





"""simple docstring"""




from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowerCamelCase_	:		Tuple         =   logging.get_logger(__name__)

lowerCamelCase_	:		List[str]         =   {
    'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json',
    # See all MBART models at https://huggingface.co/models?filter=mbart
}

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      lowercase_ :					int             =  """mbart"""
      lowercase_ :					Any             =  ["""past_key_values"""]
      lowercase_ :					Any             =  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

      def __init__(	self   , snake_case_=5_0_2_6_5   , snake_case_=1_0_2_4   , snake_case_=1_2   , snake_case_=4_0_9_6   , snake_case_=1_6   , snake_case_=1_2   , snake_case_=4_0_9_6   , snake_case_=1_6   , snake_case_=0.0   , snake_case_=0.0   , snake_case_=True   , snake_case_=True   , snake_case_="gelu"   , snake_case_=1_0_2_4   , snake_case_=0.1   , snake_case_=0.0   , snake_case_=0.0   , snake_case_=0.02   , snake_case_=0.0   , snake_case_=False   , snake_case_=1   , snake_case_=0   , snake_case_=2   , snake_case_=2   , **snake_case_   , ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    vocab_size
        A_    :			Any									=    max_position_embeddings
        A_    :			Union[str, Any]									=    d_model
        A_    :			Any									=    encoder_ffn_dim
        A_    :			int									=    encoder_layers
        A_    :			Any									=    encoder_attention_heads
        A_    :			List[str]									=    decoder_ffn_dim
        A_    :			List[Any]									=    decoder_layers
        A_    :			Union[str, Any]									=    decoder_attention_heads
        A_    :			List[Any]									=    dropout
        A_    :			Optional[Any]									=    attention_dropout
        A_    :			Optional[int]									=    activation_dropout
        A_    :			Union[str, Any]									=    activation_function
        A_    :			Dict									=    init_std
        A_    :			List[str]									=    encoder_layerdrop
        A_    :			List[str]									=    decoder_layerdrop
        A_    :			Dict									=    classifier_dropout
        A_    :			Dict									=    use_cache
        A_    :			str									=    encoder_layers
        A_    :			List[str]									=    scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=snake_case_   , bos_token_id=snake_case_   , eos_token_id=snake_case_   , is_encoder_decoder=snake_case_   , forced_eos_token_id=snake_case_   , **snake_case_   , )


class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        if self.task in ["default", "seq2seq-lm"]:
          A_    :			Optional[Any]									=    OrderedDict(
              [
                  ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                  ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
              ]    )

          if self.use_past:
            A_    :			Dict									=    {0: 'batch'}
            A_    :			List[str]									=    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
          else:
            A_    :			Optional[int]									=    {0: 'batch', 1: 'decoder_sequence'}
            A_    :			Optional[int]									=    {0: 'batch', 1: 'decoder_sequence'}

          if self.use_past:
            self.fill_with_past_key_values_(snake_case_   , direction='inputs'    )
        elif self.task == "causal-lm":
          # TODO: figure this case out.
          A_    :			str									=    OrderedDict(
              [
                  ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                  ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
              ]    )
          if self.use_past:
            A_		,			A_    :			List[str]									=    self.num_layers
            for i in range(snake_case_    ):
              A_    :			List[str]									=    {0: 'batch', 2: 'past_sequence + sequence'}
              A_    :			Dict									=    {0: 'batch', 2: 'past_sequence + sequence'}
        else:
          A_    :			Tuple									=    OrderedDict(
              [
                  ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                  ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                  ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                  ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
              ]    )

        return common_inputs

      @property
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        if self.task in ["default", "seq2seq-lm"]:
          A_    :			int									=    super().outputs
        else:
          A_    :			str									=    super(snake_case_   , self    ).outputs
          if self.use_past:
            A_		,			A_    :			Union[str, Any]									=    self.num_layers
            for i in range(snake_case_    ):
              A_    :			Tuple									=    {0: 'batch', 2: 'past_sequence + sequence'}
              A_    :			Any									=    {0: 'batch', 2: 'past_sequence + sequence'}
        return common_outputs

