



'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TFDebertaVaForMaskedLM,
         TFDebertaVaForQuestionAnswering,
         TFDebertaVaForSequenceClassification,
         TFDebertaVaForTokenClassification,
         TFDebertaVaModel,
     )

class 							a__:







    def __init__(				self			:  List[str]						,      __snake_case			:  int						,      __snake_case			:  int=13						,      __snake_case			:  Dict=7						,      __snake_case			:  Tuple=True						,      __snake_case			:  Optional[Any]=True						,      __snake_case			:  List[str]=True						,      __snake_case			:  Any=True						,      __snake_case			:  Optional[Any]=99						,      __snake_case			:  List[str]=32						,      __snake_case			:  Any=2						,      __snake_case			:  Optional[Any]=4						,      __snake_case			:  Any=37						,      __snake_case			:  Tuple="gelu"						,      __snake_case			:  Tuple=0.1						,      __snake_case			:  Any=0.1						,      __snake_case			:  List[Any]=5_12						,      __snake_case			:  List[str]=16						,      __snake_case			:  int=2						,      __snake_case			:  List[str]=0.02						,      __snake_case			:  Any=False						,      __snake_case			:  Optional[Any]=True						,      __snake_case			:  Union[str, Any]="None"						,      __snake_case			:  Union[str, Any]=3						,      __snake_case			:  Optional[Any]=4						,      __snake_case			:  Optional[Any]=None						,      ):
      a						: List[Any]					       =					parent
      a						: Tuple					       =					batch_size
      a						: int					       =					seq_length
      a						: Union[str, Any]					       =					is_training
      a						: Dict					       =					use_input_mask
      a						: int					       =					use_token_type_ids
      a						: List[Any]					       =					use_labels
      a						: Tuple					       =					vocab_size
      a						: Any					       =					hidden_size
      a						: Optional[Any]					       =					num_hidden_layers
      a						: int					       =					num_attention_heads
      a						: int					       =					intermediate_size
      a						: int					       =					hidden_act
      a						: Any					       =					hidden_dropout_prob
      a						: Optional[int]					       =					attention_probs_dropout_prob
      a						: Tuple					       =					max_position_embeddings
      a						: Optional[int]					       =					type_vocab_size
      a						: Dict					       =					type_sequence_label_size
      a						: Tuple					       =					initializer_range
      a						: Optional[Any]					       =					num_labels
      a						: Dict					       =					num_choices
      a						: List[str]					       =					relative_attention
      a						: str					       =					position_biased_input
      a						: Dict					       =					pos_att_type
      a						: Optional[Any]					       =					scope







    def       lowercase_	(				self			:  Union[str, Any]     ):
      a						: List[Any]					       =					ids_tensor([self.batch_size, self.seq_length]						,      self.vocab_size     )

      a						: Optional[Any]					       =					None
      if self.use_input_mask:
        a						: Any					       =					random_attention_mask([self.batch_size, self.seq_length]     )

      a						: List[str]					       =					None
      if self.use_token_type_ids:
        a						: Any					       =					ids_tensor([self.batch_size, self.seq_length]						,      self.type_vocab_size     )

      a						: List[str]					       =					None
      a						: Tuple					       =					None
      a						: List[str]					       =					None
      if self.use_labels:
        a						: List[str]					       =					ids_tensor([self.batch_size]						,      self.type_sequence_label_size     )
        a						: Union[str, Any]					       =					ids_tensor([self.batch_size, self.seq_length]						,      self.num_labels     )

      a						: List[Any]					       =					DebertaVaConfig(
          vocab_size=self.vocab_size						,      hidden_size=self.hidden_size						,      num_hidden_layers=self.num_hidden_layers						,      num_attention_heads=self.num_attention_heads						,      intermediate_size=self.intermediate_size						,      hidden_act=self.hidden_act						,      hidden_dropout_prob=self.hidden_dropout_prob						,      attention_probs_dropout_prob=self.attention_probs_dropout_prob						,      max_position_embeddings=self.max_position_embeddings						,      type_vocab_size=self.type_vocab_size						,      relative_attention=self.relative_attention						,      position_biased_input=self.position_biased_input						,      initializer_range=self.initializer_range						,      return_dict=_UpperCAmelCase						,      )

