


import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE						:List[str]			=  get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
   from transformers.models.plbart.modeling_plbart import shift_tokens_right

SCREAMING_SNAKE_CASE						:List[Any]			=  5_00_03
SCREAMING_SNAKE_CASE						:int			=  5_00_02


@require_sentencepiece
@require_tokenizers
class      __lowerCAmelCase  (			_UpperCAmelCase			,	unittest.TestCase       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							PLBartTokenizer
  _SCREAMING_SNAKE_CASE		     =							None
  _SCREAMING_SNAKE_CASE		     =							False



  def        lowerCAmelCase__	(  self							:							int	) ->			Tuple:




     """simple docstring"""
     super().setUp()

     # We have a SentencePiece fixture for testing
     snake_case_            =       PLBartTokenizer(SCREAMING_SNAKE_CASE_     ,					language_codes="base"     ,					keep_accents=SCREAMING_SNAKE_CASE_	)
     tokenizer.save_pretrained(self.tmpdirname	)



  def        lowerCAmelCase__	(  self							:							List[str]	) ->			Dict:




     """simple docstring"""
     snake_case_            =       PLBartTokenizer(SCREAMING_SNAKE_CASE_     ,					language_codes="base"     ,					keep_accents=SCREAMING_SNAKE_CASE_	)

     snake_case_            =       tokenizer.tokenize("This is a test"	)
     self.assertListEqual(SCREAMING_SNAKE_CASE_     ,					["▁This", "▁is", "▁a", "▁t", "est"]	)

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_	)     ,					[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]     ,					)

     snake_case_            =       tokenizer.tokenize("I was born in 92000, and this is falsé."	)
     self.assertListEqual(
         SCREAMING_SNAKE_CASE_     ,					[
             SPIECE_UNDERLINE + "I",
             SPIECE_UNDERLINE + "was",
             SPIECE_UNDERLINE + "b",
             "or",
             "n",
             SPIECE_UNDERLINE + "in",
             SPIECE_UNDERLINE + "",
             "9",
             "2",
             "0",
             "0",
             "0",
             ",",
             SPIECE_UNDERLINE + "and",
             SPIECE_UNDERLINE + "this",
             SPIECE_UNDERLINE + "is",
             SPIECE_UNDERLINE + "f",
             "al",
             "s",
             "é",
             ".",
         ]     ,					)
     snake_case_            =       tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_	)
     self.assertListEqual(
         SCREAMING_SNAKE_CASE_     ,					[
             value + tokenizer.fairseq_offset
             for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
         ]     ,					)

     snake_case_            =       tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	)
     self.assertListEqual(
         SCREAMING_SNAKE_CASE_     ,					[
             SPIECE_UNDERLINE + "I",
             SPIECE_UNDERLINE + "was",
             SPIECE_UNDERLINE + "b",
             "or",
             "n",
             SPIECE_UNDERLINE + "in",
             SPIECE_UNDERLINE + "",
             "<unk>",
             "2",
             "0",
             "0",
             "0",
             ",",
             SPIECE_UNDERLINE + "and",
             SPIECE_UNDERLINE + "this",
             SPIECE_UNDERLINE + "is",
             SPIECE_UNDERLINE + "f",
             "al",
             "s",
             "<unk>",
             ".",
         ]     ,					)

     snake_case_            =       tokenizer.vocab_size
     snake_case_            =       [tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	) for x in range(end - 4     ,					SCREAMING_SNAKE_CASE_	)]

     self.assertListEqual(SCREAMING_SNAKE_CASE_     ,					["__java__", "__python__", "__en_XX__", "<mask>"]	)

     snake_case_            =       'java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'
     snake_case_            =       tokenizer(SCREAMING_SNAKE_CASE_	).input_ids
     self.assertEqual(
         tokenizer.decode(SCREAMING_SNAKE_CASE_     ,					skip_special_tokens=SCREAMING_SNAKE_CASE_     ,					clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_	)     ,					SCREAMING_SNAKE_CASE_     ,					)



