



"""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      UpperCAmelCase__		(      lowerCAmelCase__     ):
		lowerCAmelCase_       :	Union[str, Any] 							=     "char"
		lowerCAmelCase_       :	Union[str, Any] 							=     "bpe"
		lowerCAmelCase_       :	List[Any] 							=     "wp"



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






class      UpperCAmelCase__		(      lowerCAmelCase__     ):
		lowerCAmelCase_       :	Optional[Any] 							=     ["image_processor", "char_tokenizer"]
		lowerCAmelCase_       :	str 							=     "ViTImageProcessor"
		lowerCAmelCase_       :	List[Any] 							=     "MgpstrTokenizer"



		def __init__( self  :    List[str]      ,							snake_case  :    int=None      ,							snake_case  :    List[Any]=None      ,							**snake_case  :    Union[str, Any]				)			->						Dict:






									'''simple docstring'''

									A         =					None
									if "feature_extractor" in kwargs:
																warnings.warn(
																    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																    ' instead.'      ,							_SCREAMING_SNAKE_CASE      ,							)
																A         =					kwargs.pop('feature_extractor'				)

									A         =					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`.'				)

									A         =					tokenizer
									A         =					AutoTokenizer.from_pretrained('gpt2'				)
									A         =					AutoTokenizer.from_pretrained('bert-base-uncased'				)

									super().__init__(_SCREAMING_SNAKE_CASE      ,							_SCREAMING_SNAKE_CASE				)



		def __call__( self  :    Tuple      ,							snake_case  :    str=None      ,							snake_case  :    List[str]=None      ,							snake_case  :    Tuple=None      ,							**snake_case  :    Dict				)			->						Optional[int]:






									'''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:
																A         =					self.image_processor(_SCREAMING_SNAKE_CASE      ,							return_tensors=_SCREAMING_SNAKE_CASE      ,							**_SCREAMING_SNAKE_CASE				)
									if text is not None:
																A         =					self.char_tokenizer(_SCREAMING_SNAKE_CASE      ,							return_tensors=_SCREAMING_SNAKE_CASE      ,							**_SCREAMING_SNAKE_CASE				)

									if text is None:
																return inputs
									elif images is None:
																return encodings
									else:
																A         =					encodings['input_ids']
																return inputs



		def 					A_		( self  :    List[Any]      ,							snake_case  :    Union[str, Any]				)			->						Optional[Any]:






									'''simple docstring'''

									A      ,  A      ,  A         =					sequences
									A         =					char_preds.size(0				)

									A      ,  A         =					self._decode_helper(_SCREAMING_SNAKE_CASE      ,							'char'				)
									A      ,  A         =					self._decode_helper(_SCREAMING_SNAKE_CASE      ,							'bpe'				)
									A      ,  A         =					self._decode_helper(_SCREAMING_SNAKE_CASE      ,							'wp'				)

									A         =					[]
									A         =					[]
									for i in range(_SCREAMING_SNAKE_CASE				):
																A         =					[char_scores[i], bpe_scores[i], wp_scores[i]]
																A         =					[char_strs[i], bpe_strs[i], wp_strs[i]]
																A         =					scores.index(max(_SCREAMING_SNAKE_CASE				)				)
																final_strs.append(strs[max_score_index]				)
																final_scores.append(scores[max_score_index]				)

									A         =					{}
									A         =					final_strs
									A         =					final_scores
									A         =					char_strs
									A         =					bpe_strs
									A         =					wp_strs
									return out



		def 					A_		( self  :    List[Any]      ,							snake_case  :    str      ,							snake_case  :    Optional[int]				)			->						List[Any]:






									'''simple docstring'''

									if format == DecodeType.CHARACTER:
																A         =					self.char_decode
																A         =					1
																A         =					'[s]'
									elif format == DecodeType.BPE:
																A         =					self.bpe_decode
																A         =					2
																A         =					'#'
									elif format == DecodeType.WORDPIECE:
																A         =					self.wp_decode
																A         =					102
																A         =					'[SEP]'
									else:
																raise ValueError(f"""Format {format} is not supported."""				)

									A      ,  A         =					[], []
									A         =					pred_logits.size(0				)
									A         =					pred_logits.size(1				)
									A      ,  A         =					pred_logits.topk(1      ,							dim=-1      ,							largest=_SCREAMING_SNAKE_CASE      ,							sorted=_SCREAMING_SNAKE_CASE				)
									A         =					preds_index.view(-1      ,							_SCREAMING_SNAKE_CASE				)[:, 1:]
									A         =					decoder(_SCREAMING_SNAKE_CASE				)
									A      ,  A         =					torch.nn.functional.softmax(_SCREAMING_SNAKE_CASE      ,							dim=2				).max(dim=2				)
									A         =					preds_max_prob[:, 1:]

									for index in range(_SCREAMING_SNAKE_CASE				):
																A         =					preds_str[index].find(_SCREAMING_SNAKE_CASE				)
																A         =					preds_str[index][:pred_eos]
																A         =					preds_index[index].cpu().tolist()
																A         =					pred_index.index(_SCREAMING_SNAKE_CASE				) if eos_token in pred_index else -1
																A         =					preds_max_prob[index][: pred_eos_index + 1]
																A         =					pred_max_prob.cumprod(dim=0				)[-1] if pred_max_prob.nelement() != 0 else 0.0
																dec_strs.append(_SCREAMING_SNAKE_CASE				)
																conf_scores.append(_SCREAMING_SNAKE_CASE				)

									return dec_strs, conf_scores



		def 					A_		( self  :    Any      ,							snake_case  :    Dict				)			->						Optional[int]:






