



"""simple docstring"""







import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM







@require_torch
class       UpperCamelCase__   :




      """simple docstring"""




      def __init__(      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=9_9    ,			SCREAMING_SNAKE_CASE_				:      Any=1_3    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=1_6    ,			SCREAMING_SNAKE_CASE_				:      str=7    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=True    ,			SCREAMING_SNAKE_CASE_				:      Tuple=True    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=True    ,			SCREAMING_SNAKE_CASE_				:      Dict=False    ,			SCREAMING_SNAKE_CASE_				:      Dict=True    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=2    ,			SCREAMING_SNAKE_CASE_				:      Dict=3_2    ,			SCREAMING_SNAKE_CASE_				:      Dict=4    ,			SCREAMING_SNAKE_CASE_				:      List[str]=4    ,			SCREAMING_SNAKE_CASE_				:      int=3_0    ,			SCREAMING_SNAKE_CASE_				:      str=0    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=1    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=2    ,			SCREAMING_SNAKE_CASE_				:      int=None    ,			):
        lowerCAmelCase_   :				List[str]								= parent
        lowerCAmelCase_   :				Optional[int]								= batch_size
        lowerCAmelCase_   :				Tuple								= decoder_seq_length
        # For common tests
        lowerCAmelCase_   :				List[str]								= self.decoder_seq_length
        lowerCAmelCase_   :				Union[str, Any]								= is_training
        lowerCAmelCase_   :				Tuple								= use_attention_mask
        lowerCAmelCase_   :				Tuple								= use_labels

        lowerCAmelCase_   :				List[Any]								= vocab_size
        lowerCAmelCase_   :				Union[str, Any]								= d_model
        lowerCAmelCase_   :				Tuple								= d_model
        lowerCAmelCase_   :				Any								= decoder_layers
        lowerCAmelCase_   :				Tuple								= decoder_layers
        lowerCAmelCase_   :				Dict								= decoder_ffn_dim
        lowerCAmelCase_   :				Tuple								= decoder_attention_heads
        lowerCAmelCase_   :				Any								= decoder_attention_heads
        lowerCAmelCase_   :				Union[str, Any]								= eos_token_id
        lowerCAmelCase_   :				int								= bos_token_id
        lowerCAmelCase_   :				Optional[Any]								= pad_token_id
        lowerCAmelCase_   :				Any								= decoder_start_token_id
        lowerCAmelCase_   :				Optional[int]								= use_cache
        lowerCAmelCase_   :				str								= max_position_embeddings

        lowerCAmelCase_   :				Dict								= None
        lowerCAmelCase_   :				str								= decoder_seq_length
        lowerCAmelCase_   :				Optional[Any]								= 2
        lowerCAmelCase_   :				List[Any]								= 1



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        lowerCAmelCase_   :				Union[str, Any]								= ids_tensor([self.batch_size, self.decoder_seq_length]    ,			self.vocab_size       )

        lowerCAmelCase_   :				Optional[Any]								= None
        if self.use_attention_mask:
          lowerCAmelCase_   :				Any								= ids_tensor([self.batch_size, self.decoder_seq_length]    ,			vocab_size=2       )

        lowerCAmelCase_   :				int								= None
        if self.use_labels:
          lowerCAmelCase_   :				Union[str, Any]								= ids_tensor([self.batch_size, self.decoder_seq_length]    ,			self.vocab_size       )

        lowerCAmelCase_   :				Any								= TrOCRConfig(
            vocab_size=self.vocab_size    ,			d_model=self.d_model    ,			decoder_layers=self.decoder_layers    ,			decoder_ffn_dim=self.decoder_ffn_dim    ,			decoder_attention_heads=self.decoder_attention_heads    ,			eos_token_id=self.eos_token_id    ,			bos_token_id=self.bos_token_id    ,			use_cache=self.use_cache    ,			pad_token_id=self.pad_token_id    ,			decoder_start_token_id=self.decoder_start_token_id    ,			max_position_embeddings=self.max_position_embeddings    ,			)

        return (config, input_ids, attention_mask, lm_labels)



