





"""simple docstring"""




import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


a						: Tuple     	=			'''bert-base-cased'''
a						: List[Any]     	=			'''google/pegasus-xsum'''
a						: List[Any]     	=			[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
a						: Dict     	=			['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
a						: str     	=			'''patrickvonplaten/t5-tiny-random'''
a						: List[str]     	=			'''sshleifer/bart-tiny-random'''
a						: int     	=			'''sshleifer/tiny-mbart'''
a						: int     	=			'''sshleifer/tiny-marian-en-de'''






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Path    ,       _lowercase						:    list					) ->Optional[Any]:






	'''simple docstring'''






	a      :		str	=				"\n".join(_lowercase					)
	Path(_lowercase					).open("w"					).writelines(_lowercase					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Tuple					) ->Optional[Any]:






	'''simple docstring'''






	for split in ["train", "val", "test"]:
		_dump_articles(os.path.join(_lowercase    ,       F"""{split}.source"""					)    ,       _lowercase					)
		_dump_articles(os.path.join(_lowercase    ,       F"""{split}.target"""					)    ,       _lowercase					)
	return tmp_dir







class        __UpperCamelCase				(						a__						):






							@parameterized.expand(
							    [
							        MBART_TINY,
							        MARIAN_TINY,
							        T5_TINY,
							        BART_TINY,
							        PEGASUS_XSUM,
							    ]      ,					)
							@slow
							def     __a   (   self      ,					lowerCAmelCase__      )	-> Optional[Any]:
								a      :		List[Any]	=				AutoTokenizer.from_pretrained(lowerCAmelCase__      )
								a      :		int	=				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
								a      :		Tuple	=				max(len(tokenizer.encode(lowerCAmelCase__      )      ) for a in ARTICLES      )
								a      :		Dict	=				max(len(tokenizer.encode(lowerCAmelCase__      )      ) for a in SUMMARIES      )
								a      :		int	=				4
								a      :		Dict	=				8
								assert max_len_target > max_src_len  # Will be truncated
								assert max_len_source > max_src_len  # Will be truncated
								a,       a      :		str	=				"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
								a      :		Union[str, Any]	=				SeqaSeqDataset(
								    lowerCAmelCase__      ,					data_dir=lowerCAmelCase__      ,					type_path="train"      ,					max_source_length=lowerCAmelCase__      ,					max_target_length=lowerCAmelCase__      ,					src_lang=lowerCAmelCase__      ,					tgt_lang=lowerCAmelCase__      ,					)
								a      :		List[Any]	=				DataLoader(lowerCAmelCase__      ,					batch_size=2      ,					collate_fn=train_dataset.collate_fn      )
								for batch in dataloader:
									assert isinstance(lowerCAmelCase__      ,					lowerCAmelCase__      )
									assert batch["attention_mask"].shape == batch["input_ids"].shape
									# show that articles were trimmed.
									assert batch["input_ids"].shape[1] == max_src_len
									# show that targets are the same len
									assert batch["labels"].shape[1] == max_tgt_len
									if tok_name != MBART_TINY:
										continue
									# check language codes in correct place
									a      :		Optional[int]	=				shift_tokens_right(batch["labels"]      ,					tokenizer.pad_token_id      )
									assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
									assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
									assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
									assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

									break  # No need to test every batch






							@parameterized.expand([BART_TINY, BERT_BASE_CASED]      )
							def     __a   (   self      ,					lowerCAmelCase__      )	-> Dict:
								a      :		Tuple	=				AutoTokenizer.from_pretrained(lowerCAmelCase__      )
								a      :		Dict	=				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
								a      :		Optional[int]	=				max(len(tokenizer.encode(lowerCAmelCase__      )      ) for a in ARTICLES      )
								a      :		str	=				max(len(tokenizer.encode(lowerCAmelCase__      )      ) for a in SUMMARIES      )
								a      :		str	=				4
								a      :		Dict	=				LegacySeqaSeqDataset(
								    lowerCAmelCase__      ,					data_dir=lowerCAmelCase__      ,					type_path="train"      ,					max_source_length=20      ,					max_target_length=lowerCAmelCase__      ,					)
								a      :		str	=				DataLoader(lowerCAmelCase__      ,					batch_size=2      ,					collate_fn=train_dataset.collate_fn      )
								for batch in dataloader:
									assert batch["attention_mask"].shape == batch["input_ids"].shape
									# show that articles were trimmed.
									assert batch["input_ids"].shape[1] == max_len_source
									assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
									# show that targets were truncated
									assert batch["labels"].shape[1] == trunc_target  # Truncated
									assert max_len_target > trunc_target  # Truncated
									break  # No need to test every batch






