


'''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 _lowerCamelCase       (						UpperCamelCase_							):



		'''simple docstring'''







		A_	:			Optional[int]          =				['image_processor', 'tokenizer']
		A_	:			Optional[int]          =				'BlipImageProcessor'
		A_	:			str          =				'AutoTokenizer'







		def __init__(					self		:						Dict  ,     _A		:						Any  ,     _A		:						Union[str, Any]  ,     _A		:						List[str]			)  -> str:
			super().__init__(lowercase_  ,     lowercase_			)

			# add QFormer tokenizer
			__magic_name__       :    Any     	=     qformer_tokenizer







		def __call__(					self		:						List[str]  ,     _A		:						ImageInput = None  ,     _A		:						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,     _A		:						bool = True  ,     _A		:						Union[bool, str, PaddingStrategy] = False  ,     _A		:						Union[bool, str, TruncationStrategy] = None  ,     _A		:						Optional[int] = None  ,     _A		:						int = 0  ,     _A		:						Optional[int] = None  ,     _A		:						Optional[bool] = None  ,     _A		:						bool = False  ,     _A		:						bool = False  ,     _A		:						bool = False  ,     _A		:						bool = False  ,     _A		:						bool = False  ,     _A		:						bool = True  ,     _A		:						Optional[Union[str, TensorType]] = None  ,     **_A		:						Union[str, Any]  ,     )  -> BatchFeature:




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

			__magic_name__       :    str     	=     BatchFeature()

			if text is not None:
				__magic_name__       :    Dict     	=     self.tokenizer(
				    text=lowercase_  ,     add_special_tokens=lowercase_  ,     padding=lowercase_  ,     truncation=lowercase_  ,     max_length=lowercase_  ,     stride=lowercase_  ,     pad_to_multiple_of=lowercase_  ,     return_attention_mask=lowercase_  ,     return_overflowing_tokens=lowercase_  ,     return_special_tokens_mask=lowercase_  ,     return_offsets_mapping=lowercase_  ,     return_token_type_ids=lowercase_  ,     return_length=lowercase_  ,     verbose=lowercase_  ,     return_tensors=lowercase_  ,     **lowercase_  ,     )
				encoding.update(lowercase_			)
				__magic_name__       :    Optional[int]     	=     self.qformer_tokenizer(
				    text=lowercase_  ,     add_special_tokens=lowercase_  ,     padding=lowercase_  ,     truncation=lowercase_  ,     max_length=lowercase_  ,     stride=lowercase_  ,     pad_to_multiple_of=lowercase_  ,     return_attention_mask=lowercase_  ,     return_overflowing_tokens=lowercase_  ,     return_special_tokens_mask=lowercase_  ,     return_offsets_mapping=lowercase_  ,     return_token_type_ids=lowercase_  ,     return_length=lowercase_  ,     verbose=lowercase_  ,     return_tensors=lowercase_  ,     **lowercase_  ,     )
				__magic_name__       :    Any     	=     qformer_text_encoding.pop('input_ids'			)
				__magic_name__       :    Tuple     	=     qformer_text_encoding.pop('attention_mask'			)

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

			return encoding







		def    __lowerCAmelCase		(					self		:						Optional[Any]  ,     *_A		:						int  ,     **_A		:						List[str]			)  -> Optional[int]:
			return self.tokenizer.batch_decode(*lowercase_  ,     **lowercase_			)







		def    __lowerCAmelCase		(					self		:						Union[str, Any]  ,     *_A		:						List[Any]  ,     **_A		:						Dict			)  -> Optional[Any]:
			return self.tokenizer.decode(*lowercase_  ,     **lowercase_			)







		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def    __lowerCAmelCase		(					self		:						Tuple			)  -> List[Any]:
			__magic_name__       :    List[Any]     	=     self.tokenizer.model_input_names
			__magic_name__       :    int     	=     self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)







		def    __lowerCAmelCase		(					self		:						Optional[Any]  ,     _A		:						List[Any]  ,     **_A		:						Optional[int]			)  -> List[str]:




			if os.path.isfile(lowercase_			):
				raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'			)
			os.makedirs(lowercase_  ,     exist_ok=lowercase_			)
			__magic_name__       :    List[Any]     	=     os.path.join(lowercase_  ,     'qformer_tokenizer'			)
			self.qformer_tokenizer.save_pretrained(lowercase_			)
			return super().save_pretrained(lowercase_  ,     **lowercase_			)







		@classmethod
		def    __lowerCAmelCase		(					cls		:						str  ,     _A		:						str  ,     **_A		:						Optional[Any]			)  -> Tuple:
			__magic_name__       :    Optional[int]     	=     AutoTokenizer.from_pretrained(lowercase_  ,     subfolder='qformer_tokenizer'			)
			__magic_name__       :    Optional[int]     	=     cls._get_arguments_from_pretrained(lowercase_  ,     **lowercase_			)
			args.append(lowercase_			)
			return cls(*lowercase_			)



from typing import TYPE_CHECKING

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


if is_sentencepiece_available():
 from ..ta.tokenization_ta import TaTokenizer
else:
 from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCamelCase											=       TaTokenizer

if is_tokenizers_available():
 from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
 from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCamelCase											=       TaTokenizerFast

lowerCamelCase											=       {'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase											=       [
     'MT5EncoderModel',
     'MT5ForConditionalGeneration',
     'MT5ForQuestionAnswering',
     'MT5Model',
     'MT5PreTrainedModel',
     'MT5Stack',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase											=       ['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase											=       ['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
 from .configuration_mta import MTaConfig, MTaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mta import (
      MTaEncoderModel,
      MTaForConditionalGeneration,
      MTaForQuestionAnswering,
      MTaModel,
      MTaPreTrainedModel,
      MTaStack,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
 import sys

 lowerCamelCase											=       _LazyModule(
     __name__,
     globals()['__file__'],
     _import_structure,
     extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
     module_spec=__spec__,
 )