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			):
        lowerCAmelCase_   :				Union[str, Any]								= True
        lowerCAmelCase_   :				int								= TrOCRDecoder(config=SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       ).eval()
        lowerCAmelCase_   :				int								= input_ids[:2]

        input_ids[input_ids == 0] += 1
        # first forward pass
        lowerCAmelCase_   :				Union[str, Any]								= model(SCREAMING_SNAKE_CASE_    ,			use_cache=SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Union[str, Any]								= model(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				int								= model(SCREAMING_SNAKE_CASE_    ,			use_cache=SCREAMING_SNAKE_CASE_       )

        self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_       ) == len(SCREAMING_SNAKE_CASE_       )       )
        self.parent.assertTrue(len(SCREAMING_SNAKE_CASE_       ) == len(SCREAMING_SNAKE_CASE_       ) + 1       )

        lowerCAmelCase_   :				Optional[Any]								= outputs['past_key_values']

        # create hypothetical next token and extent to next_input_ids
        lowerCAmelCase_   :				Dict								= ids_tensor((2, 1)    ,			config.vocab_size - 1       ) + 1

        # append to next input_ids and
        lowerCAmelCase_   :				Optional[Any]								= torch.cat([input_ids, next_tokens]    ,			dim=-1       )

        lowerCAmelCase_   :				List[str]								= model(SCREAMING_SNAKE_CASE_       )['last_hidden_state']
        lowerCAmelCase_   :				Optional[Any]								= model(SCREAMING_SNAKE_CASE_    ,			past_key_values=SCREAMING_SNAKE_CASE_       )['last_hidden_state']

        # select random slice
        lowerCAmelCase_   :				Any								= ids_tensor((1,)    ,			output_from_past.shape[-1]       ).item()
        lowerCAmelCase_   :				Optional[Any]								= output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
        lowerCAmelCase_   :				List[Any]								= output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        assert torch.allclose(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			atol=1E-3       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        lowerCAmelCase_   :				Optional[int]								= self.prepare_config_and_inputs()
        lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_   :				Optional[int]								= config_and_inputs

        lowerCAmelCase_   :				Optional[Any]								= {'input_ids': input_ids, 'attention_mask': attention_mask}
        return config, inputs_dict







@require_torch
class       UpperCamelCase__   (	lowercase_,					lowercase_,					lowercase_,					unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
      _SCREAMING_SNAKE_CASE  				=     (TrOCRForCausalLM,) if is_torch_available() else ()
      _SCREAMING_SNAKE_CASE  				=     {"""text-generation""": TrOCRForCausalLM} if is_torch_available() else {}
      _SCREAMING_SNAKE_CASE  				=     True
      _SCREAMING_SNAKE_CASE  				=     False



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        lowerCAmelCase_   :				List[Any]								= TrOCRStandaloneDecoderModelTester(self    ,			is_training=SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Any								= ConfigTester(self    ,			config_class=SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        pass



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        pass



      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple       ):
        pass



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        self.config_tester.run_common_tests()



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        lowerCAmelCase_   :				int								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past(*SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        return





      @unittest.skip('The model doesn\'t support left padding'       )  # and it's not used enough to be worth fixing :)
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        pass










"""simple docstring"""







from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
     import tensorflow as tf

     from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor







class       UpperCamelCase__   :




      """simple docstring"""