							def     __a   (   self      )	-> Dict:
								a      :		Union[str, Any]	=				AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"      )

								a      :		List[Any]	=				Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
								a      :		Any	=				tmp_dir.joinpath("train.source"      ).open().readlines()
								a      :		Any	=				Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
								pack_data_dir(lowerCAmelCase__      ,					lowerCAmelCase__      ,					128      ,					lowerCAmelCase__      )
								a      :		List[str]	=				{x.name for x in tmp_dir.iterdir()}
								a      :		Tuple	=				{x.name for x in save_dir.iterdir()}
								a      :		Tuple	=				save_dir.joinpath("train.source"      ).open().readlines()
								# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
								# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
								assert len(lowerCAmelCase__      ) < len(lowerCAmelCase__      )
								assert len(lowerCAmelCase__      ) == 1
								assert len(packed_examples[0]      ) == sum(len(lowerCAmelCase__      ) for x in orig_examples      )
								assert orig_paths == new_paths






							@pytest.mark.skipif(not FAIRSEQ_AVAILABLE      ,					reason="This test requires fairseq"      )
							def     __a   (   self      )	-> int:
								if not FAIRSEQ_AVAILABLE:
									return
								a,       a,       a      :		List[str]	=				self._get_dataset(max_len=64      )
								a      :		str	=				64
								a      :		Optional[int]	=				ds.make_dynamic_sampler(lowerCAmelCase__      ,					required_batch_size_multiple=lowerCAmelCase__      )
								a      :		Dict	=				[len(lowerCAmelCase__      ) for x in batch_sampler]
								assert len(set(lowerCAmelCase__      )      ) > 1  # it's not dynamic batch size if every batch is the same length
								assert sum(lowerCAmelCase__      ) == len(lowerCAmelCase__      )  # no dropped or added examples
								a      :		str	=				DataLoader(lowerCAmelCase__      ,					batch_sampler=lowerCAmelCase__      ,					collate_fn=ds.collate_fn      ,					num_workers=2      )
								a      :		Optional[Any]	=				[]
								a      :		Optional[int]	=				[]
								for batch in data_loader:
									a      :		int	=				batch["input_ids"].shape
									a      :		Dict	=				src_shape[0]
									assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
									a      :		Tuple	=				np.product(batch["input_ids"].shape      )
									num_src_per_batch.append(lowerCAmelCase__      )
									if num_src_tokens > (max_tokens * 1.1):
										failures.append(lowerCAmelCase__      )
								assert num_src_per_batch[0] == max(lowerCAmelCase__      )
								if failures:
									raise AssertionError(f"""too many tokens in {len(lowerCAmelCase__      )} batches"""      )






							def     __a   (   self      )	-> Any:
								a,       a,       a      :		Optional[int]	=				self._get_dataset(max_len=512      )
								a      :		Optional[int]	=				2
								a      :		List[str]	=				ds.make_sortish_sampler(lowerCAmelCase__      ,					shuffle=lowerCAmelCase__      )

								a      :		Union[str, Any]	=				DataLoader(lowerCAmelCase__      ,					batch_size=lowerCAmelCase__      ,					collate_fn=ds.collate_fn      ,					num_workers=2      )
								a      :		Any	=				DataLoader(lowerCAmelCase__      ,					batch_size=lowerCAmelCase__      ,					collate_fn=ds.collate_fn      ,					num_workers=2      ,					sampler=lowerCAmelCase__      )

