



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__snake_case		:Union[str, Any]   	=  {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:List[str]   	=  ['''ViTFeatureExtractor''']
 __snake_case		:Optional[Any]   	=  ['''ViTImageProcessor''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:str   	=  [
     '''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ViTForImageClassification''',
     '''ViTForMaskedImageModeling''',
     '''ViTModel''',
     '''ViTPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:Tuple   	=  [
     '''TFViTForImageClassification''',
     '''TFViTModel''',
     '''TFViTPreTrainedModel''',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case		:Tuple   	=  [
     '''FlaxViTForImageClassification''',
     '''FlaxViTModel''',
     '''FlaxViTPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_vit import ViTFeatureExtractor
  from .image_processing_vit import ViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit import (
      VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTForImageClassification,
      ViTForMaskedImageModeling,
      ViTModel,
      ViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
 import sys

 __snake_case		:Optional[int]   	=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










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


__snake_case		:Dict   	=  get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')

@require_sentencepiece
@require_tokenizers
class 					_A (			__UpperCAmelCase   ,unittest.TestCase					):
       UpperCamelCase__						:					List[str]		      =		GPTSwaTokenizer
       UpperCamelCase__						:					Dict		      =		False
       UpperCamelCase__						:					int		      =		True
       UpperCamelCase__						:					List[Any]		      =		False



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           super().setUp()

           # We have a SentencePiece fixture for testing
           __a		 =       GPTSwaTokenizer(__SCREAMING_SNAKE_CASE  ,	eos_token='''<unk>'''  ,	bos_token='''<unk>'''  ,	pad_token='''<unk>''')

           tokenizer.save_pretrained(self.tmpdirname)



       def 	_lowerCamelCase    (	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  int):


           '''simple docstring'''

           __a		 =       '''This is a test'''
           __a		 =       '''This is a test'''
           return input_text, output_text



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           __a		 =       '''<s>'''
           __a		 =       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 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       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)  ,	2_000)



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           self.assertEqual(self.get_tokenizer().vocab_size  ,	2_000)



       def 	_lowerCamelCase    (	self							:  List[str]):


           '''simple docstring'''

           __a		 =       GPTSwaTokenizer(__SCREAMING_SNAKE_CASE)

           __a		 =       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)  ,	[465, 287, 265, 631, 842])

           __a		 =       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

           __a		 =       tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE)
           self.assertListEqual(
               __SCREAMING_SNAKE_CASE  ,	[262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]  ,	)

           __a		 =       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 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a		 =       GPTSwaTokenizer(__SCREAMING_SNAKE_CASE)
           __a		 =       ['''This is a test''', '''I was born in 92000, and this is falsé.''']
           __a		 =       [
               [465, 287, 265, 631, 842],
               [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
           ]

           # 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 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a		 =       [
               '''<|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
           __a		 =       {'''input_ids''': [[63_423, 5, 6_811, 14_954, 282, 816, 3_821, 63_466, 63_425, 63_462, 18, 63_978, 678, 301, 1_320, 63_423, 63_455, 63_458, 18, 63_982, 4_246, 3_940, 1_901, 47_789, 5_547, 18_994], [19_630, 1_100, 63_446, 1_342, 633, 544, 4_488, 593, 5_102, 2_416, 63_495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_652, 428, 268, 1_936, 515, 268, 58_593, 22_413, 9_106, 546, 268, 33_213, 63_979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55_130, 63_450, 924, 63_449, 2_249, 4_062, 1_558, 318, 63_504, 21_498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2_827, 2_559, 332, 6_575, 63_443, 26_801, 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  ,	)