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  Dict						,      __snake_case			:  str						,      __snake_case			:  Dict						,      __snake_case			:  Tuple						,      __snake_case			:  Any						,      __snake_case			:  str						,      __snake_case			:  str     ):
      a						: List[str]					       =					TFDebertaVaModel(config=_UpperCAmelCase     )
      a						: Any					       =					{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

      a						: Any					       =					[input_ids, input_mask]
      a						: Dict					       =					model(_UpperCAmelCase     )

      a						: str					       =					model(_UpperCAmelCase     )

      self.parent.assertEqual(result.last_hidden_state.shape						,      (self.batch_size, self.seq_length, self.hidden_size)     )







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  List[Any]						,      __snake_case			:  str						,      __snake_case			:  int						,      __snake_case			:  Optional[Any]						,      __snake_case			:  Any						,      __snake_case			:  Optional[int]						,      __snake_case			:  Optional[int]     ):
      a						: Optional[Any]					       =					TFDebertaVaForMaskedLM(config=_UpperCAmelCase     )
      a						: List[Any]					       =					{
          'input_ids': input_ids,
          'attention_mask': input_mask,
          'token_type_ids': token_type_ids,
      }
      a						: Optional[int]					       =					model(_UpperCAmelCase     )
      self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.seq_length, self.vocab_size)     )







    def       lowercase_	(				self			:  str						,      __snake_case			:  Dict						,      __snake_case			:  Dict						,      __snake_case			:  List[Any]						,      __snake_case			:  str						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  List[str]						,      __snake_case			:  List[Any]     ):
      a						: List[Any]					       =					self.num_labels
      a						: int					       =					TFDebertaVaForSequenceClassification(config=_UpperCAmelCase     )
      a						: Tuple					       =					{
          'input_ids': input_ids,
          'attention_mask': input_mask,
          'token_type_ids': token_type_ids,
      }

      a						: Dict					       =					model(_UpperCAmelCase     )
      self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.num_labels)     )







    def       lowercase_	(				self			:  List[Any]						,      __snake_case			:  Any						,      __snake_case			:  List[Any]						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  Any						,      __snake_case			:  Tuple						,      __snake_case			:  Any						,      __snake_case			:  List[str]     ):
      a						: str					       =					self.num_labels
      a						: Any					       =					TFDebertaVaForTokenClassification(config=_UpperCAmelCase     )
      a						: List[Any]					       =					{
          'input_ids': input_ids,
          'attention_mask': input_mask,
          'token_type_ids': token_type_ids,
      }
      a						: List[Any]					       =					model(_UpperCAmelCase     )
      self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.seq_length, self.num_labels)     )







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  List[str]						,      __snake_case			:  Dict						,      __snake_case			:  List[Any]						,      __snake_case			:  Union[str, Any]						,      __snake_case			:  str						,      __snake_case			:  Optional[int]						,      __snake_case			:  Optional[Any]     ):
      a						: List[str]					       =					TFDebertaVaForQuestionAnswering(config=_UpperCAmelCase     )
      a						: str					       =					{
          'input_ids': input_ids,
          'attention_mask': input_mask,
          'token_type_ids': token_type_ids,
      }

      a						: Optional[Any]					       =					model(_UpperCAmelCase     )
      self.parent.assertEqual(result.start_logits.shape						,      (self.batch_size, self.seq_length)     )
      self.parent.assertEqual(result.end_logits.shape						,      (self.batch_size, self.seq_length)     )







    def       lowercase_	(				self			:  Optional[Any]     ):
      a						: Dict					       =					self.prepare_config_and_inputs()
      (
          a 
      )						: int					       =					config_and_inputs
      a						: Optional[int]					       =					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict





@require_tf
class 							a__( _UpperCamelCase		,			_UpperCamelCase		,			unittest.TestCase     ):
    lowercase__			=   (
        (
            TFDebertaVaModel,
            TFDebertaVaForMaskedLM,
            TFDebertaVaForQuestionAnswering,
            TFDebertaVaForSequenceClassification,
            TFDebertaVaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    lowercase__			=   (
        {
            'feature-extraction': TFDebertaVaModel,
            'fill-mask': TFDebertaVaForMaskedLM,
            'question-answering': TFDebertaVaForQuestionAnswering,
            'text-classification': TFDebertaVaForSequenceClassification,
            'token-classification': TFDebertaVaForTokenClassification,
            'zero-shot': TFDebertaVaForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )

    lowercase__			=   False
    lowercase__			=   False







    def       lowercase_	(				self			:  List[Any]     ):
      a						: Dict					       =					TFDebertaVaModelTester(self     )
      a						: Optional[int]					       =					ConfigTester(self						,      config_class=_UpperCAmelCase						,      hidden_size=37     )







    def       lowercase_	(				self			:  Dict     ):
      self.config_tester.run_common_tests()







    def       lowercase_	(				self			:  int     ):
      a						: Tuple					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_UpperCAmelCase     )







    def       lowercase_	(				self			:  List[Any]     ):
      a						: Optional[int]					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase     )







    def       lowercase_	(				self			:  Dict     ):
      a						: Tuple					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase     )







    def       lowercase_	(				self			:  Optional[int]     ):
      a						: Union[str, Any]					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase     )







    def       lowercase_	(				self			:  Tuple     ):
      a						: Any					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase     )







    @slow
    def       lowercase_	(				self			:  Optional[Any]     ):
      a						: List[str]					       =					TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'     )
      self.assertIsNotNone(_UpperCAmelCase     )





@require_tf
class 							a__( unittest.TestCase     ):







    @unittest.skip(reason='Model not available yet'     )
    def       lowercase_	(				self			:  Union[str, Any]     ):
      pass







    @slow
    def       lowercase_	(				self			:  str     ):
      a						: Optional[Any]					       =					TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'     )
      a						: List[Any]					       =					tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]     )
      a						: Any					       =					tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
      a						: Tuple					       =					model(_UpperCAmelCase						,      attention_mask=_UpperCAmelCase     )[0]

      a						: Dict					       =					tf.constant(
          [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]     )
      tf.debugging.assert_near(output[:, 1:4, 1:4]						,      _UpperCAmelCase						,      atol=1e-4     )




'''simple docstring'''






from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


lowerCAmelCase: List[Any]							=						logging.get_logger(__name__)

# General docstring
lowerCAmelCase: Optional[int]							=						'RegNetConfig'

# Base docstring
lowerCAmelCase: Tuple							=						'facebook/regnet-y-040'
lowerCAmelCase: str							=						[1, 1_0_8_8, 7, 7]

# Image classification docstring
lowerCAmelCase: Any							=						'facebook/regnet-y-040'
lowerCAmelCase: List[Any]							=						'tabby, tabby cat'

lowerCAmelCase: Optional[Any]							=						[
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]

class 							a__( nn.Module     ):







    def __init__(				self			:  str						,      __snake_case			:  int						,      __snake_case			:  int						,      __snake_case			:  int = 3						,      __snake_case			:  int = 1						,      __snake_case			:  int = 1						,      __snake_case			:  Optional[str] = "relu"						,      ):
      super().__init__()
      a						: Tuple					       =					nn.Convad(
          __snake_case						,      __snake_case						,      kernel_size=__snake_case						,      stride=__snake_case						,      padding=kernel_size // 2						,      groups=__snake_case						,      bias=__snake_case						,      )
      a						: Dict					       =					nn.BatchNormad(__snake_case     )
      a						: Tuple					       =					ACTaFN[activation] if activation is not None else nn.Identity()







