






from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE 							=						{
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}

class 							A_    (						lowercase_			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Tuple =					'''swinv2'''

 _SCREAMING_SNAKE_CASE							:			str =					{
     '''num_attention_heads''': '''num_heads''',
     '''num_hidden_layers''': '''num_layers''',
 }


 def __init__(	self							,  _A=224							,  _A=4							,  _A=3							,  _A=96							,  _A=[2, 2, 6, 2]							,  _A=[3, 6, 12, 24]							,  _A=7							,  _A=4.0							,  _A=True							,  _A=0.0							,  _A=0.0							,  _A=0.1							,  _A="gelu"							,  _A=False							,  _A=0.02							,  _A=1e-5							,  _A=32							,  **_A							,  ) ->		Optional[Any]:

     """simple docstring"""



     super().__init__(**UpperCamelCase__)

     _UpperCAmelCase				:      int			  =				image_size
     _UpperCAmelCase				:      Tuple			  =				patch_size
     _UpperCAmelCase				:      Union[str, Any]			  =				num_channels
     _UpperCAmelCase				:      List[Any]			  =				embed_dim
     _UpperCAmelCase				:      Optional[Any]			  =				depths
     _UpperCAmelCase				:      Any			  =				len(UpperCamelCase__)
     _UpperCAmelCase				:      int			  =				num_heads
     _UpperCAmelCase				:      Any			  =				window_size
     _UpperCAmelCase				:      List[Any]			  =				mlp_ratio
     _UpperCAmelCase				:      Tuple			  =				qkv_bias
     _UpperCAmelCase				:      Union[str, Any]			  =				hidden_dropout_prob
     _UpperCAmelCase				:      Any			  =				attention_probs_dropout_prob
     _UpperCAmelCase				:      Optional[int]			  =				drop_path_rate
     _UpperCAmelCase				:      List[Any]			  =				hidden_act
     _UpperCAmelCase				:      Any			  =				use_absolute_embeddings
     _UpperCAmelCase				:      List[Any]			  =				layer_norm_eps
     _UpperCAmelCase				:      Union[str, Any]			  =				initializer_range
     _UpperCAmelCase				:      List[str]			  =				encoder_stride
     # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     _UpperCAmelCase				:      Union[str, Any]			  =				int(embed_dim * 2 ** (len(UpperCamelCase__) - 1))
     _UpperCAmelCase				:      Optional[Any]			  =				(0, 0, 0, 0)












import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 							A_    (						__lowercase ,   unittest.TestCase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			int =					None
 _SCREAMING_SNAKE_CASE							:			str =					BloomTokenizerFast
 _SCREAMING_SNAKE_CASE							:			str =					BloomTokenizerFast
 _SCREAMING_SNAKE_CASE							:			Any =					True
 _SCREAMING_SNAKE_CASE							:			Optional[int] =					False
 _SCREAMING_SNAKE_CASE							:			Optional[int] =					"tokenizer_file"
 _SCREAMING_SNAKE_CASE							:			Optional[Any] =					{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}


 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     super().setUp()
     _UpperCAmelCase				:      List[Any]			  =				BloomTokenizerFast.from_pretrained('''bigscience/tokenizer''')
     tokenizer.save_pretrained(self.tmpdirname)


 def  snake_case__				(	self							,  **_A) ->		List[Any]:

     """simple docstring"""



     kwargs.update(self.special_tokens_map)
     return BloomTokenizerFast.from_pretrained(self.tmpdirname							,  **_A)


 def  snake_case__				(	self) ->		List[str]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				self.get_rust_tokenizer()

     _UpperCAmelCase				:      int			  =				['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
     _UpperCAmelCase				:      List[Any]			  =				[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

     _UpperCAmelCase				:      Any			  =				tokenizer.batch_encode_plus(_A)['''input_ids''']
     self.assertListEqual(_A							,  _A)

     _UpperCAmelCase				:      Any			  =				tokenizer.batch_decode(_A)
     self.assertListEqual(_A							,  _A)


 def  snake_case__				(	self							,  _A=6) ->		Optional[Any]:

     """simple docstring"""



     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''):
             _UpperCAmelCase				:      Optional[int]			  =				self.rust_tokenizer_class.from_pretrained(_A							,  **_A)
             # tokenizer_r.pad_token = None # Hotfixing padding = None
             # Simple input
             _UpperCAmelCase				:      List[Any]			  =				'''This is a simple input'''
             _UpperCAmelCase				:      Any			  =				['''This is a simple input 1''', '''This is a simple input 2''']
             _UpperCAmelCase				:      int			  =				('''This is a simple input''', '''This is a pair''')
             _UpperCAmelCase				:      Optional[int]			  =				[
                 ('''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
             try:
                 tokenizer_r.encode(_A							,  max_length=_A)
                 tokenizer_r.encode_plus(_A							,  max_length=_A)

                 tokenizer_r.batch_encode_plus(_A							,  max_length=_A)
                 tokenizer_r.encode(_A							,  max_length=_A)
                 tokenizer_r.batch_encode_plus(_A							,  max_length=_A)
             except ValueError:
                 self.fail('''Bloom Tokenizer should be able to deal with padding''')

             _UpperCAmelCase				:      Tuple			  =				None  # Hotfixing padding = None
             self.assertRaises(_A							,  tokenizer_r.encode							,  _A							,  max_length=_A							,  padding='''max_length''')

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

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

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

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

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


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				self.get_rust_tokenizer()
     _UpperCAmelCase				:      int			  =				load_dataset('''xnli'''							,  '''all_languages'''							,  split='''test'''							,  streaming=_A)

     _UpperCAmelCase				:      Tuple			  =				next(iter(_A))['''premise''']  # pick up one data
     _UpperCAmelCase				:      List[Any]			  =				list(sample_data.values())

     _UpperCAmelCase				:      Any			  =				list(map(tokenizer.encode							,  _A))
     _UpperCAmelCase				:      List[str]			  =				[tokenizer.decode(_A							,  clean_up_tokenization_spaces=_A) for x in output_tokens]
     self.assertListEqual(_A							,  _A)


 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map)							,  1)
     self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values())[0])							,  1)




