






'''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


SCREAMING_SNAKE_CASE__											=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__											=     {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class        a_		(    lowerCamelCase       ):
  lowercase  	=   """marian"""
  lowercase  	=   ["""past_key_values"""]
  lowercase  	=   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE=58101    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=1024    ,	_SCREAMING_SNAKE_CASE=12    ,	_SCREAMING_SNAKE_CASE=4096    ,	_SCREAMING_SNAKE_CASE=16    ,	_SCREAMING_SNAKE_CASE=12    ,	_SCREAMING_SNAKE_CASE=4096    ,	_SCREAMING_SNAKE_CASE=16    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE="gelu"    ,	_SCREAMING_SNAKE_CASE=1024    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=0.0_2    ,	_SCREAMING_SNAKE_CASE=58100    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE=58100    ,	_SCREAMING_SNAKE_CASE=0    ,	_SCREAMING_SNAKE_CASE=0    ,	_SCREAMING_SNAKE_CASE=True    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      vocab_size
        UpperCamelCase       						=      decoder_vocab_size or vocab_size
        UpperCamelCase       						=      max_position_embeddings
        UpperCamelCase       						=      d_model
        UpperCamelCase       						=      encoder_ffn_dim
        UpperCamelCase       						=      encoder_layers
        UpperCamelCase       						=      encoder_attention_heads
        UpperCamelCase       						=      decoder_ffn_dim
        UpperCamelCase       						=      decoder_layers
        UpperCamelCase       						=      decoder_attention_heads
        UpperCamelCase       						=      dropout
        UpperCamelCase       						=      attention_dropout
        UpperCamelCase       						=      activation_dropout
        UpperCamelCase       						=      activation_function
        UpperCamelCase       						=      init_std
        UpperCamelCase       						=      encoder_layerdrop
        UpperCamelCase       						=      decoder_layerdrop
        UpperCamelCase       						=      use_cache
        UpperCamelCase       						=      encoder_layers
        UpperCamelCase       						=      scale_embedding  # scale factor will be sqrt(d_model) if True
        UpperCamelCase       						=      share_encoder_decoder_embeddings
        super().__init__(
            pad_token_id=_SCREAMING_SNAKE_CASE    ,	eos_token_id=_SCREAMING_SNAKE_CASE    ,	is_encoder_decoder=_SCREAMING_SNAKE_CASE    ,	decoder_start_token_id=_SCREAMING_SNAKE_CASE    ,	forced_eos_token_id=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE    ,	)






class        a_		(    lowerCamelCase       ):

  @property
  # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
  def 						A__		(   self   )						->   Mapping[str, Mapping[int, str]]:



        """simple docstring"""






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

              if self.use_past:
                    UpperCamelCase       						=      {0: """batch"""}
                    UpperCamelCase       						=      {0: """batch""", 1: """past_decoder_sequence + sequence"""}
              else:
                    UpperCamelCase       						=      {0: """batch""", 1: """decoder_sequence"""}
                    UpperCamelCase       						=      {0: """batch""", 1: """decoder_sequence"""}

              if self.use_past:
                    self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE    ,	direction="""inputs"""   )
        elif self.task == "causal-lm":
              # TODO: figure this case out.
              UpperCamelCase       						=      OrderedDict(
                  [
                      ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                      ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                  ]   )
              if self.use_past:
                    UpperCamelCase						,UpperCamelCase       						=      self.num_layers
                    for i in range(_SCREAMING_SNAKE_CASE   ):
                          UpperCamelCase       						=      {0: """batch""", 2: """past_sequence + sequence"""}
                          UpperCamelCase       						=      {0: """batch""", 2: """past_sequence + sequence"""}
        else:
              UpperCamelCase       						=      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
  # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
  def 						A__		(   self   )						->   Mapping[str, Mapping[int, str]]:



        """simple docstring"""






        if self.task in ["default", "seq2seq-lm"]:
              UpperCamelCase       						=      super().outputs
        else:
              UpperCamelCase       						=      super(_SCREAMING_SNAKE_CASE    ,	self   ).outputs
              if self.use_past:
                    UpperCamelCase						,UpperCamelCase       						=      self.num_layers
                    for i in range(_SCREAMING_SNAKE_CASE   ):
                          UpperCamelCase       						=      {0: """batch""", 2: """past_sequence + sequence"""}
                          UpperCamelCase       						=      {0: """batch""", 2: """past_sequence + sequence"""}
        return common_outputs

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = -1    ,	_SCREAMING_SNAKE_CASE = -1    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = None    ,	)						->   Mapping[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      self._generate_dummy_inputs_for_encoder_and_decoder(
            _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

