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	# coding=utf-8
	# Copyright 2022 The OFA-Sys Team Authors and The HuggingFace Team. All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	""" Chinese-CLIP model configuration"""

	import os
	from collections import OrderedDict
	from typing import TYPE_CHECKING, Any, Mapping, Optional, Union


	if TYPE_CHECKING:
	from ...processing_utils import ProcessorMixin
	from ...utils import TensorType

	from ...configuration_utils import PretrainedConfig
	from ...onnx import OnnxConfig
	from ...utils import logging


	logger = logging.get_logger(__name__)

	CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP = {
	"OFA-Sys/chinese-clip-vit-base-patch16": (
	"https://huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16/resolve/main/config.json"
	),
	}


	class ChineseCLIPTextConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`ChineseCLIPModel`]. It is used to instantiate a
	Chinese CLIP model according to the specified arguments, defining the model architecture. Instantiating a
	configuration with the defaults will yield a similar configuration to that of the Chinese CLIP
	[OFA-Sys/chinese-clip-vit-base-patch16](https:
	//huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16) architecture.

	Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
	documentation from [`PretrainedConfig`] for more information.


	Args:
	vocab_size (`int`, optional, defaults to 30522):
	Vocabulary size of the CHINESE_CLIP model. Defines the number of different tokens that can be represented
	by the `inputs_ids` passed when calling [`ChineseCLIPModel`].
	hidden_size (`int`, optional, defaults to 768):
	Dimensionality of the encoder layers and the pooler layer.
	num_hidden_layers (`int`, optional, defaults to 12):
	Number of hidden layers in the Transformer encoder.
	num_attention_heads (`int`, optional, defaults to 12):
	Number of attention heads for each attention layer in the Transformer encoder.
	intermediate_size (`int`, optional, defaults to 3072):
	Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
	hidden_act (`str` or `Callable`, optional, defaults to `"gelu"`):
	The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
	`"relu"`, `"silu"` and `"gelu_new"` are supported.
	hidden_dropout_prob (`float`, optional, defaults to 0.1):
	The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
	attention_probs_dropout_prob (`float`, optional, defaults to 0.1):
	The dropout ratio for the attention probabilities.
	max_position_embeddings (`int`, optional, defaults to 512):
	The maximum sequence length that this model might ever be used with. Typically set this to something large
	just in case (e.g., 512 or 1024 or 2048).
	type_vocab_size (`int`, optional, defaults to 2):
	The vocabulary size of the `token_type_ids` passed when calling [`ChineseCLIPModel`].
	initializer_range (`float`, optional, defaults to 0.02):
	The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
	layer_norm_eps (`float`, optional, defaults to 1e-12):
	The epsilon used by the layer normalization layers.
	position_embedding_type (`str`, optional, defaults to `"absolute"`):
	Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
	positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
	[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
	For more information on `"relative_key_query"`, please refer to Method 4 in [Improve Transformer Models
	with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
	use_cache (`bool`, optional, defaults to `True`):
	Whether or not the model should return the last key/values attentions (not used by all models). Only
	relevant if `config.is_decoder=True`.