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  int     ):
      a						: int					       =					self.convolution(__snake_case     )
      a						: int					       =					self.normalization(__snake_case     )
      a						: Any					       =					self.activation(__snake_case     )
      return hidden_state

class 							a__( nn.Module     ):







    def __init__(				self			:  List[str]						,      __snake_case			:  RegNetConfig     ):
      super().__init__()
      a						: Optional[int]					       =					RegNetConvLayer(
          config.num_channels						,      config.embedding_size						,      kernel_size=3						,      stride=2						,      activation=config.hidden_act     )
      a						: Optional[int]					       =					config.num_channels







    def       lowercase_	(				self			:  int						,      __snake_case			:  List[str]     ):
      a						: List[Any]					       =					pixel_values.shape[1]
      if num_channels != self.num_channels:
        raise ValueError(
            'Make sure that the channel dimension of the pixel values match with the one set in the configuration.'     )
      a						: List[str]					       =					self.embedder(__snake_case     )
      return hidden_state

class 							a__( nn.Module     ):







    def __init__(				self			:  Tuple						,      __snake_case			:  int						,      __snake_case			:  int						,      __snake_case			:  int = 2     ):
      super().__init__()
      a						: Optional[int]					       =					nn.Convad(__snake_case						,      __snake_case						,      kernel_size=1						,      stride=__snake_case						,      bias=__snake_case     )
      a						: Optional[Any]					       =					nn.BatchNormad(__snake_case     )







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  Tensor     ):
      a						: Any					       =					self.convolution(__snake_case     )
      a						: Dict					       =					self.normalization(__snake_case     )
      return hidden_state

class 							a__( nn.Module     ):







    def __init__(				self			:  Union[str, Any]						,      __snake_case			:  int						,      __snake_case			:  int     ):
      super().__init__()

      a						: Any					       =					nn.AdaptiveAvgPoolad((1, 1)     )
      a						: Dict					       =					nn.Sequential(
          nn.Convad(__snake_case						,      __snake_case						,      kernel_size=1     )						,      nn.ReLU()						,      nn.Convad(__snake_case						,      __snake_case						,      kernel_size=1     )						,      nn.Sigmoid()						,      )







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  List[Any]     ):
      # b c h w -> b c 1 1
      a						: Any					       =					self.pooler(__snake_case     )
      a						: Optional[Any]					       =					self.attention(__snake_case     )
      a						: Tuple					       =					hidden_state * attention
      return hidden_state

class 							a__( nn.Module     ):







    def __init__(				self			:  List[str]						,      __snake_case			:  RegNetConfig						,      __snake_case			:  int						,      __snake_case			:  int						,      __snake_case			:  int = 1     ):
      super().__init__()
      a						: Union[str, Any]					       =					in_channels != out_channels or stride != 1
      a						: Dict					       =					max(1						,      out_channels // config.groups_width     )
      a						: List[str]					       =					(
          RegNetShortCut(__snake_case						,      __snake_case						,      stride=__snake_case     ) if should_apply_shortcut else nn.Identity()
      )
      a						: int					       =					nn.Sequential(
          RegNetConvLayer(__snake_case						,      __snake_case						,      kernel_size=1						,      activation=config.hidden_act     )						,      RegNetConvLayer(__snake_case						,      __snake_case						,      stride=__snake_case						,      groups=__snake_case						,      activation=config.hidden_act     )						,      RegNetConvLayer(__snake_case						,      __snake_case						,      kernel_size=1						,      activation=__snake_case     )						,      )
      a						: int					       =					ACTaFN[config.hidden_act]







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  Tuple     ):
      a						: List[str]					       =					hidden_state
      a						: Dict					       =					self.layer(__snake_case     )
      a						: Tuple					       =					self.shortcut(__snake_case     )
      hidden_state += residual
      a						: Optional[int]					       =					self.activation(__snake_case     )
      return hidden_state

class 							a__( nn.Module     ):







    def __init__(				self			:  List[str]						,      __snake_case			:  RegNetConfig						,      __snake_case			:  int						,      __snake_case			:  int						,      __snake_case			:  int = 1     ):
      super().__init__()
      a						: Union[str, Any]					       =					in_channels != out_channels or stride != 1
      a						: Dict					       =					max(1						,      out_channels // config.groups_width     )
      a						: Dict					       =					(
          RegNetShortCut(__snake_case						,      __snake_case						,      stride=__snake_case     ) if should_apply_shortcut else nn.Identity()
      )
      a						: List[str]					       =					nn.Sequential(
          RegNetConvLayer(__snake_case						,      __snake_case						,      kernel_size=1						,      activation=config.hidden_act     )						,      RegNetConvLayer(__snake_case						,      __snake_case						,      stride=__snake_case						,      groups=__snake_case						,      activation=config.hidden_act     )						,      RegNetSELayer(__snake_case						,      reduced_channels=int(round(in_channels / 4     )     )     )						,      RegNetConvLayer(__snake_case						,      __snake_case						,      kernel_size=1						,      activation=__snake_case     )						,      )
      a						: Union[str, Any]					       =					ACTaFN[config.hidden_act]