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




     """simple docstring"""
     snake_case_            =       PLBartTokenizer(SCREAMING_SNAKE_CASE_     ,					language_codes="multi"     ,					keep_accents=SCREAMING_SNAKE_CASE_	)

     snake_case_            =       tokenizer.tokenize("This is a test"	)
     self.assertListEqual(SCREAMING_SNAKE_CASE_     ,					["▁This", "▁is", "▁a", "▁t", "est"]	)

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_	)     ,					[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]     ,					)

     snake_case_            =       tokenizer.tokenize("I was born in 92000, and this is falsé."	)
     self.assertListEqual(
         SCREAMING_SNAKE_CASE_     ,					[
             SPIECE_UNDERLINE + "I",
             SPIECE_UNDERLINE + "was",
             SPIECE_UNDERLINE + "b",
             "or",
             "n",
             SPIECE_UNDERLINE + "in",
             SPIECE_UNDERLINE + "",
             "9",
             "2",
             "0",
             "0",
             "0",
             ",",
             SPIECE_UNDERLINE + "and",
             SPIECE_UNDERLINE + "this",
             SPIECE_UNDERLINE + "is",
             SPIECE_UNDERLINE + "f",
             "al",
             "s",
             "é",
             ".",
         ]     ,					)
     snake_case_            =       tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_	)
     self.assertListEqual(
         SCREAMING_SNAKE_CASE_     ,					[
             value + tokenizer.fairseq_offset
             for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
         ]     ,					)

     snake_case_            =       tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	)
     self.assertListEqual(
         SCREAMING_SNAKE_CASE_     ,					[
             SPIECE_UNDERLINE + "I",
             SPIECE_UNDERLINE + "was",
             SPIECE_UNDERLINE + "b",
             "or",
             "n",
             SPIECE_UNDERLINE + "in",
             SPIECE_UNDERLINE + "",
             "<unk>",
             "2",
             "0",
             "0",
             "0",
             ",",
             SPIECE_UNDERLINE + "and",
             SPIECE_UNDERLINE + "this",
             SPIECE_UNDERLINE + "is",
             SPIECE_UNDERLINE + "f",
             "al",
             "s",
             "<unk>",
             ".",
         ]     ,					)
     snake_case_            =       tokenizer.vocab_size
     snake_case_            =       [tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_	) for x in range(end - 7     ,					SCREAMING_SNAKE_CASE_	)]

     self.assertListEqual(
         SCREAMING_SNAKE_CASE_     ,					["__java__", "__python__", "__en_XX__", "__javascript__", "__php__", "__ruby__", "__go__"]	)
     snake_case_            =       'java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'
     snake_case_            =       tokenizer(SCREAMING_SNAKE_CASE_	).input_ids
     self.assertEqual(
         tokenizer.decode(SCREAMING_SNAKE_CASE_     ,					skip_special_tokens=SCREAMING_SNAKE_CASE_     ,					clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_	)     ,					SCREAMING_SNAKE_CASE_     ,					)






@require_torch
@require_sentencepiece
@require_tokenizers
class      __lowerCAmelCase  (			unittest.TestCase       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'uclanlp/plbart-python-en_XX'
  _SCREAMING_SNAKE_CASE		     =							[
      'def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])',
      'def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])',
  ]
  _SCREAMING_SNAKE_CASE		     =							[
      'Returns the maximum value of a b c.',
      'Sums the values of a b c.',
  ]
  _SCREAMING_SNAKE_CASE		     =							[
      134,
      5452,
      33460,
      33441,
      33463,
      33465,
      33463,
      33449,
      988,
      20,
      33456,
      19,
      33456,
      771,
      39,
      4258,
      889,
      3318,
      33441,
      33463,
      33465,
      33463,
      33449,
      2471,
      2,
      PYTHON_CODE,
  ]



  @classmethod
  def        lowerCAmelCase__	(  cls							:							Union[str, Any]	) ->			str:




