




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						["image_processor", "tokenizer"]
     _a							 =						"CLIPImageProcessor"
     _a							 =						("CLIPTokenizer", "CLIPTokenizerFast")

     def __init__(  self		,     _a=None		,     _a=None		,     **_a				)						->							Dict:
           _A							:     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		,     )
                 _A							:     Union[str, Any]			=						kwargs.pop("""feature_extractor"""				)

           _A							:     Optional[int]			=						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				)

     def __call__(  self		,     _a=None		,     _a=None		,     _a=None		,     **_a				)						->							int:

           if text is None and images is None:
                 raise ValueError("""You have to specify either text or images. Both cannot be none."""				)

           if text is not None:
                 _A							:     List[str]			=						self.tokenizer(_a		,     return_tensors=_a		,     **_a				)

           if images is not None:
                 _A							:     Dict			=						self.image_processor(_a		,     return_tensors=_a		,     **_a				)

           if text is not None and images is not None:
                 _A							:     Tuple			=						image_features.pixel_values
                 return encoding
           elif text is not None:
                 return encoding
           else:
                 return BatchEncoding(data=dict(**_a				)		,     tensor_type=_a				)

     def 					a__	(  self		,     *_a		,     **_a				)						->							List[Any]:
           return self.tokenizer.batch_decode(*_a		,     **_a				)

     def 					a__	(  self		,     *_a		,     **_a				)						->							Any:
           return self.tokenizer.decode(*_a		,     **_a				)

     @property
     def 					a__	(  self				)						->							List[Any]:
           _A							:     Union[str, Any]			=						self.tokenizer.model_input_names
           _A							:     str			=						self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)

     @property
     def 					a__	(  self				)						->							List[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				)						->							List[Any]:
           warnings.warn(
               """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""		,     _a		,     )
           return self.image_processor



'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
							from transformers import AutoModelForSeqaSeqLM, AutoTokenizer






@require_torch
@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase__   (  unittest.TestCase     ):






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''				,							return_dict=__SCREAMING_SNAKE_CASE		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       AutoTokenizer.from_pretrained('''google/mt5-small'''		)

											lowercase_	:     int													=       tokenizer('''Hello there'''				,							return_tensors='''pt'''		).input_ids
											lowercase_	:     Union[str, Any]													=       tokenizer('''Hi I am'''				,							return_tensors='''pt'''		).input_ids

											lowercase_	:     Union[str, Any]													=       model(input_ids.to(__SCREAMING_SNAKE_CASE		)				,							labels=labels.to(__SCREAMING_SNAKE_CASE		)		).loss
											lowercase_	:     int													=       -(labels.shape[-1] * loss.item())

											lowercase_	:     Any													=       -84.9_127
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)



