



'''simple docstring'''





import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						["image_processor", "tokenizer"]
    lowercase   =						"BlipImageProcessor"
    lowercase   =						"AutoTokenizer"




    def __init__(       self   :	str					,	snake_case_   :	Dict					,	snake_case_   :	List[str]					,	snake_case_   :	Dict		):
          super().__init__(snake_case_					,	snake_case_		)

          # add QFormer tokenizer
          snake_case__    :       Optional[Any]       		= qformer_tokenizer




    def __call__(       self   :	Dict					,	snake_case_   :	ImageInput = None					,	snake_case_   :	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					,	snake_case_   :	bool = True					,	snake_case_   :	Union[bool, str, PaddingStrategy] = False					,	snake_case_   :	Union[bool, str, TruncationStrategy] = None					,	snake_case_   :	Optional[int] = None					,	snake_case_   :	int = 0					,	snake_case_   :	Optional[int] = None					,	snake_case_   :	Optional[bool] = None					,	snake_case_   :	bool = False					,	snake_case_   :	bool = False					,	snake_case_   :	bool = False					,	snake_case_   :	bool = False					,	snake_case_   :	bool = False					,	snake_case_   :	bool = True					,	snake_case_   :	Optional[Union[str, TensorType]] = None					,	**snake_case_   :	Any					,	):
          if images is None and text is None:
                raise ValueError("""You have to specify at least images or text."""		)

          snake_case__    :       Optional[int]       		= BatchFeature()

          if text is not None:
                snake_case__    :       List[str]       		= self.tokenizer(
                    text=snake_case_					,	add_special_tokens=snake_case_					,	padding=snake_case_					,	truncation=snake_case_					,	max_length=snake_case_					,	stride=snake_case_					,	pad_to_multiple_of=snake_case_					,	return_attention_mask=snake_case_					,	return_overflowing_tokens=snake_case_					,	return_special_tokens_mask=snake_case_					,	return_offsets_mapping=snake_case_					,	return_token_type_ids=snake_case_					,	return_length=snake_case_					,	verbose=snake_case_					,	return_tensors=snake_case_					,	**snake_case_					,	)
                encoding.update(snake_case_		)
                snake_case__    :       str       		= self.qformer_tokenizer(
                    text=snake_case_					,	add_special_tokens=snake_case_					,	padding=snake_case_					,	truncation=snake_case_					,	max_length=snake_case_					,	stride=snake_case_					,	pad_to_multiple_of=snake_case_					,	return_attention_mask=snake_case_					,	return_overflowing_tokens=snake_case_					,	return_special_tokens_mask=snake_case_					,	return_offsets_mapping=snake_case_					,	return_token_type_ids=snake_case_					,	return_length=snake_case_					,	verbose=snake_case_					,	return_tensors=snake_case_					,	**snake_case_					,	)
                snake_case__    :       str       		= qformer_text_encoding.pop("""input_ids"""		)
                snake_case__    :       Optional[Any]       		= qformer_text_encoding.pop("""attention_mask"""		)

          if images is not None:
                snake_case__    :       Dict       		= self.image_processor(snake_case_					,	return_tensors=snake_case_		)
                encoding.update(snake_case_		)

          return encoding




    def lowerCamelCase			(       self   :	Optional[Any]					,	*snake_case_   :	int					,	**snake_case_   :	Union[str, Any]		):
          return self.tokenizer.batch_decode(*snake_case_					,	**snake_case_		)




    def lowerCamelCase			(       self   :	Any					,	*snake_case_   :	int					,	**snake_case_   :	Any		):
          return self.tokenizer.decode(*snake_case_					,	**snake_case_		)




    @property
    # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
    def lowerCamelCase			(       self   :	List[str]		):
          snake_case__    :       Dict       		= self.tokenizer.model_input_names
          snake_case__    :       str       		= self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)




    def lowerCamelCase			(       self   :	List[str]					,	snake_case_   :	Union[str, Any]					,	**snake_case_   :	Optional[Any]		):
          if os.path.isfile(snake_case_		):
                raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file"		)
          os.makedirs(snake_case_					,	exist_ok=snake_case_		)
          snake_case__    :       Any       		= os.path.join(snake_case_					,	"""qformer_tokenizer"""		)
          self.qformer_tokenizer.save_pretrained(snake_case_		)
          return super().save_pretrained(snake_case_					,	**snake_case_		)




    @classmethod
    def lowerCamelCase			(       cls   :	Dict					,	snake_case_   :	Optional[int]					,	**snake_case_   :	Optional[Any]		):
          snake_case__    :       Tuple       		= AutoTokenizer.from_pretrained(snake_case_					,	subfolder="""qformer_tokenizer"""		)
          snake_case__    :       Any       		= cls._get_arguments_from_pretrained(snake_case_					,	**snake_case_		)
          args.append(snake_case_		)
          return cls(*snake_case_		)





'''simple docstring'''





from typing import TYPE_CHECKING

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


__a  			=						{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a  			=						[
           "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GPTBigCodeForSequenceClassification",
           "GPTBigCodeForTokenClassification",
           "GPTBigCodeForCausalLM",
           "GPTBigCodeModel",
           "GPTBigCodePreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_bigcode import (
                  GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTBigCodeForCausalLM,
                  GPTBigCodeForSequenceClassification,
                  GPTBigCodeForTokenClassification,
                  GPTBigCodeModel,
                  GPTBigCodePreTrainedModel,
              )


else:
       import sys

       __a  			=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
