


'''simple docstring'''



import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


A__						:   str				=    get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model""")





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





    """simple docstring"""






    lowerCamelCase    :   int					=     GPTSwaTokenizer
    lowerCamelCase    :   List[str]					=     False
    lowerCamelCase    :   Optional[Any]					=     True
    lowerCamelCase    :   int					=     False







    def  lowercase_							(				self      )							->	Tuple:
         super().setUp()

         # We have a SentencePiece fixture for testing
         __lowerCamelCase  :      Tuple														=						GPTSwaTokenizer(SCREAMING_SNAKE_CASE_     ,	eos_token='<unk>'     ,	bos_token='<unk>'     ,	pad_token='<unk>'      )

         tokenizer.save_pretrained(self.tmpdirname      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	int:
         __lowerCamelCase  :      int														=						'This is a test'
         __lowerCamelCase  :      Tuple														=						'This is a test'
         return input_text, output_text







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      Any														=						'<s>'
         __lowerCamelCase  :      List[Any]														=						1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE_      )     ,	SCREAMING_SNAKE_CASE_      )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE_      )     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Optional[int]:
         __lowerCamelCase  :      Optional[Any]														=						list(self.get_tokenizer().get_vocab().keys()      )

         self.assertEqual(vocab_keys[0]     ,	'<unk>'      )
         self.assertEqual(vocab_keys[1]     ,	'<s>'      )
         self.assertEqual(vocab_keys[-1]     ,	'j'      )
         self.assertEqual(len(SCREAMING_SNAKE_CASE_      )     ,	20_00      )







    def  lowercase_							(				self      )							->	List[Any]:
         self.assertEqual(self.get_tokenizer().vocab_size     ,	20_00      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      int														=						GPTSwaTokenizer(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Optional[Any]														=						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_      )     ,	[4_65, 2_87, 2_65, 6_31, 8_42]      )

         __lowerCamelCase  :      Optional[int]														=						tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
         # fmt: off
         self.assertListEqual(
             SCREAMING_SNAKE_CASE_     ,	['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']     ,	)
         # fmt: on

         __lowerCamelCase  :      Any														=						tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_      )
         self.assertListEqual(
             SCREAMING_SNAKE_CASE_     ,	[2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60]     ,	)

         __lowerCamelCase  :      Dict														=						tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_      )
         # fmt: off
         self.assertListEqual(
             SCREAMING_SNAKE_CASE_     ,	['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']      )
         # fmt: on







    def  lowercase_							(				self      )							->	Tuple:
         __lowerCamelCase  :      str														=						GPTSwaTokenizer(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Optional[int]														=						['This is a test', 'I was born in 92000, and this is falsé.']
         __lowerCamelCase  :      Tuple														=						[
             [4_65, 2_87, 2_65, 6_31, 8_42],
             [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
         ]

         # Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
         for text, expected_ids in zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
              self.assertListEqual(tokenizer.encode_fast(SCREAMING_SNAKE_CASE_      )     ,	SCREAMING_SNAKE_CASE_      )

         # Test that decode_fast returns the input text
         for text, token_ids in zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
              self.assertEqual(tokenizer.decode_fast(SCREAMING_SNAKE_CASE_      )     ,	SCREAMING_SNAKE_CASE_      )







    @slow
    def  lowercase_							(				self      )							->	Union[str, Any]:
         __lowerCamelCase  :      Union[str, Any]														=						[
             '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
             'Hey there, how are you doing this fine day?',
             'This is a text with a trailing spaces followed by a dot     .',
             'Häj sväjs lillebrör! =)',
             'Det är inget fel på Mr. Cool',
         ]

         # fmt: off
         __lowerCamelCase  :      Union[str, Any]														=						{'input_ids': [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
         # fmt: on
         self.tokenizer_integration_test_util(
             expected_encoding=SCREAMING_SNAKE_CASE_     ,	model_name='AI-Sweden/gpt-sw3-126m'     ,	sequences=SCREAMING_SNAKE_CASE_     ,	)










'''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_      )