     """simple docstring"""
     snake_case_            =       PLBartTokenizer.from_pretrained(
         cls.checkpoint_name     ,					language_codes="base"     ,					src_lang="python"     ,					tgt_lang="en_XX"	)
     snake_case_            =       1
     return cls



  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			Dict:




     """simple docstring"""
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__java__"]     ,					5_0_0_0_1	)
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__python__"]     ,					5_0_0_0_2	)
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__en_XX__"]     ,					5_0_0_0_3	)



  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       self.tokenizer.batch_encode_plus(self.src_text	).input_ids[0]
     self.assertListEqual(self.expected_src_tokens     ,					SCREAMING_SNAKE_CASE_	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]	) ->			List[Any]:




     """simple docstring"""
     self.assertIn(SCREAMING_SNAKE_CASE_     ,					self.tokenizer.all_special_ids	)
     snake_case_            =       [EN_CODE, 9_0_3_7, 3_3_4_4_2, 5_7, 7_5_2, 1_5_3, 1_4, 5_6, 1_8, 9, 2]
     snake_case_            =       self.tokenizer.decode(SCREAMING_SNAKE_CASE_     ,					skip_special_tokens=SCREAMING_SNAKE_CASE_	)
     snake_case_            =       self.tokenizer.decode(generated_ids[1:]     ,					skip_special_tokens=SCREAMING_SNAKE_CASE_	)
     self.assertEqual(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_	)
     self.assertNotIn(self.tokenizer.eos_token     ,					SCREAMING_SNAKE_CASE_	)



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




     """simple docstring"""
     snake_case_            =       ['def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])' * 2_0]
     self.assertIsInstance(src_text[0]     ,					SCREAMING_SNAKE_CASE_	)
     snake_case_            =       1_0
     snake_case_            =       self.tokenizer(SCREAMING_SNAKE_CASE_     ,					max_length=SCREAMING_SNAKE_CASE_     ,					truncation=SCREAMING_SNAKE_CASE_	).input_ids[0]
     self.assertEqual(ids[-2]     ,					2	)
     self.assertEqual(ids[-1]     ,					SCREAMING_SNAKE_CASE_	)
     self.assertEqual(len(SCREAMING_SNAKE_CASE_	)     ,					SCREAMING_SNAKE_CASE_	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]	) ->			Optional[int]:




     """simple docstring"""
     self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "__java__"]	)     ,					[5_0_0_0_4, 5_0_0_0_1]	)



  def        lowerCAmelCase__	(  self							:							Dict	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       tempfile.mkdtemp()
     snake_case_            =       self.tokenizer.fairseq_tokens_to_ids
     self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_	)
     snake_case_            =       PLBartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_	)
     self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,					SCREAMING_SNAKE_CASE_	)



  @require_torch
  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Optional[int]:




     """simple docstring"""
     snake_case_            =       self.tokenizer(self.src_text     ,					text_target=self.tgt_text     ,					padding=SCREAMING_SNAKE_CASE_     ,					return_tensors="pt"	)
     snake_case_            =       shift_tokens_right(batch["labels"]     ,					self.tokenizer.pad_token_id	)

     # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
     self.assertEqual(batch.input_ids[1][-2:].tolist()     ,					[2, PYTHON_CODE]	)
     self.assertEqual(batch.decoder_input_ids[1][0]     ,					SCREAMING_SNAKE_CASE_	)
     self.assertEqual(batch.decoder_input_ids[1][-1]     ,					2	)
     self.assertEqual(batch.labels[1][-2:].tolist()     ,					[2, EN_CODE]	)



  @require_torch
  def        lowerCAmelCase__	(  self							:							List[str]	) ->			int:




