Update requirements.txt.file

requirements.txt.file

@@ -1,198 +1,2 @@
-# coding=utf-8
-# Copyright 2020, Microsoft and the HuggingFace Inc. team.
-#
-# 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.
-""" DeBERTa model configuration"""
-from collections import OrderedDict
-from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
-
-from ...configuration_utils import PretrainedConfig
-from ...onnx import OnnxConfig
-from ...utils import logging
-
-
-if TYPE_CHECKING:
-    from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
-
-
-logger = logging.get_logger(__name__)
-
-DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
-    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
-    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
-    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
-    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
-    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
-}
-
-
-class DebertaConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`DebertaModel`] or a [`TFDebertaModel`]. It is
-    used to instantiate a DeBERTa 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 DeBERTa
-    [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) architecture.
-
-    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
-    documentation from [`PretrainedConfig`] for more information.
-
-    Arguments:
-        vocab_size (`int`, *optional*, defaults to 30522):
-            Vocabulary size of the DeBERTa model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`DebertaModel`] or [`TFDebertaModel`].
-        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"`, `"gelu"`, `"tanh"`, `"gelu_fast"`, `"mish"`, `"linear"`, `"sigmoid"` 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 [`DebertaModel`] or [`TFDebertaModel`].
-        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.
-        relative_attention (`bool`, *optional*, defaults to `False`):
-            Whether use relative position encoding.
-        max_relative_positions (`int`, *optional*, defaults to 1):
-            The range of relative positions `[-max_position_embeddings, max_position_embeddings]`. Use the same value
-            as `max_position_embeddings`.
-        pad_token_id (`int`, *optional*, defaults to 0):
-            The value used to pad input_ids.
-        position_biased_input (`bool`, *optional*, defaults to `True`):
-            Whether add absolute position embedding to content embedding.
-        pos_att_type (`List[str]`, *optional*):
-            The type of relative position attention, it can be a combination of `["p2c", "c2p"]`, e.g. `["p2c"]`,
-            `["p2c", "c2p"]`.
-        layer_norm_eps (`float`, optional, defaults to 1e-12):
-            The epsilon used by the layer normalization layers.
-
-    Example:
-
-    ```python
-    >>> from transformers import DebertaConfig, DebertaModel
-
-    >>> # Initializing a DeBERTa microsoft/deberta-base style configuration
-    >>> configuration = DebertaConfig()
-
-    >>> # Initializing a model (with random weights) from the microsoft/deberta-base style configuration
-    >>> model = DebertaModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-    model_type = "deberta"
-
-    def __init__(
-        self,
-        vocab_size=50265,
-        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=0,
-        initializer_range=0.02,
-        layer_norm_eps=1e-7,
-        relative_attention=False,
-        max_relative_positions=-1,
-        pad_token_id=0,
-        position_biased_input=True,
-        pos_att_type=None,
-        pooler_dropout=0,
-        pooler_hidden_act="gelu",
-        **kwargs
-    ):
-        super().__init__(**kwargs)
-
-        self.hidden_size = hidden_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.intermediate_size = intermediate_size
-        self.hidden_act = hidden_act
-        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.relative_attention = relative_attention
-        self.max_relative_positions = max_relative_positions
-        self.pad_token_id = pad_token_id
-        self.position_biased_input = position_biased_input
-
-        # Backwards compatibility
-        if type(pos_att_type) == str:
-            pos_att_type = [x.strip() for x in pos_att_type.lower().split("|")]
-
-        self.pos_att_type = pos_att_type
-        self.vocab_size = vocab_size
-        self.layer_norm_eps = layer_norm_eps
-
-        self.pooler_hidden_size = kwargs.get("pooler_hidden_size", hidden_size)
-        self.pooler_dropout = pooler_dropout
-        self.pooler_hidden_act = pooler_hidden_act
-
-
-# Copied from transformers.models.deberta_v2.configuration_deberta_v2.DebertaV2OnnxConfig
-class DebertaOnnxConfig(OnnxConfig):
-    @property
-    def inputs(self) -> Mapping[str, Mapping[int, str]]:
-        if self.task == "multiple-choice":
-            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
-        else:
-            dynamic_axis = {0: "batch", 1: "sequence"}
-        if self._config.type_vocab_size > 0:
-            return OrderedDict(
-                [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]
-            )
-        else:
-            return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)])
-
-    @property
-    def default_onnx_opset(self) -> int:
-        return 12
-
-    def generate_dummy_inputs(
-        self,
-        preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],
-        batch_size: int = -1,
-        seq_length: int = -1,
-        num_choices: int = -1,
-        is_pair: bool = False,
-        framework: Optional["TensorType"] = None,
-        num_channels: int = 3,
-        image_width: int = 40,
-        image_height: int = 40,
-        tokenizer: "PreTrainedTokenizerBase" = None,
-    ) -> Mapping[str, Any]:
-        dummy_inputs = super().generate_dummy_inputs(preprocessor=preprocessor, framework=framework)
-        if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
-            del dummy_inputs["token_type_ids"]
-        return dummy_inputs
+transformers
+torch