									'''simple docstring'''

									A         =					[seq.replace(' '      ,							''				) for seq in self.char_tokenizer.batch_decode(_SCREAMING_SNAKE_CASE				)]
									return decode_strs



		def 					A_		( self  :    List[Any]      ,							snake_case  :    Any				)			->						Any:






									'''simple docstring'''

									return self.bpe_tokenizer.batch_decode(_SCREAMING_SNAKE_CASE				)



		def 					A_		( self  :    int      ,							snake_case  :    Any				)			->						Optional[int]:






									'''simple docstring'''

									A         =					[seq.replace(' '      ,							''				) for seq in self.wp_tokenizer.batch_decode(_SCREAMING_SNAKE_CASE				)]
									return decode_strs








"""simple docstring"""







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
							import tensorflow as tf

if is_tensorflow_text_available():
							from transformers.models.bert import TFBertTokenizer


A    =			['bert-base-uncased', 'bert-base-cased']
A    =			'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():






							class      UpperCAmelCase__		(      tf.keras.Model     ):



									def __init__( self  :    Tuple      ,							snake_case  :    Optional[int]				)			->						List[str]:






																'''simple docstring'''

																super().__init__()
																A         =					tokenizer
																A         =					AutoConfig.from_pretrained(snake_case				)
																A         =					TFAutoModel.from_config(snake_case				)



									def 					A_		( self  :    Any      ,							snake_case  :    Optional[int]				)			->						Dict:






																'''simple docstring'''

																A         =					self.tokenizer(snake_case				)
																A         =					self.bert(**snake_case				)
																return out["pooler_output"]






@require_tf
@require_tensorflow_text
class      UpperCAmelCase__		(      unittest.TestCase     ):



		def 					A_		( self  :    int				)			->						Tuple:






									'''simple docstring'''

									super().setUp()

									A         =					[
									    BertTokenizer.from_pretrained(snake_case				) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
									]  # repeat for when fast_bert_tokenizer=false
									A         =					[TFBertTokenizer.from_pretrained(snake_case				) for checkpoint in TOKENIZER_CHECKPOINTS] + [
									    TFBertTokenizer.from_pretrained(snake_case      ,							use_fast_bert_tokenizer=snake_case				)
									    for checkpoint in TOKENIZER_CHECKPOINTS
									]
									assert len(self.tokenizers				) == len(self.tf_tokenizers				)

									A         =					[
									    'This is a straightforward English test sentence.',
									    'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
									    'Now we\'re going to add some Chinese: 一 二 三 一二三',
									    'And some much more rare Chinese: 齉 堃 齉堃',
									    'Je vais aussi écrire en français pour tester les accents',
									    'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
									]
									A         =					list(zip(self.test_sentences      ,							self.test_sentences[::-1]				)				)



		def 					A_		( self  :    List[Any]				)			->						Dict:






									'''simple docstring'''

									for tokenizer, tf_tokenizer in zip(self.tokenizers      ,							self.tf_tokenizers				):
																for test_inputs in (self.test_sentences, self.paired_sentences):
																							A         =					tokenizer(snake_case      ,							return_tensors='tf'      ,							padding='longest'				)
																							A         =					tf_tokenizer(snake_case				)

																							for key in python_outputs.keys():
																														self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape				)				)
																														self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]      ,							tf.intaa				) == tf_outputs[key]				)				)



		@slow
		def 					A_		( self  :    Tuple				)			->						Tuple:






									'''simple docstring'''

									for tf_tokenizer in self.tf_tokenizers:
																A         =					tf_tokenizer(self.paired_sentences				)
																A         =					tf_tokenizer(
																    text=[sentence[0] for sentence in self.paired_sentences]      ,							text_pair=[sentence[1] for sentence in self.paired_sentences]      ,							)
																for key in merged_outputs.keys():
																							self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]      ,							tf.intaa				) == separated_outputs[key]				)				)



		@slow
		def 					A_		( self  :    Any				)			->						Tuple:






									'''simple docstring'''

									for tf_tokenizer in self.tf_tokenizers:
																A         =					tf.function(snake_case				)
																for test_inputs in (self.test_sentences, self.paired_sentences):
																							A         =					tf.constant(snake_case				)
																							A         =					compiled_tokenizer(snake_case				)
																							A         =					tf_tokenizer(snake_case				)

																							for key in eager_outputs.keys():
																														self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]				)				)



		@slow
		def 					A_		( self  :    Optional[Any]				)			->						Optional[Any]:






									'''simple docstring'''

									for tf_tokenizer in self.tf_tokenizers:
																A         =					ModelToSave(tokenizer=snake_case				)
																A         =					tf.convert_to_tensor(self.test_sentences				)
																A         =					model(snake_case				)  # Build model with some sample inputs
																with TemporaryDirectory() as tempdir:
																							A         =					Path(snake_case				) / 'saved.model'
																							model.save(snake_case				)
																							A         =					tf.keras.models.load_model(snake_case				)
																A         =					loaded_model(snake_case				)
																# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output				)				)      ,							1E-5				)