    def       lowercase_	(				self			:  Any						,      __snake_case			:  int     ):
      a						: Tuple					       =					hidden_state
      a						: Tuple					       =					self.layer(__snake_case     )
      a						: Optional[int]					       =					self.shortcut(__snake_case     )
      hidden_state += residual
      a						: Any					       =					self.activation(__snake_case     )
      return hidden_state

class 							a__( nn.Module     ):







    def __init__(				self			:  int						,      __snake_case			:  RegNetConfig						,      __snake_case			:  int						,      __snake_case			:  int						,      __snake_case			:  int = 2						,      __snake_case			:  int = 2						,      ):
      super().__init__()

      a						: int					       =					RegNetXLayer if config.layer_type == 'x' else RegNetYLayer

      a						: Any					       =					nn.Sequential(
          # downsampling is done in the first layer with stride of 2
          layer(
              __snake_case						,      __snake_case						,      __snake_case						,      stride=__snake_case						,      )						,      *[layer(__snake_case						,      __snake_case						,      __snake_case     ) for _ in range(depth - 1     )]						,      )







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  str     ):
      a						: Dict					       =					self.layers(__snake_case     )
      return hidden_state

class 							a__( nn.Module     ):







    def __init__(				self			:  List[Any]						,      __snake_case			:  RegNetConfig     ):
      super().__init__()
      a						: Dict					       =					nn.ModuleList([]     )
      # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
      self.stages.append(
          RegNetStage(
              __snake_case						,      config.embedding_size						,      config.hidden_sizes[0]						,      stride=2 if config.downsample_in_first_stage else 1						,      depth=config.depths[0]						,      )     )
      a						: str					       =					zip(config.hidden_sizes						,      config.hidden_sizes[1:]     )
      for (in_channels, out_channels), depth in zip(__snake_case						,      config.depths[1:]     ):
        self.stages.append(RegNetStage(__snake_case						,      __snake_case						,      __snake_case						,      depth=__snake_case     )     )







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  Tensor						,      __snake_case			:  bool = False						,      __snake_case			:  bool = True     ):
      a						: Union[str, Any]					       =					() if output_hidden_states else None

      for stage_module in self.stages:
        if output_hidden_states:
          a						: List[str]					       =					hidden_states + (hidden_state,)

        a						: int					       =					stage_module(__snake_case     )

      if output_hidden_states:
        a						: Union[str, Any]					       =					hidden_states + (hidden_state,)

      if not return_dict:
        return tuple(v for v in [hidden_state, hidden_states] if v is not None     )

      return BaseModelOutputWithNoAttention(last_hidden_state=__snake_case						,      hidden_states=__snake_case     )

class 							a__( lowerCamelCase__     ):
    lowercase__			=   RegNetConfig
    lowercase__			=   """regnet"""
    lowercase__			=   """pixel_values"""
    lowercase__			=   True







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  Optional[int]     ):
      if isinstance(__snake_case						,      nn.Convad     ):
        nn.init.kaiming_normal_(module.weight						,      mode='fan_out'						,      nonlinearity='relu'     )
      elif isinstance(__snake_case						,      (nn.BatchNormad, nn.GroupNorm)     ):
        nn.init.constant_(module.weight						,      1     )
        nn.init.constant_(module.bias						,      0     )







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  List[str]						,      __snake_case			:  Dict=False     ):
      if isinstance(__snake_case						,      __snake_case     ):
        a						: Optional[Any]					       =					value
lowerCAmelCase: Union[str, Any]							=						r'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

lowerCAmelCase: int							=						r'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n'

