

from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class        lowercase		(			_a   ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE   				=							["""image_processor""", """tokenizer"""]
 __SCREAMING_SNAKE_CASE   				=							"""BlipImageProcessor"""
 __SCREAMING_SNAKE_CASE   				=							("""BertTokenizer""", """BertTokenizerFast""")




 def __init__(self			,			__a			,			__a					)					->   List[str]:






  """simple docstring"""





  UpperCAmelCase__     							=							False
  super().__init__(lowerCAmelCase_			,			lowerCAmelCase_					)
  UpperCAmelCase__     							=							self.image_processor




 def __call__(self			,			__a = None			,			__a = None			,			__a = True			,			__a = False			,			__a = None			,			__a = None			,			__a = 0			,			__a = None			,			__a = None			,			__a = False			,			__a = False			,			__a = False			,			__a = False			,			__a = False			,			__a = True			,			__a = None			,			**__a			,			)					->   BatchEncoding:






  """simple docstring"""





  if images is None and text is None:
   raise ValueError('You have to specify either images or text.'					)

  # Get only text
  if images is None:
   UpperCAmelCase__     							=							self.tokenizer
   UpperCAmelCase__     							=							self.tokenizer(
       text=lowerCAmelCase_			,			add_special_tokens=lowerCAmelCase_			,			padding=lowerCAmelCase_			,			truncation=lowerCAmelCase_			,			max_length=lowerCAmelCase_			,			stride=lowerCAmelCase_			,			pad_to_multiple_of=lowerCAmelCase_			,			return_attention_mask=lowerCAmelCase_			,			return_overflowing_tokens=lowerCAmelCase_			,			return_special_tokens_mask=lowerCAmelCase_			,			return_offsets_mapping=lowerCAmelCase_			,			return_token_type_ids=lowerCAmelCase_			,			return_length=lowerCAmelCase_			,			verbose=lowerCAmelCase_			,			return_tensors=lowerCAmelCase_			,			**lowerCAmelCase_			,			)
   return text_encoding

  # add pixel_values
  UpperCAmelCase__     							=							self.image_processor(lowerCAmelCase_			,			return_tensors=lowerCAmelCase_					)

  if text is not None:
   UpperCAmelCase__     							=							self.tokenizer(
       text=lowerCAmelCase_			,			add_special_tokens=lowerCAmelCase_			,			padding=lowerCAmelCase_			,			truncation=lowerCAmelCase_			,			max_length=lowerCAmelCase_			,			stride=lowerCAmelCase_			,			pad_to_multiple_of=lowerCAmelCase_			,			return_attention_mask=lowerCAmelCase_			,			return_overflowing_tokens=lowerCAmelCase_			,			return_special_tokens_mask=lowerCAmelCase_			,			return_offsets_mapping=lowerCAmelCase_			,			return_token_type_ids=lowerCAmelCase_			,			return_length=lowerCAmelCase_			,			verbose=lowerCAmelCase_			,			return_tensors=lowerCAmelCase_			,			**lowerCAmelCase_			,			)
  else:
   UpperCAmelCase__     							=							None

  if text_encoding is not None:
   encoding_image_processor.update(lowerCAmelCase_					)

  return encoding_image_processor




 def     UpperCamelCase__				(self			,			*__a			,			**__a					)					->   Optional[int]:






  """simple docstring"""





  return self.tokenizer.batch_decode(*lowerCAmelCase_			,			**lowerCAmelCase_					)




 def     UpperCamelCase__				(self			,			*__a			,			**__a					)					->   Optional[int]:






  """simple docstring"""





  return self.tokenizer.decode(*lowerCAmelCase_			,			**lowerCAmelCase_					)




 @property
 def     UpperCamelCase__				(self					)					->   Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__     							=							self.tokenizer.model_input_names
  UpperCAmelCase__     							=							self.image_processor.model_input_names
  return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)









'''simple docstring'''


import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
				import optax
				from flax.training.common_utils import onehot

				from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
				from transformers.models.ta.modeling_flax_ta import shift_tokens_right

@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class      A       (						unittest.TestCase       ):







							@slow
							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''   )
														_a      	=							AutoTokenizer.from_pretrained('''google/mt5-small'''   )

														_a      	=							tokenizer('''Hello there'''				,     return_tensors='''np'''   ).input_ids
														_a      	=							tokenizer('''Hi I am'''				,     return_tensors='''np'''   ).input_ids

														_a      	=							shift_tokens_right(lowerCAmelCase_				,     model.config.pad_token_id				,     model.config.decoder_start_token_id   )

														_a      	=							model(lowerCAmelCase_				,     decoder_input_ids=lowerCAmelCase_   ).logits
														_a      	=							optax.softmax_cross_entropy(lowerCAmelCase_				,     onehot(lowerCAmelCase_				,     logits.shape[-1]   )   ).mean()

														_a      	=							-(labels.shape[-1] * loss.item())

														_a      	=							-8_4.9_1_2_7
														self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1e-4   )