        # Generate decoder inputs
        UpperCamelCase       						=      seq_length if not self.use_past else 1
        UpperCamelCase       						=      self._generate_dummy_inputs_for_encoder_and_decoder(
            _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      {F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
        UpperCamelCase       						=      dict(**_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_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
              UpperCamelCase						,UpperCamelCase       						=      common_inputs["""input_ids"""].shape
              UpperCamelCase       						=      common_inputs["""decoder_input_ids"""].shape[1]
              UpperCamelCase						,UpperCamelCase       						=      self.num_attention_heads
              UpperCamelCase       						=      (
                  batch,
                  num_encoder_attention_heads,
                  encoder_seq_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )
              UpperCamelCase       						=      decoder_seq_length + 3
              UpperCamelCase       						=      (
                  batch,
                  num_decoder_attention_heads,
                  decoder_past_length,
                  self._config.hidden_size // num_decoder_attention_heads,
              )

              UpperCamelCase       						=      torch.cat(
                  [common_inputs["""decoder_attention_mask"""], torch.ones(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )]    ,	dim=1   )

              UpperCamelCase       						=      []
              # If the number of encoder and decoder layers are present in the model configuration, both are considered
              UpperCamelCase						,UpperCamelCase       						=      self.num_layers
              UpperCamelCase       						=      min(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
              UpperCamelCase       						=      max(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ) - min_num_layers
              UpperCamelCase       						=      """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = -1    ,	_SCREAMING_SNAKE_CASE = -1    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = None    ,	)						->   Mapping[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      self._generate_dummy_inputs_for_encoder_and_decoder(
            _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_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
              UpperCamelCase						,UpperCamelCase       						=      common_inputs["""input_ids"""].shape
              # Not using the same length for past_key_values
              UpperCamelCase       						=      seqlen + 2
              UpperCamelCase						,UpperCamelCase       						=      self.num_layers
              UpperCamelCase						,UpperCamelCase       						=      self.num_attention_heads
              UpperCamelCase       						=      (
                  batch,
                  num_encoder_attention_heads,
                  past_key_values_length,
                  self._config.hidden_size // num_encoder_attention_heads,
              )

              UpperCamelCase       						=      common_inputs["""attention_mask"""].dtype
              UpperCamelCase       						=      torch.cat(
                  [common_inputs["""attention_mask"""], torch.ones(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	dtype=_SCREAMING_SNAKE_CASE   )]    ,	dim=1   )
              UpperCamelCase       						=      [
                  (torch.zeros(_SCREAMING_SNAKE_CASE   ), torch.zeros(_SCREAMING_SNAKE_CASE   )) for _ in range(_SCREAMING_SNAKE_CASE   )
              ]
        return common_inputs

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = -1    ,	_SCREAMING_SNAKE_CASE = -1    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = None    ,	)						->   Mapping[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      compute_effective_axis_dimension(
            _SCREAMING_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
        UpperCamelCase       						=      tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      compute_effective_axis_dimension(
            _SCREAMING_SNAKE_CASE    ,	fixed_dimension=OnnxConfig.default_fixed_sequence    ,	num_token_to_add=_SCREAMING_SNAKE_CASE   )

        # Generate dummy inputs according to compute batch and sequence
        UpperCamelCase       						=      [""" """.join([tokenizer.unk_token]   ) * seq_length] * batch_size
        UpperCamelCase       						=      dict(tokenizer(_SCREAMING_SNAKE_CASE    ,	return_tensors=_SCREAMING_SNAKE_CASE   )   )
        return common_inputs

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = -1    ,	_SCREAMING_SNAKE_CASE = -1    ,	_SCREAMING_SNAKE_CASE = False    ,	_SCREAMING_SNAKE_CASE = None    ,	)						->   Mapping[str, Any]:



        """simple docstring"""






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

        else:
              UpperCamelCase       						=      self._generate_dummy_inputs_for_causal_lm(
                  _SCREAMING_SNAKE_CASE    ,	batch_size=_SCREAMING_SNAKE_CASE    ,	seq_length=_SCREAMING_SNAKE_CASE    ,	is_pair=_SCREAMING_SNAKE_CASE    ,	framework=_SCREAMING_SNAKE_CASE   )

        return common_inputs

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   List[Any]:



        """simple docstring"""






        if self.task in ["default", "seq2seq-lm"]:
              UpperCamelCase       						=      super()._flatten_past_key_values_(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
        else:
              UpperCamelCase       						=      super(_SCREAMING_SNAKE_CASE    ,	self   )._flatten_past_key_values_(
                  _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )


  @property
  def 						A__		(   self   )						->   float:



        """simple docstring"""






        return 1e-4








'''simple docstring'''

import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import MobileNetVaForImageClassification, MobileNetVaModel
     from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileNetVaImageProcessor






class        a_		(    lowerCamelCase       ):