	Example:

	```python
	>>> from transformers import ChineseCLIPTextConfig, ChineseCLIPTextModel

	>>> # Initializing a ChineseCLIPTextConfig with OFA-Sys/chinese-clip-vit-base-patch16 style configuration
	>>> configuration = ChineseCLIPTextConfig()

	>>> # Initializing a ChineseCLIPTextModel (with random weights) from the OFA-Sys/chinese-clip-vit-base-patch16 style configuration
	>>> model = ChineseCLIPTextModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```"""
	model_type = "chinese_clip_text_model"

	def __init__(
	self,
	vocab_size=30522,
	hidden_size=768,
	num_hidden_layers=12,
	num_attention_heads=12,
	intermediate_size=3072,
	hidden_act="gelu",
	hidden_dropout_prob=0.1,
	attention_probs_dropout_prob=0.1,
	max_position_embeddings=512,
	type_vocab_size=2,
	initializer_range=0.02,
	initializer_factor=1.0,
	layer_norm_eps=1e-12,
	pad_token_id=0,
	position_embedding_type="absolute",
	use_cache=True,
	**kwargs,
	):
	super().__init__(pad_token_id=pad_token_id, **kwargs)

	self.vocab_size = vocab_size
	self.hidden_size = hidden_size
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads
	self.hidden_act = hidden_act
	self.intermediate_size = intermediate_size
	self.hidden_dropout_prob = hidden_dropout_prob
	self.attention_probs_dropout_prob = attention_probs_dropout_prob
	self.max_position_embeddings = max_position_embeddings
	self.type_vocab_size = type_vocab_size
	self.initializer_range = initializer_range
	self.initializer_factor = initializer_factor
	self.layer_norm_eps = layer_norm_eps
	self.position_embedding_type = position_embedding_type
	self.use_cache = use_cache

	@classmethod
	def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
	cls._set_token_in_kwargs(kwargs)

	config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

	# get the vision config dict if we are loading from ChineseCLIPConfig
	if config_dict.get("model_type") == "chinese_clip":
	config_dict = config_dict["text_config"]

	if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
	logger.warning(
	f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
	f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
	)

	return cls.from_dict(config_dict, **kwargs)


	class ChineseCLIPVisionConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`ChineseCLIPModel`]. It is used to instantiate an
	ChineseCLIP model according to the specified arguments, defining the model architecture. Instantiating a
	configuration with the defaults will yield a similar configuration to that of the ChineseCLIP
	[OFA-Sys/chinese-clip-vit-base-patch16](https:
	//huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16) architecture.

	Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
	documentation from [`PretrainedConfig`] for more information.


	Args:
	hidden_size (`int`, optional, defaults to 768):
	Dimensionality of the encoder layers and the pooler layer.
	intermediate_size (`int`, optional, defaults to 3072):
	Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
	num_hidden_layers (`int`, optional, defaults to 12):
	Number of hidden layers in the Transformer encoder.
	num_attention_heads (`int`, optional, defaults to 12):
	Number of attention heads for each attention layer in the Transformer encoder.
	image_size (`int`, optional, defaults to 224):
	The size (resolution) of each image.
	patch_size (`int`, optional, defaults to 32):
	The size (resolution) of each patch.
	hidden_act (`str` or `function`, optional, defaults to `"quick_gelu"`):
	The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
	`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
	layer_norm_eps (`float`, optional, defaults to 1e-5):
	The epsilon used by the layer normalization layers.
	attention_dropout (`float`, optional, defaults to 0.0):
	The dropout ratio for the attention probabilities.
	initializer_range (`float`, optional, defaults to 0.02):
	The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
	initializer_factor (`float``, optional, defaults to 1):
	A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
	testing).
	Example:
	```python
	>>> from transformers import ChineseCLIPVisionConfig, ChineseCLIPVisionModel

	>>> # Initializing a ChineseCLIPVisionConfig with OFA-Sys/chinese-clip-vit-base-patch16 style configuration
	>>> configuration = ChineseCLIPVisionConfig()

	>>> # Initializing a ChineseCLIPVisionModel (with random weights) from the OFA-Sys/chinese-clip-vit-base-patch16 style configuration
	>>> model = ChineseCLIPVisionModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```"""

	model_type = "chinese_clip_vision_model"

	def __init__(
	self,
	hidden_size=768,
	intermediate_size=3072,
	projection_dim=512,
	num_hidden_layers=12,
	num_attention_heads=12,
	num_channels=3,
	image_size=224,
	patch_size=32,
	hidden_act="quick_gelu",
	layer_norm_eps=1e-5,
	attention_dropout=0.0,
	initializer_range=0.02,
	initializer_factor=1.0,
	**kwargs,
	):
	super().__init__(**kwargs)

	self.hidden_size = hidden_size
	self.intermediate_size = intermediate_size
	self.projection_dim = projection_dim
	self.num_hidden_layers = num_hidden_layers
	self.num_attention_heads = num_attention_heads
	self.num_channels = num_channels
	self.patch_size = patch_size
	self.image_size = image_size
	self.initializer_range = initializer_range
	self.initializer_factor = initializer_factor
	self.attention_dropout = attention_dropout
	self.layer_norm_eps = layer_norm_eps
	self.hidden_act = hidden_act