     """simple docstring"""
     snake_case_            =       self.tokenizer(
         self.src_text     ,					text_target=self.tgt_text     ,					padding=SCREAMING_SNAKE_CASE_     ,					truncation=SCREAMING_SNAKE_CASE_     ,					max_length=len(self.expected_src_tokens	)     ,					return_tensors="pt"     ,					)
     snake_case_            =       shift_tokens_right(batch["labels"]     ,					self.tokenizer.pad_token_id	)

     self.assertIsInstance(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_	)

     self.assertEqual((2, 2_6)     ,					batch.input_ids.shape	)
     self.assertEqual((2, 2_6)     ,					batch.attention_mask.shape	)
     snake_case_            =       batch.input_ids.tolist()[0]
     self.assertListEqual(self.expected_src_tokens     ,					SCREAMING_SNAKE_CASE_	)
     self.assertEqual(2     ,					batch.decoder_input_ids[0, -1]	)  # EOS
     # Test that special tokens are reset
     self.assertEqual(self.tokenizer.prefix_tokens     ,					[]	)
     self.assertEqual(self.tokenizer.suffix_tokens     ,					[self.tokenizer.eos_token_id, PYTHON_CODE]	)



  def        lowerCAmelCase__	(  self							:							Dict	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       self.tokenizer(self.src_text     ,					padding=SCREAMING_SNAKE_CASE_     ,					truncation=SCREAMING_SNAKE_CASE_     ,					max_length=3     ,					return_tensors="pt"	)
     snake_case_            =       self.tokenizer(
         text_target=self.tgt_text     ,					padding=SCREAMING_SNAKE_CASE_     ,					truncation=SCREAMING_SNAKE_CASE_     ,					max_length=1_0     ,					return_tensors="pt"	)
     snake_case_            =       targets['input_ids']
     snake_case_            =       shift_tokens_right(SCREAMING_SNAKE_CASE_     ,					self.tokenizer.pad_token_id	)

     self.assertEqual(batch.input_ids.shape[1]     ,					3	)
     self.assertEqual(batch.decoder_input_ids.shape[1]     ,					1_0	)



  @require_torch
  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Dict:




     """simple docstring"""
     snake_case_            =       self.tokenizer._build_translation_inputs(
         "A test"     ,					return_tensors="pt"     ,					src_lang="en_XX"     ,					tgt_lang="java"	)

     self.assertEqual(
         nested_simplify(SCREAMING_SNAKE_CASE_	)     ,					{
             # A, test, EOS, en_XX
             "input_ids": [[1_5_0, 2_4_2, 2, 5_0_0_0_3]],
             "attention_mask": [[1, 1, 1, 1]],
             # java
             "forced_bos_token_id": 5_0_0_0_1,
         }     ,					)








'''simple docstring'''



import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
      import jax

      from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel





class   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=13     ,	SCREAMING_SNAKE_CASE_=30     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=5     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=37     ,	SCREAMING_SNAKE_CASE_="gelu"     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=10     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	)							->	Optional[int]:
         __lowerCamelCase  :      Optional[int]														=						parent
         __lowerCamelCase  :      Dict														=						batch_size
         __lowerCamelCase  :      int														=						image_size
         __lowerCamelCase  :      List[str]														=						patch_size
         __lowerCamelCase  :      Optional[int]														=						num_channels
         __lowerCamelCase  :      Any														=						is_training
         __lowerCamelCase  :      Dict														=						use_labels
         __lowerCamelCase  :      List[Any]														=						hidden_size
         __lowerCamelCase  :      List[Any]														=						num_hidden_layers
         __lowerCamelCase  :      Optional[Any]														=						num_attention_heads
         __lowerCamelCase  :      Dict														=						intermediate_size
         __lowerCamelCase  :      Union[str, Any]														=						hidden_act
         __lowerCamelCase  :      Optional[int]														=						hidden_dropout_prob
         __lowerCamelCase  :      Tuple														=						attention_probs_dropout_prob
         __lowerCamelCase  :      str														=						type_sequence_label_size
         __lowerCamelCase  :      List[str]														=						initializer_range

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         __lowerCamelCase  :      str														=						(image_size // patch_size) ** 2
         __lowerCamelCase  :      Optional[int]														=						num_patches + 1







    def  lowercase_							(				self      )							->	Union[str, Any]:
         __lowerCamelCase  :      int														=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