@add_start_docstrings(
    """The bare RegNet model outputting raw features without any specific head on top."""		,			lowerCamelCase__		,			)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 							a__( lowerCamelCase__     ):







    def __init__(				self			:  Dict						,      __snake_case			:  Dict     ):
      super().__init__(__snake_case     )
      a						: str					       =					config
      a						: str					       =					RegNetEmbeddings(__snake_case     )
      a						: Dict					       =					RegNetEncoder(__snake_case     )
      a						: int					       =					nn.AdaptiveAvgPoolad((1, 1)     )
      # Initialize weights and apply final processing
      self.post_init()







    @add_start_docstrings_to_model_forward(__snake_case     )
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC						,      output_type=__snake_case						,      config_class=_CONFIG_FOR_DOC						,      modality='vision'						,      expected_output=_EXPECTED_OUTPUT_SHAPE						,      )
    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  Tensor						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[bool] = None     ):
      a						: Dict					       =					(
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      a						: List[Any]					       =					return_dict if return_dict is not None else self.config.use_return_dict

      a						: int					       =					self.embedder(__snake_case     )

      a						: Optional[int]					       =					self.encoder(
          __snake_case						,      output_hidden_states=__snake_case						,      return_dict=__snake_case     )

      a						: List[Any]					       =					encoder_outputs[0]

      a						: Any					       =					self.pooler(__snake_case     )

      if not return_dict:
        return (last_hidden_state, pooled_output) + encoder_outputs[1:]

      return BaseModelOutputWithPoolingAndNoAttention(
          last_hidden_state=__snake_case						,      pooler_output=__snake_case						,      hidden_states=encoder_outputs.hidden_states						,      )

@add_start_docstrings(
    """
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """		,			lowerCamelCase__		,			)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 							a__( lowerCamelCase__     ):







    def __init__(				self			:  int						,      __snake_case			:  Optional[int]     ):
      super().__init__(__snake_case     )
      a						: Optional[Any]					       =					config.num_labels
      a						: Tuple					       =					RegNetModel(__snake_case     )
      # classification head
      a						: List[Any]					       =					nn.Sequential(
          nn.Flatten()						,      nn.Linear(config.hidden_sizes[-1]						,      config.num_labels     ) if config.num_labels > 0 else nn.Identity()						,      )
      # initialize weights and apply final processing
      self.post_init()







    @add_start_docstrings_to_model_forward(__snake_case     )
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT						,      output_type=__snake_case						,      config_class=_CONFIG_FOR_DOC						,      expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,      )
    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  Optional[torch.FloatTensor] = None						,      __snake_case			:  Optional[torch.LongTensor] = None						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[bool] = None						,      ):
      a						: str					       =					return_dict if return_dict is not None else self.config.use_return_dict

      a						: Optional[Any]					       =					self.regnet(__snake_case						,      output_hidden_states=__snake_case						,      return_dict=__snake_case     )

      a						: Any					       =					outputs.pooler_output if return_dict else outputs[1]

      a						: str					       =					self.classifier(__snake_case     )

      a						: Optional[int]					       =					None

      if labels is not None:
        if self.config.problem_type is None:
          if self.num_labels == 1:
            a						: List[str]					       =					'regression'
          elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
            a						: int					       =					'single_label_classification'
          else:
            a						: Dict					       =					'multi_label_classification'
        if self.config.problem_type == "regression":
          a						: Any					       =					MSELoss()
          if self.num_labels == 1:
            a						: int					       =					loss_fct(logits.squeeze()						,      labels.squeeze()     )
          else:
            a						: List[str]					       =					loss_fct(__snake_case						,      __snake_case     )
        elif self.config.problem_type == "single_label_classification":
          a						: Union[str, Any]					       =					CrossEntropyLoss()
          a						: Tuple					       =					loss_fct(logits.view(-1						,      self.num_labels     )						,      labels.view(-1     )     )
        elif self.config.problem_type == "multi_label_classification":
          a						: Any					       =					BCEWithLogitsLoss()
          a						: Tuple					       =					loss_fct(__snake_case						,      __snake_case     )

      if not return_dict:
        a						: Optional[int]					       =					(logits,) + outputs[2:]
        return (loss,) + output if loss is not None else output

      return ImageClassifierOutputWithNoAttention(loss=__snake_case						,      logits=__snake_case						,      hidden_states=outputs.hidden_states     )