	@classmethod
	def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
	cls._set_token_in_kwargs(kwargs)

	config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

	# get the vision config dict if we are loading from ChineseCLIPConfig
	if config_dict.get("model_type") == "chinese_clip":
	config_dict = config_dict["vision_config"]

	if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
	logger.warning(
	f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
	f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
	)

	return cls.from_dict(config_dict, **kwargs)


	class ChineseCLIPConfig(PretrainedConfig):
	r"""
	[`ChineseCLIPConfig`] is the configuration class to store the configuration of a [`ChineseCLIPModel`]. It is used
	to instantiate Chinese-CLIP model according to the specified arguments, defining the text model and vision model
	configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the
	Chinese-CLIP [OFA-Sys/chinese-clip-vit-base-patch16](https://huggingface.co/OFA-Sys/chinese-clip-vit-base-patch16)
	architecture.

	Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
	documentation from [`PretrainedConfig`] for more information.

	Args:
	text_config (`dict`, optional):
	Dictionary of configuration options used to initialize [`ChineseCLIPTextConfig`].
	vision_config (`dict`, optional):
	Dictionary of configuration options used to initialize [`ChineseCLIPVisionConfig`].
	projection_dim (`int`, optional, defaults to 512):
	Dimentionality of text and vision projection layers.
	logit_scale_init_value (`float`, optional, defaults to 2.6592):
	The inital value of the logit_scale paramter. Default is used as per the original ChineseCLIP
	implementation.
	kwargs (optional):
	Dictionary of keyword arguments.

	Example:

	```python
	>>> from transformers import ChineseCLIPConfig, ChineseCLIPModel

	>>> # Initializing a ChineseCLIPConfig with OFA-Sys/chinese-clip-vit-base-patch16 style configuration
	>>> configuration = ChineseCLIPConfig()

	>>> # Initializing a ChineseCLIPModel (with random weights) from the OFA-Sys/chinese-clip-vit-base-patch16 style configuration
	>>> model = ChineseCLIPModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config

	>>> # We can also initialize a ChineseCLIPConfig from a ChineseCLIPTextConfig and a ChineseCLIPVisionConfig

	>>> # Initializing a ChineseCLIPTextConfig and ChineseCLIPVisionConfig configuration
	>>> config_text = ChineseCLIPTextConfig()
	>>> config_vision = ChineseCLIPVisionConfig()

	>>> config = ChineseCLIPConfig.from_text_vision_configs(config_text, config_vision)
	```"""

	model_type = "chinese_clip"

	def __init__(
	self, text_config=None, vision_config=None, projection_dim=512, logit_scale_init_value=2.6592, **kwargs
	):
	# If `_config_dict` exist, we use them for the backward compatibility.
	# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
	# of confusion!).
	text_config_dict = kwargs.pop("text_config_dict", None)
	vision_config_dict = kwargs.pop("vision_config_dict", None)

	super().__init__(**kwargs)

	# Instead of simply assigning `[text\|vision]_config_dict` to `[text\|vision]_config`, we use the values in
	# `[text\|vision]_config_dict` to update the values in `[text\|vision]_config`. The values should be same in most
	# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
	if text_config_dict is not None:
	if text_config is None:
	text_config = {}

	# This is the complete result when using `text_config_dict`.
	_text_config_dict = ChineseCLIPTextConfig(**text_config_dict).to_dict()

	# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
	for key, value in _text_config_dict.items():
	if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
	# If specified in `text_config_dict`
	if key in text_config_dict:
	message = (
	f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
	f'The value `text_config_dict["{key}"]` will be used instead.'
	)
	# If inferred from default argument values (just to be super careful)
	else:
	message = (
	f"`text_config_dict` is provided which will be used to initialize `ChineseCLIPTextConfig`. "
	f'The value `text_config["{key}"]` will be overriden.'
	)
	logger.warning(message)

	# Update all values in `text_config` with the ones in `_text_config_dict`.
	text_config.update(_text_config_dict)

	if vision_config_dict is not None:
	if vision_config is None:
	vision_config = {}

	# This is the complete result when using `vision_config_dict`.
	_vision_config_dict = ChineseCLIPVisionConfig(**vision_config_dict).to_dict()
	# convert keys to string instead of integer
	if "id2label" in _vision_config_dict:
	_vision_config_dict["id2label"] = {
	str(key): value for key, value in _vision_config_dict["id2label"].items()
	}

	# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
	for key, value in _vision_config_dict.items():
	if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
	# If specified in `vision_config_dict`
	if key in vision_config_dict:
	message = (
	f"`{key}` is found in both `vision_config_dict` and `vision_config` but with different "
	f'values. The value `vision_config_dict["{key}"]` will be used instead.'
	)
	# If inferred from default argument values (just to be super careful)
	else:
	message = (
	f"`vision_config_dict` is provided which will be used to initialize "
	f'`ChineseCLIPVisionConfig`. The value `vision_config["{key}"]` will be overriden.'
	)
	logger.warning(message)

	# Update all values in `vision_config` with the ones in `_vision_config_dict`.
	vision_config.update(_vision_config_dict)

	if text_config is None:
	text_config = {}
	logger.info("`text_config` is `None`. Initializing the `ChineseCLIPTextConfig` with default values.")

	if vision_config is None:
	vision_config = {}
	logger.info("`vision_config` is `None`. initializing the `ChineseCLIPVisionConfig` with default values.")

	self.text_config = ChineseCLIPTextConfig(**text_config)
	self.vision_config = ChineseCLIPVisionConfig(**vision_config)

	self.projection_dim = projection_dim
	self.logit_scale_init_value = logit_scale_init_value
	self.initializer_factor = 1.0
	self.initializer_range = 0.02

	@classmethod
	def from_text_vision_configs(
	cls, text_config: ChineseCLIPTextConfig, vision_config: ChineseCLIPVisionConfig, **kwargs
	):
	r"""
	Instantiate a [`ChineseCLIPConfig`] (or a derived class) from Chinese-CLIP text model configuration and
	Chinese-CLIP vision model configuration. Returns:
	[`ChineseCLIPConfig`]: An instance of a configuration object
	"""

	return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)


	class ChineseCLIPOnnxConfig(OnnxConfig):
	@property
	def inputs(self) -> Mapping[str, Mapping[int, str]]:
	return OrderedDict(
	[
	("input_ids", {0: "batch", 1: "sequence"}),
	("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
	("attention_mask", {0: "batch", 1: "sequence"}),
	]
	)

	@property
	def outputs(self) -> Mapping[str, Mapping[int, str]]:
	return OrderedDict(
	[
	("logits_per_image", {0: "batch"}),
	("logits_per_text", {0: "batch"}),
	("text_embeds", {0: "batch"}),
	("image_embeds", {0: "batch"}),
	]
	)

	@property
	def atol_for_validation(self) -> float:
	return 1e-4

	def generate_dummy_inputs(
	self,
	processor: "ProcessorMixin",
	batch_size: int = -1,
	seq_length: int = -1,
	framework: Optional["TensorType"] = None,
	) -> Mapping[str, Any]:
	text_input_dict = super().generate_dummy_inputs(
	processor.tokenizer, batch_size=batch_size, seq_length=seq_length, framework=framework
	)
	image_input_dict = super().generate_dummy_inputs(
	processor.image_processor, batch_size=batch_size, framework=framework
	)
	return {text_input_dict, image_input_dict}

	@property
	def default_onnx_opset(self) -> int:
	return 14