



import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 					_A (			__a   ,unittest.TestCase					):
       UpperCamelCase__						:					Tuple		      =		GPTaTokenizer
       UpperCamelCase__						:					Optional[int]		      =		GPTaTokenizerFast
       UpperCamelCase__						:					Optional[int]		      =		True
       UpperCamelCase__						:					Optional[Any]		      =		{"add_prefix_space": True}
       UpperCamelCase__						:					List[str]		      =		False



       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           __a		 =       [
               '''l''',
               '''o''',
               '''w''',
               '''e''',
               '''r''',
               '''s''',
               '''t''',
               '''i''',
               '''d''',
               '''n''',
               '''\u0120''',
               '''\u0120l''',
               '''\u0120n''',
               '''\u0120lo''',
               '''\u0120low''',
               '''er''',
               '''\u0120lowest''',
               '''\u0120newer''',
               '''\u0120wider''',
               '''<unk>''',
               '''<|endoftext|>''',
           ]
           __a		 =       dict(zip(lowerCAmelCase_  ,	range(len(lowerCAmelCase_))))
           __a		 =       ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
           __a		 =       {'''unk_token''': '''<unk>'''}

           __a		 =       os.path.join(self.tmpdirname  ,	VOCAB_FILES_NAMES['''vocab_file'''])
           __a		 =       os.path.join(self.tmpdirname  ,	VOCAB_FILES_NAMES['''merges_file'''])
           with open(self.vocab_file  ,	'''w'''  ,	encoding='''utf-8''') as fp:
               fp.write(json.dumps(lowerCAmelCase_) + '''\n''')
           with open(self.merges_file  ,	'''w'''  ,	encoding='''utf-8''') as fp:
               fp.write('''\n'''.join(lowerCAmelCase_))



       def 	_lowerCamelCase    (	self							:  int  ,	**__SCREAMING_SNAKE_CASE							:  Dict):


           '''simple docstring'''

           kwargs.update(self.special_tokens_map)
           return GPTaTokenizer.from_pretrained(self.tmpdirname  ,	**lowerCAmelCase_)



       def 	_lowerCamelCase    (	self							:  int  ,	**__SCREAMING_SNAKE_CASE							:  Union[str, Any]):


           '''simple docstring'''

           kwargs.update(self.special_tokens_map)
           return GPTaTokenizerFast.from_pretrained(self.tmpdirname  ,	**lowerCAmelCase_)



       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       '''lower newer'''
           __a		 =       '''lower newer'''
           return input_text, output_text



       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           __a		 =       GPTaTokenizer(self.vocab_file  ,	self.merges_file  ,	**self.special_tokens_map)
           __a		 =       '''lower newer'''
           __a		 =       ['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
           __a		 =       tokenizer.tokenize(lowerCAmelCase_  ,	add_prefix_space=lowerCAmelCase_)
           self.assertListEqual(lowerCAmelCase_  ,	lowerCAmelCase_)

           __a		 =       tokens + [tokenizer.unk_token]
           __a		 =       [14, 15, 10, 9, 3, 2, 15, 19]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_)  ,	lowerCAmelCase_)



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           if not self.test_rust_tokenizer:
               return

           __a		 =       self.get_tokenizer()
           __a		 =       self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase_)

           __a		 =       '''lower newer'''

           # Testing tokenization
           __a		 =       tokenizer.tokenize(lowerCAmelCase_  ,	add_prefix_space=lowerCAmelCase_)
           __a		 =       rust_tokenizer.tokenize(lowerCAmelCase_)
           self.assertListEqual(lowerCAmelCase_  ,	lowerCAmelCase_)

           # Testing conversion to ids without special tokens
           __a		 =       tokenizer.encode(lowerCAmelCase_  ,	add_special_tokens=lowerCAmelCase_  ,	add_prefix_space=lowerCAmelCase_)
           __a		 =       rust_tokenizer.encode(lowerCAmelCase_  ,	add_special_tokens=lowerCAmelCase_)
           self.assertListEqual(lowerCAmelCase_  ,	lowerCAmelCase_)

           # Testing conversion to ids with special tokens
           __a		 =       self.get_rust_tokenizer(add_prefix_space=lowerCAmelCase_)
           __a		 =       tokenizer.encode(lowerCAmelCase_  ,	add_prefix_space=lowerCAmelCase_)
           __a		 =       rust_tokenizer.encode(lowerCAmelCase_)
           self.assertListEqual(lowerCAmelCase_  ,	lowerCAmelCase_)

           # Testing the unknown token
           __a		 =       tokens + [rust_tokenizer.unk_token]
           __a		 =       [14, 15, 10, 9, 3, 2, 15, 19]
           self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCAmelCase_)  ,	lowerCAmelCase_)



       def 	_lowerCamelCase    (	self							:  Union[str, Any]  ,	*__SCREAMING_SNAKE_CASE							:  Tuple  ,	**__SCREAMING_SNAKE_CASE							:  List[Any]):


