






"""simple docstring"""



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   ["image_processor", "tokenizer"]
					SCREAMING_SNAKE_CASE_		:				Dict							    =   "AutoImageProcessor"
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   "AutoTokenizer"




					def __init__(				self     :						Optional[Any]    ,						A     :						Any=None    ,						A     :						List[Any]=None    ,						**A     :						List[str]   )  ->  Optional[int]:
							lowercase_ : Union[str, 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_ : Optional[Any]							       =   kwargs.pop('''feature_extractor'''   )

							lowercase_ : Optional[Any]							       =   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`.'''   )

							super().__init__(A    ,						A   )
							lowercase_ : List[Any]							       =   self.image_processor
							lowercase_ : int							       =   False




					def __call__(				self     :						Tuple    ,						*A     :						Optional[Any]    ,						**A     :						List[str]   )  ->  int:
							# For backward compatibility
							if self._in_target_context_manager:
									return self.current_processor(*A    ,						**A   )

							lowercase_ : int							       =   kwargs.pop('''images'''    ,						A   )
							lowercase_ : List[str]							       =   kwargs.pop('''text'''    ,						A   )
							if len(A   ) > 0:
									lowercase_ : List[Any]							       =   args[0]
									lowercase_ : Optional[int]							       =   args[1:]

							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_ : str							       =   self.image_processor(A    ,						*A    ,						**A   )
							if text is not None:
									lowercase_ : Dict							       =   self.tokenizer(A    ,						**A   )

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




					def   A  (				self     :						int    ,						*A     :						Tuple    ,						**A     :						Optional[Any]   )  ->  List[str]:
							return self.tokenizer.batch_decode(*A    ,						**A   )




					def   A  (				self     :						Any    ,						*A     :						List[str]    ,						**A     :						Optional[Any]   )  ->  Optional[Any]:
							return self.tokenizer.decode(*A    ,						**A   )




					@contextmanager
					def   A  (				self     :						Dict   )  ->  Union[str, Any]:
							warnings.warn(
							    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
							    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
							    '''your images inputs, or in a separate call.'''   )
							lowercase_ : Union[str, Any]							       =   True
							lowercase_ : Optional[int]							       =   self.tokenizer
							yield
							lowercase_ : Dict							       =   self.image_processor
							lowercase_ : List[Any]							       =   False




					def   A  (				self     :						Any    ,						A     :						Optional[Any]    ,						A     :						Optional[int]=False    ,						A     :						Any=None   )  ->  Dict:
							if added_vocab is None:
									lowercase_ : Tuple							       =   self.tokenizer.get_added_vocab()

							lowercase_ : Optional[int]							       =   {}

							while tokens:
									lowercase_ : int							       =   re.search(R'''<s_(.*?)>'''    ,						A    ,						re.IGNORECASE   )
									if start_token is None:
											break
									lowercase_ : Tuple							       =   start_token.group(1   )
									lowercase_ : Optional[Any]							       =   re.search(RF'''</s_{key}>'''    ,						A    ,						re.IGNORECASE   )
									lowercase_ : int							       =   start_token.group()
									if end_token is None:
											lowercase_ : int							       =   tokens.replace(A    ,						''''''   )
									else:
											lowercase_ : Any							       =   end_token.group()
											lowercase_ : int							       =   re.escape(A   )
											lowercase_ : Tuple							       =   re.escape(A   )
											lowercase_ : Optional[int]							       =   re.search(F'''{start_token_escaped}(.*?){end_token_escaped}'''    ,						A    ,						re.IGNORECASE   )
											if content is not None:
													lowercase_ : int							       =   content.group(1   ).strip()
													if r"<s_" in content and r"</s_" in content:  # non-leaf node
															lowercase_ : List[Any]							       =   self.tokenajson(A    ,						is_inner_value=A    ,						added_vocab=A   )
															if value:
																	if len(A   ) == 1:
																			lowercase_ : Optional[int]							       =   value[0]
																	lowercase_ : str							       =   value
													else:  # leaf nodes
															lowercase_ : int							       =   []
															for leaf in content.split(R'''<sep/>'''   ):
																	lowercase_ : Optional[int]							       =   leaf.strip()
																	if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																			lowercase_ : List[Any]							       =   leaf[1:-2]  # for categorical special tokens
																	output[key].append(A   )
															if len(output[key]   ) == 1:
																	lowercase_ : Dict							       =   output[key][0]

											lowercase_ : int							       =   tokens[tokens.find(A   ) + len(A   ) :].strip()
											if tokens[:6] == r"<sep/>":  # non-leaf nodes
													return [output] + self.tokenajson(tokens[6:]    ,						is_inner_value=A    ,						added_vocab=A   )

							if len(A   ):
									return [output] if is_inner_value else output
							else:
									return [] if is_inner_value else {"text_sequence": tokens}




					@property
					def   A  (				self     :						Any   )  ->  str:
							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''    ,						A    ,						)
							return self.image_processor_class






					@property
					def   A  (				self     :						Optional[int]   )  ->  Union[str, Any]:
							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''    ,						A    ,						)
							return self.image_processor




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
					import tensorflow as tf

					from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM





@require_tf
@require_sentencepiece
@require_tokenizers
class 					lowercase__						(   unittest.TestCase):


						@slow
						def        __A       (       self	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''						)
												SCREAMING_SNAKE_CASE							:    List[str]							=							AutoTokenizer.from_pretrained('''google/mt5-small'''						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							tokenizer('''Hello there'''    ,		return_tensors='''tf'''						).input_ids
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenizer('''Hi I am'''    ,		return_tensors='''tf'''						).input_ids

												SCREAMING_SNAKE_CASE							:    List[Any]							=							model(UpperCamelCase__    ,		labels=UpperCamelCase__						).loss
												SCREAMING_SNAKE_CASE							:    int							=							-tf.math.reduce_mean(UpperCamelCase__						).numpy()

												SCREAMING_SNAKE_CASE							:    List[str]							=							-21.22_8168
												self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 2E-4						)





