



import warnings

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

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):
     lowerCAmelCase       =      ["""image_processor""", """tokenizer"""]
     lowerCAmelCase       =      """AutoImageProcessor"""
     lowerCAmelCase       =      """AutoTokenizer"""





     def __init__(							self			:				int  ,	UpperCamelCase			:				int=None  ,	UpperCamelCase			:				Any=None  ,	**UpperCamelCase			:				int					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     int							=						None
      if "feature_extractor" in kwargs:
       warnings.warn(
           "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
           " instead."  ,	UpperCamelCase  ,	)
       __SCREAMING_SNAKE_CASE							:     Tuple							=						kwargs.pop("feature_extractor"					)

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
       raise ValueError("You have to specify an image_processor."					)
      if tokenizer is None:
       raise ValueError("You have to specify a tokenizer."					)

      super().__init__(UpperCamelCase  ,	UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self.image_processor





     def __call__(							self			:				Union[str, Any]  ,	UpperCamelCase			:				Optional[Any]=None  ,	UpperCamelCase			:				List[Any]=None  ,	UpperCamelCase			:				Union[str, Any]=None  ,	**UpperCamelCase			:				List[Any]					)->List[str]:

      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:
       __SCREAMING_SNAKE_CASE							:     str							=						self.tokenizer(UpperCamelCase  ,	return_tensors=UpperCamelCase  ,	**UpperCamelCase					)

      if images is not None:
       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self.image_processor(UpperCamelCase  ,	return_tensors=UpperCamelCase  ,	**UpperCamelCase					)

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





     def 						__snake_case     (							self			:				Tuple  ,	*UpperCamelCase			:				Union[str, Any]  ,	**UpperCamelCase			:				str					)->int:
      return self.tokenizer.batch_decode(*UpperCamelCase  ,	**UpperCamelCase					)





     def 						__snake_case     (							self			:				Dict  ,	*UpperCamelCase			:				Any  ,	**UpperCamelCase			:				List[Any]					)->str:
      return self.tokenizer.decode(*UpperCamelCase  ,	**UpperCamelCase					)





     @property
     def 						__snake_case     (							self			:				Any					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.tokenizer.model_input_names
      __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)





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






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














from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


snake_case_   :	Union[str, Any]							      ={'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	int							      =[
					    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''GPTJForCausalLM''',
					    '''GPTJForQuestionAnswering''',
					    '''GPTJForSequenceClassification''',
					    '''GPTJModel''',
					    '''GPTJPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	str							      =[
					    '''TFGPTJForCausalLM''',
					    '''TFGPTJForQuestionAnswering''',
					    '''TFGPTJForSequenceClassification''',
					    '''TFGPTJModel''',
					    '''TFGPTJPreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	List[Any]							      =[
					    '''FlaxGPTJForCausalLM''',
					    '''FlaxGPTJModel''',
					    '''FlaxGPTJPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gptj import (
										    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTJForCausalLM,
										    GPTJForQuestionAnswering,
										    GPTJForSequenceClassification,
										    GPTJModel,
										    GPTJPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_gptj import (
										    TFGPTJForCausalLM,
										    TFGPTJForQuestionAnswering,
										    TFGPTJForSequenceClassification,
										    TFGPTJModel,
										    TFGPTJPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
					import sys

					snake_case_   :	List[str]							      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