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = -1   , snake_case_ = -1   , snake_case_ = False   , snake_case_ = None   , ):

        """simple docstring"""

        A_    :			List[str]									=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_    )

        # Generate decoder inputs
        A_    :			Any									=    seq_length if not self.use_past else 1
        A_    :			Tuple									=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_    )
        A_    :			Tuple									=    {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
        A_    :			Tuple									=    dict(**snake_case_   , **snake_case_    )

        if self.use_past:
          if not is_torch_available():
            raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'    )
          else:
            import torch
          A_		,			A_    :			Optional[int]									=    common_inputs['input_ids'].shape
          A_    :			List[str]									=    common_inputs['decoder_input_ids'].shape[1]
          A_		,			A_    :			Union[str, Any]									=    self.num_attention_heads
          A_    :			Any									=    (
              batch,
              num_encoder_attention_heads,
              encoder_seq_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )
          A_    :			Dict									=    decoder_seq_length + 3
          A_    :			Any									=    (
              batch,
              num_decoder_attention_heads,
              decoder_past_length,
              self._config.hidden_size // num_decoder_attention_heads,
          )

          A_    :			Optional[int]									=    torch.cat(
              [common_inputs['decoder_attention_mask'], torch.ones(snake_case_   , snake_case_    )]   , dim=1    )

          A_    :			str									=    []
          # If the number of encoder and decoder layers are present in the model configuration, both are considered
          A_		,			A_    :			Optional[Any]									=    self.num_layers
          A_    :			Tuple									=    min(snake_case_   , snake_case_    )
          A_    :			Optional[int]									=    max(snake_case_   , snake_case_    ) - min_num_layers
          A_    :			Optional[int]									=    'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

          for _ in range(snake_case_    ):
            common_inputs["past_key_values"].append(
                (
                    torch.zeros(snake_case_    ),
                    torch.zeros(snake_case_    ),
                    torch.zeros(snake_case_    ),
                    torch.zeros(snake_case_    ),
                )    )
          # TODO: test this.
          A_    :			Optional[int]									=    encoder_shape if remaining_side_name == 'encoder' else decoder_shape
          for _ in range(snake_case_   , snake_case_    ):
            common_inputs["past_key_values"].append((torch.zeros(snake_case_    ), torch.zeros(snake_case_    ))    )
        return common_inputs

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = -1   , snake_case_ = -1   , snake_case_ = False   , snake_case_ = None   , ):

        """simple docstring"""

        A_    :			int									=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            snake_case_   , snake_case_   , snake_case_   , snake_case_   , snake_case_    )

        if self.use_past:
          if not is_torch_available():
            raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'    )
          else:
            import torch
          A_		,			A_    :			Optional[int]									=    common_inputs['input_ids'].shape
          # Not using the same length for past_key_values
          A_    :			Optional[int]									=    seqlen + 2
          A_		,			A_    :			Optional[Any]									=    self.num_layers
          A_		,			A_    :			Optional[int]									=    self.num_attention_heads
          A_    :			Optional[Any]									=    (
              batch,
              num_encoder_attention_heads,
              past_key_values_length,
              self._config.hidden_size // num_encoder_attention_heads,
          )

          A_    :			str									=    common_inputs['attention_mask'].dtype
          A_    :			Union[str, Any]									=    torch.cat(
              [common_inputs['attention_mask'], torch.ones(snake_case_   , snake_case_   , dtype=snake_case_    )]   , dim=1    )
          A_    :			str									=    [
              (torch.zeros(snake_case_    ), torch.zeros(snake_case_    )) for _ in range(snake_case_    )
          ]
        return common_inputs

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = -1   , snake_case_ = -1   , snake_case_ = False   , snake_case_ = None   , ):

        """simple docstring"""

        A_    :			Optional[Any]									=    compute_effective_axis_dimension(
            snake_case_   , fixed_dimension=OnnxConfig.default_fixed_batch   , num_token_to_add=0    )

