import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__lowerCamelCase      :			Dict            =		logging.get_logger(__name__)







class __snake_case    :




   def __init__(   self      :    Union[str, Any]	,     _lowercase      :    Any	,     _lowercase      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				question_encoder
     SCREAMING_SNAKE_CASE__      							=				generator
     SCREAMING_SNAKE_CASE__      							=				self.question_encoder




   def      __a						(   self      :    Dict	,     _lowercase      :    Dict				):



     """simple docstring"""






     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				)
     SCREAMING_SNAKE_CASE__      							=				os.path.join(__lowerCamelCase	,     """question_encoder_tokenizer"""				)
     SCREAMING_SNAKE_CASE__      							=				os.path.join(__lowerCamelCase	,     """generator_tokenizer"""				)
     self.question_encoder.save_pretrained(__lowerCamelCase				)
     self.generator.save_pretrained(__lowerCamelCase				)




   @classmethod
   def      __a						(   cls      :    Union[str, Any]	,     _lowercase      :    Optional[int]	,     **_lowercase      :    Optional[int]				):



     """simple docstring"""






     from ..auto.tokenization_auto import AutoTokenizer

     SCREAMING_SNAKE_CASE__      							=				kwargs.pop("""config"""	,     __lowerCamelCase				)

     if config is None:
       SCREAMING_SNAKE_CASE__      							=				RagConfig.from_pretrained(__lowerCamelCase				)

     SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained(
         __lowerCamelCase	,     config=config.question_encoder	,     subfolder="""question_encoder_tokenizer"""				)
     SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained(
         __lowerCamelCase	,     config=config.generator	,     subfolder="""generator_tokenizer"""				)
     return cls(question_encoder=__lowerCamelCase	,     generator=__lowerCamelCase				)




   def __call__(   self      :    Tuple	,     *_lowercase      :    int	,     **_lowercase      :    Optional[int]				):



     """simple docstring"""






     return self.current_tokenizer(*__lowerCamelCase	,     **__lowerCamelCase				)




   def      __a						(   self      :    Tuple	,     *_lowercase      :    int	,     **_lowercase      :    List[Any]				):



     """simple docstring"""






     return self.generator.batch_decode(*__lowerCamelCase	,     **__lowerCamelCase				)




   def      __a						(   self      :    Union[str, Any]	,     *_lowercase      :    Any	,     **_lowercase      :    Optional[int]				):



     """simple docstring"""






     return self.generator.decode(*__lowerCamelCase	,     **__lowerCamelCase				)




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.question_encoder




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.generator




   def      __a						(   self      :    int	,     _lowercase      :    Dict	,     _lowercase      :    str = None	,     _lowercase      :    List[str] = None	,     _lowercase      :    Optional[int] = None	,     _lowercase      :    List[str] = "longest"	,     _lowercase      :    List[str] = None	,     _lowercase      :    List[Any] = True	,     **_lowercase      :    Union[str, Any]	,     ):



     """simple docstring"""






     warnings.warn(
         """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
         """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
         """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
         """details"""	,     __lowerCamelCase	,     )
     if max_length is None:
       SCREAMING_SNAKE_CASE__      							=				self.current_tokenizer.model_max_length
     SCREAMING_SNAKE_CASE__      							=				self(
         __lowerCamelCase	,     add_special_tokens=__lowerCamelCase	,     return_tensors=__lowerCamelCase	,     max_length=__lowerCamelCase	,     padding=__lowerCamelCase	,     truncation=__lowerCamelCase	,     **__lowerCamelCase	,     )
     if tgt_texts is None:
       return model_inputs
     # Process tgt_texts
     if max_target_length is None:
       SCREAMING_SNAKE_CASE__      							=				self.current_tokenizer.model_max_length
     SCREAMING_SNAKE_CASE__      							=				self(
         text_target=__lowerCamelCase	,     add_special_tokens=__lowerCamelCase	,     return_tensors=__lowerCamelCase	,     padding=__lowerCamelCase	,     max_length=__lowerCamelCase	,     truncation=__lowerCamelCase	,     **__lowerCamelCase	,     )
     SCREAMING_SNAKE_CASE__      							=				labels["""input_ids"""]
     return model_inputs

from typing import TYPE_CHECKING

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


__lowerCamelCase      :			Optional[Any]							       =		{
    '''configuration_efficientformer''': [
        '''EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientFormerConfig''',
    ]
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Dict							       =		['''EfficientFormerImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Optional[int]							       =		[
       '''EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''EfficientFormerForImageClassification''',
       '''EfficientFormerForImageClassificationWithTeacher''',
       '''EfficientFormerModel''',
       '''EfficientFormerPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Any							       =		[
       '''TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFEfficientFormerForImageClassification''',
       '''TFEfficientFormerForImageClassificationWithTeacher''',
       '''TFEfficientFormerModel''',
       '''TFEfficientFormerPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_efficientformer import EfficientFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_efficientformer import (
          EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          EfficientFormerForImageClassification,
          EfficientFormerForImageClassificationWithTeacher,
          EfficientFormerModel,
          EfficientFormerPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_efficientformer import (
          TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFEfficientFormerForImageClassification,
          TFEfficientFormerForImageClassificationWithTeacher,
          TFEfficientFormerModel,
          TFEfficientFormerPreTrainedModel,
      )

else:
   import sys

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