      def __init__(      self				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=3    ,			SCREAMING_SNAKE_CASE_				:      Any=3_2    ,			SCREAMING_SNAKE_CASE_				:      int=3    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=1_0    ,			SCREAMING_SNAKE_CASE_				:      Dict=[1_0, 2_0, 3_0, 4_0]    ,			SCREAMING_SNAKE_CASE_				:      int=[1, 1, 2, 1]    ,			SCREAMING_SNAKE_CASE_				:      int=True    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=True    ,			SCREAMING_SNAKE_CASE_				:      int="relu"    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=3    ,			SCREAMING_SNAKE_CASE_				:      List[str]=None    ,			):
        lowerCAmelCase_   :				Optional[int]								= parent
        lowerCAmelCase_   :				Union[str, Any]								= batch_size
        lowerCAmelCase_   :				Optional[int]								= image_size
        lowerCAmelCase_   :				Optional[int]								= num_channels
        lowerCAmelCase_   :				Any								= embeddings_size
        lowerCAmelCase_   :				Dict								= hidden_sizes
        lowerCAmelCase_   :				Any								= depths
        lowerCAmelCase_   :				Optional[int]								= is_training
        lowerCAmelCase_   :				int								= use_labels
        lowerCAmelCase_   :				List[Any]								= hidden_act
        lowerCAmelCase_   :				Dict								= num_labels
        lowerCAmelCase_   :				Optional[int]								= scope
        lowerCAmelCase_   :				Tuple								= len(SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        lowerCAmelCase_   :				List[str]								= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

        lowerCAmelCase_   :				Union[str, Any]								= None
        if self.use_labels:
          lowerCAmelCase_   :				int								= ids_tensor([self.batch_size]    ,			self.num_labels       )

        lowerCAmelCase_   :				List[str]								= self.get_config()
        return config, pixel_values, labels



      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        return RegNetConfig(
            num_channels=self.num_channels    ,			embeddings_size=self.embeddings_size    ,			hidden_sizes=self.hidden_sizes    ,			depths=self.depths    ,			hidden_act=self.hidden_act    ,			num_labels=self.num_labels    ,			)



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      int       ):
        lowerCAmelCase_   :				List[Any]								= TFRegNetModel(config=SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				List[Any]								= model(SCREAMING_SNAKE_CASE_    ,			training=SCREAMING_SNAKE_CASE_       )
        # expected last hidden states: B, C, H // 32, W // 32
        self.parent.assertEqual(
            result.last_hidden_state.shape    ,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)    ,			)



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        lowerCAmelCase_   :				int								= self.num_labels
        lowerCAmelCase_   :				int								= TFRegNetForImageClassification(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Dict								= model(SCREAMING_SNAKE_CASE_    ,			labels=SCREAMING_SNAKE_CASE_    ,			training=SCREAMING_SNAKE_CASE_       )
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_labels)       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        lowerCAmelCase_   :				str								= self.prepare_config_and_inputs()
        lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_   :				Tuple								= config_and_inputs
        lowerCAmelCase_   :				Optional[Any]								= {'pixel_values': pixel_values}
        return config, inputs_dict







@require_tf
class       UpperCamelCase__   (	lowercase_,					lowercase_,					unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
      _SCREAMING_SNAKE_CASE  				=     (
          {"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
          if is_tf_available()
          else {}
      )

      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				List[Any]								= TFRegNetModelTester(self       )
        lowerCAmelCase_   :				Tuple								= ConfigTester(self    ,			config_class=SCREAMING_SNAKE_CASE_    ,			has_text_modality=SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        return



      @unittest.skip(reason='RegNet does not use inputs_embeds'       )
      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        pass



      @unittest.skipIf(
          not is_tf_available() or len(tf.config.list_physical_devices('GPU'       )       ) == 0    ,			reason='TF does not support backprop for grouped convolutions on CPU.'    ,			)
      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple       ):
        super().test_keras_fit()



      @unittest.skip(reason='RegNet does not support input and output embeddings'       )
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        pass



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        lowerCAmelCase_						,lowerCAmelCase_   :				Optional[int]								= self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowerCAmelCase_   :				int								= model_class(SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Dict								= inspect.signature(model.call       )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          lowerCAmelCase_   :				List[Any]								= [*signature.parameters.keys()]

          lowerCAmelCase_   :				Any								= ['pixel_values']
          self.assertListEqual(arg_names[:1]    ,			SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        lowerCAmelCase_   :				Dict								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        def check_hidden_states_output(SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      Tuple       ):
          lowerCAmelCase_   :				List[Any]								= model_class(SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Union[str, Any]								= model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )    ,			training=SCREAMING_SNAKE_CASE_       )

          lowerCAmelCase_   :				List[str]								= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          lowerCAmelCase_   :				Union[str, Any]								= self.model_tester.num_stages
          self.assertEqual(len(SCREAMING_SNAKE_CASE_       )    ,			expected_num_stages + 1       )

          # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
          self.assertListEqual(
              list(hidden_states[0].shape[-2:]       )    ,			[self.model_tester.image_size // 2, self.model_tester.image_size // 2]    ,			)

        lowerCAmelCase_						,lowerCAmelCase_   :				List[Any]								= self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase_   :				Optional[int]								= ['basic', 'bottleneck']
        for model_class in self.all_model_classes:
          for layer_type in layers_type:
            lowerCAmelCase_   :				str								= layer_type
            lowerCAmelCase_   :				List[str]								= 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"]
            lowerCAmelCase_   :				List[str]								= True

            check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        lowerCAmelCase_						,lowerCAmelCase_   :				int								= self.model_tester.prepare_config_and_inputs_for_common()

        def check_equivalence(SCREAMING_SNAKE_CASE_				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      str={}       ):
          lowerCAmelCase_   :				Optional[Any]								= model(SCREAMING_SNAKE_CASE_    ,			return_dict=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				List[Any]								= model(SCREAMING_SNAKE_CASE_    ,			return_dict=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       ).to_tuple()

          def recursive_check(SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      int       ):
            if isinstance(SCREAMING_SNAKE_CASE_    ,			(List, Tuple)       ):
              for tuple_iterable_value, dict_iterable_value in zip(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       ):
                recursive_check(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
            elif tuple_object is None:
              return
            else:
              self.assertTrue(
                  all(tf.equal(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )       )    ,			msg=(
                      'Tuple and dict output are not equal. Difference:'
                      F" {tf.math.reduce_max(tf.abs(tuple_object - dict_object       )       )}"
                  )    ,			)

            recursive_check(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )

        for model_class in self.all_model_classes:
          lowerCAmelCase_   :				Union[str, Any]								= model_class(SCREAMING_SNAKE_CASE_       )

          lowerCAmelCase_   :				List[Any]								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Union[str, Any]								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
          check_equivalence(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )

          lowerCAmelCase_   :				Optional[int]								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			return_labels=SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				str								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			return_labels=SCREAMING_SNAKE_CASE_       )
          check_equivalence(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )

          lowerCAmelCase_   :				Tuple								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				int								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
          check_equivalence(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			{'output_hidden_states': True}       )

          lowerCAmelCase_   :				List[Any]								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			return_labels=SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Any								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			return_labels=SCREAMING_SNAKE_CASE_       )
          check_equivalence(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			{'output_hidden_states': True}       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        lowerCAmelCase_   :				List[str]								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_       )





      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_   :				int								= TFRegNetModel.from_pretrained(SCREAMING_SNAKE_CASE_       )
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )




def 				UpperCamelCase_    (    )						->   List[str]:

  """simple docstring"""




  lowerCAmelCase_   :				Optional[Any]								= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
  return image







@require_tf
@require_vision
class       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      @cached_property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        return (
            AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
            if is_vision_available()
            else None
        )





      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				int								= TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

        lowerCAmelCase_   :				Tuple								= self.default_image_processor
        lowerCAmelCase_   :				Dict								= prepare_img()
        lowerCAmelCase_   :				Union[str, Any]								= image_processor(images=SCREAMING_SNAKE_CASE_    ,			return_tensors='tf'       )

        # forward pass
        lowerCAmelCase_   :				List[str]								= model(**SCREAMING_SNAKE_CASE_    ,			training=SCREAMING_SNAKE_CASE_       )

        # verify the logits
        lowerCAmelCase_   :				Optional[Any]								= tf.TensorShape((1, 1_0_0_0)       )
        self.assertEqual(outputs.logits.shape    ,			SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				List[Any]								= tf.constant([-0.41_80, -1.50_51, -3.48_36]       )

        tf.debugging.assert_near(outputs.logits[0, :3]    ,			SCREAMING_SNAKE_CASE_    ,			atol=1E-4       )