         __lowerCamelCase  :      Optional[int]														=						ViTConfig(
             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=SCREAMING_SNAKE_CASE_     ,	initializer_range=self.initializer_range     ,	)

         return config, pixel_values







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Union[str, Any]:
         __lowerCamelCase  :      Union[str, Any]														=						FlaxViTModel(config=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      str														=						model(SCREAMING_SNAKE_CASE_      )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         __lowerCamelCase  :      str														=						(self.image_size, self.image_size)
         __lowerCamelCase  :      str														=						(self.patch_size, self.patch_size)
         __lowerCamelCase  :      Any														=						(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, num_patches + 1, self.hidden_size)      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         __lowerCamelCase  :      Tuple														=						self.type_sequence_label_size
         __lowerCamelCase  :      Any														=						FlaxViTForImageClassification(config=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Dict														=						model(SCREAMING_SNAKE_CASE_      )
         self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.type_sequence_label_size)      )

         # test greyscale images
         __lowerCamelCase  :      List[str]														=						1
         __lowerCamelCase  :      List[Any]														=						FlaxViTForImageClassification(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      str														=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
         __lowerCamelCase  :      List[Any]														=						model(SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Optional[int]:
         __lowerCamelCase  :      List[Any]														=						self.prepare_config_and_inputs()
         (
             (
             __lowerCamelCase
         )							,			(
             __lowerCamelCase
         )							,			
         )  :      int														=						config_and_inputs
         __lowerCamelCase  :      Union[str, Any]														=						{'pixel_values': pixel_values}
         return config, inputs_dict







@require_flax
class   UpperCAmelCase_    (_UpperCAmelCase   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   str					=     (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







    def  lowercase_							(				self      )							->	None:
         __lowerCamelCase  :      str														=						FlaxViTModelTester(self      )
         __lowerCamelCase  :      Union[str, Any]														=						ConfigTester(self     ,	config_class=SCREAMING_SNAKE_CASE_     ,	has_text_modality=SCREAMING_SNAKE_CASE_     ,	hidden_size=37      )







    def  lowercase_							(				self      )							->	List[Any]:
         self.config_tester.run_common_tests()







    def  lowercase_							(				self      )							->	Optional[Any]:
         __lowerCamelCase  :      Optional[int]														=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      List[Any]														=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Optional[Any]:
         __lowerCamelCase							,			__lowerCamelCase  :      Optional[int]														=						self.model_tester.prepare_config_and_inputs_for_common()

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

              __lowerCamelCase  :      Optional[int]														=						['pixel_values']
              self.assertListEqual(arg_names[:1]     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase							,			__lowerCamelCase  :      Dict														=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__      ):
                   __lowerCamelCase  :      List[Any]														=						self._prepare_for_class(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
                   __lowerCamelCase  :      Optional[int]														=						model_class(SCREAMING_SNAKE_CASE_      )

                   @jax.jit
                   def model_jitted(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      ):
                        return model(pixel_values=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )

                   with self.subTest('JIT Enabled'      ):
                        __lowerCamelCase  :      Optional[int]														=						model_jitted(**SCREAMING_SNAKE_CASE_      ).to_tuple()

                   with self.subTest('JIT Disabled'      ):
                        with jax.disable_jit():
                             __lowerCamelCase  :      Union[str, Any]														=						model_jitted(**SCREAMING_SNAKE_CASE_      ).to_tuple()

                   self.assertEqual(len(SCREAMING_SNAKE_CASE_      )     ,	len(SCREAMING_SNAKE_CASE_      )      )
                   for jitted_output, output in zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
                        self.assertEqual(jitted_output.shape     ,	output.shape      )







    @slow
    def  lowercase_							(				self      )							->	List[Any]:
         for model_class_name in self.all_model_classes:
              __lowerCamelCase  :      Union[str, Any]														=						model_class_name.from_pretrained('google/vit-base-patch16-224'      )
              __lowerCamelCase  :      Union[str, Any]														=						model(np.ones((1, 3, 2_24, 2_24)      )      )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE_      )