        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
        A_    :			List[str]									=    tokenizer.num_special_tokens_to_add(snake_case_    )
        A_    :			List[Any]									=    compute_effective_axis_dimension(
            snake_case_   , fixed_dimension=OnnxConfig.default_fixed_sequence   , num_token_to_add=snake_case_    )

        # Generate dummy inputs according to compute batch and sequence
        A_    :			Any									=    [' '.join([tokenizer.unk_token]    ) * seq_length] * batch_size
        A_    :			str									=    dict(tokenizer(snake_case_   , return_tensors=snake_case_    )    )
        return common_inputs

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = -1   , snake_case_ = -1   , snake_case_ = False   , snake_case_ = None   , ):

        """simple docstring"""

        if self.task in ["default", "seq2seq-lm"]:
          A_    :			Tuple									=    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
              snake_case_   , batch_size=snake_case_   , seq_length=snake_case_   , is_pair=snake_case_   , framework=snake_case_    )

        elif self.task == "causal-lm":
          A_    :			Tuple									=    self._generate_dummy_inputs_for_causal_lm(
              snake_case_   , batch_size=snake_case_   , seq_length=snake_case_   , is_pair=snake_case_   , framework=snake_case_    )
        else:
          A_    :			Optional[Any]									=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              snake_case_   , batch_size=snake_case_   , seq_length=snake_case_   , is_pair=snake_case_   , framework=snake_case_    )

        return common_inputs

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_   , snake_case_    ):

        """simple docstring"""

        if self.task in ["default", "seq2seq-lm"]:
          A_    :			Optional[int]									=    super()._flatten_past_key_values_(snake_case_   , snake_case_   , snake_case_   , snake_case_    )
        else:
          A_    :			Optional[Any]									=    super(snake_case_   , self    )._flatten_past_key_values_(
              snake_case_   , snake_case_   , snake_case_   , snake_case_    )







import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import (
		    MODEL_MAPPING,
		    DataaVecVisionForImageClassification,
		    DataaVecVisionForSemanticSegmentation,
		    DataaVecVisionModel,
		)
		from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import BeitImageProcessor



class  __UpperCAmelCase						:







			def __init__(      self:    str ,      UpperCAmelCase_:    List[Any] ,      UpperCAmelCase_:    Tuple=100 ,      UpperCAmelCase_:    str=13 ,      UpperCAmelCase_:    str=30 ,      UpperCAmelCase_:    Tuple=2 ,      UpperCAmelCase_:    Tuple=3 ,      UpperCAmelCase_:    List[str]=True ,      UpperCAmelCase_:    List[Any]=True ,      UpperCAmelCase_:    Optional[int]=32 ,      UpperCAmelCase_:    Optional[int]=4 ,      UpperCAmelCase_:    Tuple=4 ,      UpperCAmelCase_:    List[Any]=37 ,      UpperCAmelCase_:    Optional[Any]="gelu" ,      UpperCAmelCase_:    Optional[int]=0.1 ,      UpperCAmelCase_:    int=0.1 ,      UpperCAmelCase_:    Dict=10 ,      UpperCAmelCase_:    Dict=0.02 ,      UpperCAmelCase_:    Union[str, Any]=3 ,      UpperCAmelCase_:    List[Any]=None ,      UpperCAmelCase_:    Any=[0, 1, 2, 3] ,      ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    parent
									_SCREAMING_SNAKE_CASE       =    100
									_SCREAMING_SNAKE_CASE       =    batch_size
									_SCREAMING_SNAKE_CASE       =    image_size
									_SCREAMING_SNAKE_CASE       =    patch_size
									_SCREAMING_SNAKE_CASE       =    num_channels
									_SCREAMING_SNAKE_CASE       =    is_training
									_SCREAMING_SNAKE_CASE       =    use_labels
									_SCREAMING_SNAKE_CASE       =    hidden_size
									_SCREAMING_SNAKE_CASE       =    num_hidden_layers
									_SCREAMING_SNAKE_CASE       =    num_attention_heads
									_SCREAMING_SNAKE_CASE       =    intermediate_size
									_SCREAMING_SNAKE_CASE       =    hidden_act
									_SCREAMING_SNAKE_CASE       =    hidden_dropout_prob
									_SCREAMING_SNAKE_CASE       =    attention_probs_dropout_prob
									_SCREAMING_SNAKE_CASE       =    type_sequence_label_size
									_SCREAMING_SNAKE_CASE       =    initializer_range
									_SCREAMING_SNAKE_CASE       =    scope
									_SCREAMING_SNAKE_CASE       =    out_indices
									_SCREAMING_SNAKE_CASE       =    num_labels

									# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									_SCREAMING_SNAKE_CASE       =    (image_size // patch_size) ** 2
									_SCREAMING_SNAKE_CASE       =    num_patches + 1







			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

									_SCREAMING_SNAKE_CASE       =    None
									_SCREAMING_SNAKE_CASE       =    None
									if self.use_labels:
															_SCREAMING_SNAKE_CASE       =    ids_tensor([self.batch_size] ,      self.type_sequence_label_size	)
															_SCREAMING_SNAKE_CASE       =    ids_tensor([self.batch_size, self.image_size, self.image_size] ,      self.num_labels	)

									_SCREAMING_SNAKE_CASE       =    self.get_config()

									return config, pixel_values, labels, pixel_labels







			def 				UpperCamelCase (      self:    Any	):







									'''simple docstring'''




									return DataaVecVisionConfig(
									    vocab_size=self.vocab_size ,      image_size=self.image_size ,      patch_size=self.patch_size ,      num_channels=self.num_channels ,      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 ,      is_decoder=UpperCAmelCase_ ,      initializer_range=self.initializer_range ,      out_indices=self.out_indices ,      )







			def 				UpperCamelCase (      self:    Optional[Any] ,      UpperCAmelCase_:    str ,      UpperCAmelCase_:    Optional[int] ,      UpperCAmelCase_:    Optional[Any] ,      UpperCAmelCase_:    List[Any]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    DataaVecVisionModel(config=UpperCAmelCase_	)
									model.to(UpperCAmelCase_	)
									model.eval()
									_SCREAMING_SNAKE_CASE       =    model(UpperCAmelCase_	)
									# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
									_SCREAMING_SNAKE_CASE       =    (self.image_size // self.patch_size) ** 2
									self.parent.assertEqual(result.last_hidden_state.shape ,      (self.batch_size, num_patches + 1, self.hidden_size)	)







			def 				UpperCamelCase (      self:    Union[str, Any] ,      UpperCAmelCase_:    Tuple ,      UpperCAmelCase_:    Optional[int] ,      UpperCAmelCase_:    Optional[int] ,      UpperCAmelCase_:    str	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.type_sequence_label_size
									_SCREAMING_SNAKE_CASE       =    DataaVecVisionForImageClassification(UpperCAmelCase_	)
									model.to(UpperCAmelCase_	)
									model.eval()
									_SCREAMING_SNAKE_CASE       =    model(UpperCAmelCase_ ,      labels=UpperCAmelCase_	)
									self.parent.assertEqual(result.logits.shape ,      (self.batch_size, self.type_sequence_label_size)	)







			def 				UpperCamelCase (      self:    List[str] ,      UpperCAmelCase_:    str ,      UpperCAmelCase_:    List[Any] ,      UpperCAmelCase_:    Tuple ,      UpperCAmelCase_:    List[str]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.num_labels
									_SCREAMING_SNAKE_CASE       =    DataaVecVisionForSemanticSegmentation(UpperCAmelCase_	)
									model.to(UpperCAmelCase_	)
									model.eval()
									_SCREAMING_SNAKE_CASE       =    model(UpperCAmelCase_	)
									self.parent.assertEqual(
									    result.logits.shape ,      (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)
									_SCREAMING_SNAKE_CASE       =    model(UpperCAmelCase_ ,      labels=UpperCAmelCase_	)
									self.parent.assertEqual(
									    result.logits.shape ,      (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)	)







			def 				UpperCamelCase (      self:    Optional[Any]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.prepare_config_and_inputs()
									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    config_and_inputs
									_SCREAMING_SNAKE_CASE       =    {"""pixel_values""": pixel_values}
									return config, inputs_dict