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      self.config_class(**self.inputs_dict   )
        self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""tf_padding"""   )   )
        self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""depth_multiplier"""   )   )






class        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=13    ,	_SCREAMING_SNAKE_CASE=3    ,	_SCREAMING_SNAKE_CASE=32    ,	_SCREAMING_SNAKE_CASE=0.2_5    ,	_SCREAMING_SNAKE_CASE=8    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=1024    ,	_SCREAMING_SNAKE_CASE=32    ,	_SCREAMING_SNAKE_CASE="relu6"    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=0.0_2    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=10    ,	_SCREAMING_SNAKE_CASE=None    ,	)						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      parent
        UpperCamelCase       						=      batch_size
        UpperCamelCase       						=      num_channels
        UpperCamelCase       						=      image_size
        UpperCamelCase       						=      depth_multiplier
        UpperCamelCase       						=      min_depth
        UpperCamelCase       						=      tf_padding
        UpperCamelCase       						=      int(last_hidden_size * depth_multiplier   )
        UpperCamelCase       						=      output_stride
        UpperCamelCase       						=      hidden_act
        UpperCamelCase       						=      classifier_dropout_prob
        UpperCamelCase       						=      use_labels
        UpperCamelCase       						=      is_training
        UpperCamelCase       						=      num_labels
        UpperCamelCase       						=      initializer_range
        UpperCamelCase       						=      scope

  def 						A__		(   self   )						->   int:



        """simple docstring"""






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

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

        UpperCamelCase       						=      self.get_config()

        return config, pixel_values, labels, pixel_labels

  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        return MobileNetVaConfig(
            num_channels=self.num_channels    ,	image_size=self.image_size    ,	depth_multiplier=self.depth_multiplier    ,	min_depth=self.min_depth    ,	tf_padding=self.tf_padding    ,	hidden_act=self.hidden_act    ,	classifier_dropout_prob=self.classifier_dropout_prob    ,	initializer_range=self.initializer_range    ,	)

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      MobileNetVaModel(config=_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(
            result.last_hidden_state.shape    ,	(
                self.batch_size,
                self.last_hidden_size,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )    ,	)

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.num_labels
        UpperCamelCase       						=      MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE    ,	labels=_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)   )


  def 						A__		(   self   )						->   Any:



        """simple docstring"""






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






@require_torch
class        a_		(    lowerCamelCase			, lowerCamelCase			, unittest.TestCase       ):
  lowercase  	=   (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
  lowercase  	=   (
      {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
      if is_torch_available()
      else {}
  )

  lowercase  	=   False
  lowercase  	=   False
  lowercase  	=   False
  lowercase  	=   False

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      MobileNetVaModelTester(self   )
        UpperCamelCase       						=      MobileNetVaConfigTester(self    ,	config_class=_SCREAMING_SNAKE_CASE    ,	has_text_modality=_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        self.config_tester.run_common_tests()

  @unittest.skip(reason="""MobileNetV1 does not use inputs_embeds"""   )
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        pass

  @unittest.skip(reason="""MobileNetV1 does not support input and output embeddings"""   )
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        pass

  @unittest.skip(reason="""MobileNetV1 does not output attentions"""   )
  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        pass

  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

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

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

  def 						A__		(   self   )						->   str:



        """simple docstring"""






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

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        def check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      model_class(_SCREAMING_SNAKE_CASE   )
              model.to(_SCREAMING_SNAKE_CASE   )
              model.eval()

              with torch.no_grad():
                    UpperCamelCase       						=      model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )   )

              UpperCamelCase       						=      outputs.hidden_states

              UpperCamelCase       						=      26
              self.assertEqual(len(_SCREAMING_SNAKE_CASE   )    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCamelCase       						=      True
              check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCamelCase       						=      True

              check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   str:



        """simple docstring"""






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


  @slow
  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCamelCase       						=      MobileNetVaModel.from_pretrained(_SCREAMING_SNAKE_CASE   )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE   )







def 					lowercase__				(   )->				Optional[Any]:
      UpperCamelCase       						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
      return image






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

  @cached_property
  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        return (
            MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v1_1.0_224"""   ) if is_vision_available() else None
        )


  @slow
  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224"""   ).to(_SCREAMING_SNAKE_CASE   )

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

        # forward pass
        with torch.no_grad():
              UpperCamelCase       						=      model(**_SCREAMING_SNAKE_CASE   )

        # verify the logits
        UpperCamelCase       						=      torch.Size((1, 1001)   )
        self.assertEqual(outputs.logits.shape    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]   ).to(_SCREAMING_SNAKE_CASE   )

        self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,	_SCREAMING_SNAKE_CASE    ,	atol=1e-4   )   )
