


"""simple docstring"""

import os
import warnings
from typing import List, Optional

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


UpperCAmelCase__  							=logging.get_logger(__name__)





class    lowerCamelCase__						:
      def __init__(		self	:	int      ,       A_	:	Optional[Any]      ,       A_	:	List[str]							):






       '''simple docstring'''


       __lowercase									=	question_encoder
       __lowercase									=	generator
       __lowercase									=	self.question_encoder
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict      ,       A_	:	str							):






       '''simple docstring'''


       if os.path.isfile(A_							):
        raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''							)
       os.makedirs(A_      ,       exist_ok=A_							)
       __lowercase									=	os.path.join(A_      ,       """question_encoder_tokenizer"""							)
       __lowercase									=	os.path.join(A_      ,       """generator_tokenizer"""							)
       self.question_encoder.save_pretrained(A_							)
       self.generator.save_pretrained(A_							)
      @classmethod
      def SCREAMING_SNAKE_CASE_	(		cls	:	List[Any]      ,       A_	:	str      ,       **A_	:	Dict							):






       '''simple docstring'''


       from ..auto.tokenization_auto import AutoTokenizer

       __lowercase									=	kwargs.pop("""config"""      ,       A_							)

       if config is None:
        __lowercase									=	RagConfig.from_pretrained(A_							)

       __lowercase									=	AutoTokenizer.from_pretrained(
           A_      ,       config=config.question_encoder      ,       subfolder="""question_encoder_tokenizer"""							)
       __lowercase									=	AutoTokenizer.from_pretrained(
           A_      ,       config=config.generator      ,       subfolder="""generator_tokenizer"""							)
       return cls(question_encoder=A_      ,       generator=A_							)
      def __call__(		self	:	List[Any]      ,       *A_	:	Dict      ,       **A_	:	int							):






       '''simple docstring'''


       return self.current_tokenizer(*A_      ,       **A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       *A_	:	Optional[Any]      ,       **A_	:	Optional[int]							):






       '''simple docstring'''


       return self.generator.batch_decode(*A_      ,       **A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]      ,       *A_	:	str      ,       **A_	:	str							):






       '''simple docstring'''


       return self.generator.decode(*A_      ,       **A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       __lowercase									=	self.question_encoder
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       __lowercase									=	self.generator






      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	List[str]      ,       A_	:	Optional[List[str]] = None      ,       A_	:	Optional[int] = None      ,       A_	:	Optional[int] = None      ,       A_	:	str = "longest"      ,       A_	:	str = None      ,       A_	:	bool = True      ,       **A_	:	int      ,       ):






       '''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"""      ,       A_      ,       )
       if max_length is None:
        __lowercase									=	self.current_tokenizer.model_max_length
       __lowercase									=	self(
           A_      ,       add_special_tokens=A_      ,       return_tensors=A_      ,       max_length=A_      ,       padding=A_      ,       truncation=A_      ,       **A_      ,       )
       if tgt_texts is None:
        return model_inputs
       # Process tgt_texts
       if max_target_length is None:
        __lowercase									=	self.current_tokenizer.model_max_length
       __lowercase									=	self(
           text_target=A_      ,       add_special_tokens=A_      ,       return_tensors=A_      ,       padding=A_      ,       max_length=A_      ,       truncation=A_      ,       **A_      ,       )
       __lowercase									=	labels["""input_ids"""]
       return model_inputs




"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__  							=logging.get_logger(__name__)

UpperCAmelCase__  							={
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}





class    lowerCamelCase__						(		_a    ):
      a :     List[Any]											=		"""swinv2"""

      a :     Any											=		{
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }
      def __init__(		self	:	List[str]      ,       A_	:	Any=2_2_4      ,       A_	:	int=4      ,       A_	:	Optional[int]=3      ,       A_	:	List[Any]=9_6      ,       A_	:	List[Any]=[2, 2, 6, 2]      ,       A_	:	List[str]=[3, 6, 1_2, 2_4]      ,       A_	:	Union[str, Any]=7      ,       A_	:	int=4.0      ,       A_	:	List[str]=True      ,       A_	:	str=0.0      ,       A_	:	Any=0.0      ,       A_	:	Union[str, Any]=0.1      ,       A_	:	Optional[Any]="gelu"      ,       A_	:	int=False      ,       A_	:	List[Any]=0.02      ,       A_	:	Tuple=1e-5      ,       A_	:	Tuple=3_2      ,       **A_	:	int      ,       ):






       '''simple docstring'''


       super().__init__(**A_							)

       __lowercase									=	image_size
       __lowercase									=	patch_size
       __lowercase									=	num_channels
       __lowercase									=	embed_dim
       __lowercase									=	depths
       __lowercase									=	len(A_							)
       __lowercase									=	num_heads
       __lowercase									=	window_size
       __lowercase									=	mlp_ratio
       __lowercase									=	qkv_bias
       __lowercase									=	hidden_dropout_prob
       __lowercase									=	attention_probs_dropout_prob
       __lowercase									=	drop_path_rate
       __lowercase									=	hidden_act
       __lowercase									=	use_absolute_embeddings
       __lowercase									=	layer_norm_eps
       __lowercase									=	initializer_range
       __lowercase									=	encoder_stride
       # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       __lowercase									=	int(embed_dim * 2 ** (len(A_							) - 1)							)
       __lowercase									=	(0, 0, 0, 0)