@require_torch
class  __UpperCAmelCase						(_UpperCAmelCase			,_UpperCAmelCase			,unittest.TestCase       ):
			__snake_case							: Tuple							=				(
			    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			__snake_case							: Optional[Any]							=				(
			    {
			        "feature-extraction": DataaVecVisionModel,
			        "image-classification": DataaVecVisionForImageClassification,
			        "image-segmentation": DataaVecVisionForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			__snake_case							: Dict							=				False
			__snake_case							: List[str]							=				False
			__snake_case							: Dict							=				False







			def 				UpperCamelCase (      self:    str	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    DataaVecVisionModelTester(self	)
									_SCREAMING_SNAKE_CASE       =    ConfigTester(
									    self ,      config_class=UpperCAmelCase_ ,      has_text_modality=UpperCAmelCase_ ,      hidden_size=37	)







			def 				UpperCamelCase (      self:    Optional[Any]	):







									'''simple docstring'''




									self.config_tester.run_common_tests()







			def 				UpperCamelCase (      self:    str	):







									'''simple docstring'''




									pass







			@require_torch_multi_gpu
			@unittest.skip(
			    reason="""Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""	)
			def 				UpperCamelCase (      self:    List[str]	):







									'''simple docstring'''




									pass







			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE       =    model_class(UpperCAmelCase_	)
															self.assertIsInstance(model.get_input_embeddings() ,      (nn.Module)	)
															_SCREAMING_SNAKE_CASE       =    model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(UpperCAmelCase_ ,      nn.Linear	)	)







			def 				UpperCamelCase (      self:    Optional[int]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE       =    model_class(UpperCAmelCase_	)
															_SCREAMING_SNAKE_CASE       =    inspect.signature(model.forward	)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															_SCREAMING_SNAKE_CASE       =    [*signature.parameters.keys()]

															_SCREAMING_SNAKE_CASE       =    ["""pixel_values"""]
															self.assertListEqual(arg_names[:1] ,      UpperCAmelCase_	)







			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*UpperCAmelCase_	)







			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_segmentation(*UpperCAmelCase_	)







			def 				UpperCamelCase (      self:    Optional[int]	):







									'''simple docstring'''




									if not self.model_tester.is_training:
															return

									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    self.model_tester.prepare_config_and_inputs_for_common()
									_SCREAMING_SNAKE_CASE       =    True

									for model_class in self.all_model_classes:
															if model_class in [*get_values(UpperCAmelCase_	)]:
																					continue

															_SCREAMING_SNAKE_CASE       =    model_class(UpperCAmelCase_	)
															model.to(UpperCAmelCase_	)
															model.train()
															_SCREAMING_SNAKE_CASE       =    self._prepare_for_class(UpperCAmelCase_ ,      UpperCAmelCase_ ,      return_labels=UpperCAmelCase_	)
															_SCREAMING_SNAKE_CASE       =    model(**UpperCAmelCase_	).loss
															loss.backward()







			def 				UpperCamelCase (      self:    Dict	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    self.model_tester.prepare_config_and_inputs_for_common()
									if not self.model_tester.is_training:
															return