								a      :		List[Any]	=				tokenizer.pad_token_id

								def count_pad_tokens(lowerCAmelCase__      ,					lowerCAmelCase__="input_ids"      ):
									return [batch[k].eq(lowerCAmelCase__      ).sum().item() for batch in data_loader]

								assert sum(count_pad_tokens(lowerCAmelCase__      ,					k="labels"      )      ) < sum(count_pad_tokens(lowerCAmelCase__      ,					k="labels"      )      )
								assert sum(count_pad_tokens(lowerCAmelCase__      )      ) < sum(count_pad_tokens(lowerCAmelCase__      )      )
								assert len(lowerCAmelCase__      ) == len(lowerCAmelCase__      )






							def     __a   (   self      ,					lowerCAmelCase__=1000      ,					lowerCAmelCase__=128      )	-> Dict:
								if os.getenv("USE_REAL_DATA"      ,					lowerCAmelCase__      ):
									a      :		Tuple	=				"examples/seq2seq/wmt_en_ro"
									a      :		Optional[Any]	=				max_len * 2 * 64
									if not Path(lowerCAmelCase__      ).joinpath("train.len"      ).exists():
										save_len_file(lowerCAmelCase__      ,					lowerCAmelCase__      )
								else:
									a      :		Tuple	=				"examples/seq2seq/test_data/wmt_en_ro"
									a      :		Optional[int]	=				max_len * 4
									save_len_file(lowerCAmelCase__      ,					lowerCAmelCase__      )

								a      :		Dict	=				AutoTokenizer.from_pretrained(lowerCAmelCase__      )
								a      :		Tuple	=				SeqaSeqDataset(
								    lowerCAmelCase__      ,					data_dir=lowerCAmelCase__      ,					type_path="train"      ,					max_source_length=lowerCAmelCase__      ,					max_target_length=lowerCAmelCase__      ,					n_obs=lowerCAmelCase__      ,					)
								return ds, max_tokens, tokenizer






							def     __a   (   self      )	-> Optional[Any]:
								a,       a,       a      :		Tuple	=				self._get_dataset()
								a      :		List[str]	=				set(DistributedSortishSampler(lowerCAmelCase__      ,					256      ,					num_replicas=2      ,					rank=0      ,					add_extra_examples=lowerCAmelCase__      )      )
								a      :		Dict	=				set(DistributedSortishSampler(lowerCAmelCase__      ,					256      ,					num_replicas=2      ,					rank=1      ,					add_extra_examples=lowerCAmelCase__      )      )
								assert idsa.intersection(lowerCAmelCase__      ) == set()






							@parameterized.expand(
							    [
							        MBART_TINY,
							        MARIAN_TINY,
							        T5_TINY,
							        BART_TINY,
							        PEGASUS_XSUM,
							    ]      ,					)
							def     __a   (   self      ,					lowerCAmelCase__      )	-> Dict:
								a      :		Optional[int]	=				AutoTokenizer.from_pretrained(lowerCAmelCase__      ,					use_fast=lowerCAmelCase__      )
								if tok_name == MBART_TINY:
									a      :		Optional[Any]	=				SeqaSeqDataset(
									    lowerCAmelCase__      ,					data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      ,					type_path="train"      ,					max_source_length=4      ,					max_target_length=8      ,					src_lang="EN"      ,					tgt_lang="FR"      ,					)
									a      :		str	=				train_dataset.dataset_kwargs
									assert "src_lang" in kwargs and "tgt_lang" in kwargs
								else:
									a      :		Any	=				SeqaSeqDataset(
									    lowerCAmelCase__      ,					data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      ,					type_path="train"      ,					max_source_length=4      ,					max_target_length=8      ,					)
									a      :		Optional[int]	=				train_dataset.dataset_kwargs
									assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
									assert len(lowerCAmelCase__      ) == 1 if tok_name == BART_TINY else len(lowerCAmelCase__      ) == 0







"""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       )





