

'''simple docstring'''





import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
							import torch





class 		__SCREAMING_SNAKE_CASE       ( UpperCamelCase_			):
				lowerCamelCase_							=						'char'
				lowerCamelCase_							=						'bpe'
				lowerCamelCase_							=						'wp'



UpperCamelCase_							=					(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)





class 		__SCREAMING_SNAKE_CASE       ( UpperCamelCase_			):
				lowerCamelCase_							=						['image_processor', 'char_tokenizer']
				lowerCamelCase_							=						'ViTImageProcessor'
				lowerCamelCase_							=						'MgpstrTokenizer'







				def __init__(					self    :		Union[str, Any]		,				UpperCAmelCase__    :		Any=None		,				UpperCAmelCase__    :		Dict=None		,				**UpperCAmelCase__    :		Any      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=None
										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.'''		,				__A		,				)
																lowercase   :       List[str]								=kwargs.pop('''feature_extractor'''      )

										lowercase   :       List[str]								=image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
																raise ValueError('''You need to specify an `image_processor`.'''      )
										if tokenizer is None:
																raise ValueError('''You need to specify a `tokenizer`.'''      )

										lowercase   :       List[Any]								=tokenizer
										lowercase   :       List[str]								=AutoTokenizer.from_pretrained('''gpt2'''      )
										lowercase   :       Dict								=AutoTokenizer.from_pretrained('''bert-base-uncased'''      )

										super().__init__(__A		,				__A      )







				def __call__(					self    :		List[str]		,				UpperCAmelCase__    :		str=None		,				UpperCAmelCase__    :		Optional[Any]=None		,				UpperCAmelCase__    :		Dict=None		,				**UpperCAmelCase__    :		Optional[Any]      ):
										'''simple docstring'''
										if images is None and text is None:
																raise ValueError('''You need to specify either an `images` or `text` input to process.'''      )

										if images is not None:
																lowercase   :       Union[str, Any]								=self.image_processor(__A		,				return_tensors=__A		,				**__A      )
										if text is not None:
																lowercase   :       List[str]								=self.char_tokenizer(__A		,				return_tensors=__A		,				**__A      )

										if text is None:
																return inputs
										elif images is None:
																return encodings
										else:
																lowercase   :       Any								=encodings["input_ids"]
																return inputs







				def    lowerCamelCase_  (					self    :		Optional[Any]		,				UpperCAmelCase__    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       List[str]								=sequences
										lowercase   :       Optional[Any]								=char_preds.size(0      )

										lowercase   :       List[Any]								=self._decode_helper(__A		,				'''char'''      )
										lowercase   :       Tuple								=self._decode_helper(__A		,				'''bpe'''      )
										lowercase   :       int								=self._decode_helper(__A		,				'''wp'''      )

										lowercase   :       Dict								=[]
										lowercase   :       Dict								=[]
										for i in range(__A      ):
																lowercase   :       List[str]								=[char_scores[i], bpe_scores[i], wp_scores[i]]
																lowercase   :       str								=[char_strs[i], bpe_strs[i], wp_strs[i]]
																lowercase   :       str								=scores.index(max(__A      )      )
																final_strs.append(strs[max_score_index]      )
																final_scores.append(scores[max_score_index]      )

										lowercase   :       Any								={}
										lowercase   :       Union[str, Any]								=final_strs
										lowercase   :       Tuple								=final_scores
										lowercase   :       Optional[Any]								=char_strs
										lowercase   :       Tuple								=bpe_strs
										lowercase   :       Dict								=wp_strs
										return out







				def    lowerCamelCase_  (					self    :		str		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		Union[str, Any]      ):
										'''simple docstring'''
										if format == DecodeType.CHARACTER:
																lowercase   :       int								=self.char_decode
																lowercase   :       List[str]								=1
																lowercase   :       Optional[int]								="[s]"
										elif format == DecodeType.BPE:
																lowercase   :       Optional[int]								=self.bpe_decode
																lowercase   :       List[str]								=2
																lowercase   :       Dict								="#"
										elif format == DecodeType.WORDPIECE:
																lowercase   :       Union[str, Any]								=self.wp_decode
																lowercase   :       Tuple								=102
																lowercase   :       Optional[Any]								="[SEP]"
										else:
																raise ValueError(F'''Format {format} is not supported.'''      )