									_SCREAMING_SNAKE_CASE       =    False
									_SCREAMING_SNAKE_CASE       =    True

									for model_class in self.all_model_classes:
															if model_class in [*get_values(UpperCAmelCase_	)] or not model_class.supports_gradient_checkpointing:
																					continue
															# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
															# this can then be incorporated into _prepare_for_class in test_modeling_common.py
															elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
																					_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    inputs_dict["""pixel_values"""].shape
																					_SCREAMING_SNAKE_CASE       =    torch.zeros(
																					    [self.model_tester.batch_size, height, width] ,      device=UpperCAmelCase_	).long()
															_SCREAMING_SNAKE_CASE       =    model_class(UpperCAmelCase_	)
															model.gradient_checkpointing_enable()
															model.to(UpperCAmelCase_	)
															model.train()
															_SCREAMING_SNAKE_CASE       =    self._prepare_for_class(UpperCAmelCase_ ,      UpperCAmelCase_ ,      return_labels=UpperCAmelCase_	)
															_SCREAMING_SNAKE_CASE       =    model(**UpperCAmelCase_	).loss
															loss.backward()







			def 				UpperCamelCase (      self:    str	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    self.model_tester.prepare_config_and_inputs_for_common()

									_SCREAMING_SNAKE_CASE       =    _config_zero_init(UpperCAmelCase_	)
									for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE       =    model_class(config=UpperCAmelCase_	)
															for name, param in model.named_parameters():
																					# we skip lambda parameters as these require special initial values
																					# determined by config.layer_scale_init_value
																					if "lambda" in name:
																											continue
																					if param.requires_grad:
																											self.assertIn(
																											    ((param.data.mean() * 1E9).round() / 1E9).item() ,      [0.0, 1.0] ,      msg=F'Parameter {name} of model {model_class} seems not properly initialized' ,      )







			def 				UpperCamelCase (      self:    int ,      UpperCAmelCase_:    Optional[Any] ,      UpperCAmelCase_:    int ,      UpperCAmelCase_:    Any ,      UpperCAmelCase_:    Union[str, Any]=2E-4 ,      UpperCAmelCase_:    Tuple="outputs" ,      UpperCAmelCase_:    Union[str, Any]=None	):







									'''simple docstring'''




									super().check_pt_tf_outputs(UpperCAmelCase_ ,      UpperCAmelCase_ ,      UpperCAmelCase_ ,      UpperCAmelCase_ ,      UpperCAmelCase_ ,      UpperCAmelCase_	)







			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_	)







			@slow
			def 				UpperCamelCase (      self:    int	):







									'''simple docstring'''




									for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE       =    DataaVecVisionModel.from_pretrained(UpperCAmelCase_	)
															self.assertIsNotNone(UpperCAmelCase_	)

def 			__lowerCamelCase     (						)				->    int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
						return image




@require_torch
@require_vision
class  __UpperCAmelCase						(unittest.TestCase       ):







			@cached_property
			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									return (
									    BeitImageProcessor.from_pretrained("""facebook/data2vec-vision-base-ft1k"""	) if is_vision_available() else None
									)







			@slow
			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    DataaVecVisionForImageClassification.from_pretrained("""facebook/data2vec-vision-base-ft1k"""	).to(
									    UpperCAmelCase_	)

									_SCREAMING_SNAKE_CASE       =    self.default_image_processor
									_SCREAMING_SNAKE_CASE       =    prepare_img()
									_SCREAMING_SNAKE_CASE       =    image_processor(images=UpperCAmelCase_ ,      return_tensors="""pt"""	).to(UpperCAmelCase_	)

									# forward pass
									with torch.no_grad():
															_SCREAMING_SNAKE_CASE       =    model(**UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    outputs.logits

									# verify the logits
									_SCREAMING_SNAKE_CASE       =    torch.Size((1, 1_000)	)
									self.assertEqual(logits.shape ,      UpperCAmelCase_	)

									_SCREAMING_SNAKE_CASE       =    torch.tensor([0.32_77, -0.13_95, 0.09_11]	).to(UpperCAmelCase_	)

									self.assertTrue(torch.allclose(logits[0, :3] ,      UpperCAmelCase_ ,      atol=1E-4	)	)

									_SCREAMING_SNAKE_CASE       =    [model.config.labelaid[i] for i in ["""remote control, remote""", """tabby, tabby cat"""]]
									self.assertEqual(logits[0].topk(2	).indices.cpu().tolist() ,      UpperCAmelCase_	)

