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	# coding=utf-8
	# Copyright 2023 The HuggingFace Inc. 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.
	""" ALIGN model configuration"""

	import os
	from typing import TYPE_CHECKING, List, Union


	if TYPE_CHECKING:
	pass

	from ...configuration_utils import PretrainedConfig
	from ...utils import logging


	logger = logging.get_logger(__name__)

	ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP = {
	"kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
	}


	class AlignTextConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`AlignTextModel`]. It is used to instantiate a
	ALIGN text encoder according to the specified arguments, defining the model architecture. Instantiating a
	configuration with the defaults will yield a similar configuration to that of the text encoder of the ALIGN
	[kakaobrain/align-base](https://huggingface.co/kakaobrain/align-base) architecture. The default values here are
	copied from BERT.

	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 Align Text model. Defines the number of different tokens that can be represented by
	the `inputs_ids` passed when calling [`AlignTextModel`].
	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 [`AlignTextModel`].
	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.
	pad_token_id (`int`, optional, defaults to 0):
	Padding token id.
	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 AlignTextConfig, AlignTextModel

	>>> # Initializing a AlignTextConfig with kakaobrain/align-base style configuration
	>>> configuration = AlignTextConfig()

	>>> # Initializing a AlignTextModel (with random weights) from the kakaobrain/align-base style configuration
	>>> model = AlignTextModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```"""
	model_type = "align_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,
	layer_norm_eps=1e-12,
	pad_token_id=0,
	position_embedding_type="absolute",
	use_cache=True,
	**kwargs,
	):
	super().__init__(**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.layer_norm_eps = layer_norm_eps
	self.position_embedding_type = position_embedding_type
	self.use_cache = use_cache
	self.pad_token_id = pad_token_id

	@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 text config dict if we are loading from AlignConfig
	if config_dict.get("model_type") == "align":
	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 AlignVisionConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`AlignVisionModel`]. It is used to instantiate a
	ALIGN vision encoder according to the specified arguments, defining the model architecture. Instantiating a
	configuration with the defaults will yield a similar configuration to that of the vision encoder of the ALIGN
	[kakaobrain/align-base](https://huggingface.co/kakaobrain/align-base) architecture. The default values are copied
	from EfficientNet (efficientnet-b7)

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

	Args:
	num_channels (`int`, optional, defaults to 3):
	The number of input channels.
	image_size (`int`, optional, defaults to 600):
	The input image size.
	width_coefficient (`float`, optional, defaults to 2.0):
	Scaling coefficient for network width at each stage.
	depth_coefficient (`float`, optional, defaults to 3.1):
	Scaling coefficient for network depth at each stage.
	depth_divisor `int`, optional, defaults to 8):
	A unit of network width.
	kernel_sizes (`List[int]`, optional, defaults to `[3, 3, 5, 3, 5, 5, 3]`):
	List of kernel sizes to be used in each block.
	in_channels (`List[int]`, optional, defaults to `[32, 16, 24, 40, 80, 112, 192]`):
	List of input channel sizes to be used in each block for convolutional layers.
	out_channels (`List[int]`, optional, defaults to `[16, 24, 40, 80, 112, 192, 320]`):
	List of output channel sizes to be used in each block for convolutional layers.
	depthwise_padding (`List[int]`, optional, defaults to `[]`):
	List of block indices with square padding.
	strides (`List[int]`, optional, defaults to `[1, 2, 2, 2, 1, 2, 1]`):
	List of stride sizes to be used in each block for convolutional layers.
	num_block_repeats (`List[int]`, optional, defaults to `[1, 2, 2, 3, 3, 4, 1]`):
	List of the number of times each block is to repeated.
	expand_ratios (`List[int]`, optional, defaults to `[1, 6, 6, 6, 6, 6, 6]`):
	List of scaling coefficient of each block.
	squeeze_expansion_ratio (`float`, optional, defaults to 0.25):
	Squeeze expansion ratio.
	hidden_act (`str` or `function`, optional, defaults to `"silu"`):
	The non-linear activation function (function or string) in each block. If string, `"gelu"`, `"relu"`,
	`"selu", `"gelu_new"`, `"silu"` and `"mish"` are supported.
	hiddem_dim (`int`, optional, defaults to 1280):
	The hidden dimension of the layer before the classification head.
	pooling_type (`str` or `function`, optional, defaults to `"mean"`):
	Type of final pooling to be applied before the dense classification head. Available options are [`"mean"`,
	`"max"`]
	initializer_range (`float`, optional, defaults to 0.02):
	The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
	batch_norm_eps (`float`, optional, defaults to 1e-3):
	The epsilon used by the batch normalization layers.
	batch_norm_momentum (`float`, optional, defaults to 0.99):
	The momentum used by the batch normalization layers.
	drop_connect_rate (`float`, optional, defaults to 0.2):
	The drop rate for skip connections.