										lowercase   :       Union[str, Any]								=[], []
										lowercase   :       Tuple								=pred_logits.size(0      )
										lowercase   :       int								=pred_logits.size(1      )
										lowercase   :       Any								=pred_logits.topk(1		,				dim=-1		,				largest=__A		,				sorted=__A      )
										lowercase   :       Tuple								=preds_index.view(-1		,				__A      )[:, 1:]
										lowercase   :       List[Any]								=decoder(__A      )
										lowercase   :       Optional[Any]								=torch.nn.functional.softmax(__A		,				dim=2      ).max(dim=2      )
										lowercase   :       str								=preds_max_prob[:, 1:]

										for index in range(__A      ):
																lowercase   :       int								=preds_str[index].find(__A      )
																lowercase   :       Union[str, Any]								=preds_str[index][:pred_eos]
																lowercase   :       Any								=preds_index[index].cpu().tolist()
																lowercase   :       Union[str, Any]								=pred_index.index(__A      ) if eos_token in pred_index else -1
																lowercase   :       Union[str, Any]								=preds_max_prob[index][: pred_eos_index + 1]
																lowercase   :       List[str]								=pred_max_prob.cumprod(dim=0      )[-1] if pred_max_prob.nelement() != 0 else 0.0
																dec_strs.append(__A      )
																conf_scores.append(__A      )

										return dec_strs, conf_scores







				def    lowerCamelCase_  (					self    :		Union[str, Any]		,				UpperCAmelCase__    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       Tuple								=[seq.replace(''' '''		,				''''''      ) for seq in self.char_tokenizer.batch_decode(__A      )]
										return decode_strs







				def    lowerCamelCase_  (					self    :		List[Any]		,				UpperCAmelCase__    :		Any      ):
										'''simple docstring'''
										return self.bpe_tokenizer.batch_decode(__A      )







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		List[str]      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=[seq.replace(''' '''		,				''''''      ) for seq in self.wp_tokenizer.batch_decode(__A      )]
										return decode_strs








'''simple docstring'''





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 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						None
				lowerCamelCase_							=						BloomTokenizerFast
				lowerCamelCase_							=						BloomTokenizerFast
				lowerCamelCase_							=						True
				lowerCamelCase_							=						False
				lowerCamelCase_							=						'tokenizer_file'
				lowerCamelCase_							=						{'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>'}







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										super().setUp()
										lowercase   :       Union[str, Any]								=BloomTokenizerFast.from_pretrained('''bigscience/tokenizer'''      )
										tokenizer.save_pretrained(self.tmpdirname      )







				def    lowerCamelCase_  (					self    :		Union[str, Any]		,				**UpperCAmelCase__    :		Any      ):
										'''simple docstring'''
										kwargs.update(self.special_tokens_map      )
										return BloomTokenizerFast.from_pretrained(self.tmpdirname		,				**UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       str								=self.get_rust_tokenizer()

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

										lowercase   :       Any								=tokenizer.batch_encode_plus(UpperCAmelCase__      )['''input_ids''']
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )

										lowercase   :       int								=tokenizer.batch_decode(UpperCAmelCase__      )
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Tuple		,				UpperCAmelCase__    :		Any=6      ):
										'''simple docstring'''
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
																						lowercase   :       Optional[int]								=self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__		,				**UpperCAmelCase__      )
																						# tokenizer_r.pad_token = None # Hotfixing padding = None
																						# Simple input
																						lowercase   :       Tuple								='''This is a simple input'''
																						lowercase   :       int								=['''This is a simple input 1''', '''This is a simple input 2''']
																						lowercase   :       Optional[Any]								=('''This is a simple input''', '''This is a pair''')
																						lowercase   :       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(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																												tokenizer_r.encode_plus(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )

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

																						lowercase   :       Optional[int]								=None  # Hotfixing padding = None
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

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

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

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

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

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







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=self.get_rust_tokenizer()
										lowercase   :       Dict								=load_dataset('''xnli'''		,				'''all_languages'''		,				split='''test'''		,				streaming=UpperCAmelCase__      )

										lowercase   :       Union[str, Any]								=next(iter(UpperCAmelCase__      )      )['''premise''']  # pick up one data
										lowercase   :       int								=list(sample_data.values()      )

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







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
										# any sequence length constraints. This test of the parent class will fail since it relies on the
										# maximum sequence length of the positoonal embeddings.
										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      )