           '''simple docstring'''

           pass



       def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  Any=15):


           '''simple docstring'''

           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
                   __a		 =       self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_  ,	**lowerCAmelCase_)

                   # Simple input
                   __a		 =       '''This is a simple input'''
                   __a		 =       ['''This is a simple input 1''', '''This is a simple input 2''']
                   __a		 =       ('''This is a simple input''', '''This is a pair''')
                   __a		 =       [
                       ('''This is a simple input 1''', '''This is a simple input 2'''),
                       ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                   ]

                   # Simple input tests
                   self.assertRaises(lowerCAmelCase_  ,	tokenizer_r.encode  ,	lowerCAmelCase_  ,	max_length=lowerCAmelCase_  ,	padding='''max_length''')

                   # Simple input
                   self.assertRaises(lowerCAmelCase_  ,	tokenizer_r.encode_plus  ,	lowerCAmelCase_  ,	max_length=lowerCAmelCase_  ,	padding='''max_length''')

                   # Simple input
                   self.assertRaises(
                       lowerCAmelCase_  ,	tokenizer_r.batch_encode_plus  ,	lowerCAmelCase_  ,	max_length=lowerCAmelCase_  ,	padding='''max_length'''  ,	)

                   # Pair input
                   self.assertRaises(lowerCAmelCase_  ,	tokenizer_r.encode  ,	lowerCAmelCase_  ,	max_length=lowerCAmelCase_  ,	padding='''max_length''')

                   # Pair input
                   self.assertRaises(lowerCAmelCase_  ,	tokenizer_r.encode_plus  ,	lowerCAmelCase_  ,	max_length=lowerCAmelCase_  ,	padding='''max_length''')

                   # Pair input
                   self.assertRaises(
                       lowerCAmelCase_  ,	tokenizer_r.batch_encode_plus  ,	lowerCAmelCase_  ,	max_length=lowerCAmelCase_  ,	padding='''max_length'''  ,	)



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           __a		 =       GPTaTokenizer.from_pretrained(self.tmpdirname  ,	pad_token='''<pad>''')

           # Simple input
           __a		 =       '''This is a simple input'''
           __a		 =       ['''This is a simple input looooooooong''', '''This is a simple input''']
           __a		 =       ('''This is a simple input''', '''This is a pair''')
           __a		 =       [
               ('''This is a simple input loooooong''', '''This is a simple input'''),
               ('''This is a simple pair loooooong''', '''This is a simple pair'''),
           ]

           __a		 =       tokenizer.pad_token_id

           __a		 =       tokenizer(lowerCAmelCase_  ,	padding='''max_length'''  ,	max_length=30  ,	return_tensors='''np''')
           __a		 =       tokenizer(lowerCAmelCase_  ,	padding=lowerCAmelCase_  ,	truncate=lowerCAmelCase_  ,	return_tensors='''np''')
           __a		 =       tokenizer(*lowerCAmelCase_  ,	padding='''max_length'''  ,	max_length=60  ,	return_tensors='''np''')
           __a		 =       tokenizer(lowerCAmelCase_  ,	padding=lowerCAmelCase_  ,	truncate=lowerCAmelCase_  ,	return_tensors='''np''')

           # s
           # test single string max_length padding
           self.assertEqual(out_s['''input_ids'''].shape[-1]  ,	30)
           self.assertTrue(pad_token_id in out_s['''input_ids'''])
           self.assertTrue(0 in out_s['''attention_mask'''])

           # s2
           # test automatic padding
           self.assertEqual(out_sa['''input_ids'''].shape[-1]  ,	33)
           # long slice doesn't have padding
           self.assertFalse(pad_token_id in out_sa['''input_ids'''][0])
           self.assertFalse(0 in out_sa['''attention_mask'''][0])
           # short slice does have padding
           self.assertTrue(pad_token_id in out_sa['''input_ids'''][1])
           self.assertTrue(0 in out_sa['''attention_mask'''][1])

           # p
           # test single pair max_length padding
           self.assertEqual(out_p['''input_ids'''].shape[-1]  ,	60)
           self.assertTrue(pad_token_id in out_p['''input_ids'''])
           self.assertTrue(0 in out_p['''attention_mask'''])

           # p2
           # test automatic padding pair
           self.assertEqual(out_pa['''input_ids'''].shape[-1]  ,	52)
           # long slice pair doesn't have padding
           self.assertFalse(pad_token_id in out_pa['''input_ids'''][0])
           self.assertFalse(0 in out_pa['''attention_mask'''][0])
           # short slice pair does have padding
           self.assertTrue(pad_token_id in out_pa['''input_ids'''][1])
           self.assertTrue(0 in out_pa['''attention_mask'''][1])



       def 	_lowerCamelCase    (	self							:  List[str]):