	Example:

	```python
	>>> from transformers import AlignVisionConfig, AlignVisionModel

	>>> # Initializing a AlignVisionConfig with kakaobrain/align-base style configuration
	>>> configuration = AlignVisionConfig()

	>>> # Initializing a AlignVisionModel (with random weights) from the kakaobrain/align-base style configuration
	>>> model = AlignVisionModel(configuration)

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

	model_type = "align_vision_model"

	def __init__(
	self,
	num_channels: int = 3,
	image_size: int = 600,
	width_coefficient: float = 2.0,
	depth_coefficient: float = 3.1,
	depth_divisor: int = 8,
	kernel_sizes: List[int] = [3, 3, 5, 3, 5, 5, 3],
	in_channels: List[int] = [32, 16, 24, 40, 80, 112, 192],
	out_channels: List[int] = [16, 24, 40, 80, 112, 192, 320],
	depthwise_padding: List[int] = [],
	strides: List[int] = [1, 2, 2, 2, 1, 2, 1],
	num_block_repeats: List[int] = [1, 2, 2, 3, 3, 4, 1],
	expand_ratios: List[int] = [1, 6, 6, 6, 6, 6, 6],
	squeeze_expansion_ratio: float = 0.25,
	hidden_act: str = "swish",
	hidden_dim: int = 2560,
	pooling_type: str = "mean",
	initializer_range: float = 0.02,
	batch_norm_eps: float = 0.001,
	batch_norm_momentum: float = 0.99,
	drop_connect_rate: float = 0.2,
	**kwargs,
	):
	super().__init__(**kwargs)

	self.num_channels = num_channels
	self.image_size = image_size
	self.width_coefficient = width_coefficient
	self.depth_coefficient = depth_coefficient
	self.depth_divisor = depth_divisor
	self.kernel_sizes = kernel_sizes
	self.in_channels = in_channels
	self.out_channels = out_channels
	self.depthwise_padding = depthwise_padding
	self.strides = strides
	self.num_block_repeats = num_block_repeats
	self.expand_ratios = expand_ratios
	self.squeeze_expansion_ratio = squeeze_expansion_ratio
	self.hidden_act = hidden_act
	self.hidden_dim = hidden_dim
	self.pooling_type = pooling_type
	self.initializer_range = initializer_range
	self.batch_norm_eps = batch_norm_eps
	self.batch_norm_momentum = batch_norm_momentum
	self.drop_connect_rate = drop_connect_rate
	self.num_hidden_layers = sum(num_block_repeats) * 4

	@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 AlignConfig
	if config_dict.get("model_type") == "align":
	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 AlignConfig(PretrainedConfig):
	r"""
	[`AlignConfig`] is the configuration class to store the configuration of a [`AlignModel`]. It is used to
	instantiate a ALIGN 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 ALIGN
	[kakaobrain/align-base](https://huggingface.co/kakaobrain/align-base) 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 [`AlignTextConfig`].
	vision_config (`dict`, optional):
	Dictionary of configuration options used to initialize [`AlignVisionConfig`].
	projection_dim (`int`, optional, defaults to 640):
	Dimentionality of text and vision projection layers.
	temperature_init_value (`float`, optional, defaults to 1.0):
	The inital value of the temperature paramter. Default is used as per the original ALIGN implementation.
	initializer_range (`float`, optional, defaults to 0.02):
	The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
	kwargs (optional):
	Dictionary of keyword arguments.

	Example:

	```python
	>>> from transformers import AlignConfig, AlignModel

	>>> # Initializing a AlignConfig with kakaobrain/align-base style configuration
	>>> configuration = AlignConfig()

	>>> # Initializing a AlignModel (with random weights) from the kakaobrain/align-base style configuration
	>>> model = AlignModel(configuration)

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

	>>> # We can also initialize a AlignConfig from a AlignTextConfig and a AlignVisionConfig
	>>> from transformers import AlignTextConfig, AlignVisionConfig

	>>> # Initializing ALIGN Text and Vision configurations
	>>> config_text = AlignTextConfig()
	>>> config_vision = AlignVisionConfig()

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

	model_type = "align"

	def __init__(
	self,
	text_config=None,
	vision_config=None,
	projection_dim=640,
	temperature_init_value=1.0,
	initializer_range=0.02,
	**kwargs,
	):
	super().__init__(**kwargs)

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

	if vision_config is None:
	vision_config = {}
	logger.info("vision_config is None. Initializing the AlignVisionConfig with default values.")

	self.text_config = AlignTextConfig(**text_config)
	self.vision_config = AlignVisionConfig(**vision_config)

	self.projection_dim = projection_dim
	self.temperature_init_value = temperature_init_value
	self.initializer_range = initializer_range

	@classmethod
	def from_text_vision_configs(cls, text_config: AlignTextConfig, vision_config: AlignVisionConfig, **kwargs):
	r"""
	Instantiate a [`AlignConfig`] (or a derived class) from align text model configuration and align vision model
	configuration.

	Returns:
	[`AlignConfig`]: An instance of a configuration object
	"""

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