
"""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 				a			(					lowerCAmelCase_      ):
      UpperCamelCase							:   List[Any]           =     ['image_processor', 'tokenizer']
      UpperCamelCase							:   Tuple           =     'BlipImageProcessor'
      UpperCamelCase							:   Optional[int]           =     'AutoTokenizer'



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          super().__init__(__lowerCAmelCase     ,	__lowerCAmelCase      )

          # add QFormer tokenizer
          UpperCAmelCase__	:      Optional[int]     							=					qformer_tokenizer



      def __call__(   self     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = 0     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = None     ,	**UpperCamelCase_     ,	):
          if images is None and text is None:
              raise ValueError('You have to specify at least images or text.'      )

          UpperCAmelCase__	:      List[Any]     							=					BatchFeature()

          if text is not None:
              UpperCAmelCase__	:      List[Any]     							=					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     ,	)
              encoding.update(__lowerCAmelCase      )
              UpperCAmelCase__	:      List[Any]     							=					self.qformer_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     ,	)
              UpperCAmelCase__	:      Optional[int]     							=					qformer_text_encoding.pop('input_ids'      )
              UpperCAmelCase__	:      Optional[Any]     							=					qformer_text_encoding.pop('attention_mask'      )

          if images is not None:
              UpperCAmelCase__	:      str     							=					self.image_processor(__lowerCAmelCase     ,	return_tensors=__lowerCAmelCase      )
              encoding.update(__lowerCAmelCase      )

          return encoding



      def  __snake_case							(   self     ,	*UpperCamelCase_     ,	**UpperCamelCase_      ):
          return self.tokenizer.batch_decode(*__lowerCAmelCase     ,	**__lowerCAmelCase      )



      def  __snake_case							(   self     ,	*UpperCamelCase_     ,	**UpperCamelCase_      ):
          return self.tokenizer.decode(*__lowerCAmelCase     ,	**__lowerCAmelCase      )



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



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	**UpperCamelCase_      ):
          if os.path.isfile(__lowerCAmelCase      ):
              raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''      )
          os.makedirs(__lowerCAmelCase     ,	exist_ok=__lowerCAmelCase      )
          UpperCAmelCase__	:      Tuple     							=					os.path.join(__lowerCAmelCase     ,	'qformer_tokenizer'      )
          self.qformer_tokenizer.save_pretrained(__lowerCAmelCase      )
          return super().save_pretrained(__lowerCAmelCase     ,	**__lowerCAmelCase      )



      @classmethod
      def  __snake_case							(   cls     ,	UpperCamelCase_     ,	**UpperCamelCase_      ):
          UpperCAmelCase__	:      Optional[int]     							=					AutoTokenizer.from_pretrained(__lowerCAmelCase     ,	subfolder='qformer_tokenizer'      )
          UpperCAmelCase__	:      Optional[Any]     							=					cls._get_arguments_from_pretrained(__lowerCAmelCase     ,	**__lowerCAmelCase      )
          args.append(__lowerCAmelCase      )
          return cls(*__lowerCAmelCase      )


"""simple docstring"""


from typing import TYPE_CHECKING

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


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

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """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

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