           '''simple docstring'''

           __a		 =       '''$$$'''
           __a		 =       GPTaTokenizer.from_pretrained(self.tmpdirname  ,	bos_token=lowerCAmelCase_  ,	add_bos_token=lowerCAmelCase_)

           __a		 =       '''This is a simple input'''
           __a		 =       ['''This is a simple input 1''', '''This is a simple input 2''']

           __a		 =       tokenizer.bos_token_id

           __a		 =       tokenizer(lowerCAmelCase_)
           __a		 =       tokenizer(lowerCAmelCase_)

           self.assertEqual(out_s.input_ids[0]  ,	lowerCAmelCase_)
           self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids))

           __a		 =       tokenizer.decode(out_s.input_ids)
           __a		 =       tokenizer.batch_decode(out_sa.input_ids)

           self.assertEqual(decode_s.split()[0]  ,	lowerCAmelCase_)
           self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa))



       def 	_lowerCamelCase    (	self							:  List[str]):


           '''simple docstring'''

           pass





       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           __a		 =       [self.get_tokenizer(do_lower_case=lowerCAmelCase_  ,	add_bos_token=lowerCAmelCase_)]
           for tokenizer in tokenizers:
               with self.subTest(F'{tokenizer.__class__.__name__}'):
                   __a		 =       '''Encode this.'''
                   __a		 =       '''This one too please.'''
                   __a		 =       tokenizer.encode(lowerCAmelCase_  ,	add_special_tokens=lowerCAmelCase_)
                   encoded_sequence += tokenizer.encode(lowerCAmelCase_  ,	add_special_tokens=lowerCAmelCase_)
                   __a		 =       tokenizer.encode_plus(
                       lowerCAmelCase_  ,	lowerCAmelCase_  ,	add_special_tokens=lowerCAmelCase_  ,	return_special_tokens_mask=lowerCAmelCase_  ,	)
                   __a		 =       encoded_sequence_dict['''input_ids''']
                   __a		 =       encoded_sequence_dict['''special_tokens_mask''']
                   self.assertEqual(len(lowerCAmelCase_)  ,	len(lowerCAmelCase_))

                   __a		 =       [
                       (x if not special_tokens_mask[i] else None) for i, x in enumerate(lowerCAmelCase_)
                   ]
                   __a		 =       [x for x in filtered_sequence if x is not None]
                   self.assertEqual(lowerCAmelCase_  ,	lowerCAmelCase_)


@require_tokenizers
class 					_A (			unittest.TestCase					):



       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           __a		 =       AutoTokenizer.from_pretrained('''facebook/opt-350m'''  ,	from_slow=lowerCAmelCase_)
           __a		 =       '''A photo of a cat'''

           __a		 =       tokenizer.encode(
               lowerCAmelCase_  ,	)
           self.assertEqual(lowerCAmelCase_  ,	[2, 250, 1_345, 9, 10, 4_758])
           tokenizer.save_pretrained('''test_opt''')

           __a		 =       AutoTokenizer.from_pretrained('''./test_opt''')
           __a		 =       tokenizer.encode(
               lowerCAmelCase_  ,	)
           self.assertEqual(lowerCAmelCase_  ,	[2, 250, 1_345, 9, 10, 4_758])



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       AutoTokenizer.from_pretrained('''facebook/opt-350m'''  ,	use_slow=lowerCAmelCase_)
           __a		 =       '''A photo of a cat'''

           __a		 =       tokenizer.encode(
               lowerCAmelCase_  ,	)
           # Same as above
           self.assertEqual(lowerCAmelCase_  ,	[2, 250, 1_345, 9, 10, 4_758])





       @unittest.skip('''This test is failing because of a bug in the fast tokenizer''')
       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           __a		 =       AutoTokenizer.from_pretrained('''facebook/opt-350m'''  ,	from_slow=lowerCAmelCase_)

           __a		 =       '''bos'''
           __a		 =       tokenizer.get_vocab()['''bos''']

           __a		 =       '''A photo of a cat'''
           __a		 =       tokenizer.encode(
               lowerCAmelCase_  ,	)
           # We changed the bos token
           self.assertEqual(lowerCAmelCase_  ,	[31_957, 250, 1_345, 9, 10, 4_758])
           tokenizer.save_pretrained('''./tok''')
           __a		 =       AutoTokenizer.from_pretrained('''./tok''')
           self.assertTrue(tokenizer.is_fast)
           __a		 =       tokenizer.encode(
               lowerCAmelCase_  ,	)
           self.assertEqual(lowerCAmelCase_  ,	[31_957, 250, 1_345, 9, 10, 4_758])










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__)





