



"""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


lowerCAmelCase__      =    logging.get_logger(__name__)






class 	__snake_case	:




  def __init__(			self					:						Optional[Any]						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:    Any         =			question_encoder
         _lowerCamelCase						:    Union[str, Any]         =			generator
         _lowerCamelCase						:    Optional[Any]         =			self.question_encoder




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]						, __lowerCAmelCase					:						int		):






         """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		)
         _lowerCamelCase						:    Optional[Any]         =			os.path.join(__lowerCAmelCase						, '''question_encoder_tokenizer'''		)
         _lowerCamelCase						:    Tuple         =			os.path.join(__lowerCAmelCase						, '''generator_tokenizer'''		)
         self.question_encoder.save_pretrained(__lowerCAmelCase		)
         self.generator.save_pretrained(__lowerCAmelCase		)




  @classmethod
  def 						SCREAMING_SNAKE_CASE    (			cls					:						Optional[int]						, __lowerCAmelCase					:						Optional[Any]						, **__lowerCAmelCase					:						int		):






         """simple docstring"""






         from ..auto.tokenization_auto import AutoTokenizer

         _lowerCamelCase						:    List[str]         =			kwargs.pop('''config'''						, __lowerCAmelCase		)

         if config is None:
                _lowerCamelCase						:    Any         =			RagConfig.from_pretrained(__lowerCAmelCase		)

         _lowerCamelCase						:    str         =			AutoTokenizer.from_pretrained(
             __lowerCAmelCase						, config=config.question_encoder						, subfolder='''question_encoder_tokenizer'''		)
         _lowerCamelCase						:    Optional[int]         =			AutoTokenizer.from_pretrained(
             __lowerCAmelCase						, config=config.generator						, subfolder='''generator_tokenizer'''		)
         return cls(question_encoder=__lowerCAmelCase						, generator=__lowerCAmelCase		)




  def __call__(			self					:						List[str]						, *__lowerCAmelCase					:						Optional[Any]						, **__lowerCAmelCase					:						Dict		):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE    (			self					:						int						, *__lowerCAmelCase					:						Optional[Any]						, **__lowerCAmelCase					:						Union[str, Any]		):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]						, *__lowerCAmelCase					:						Dict						, **__lowerCAmelCase					:						int		):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:    int         =			self.question_encoder




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			self.generator




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any						, __lowerCAmelCase					:						List[str]						, __lowerCAmelCase					:						Optional[List[str]] = None						, __lowerCAmelCase					:						Optional[int] = None						, __lowerCAmelCase					:						Optional[int] = None						, __lowerCAmelCase					:						str = "longest"						, __lowerCAmelCase					:						str = None						, __lowerCAmelCase					:						bool = True						, **__lowerCAmelCase					:						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:
                _lowerCamelCase						:    Optional[int]         =			self.current_tokenizer.model_max_length
         _lowerCamelCase						:    Any         =			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:
                _lowerCamelCase						:    List[str]         =			self.current_tokenizer.model_max_length
         _lowerCamelCase						:    List[Any]         =			self(
             text_target=__lowerCAmelCase						, add_special_tokens=__lowerCAmelCase						, return_tensors=__lowerCAmelCase						, padding=__lowerCAmelCase						, max_length=__lowerCAmelCase						, truncation=__lowerCAmelCase						, **__lowerCAmelCase						, )
         _lowerCamelCase						:    List[str]         =			labels['''input_ids''']
         return model_inputs







"""simple docstring"""






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


lowerCAmelCase__      =    logging.get_logger(__name__)

lowerCAmelCase__      =    {
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}






class 	__snake_case	(     _lowercase):
  snake_case__    :       List[Any]        =						"xglm"
  snake_case__    :       Dict        =						["past_key_values"]

  snake_case__    :       str        =						{
      "num_attention_heads": "attention_heads",
      "hidden_size": "d_model",
      "num_hidden_layers": "num_layers",
  }




  def __init__(			self					:						List[str]						, __lowerCAmelCase					:						List[Any]=2_5_6_0_0_8						, __lowerCAmelCase					:						int=2_0_4_8						, __lowerCAmelCase					:						Dict=1_0_2_4						, __lowerCAmelCase					:						List[str]=4_0_9_6						, __lowerCAmelCase					:						Tuple=2_4						, __lowerCAmelCase					:						Dict=1_6						, __lowerCAmelCase					:						Tuple="gelu"						, __lowerCAmelCase					:						Tuple=0.1						, __lowerCAmelCase					:						Tuple=0.1						, __lowerCAmelCase					:						Optional[Any]=0.0						, __lowerCAmelCase					:						List[Any]=0.0						, __lowerCAmelCase					:						int=0.02						, __lowerCAmelCase					:						Any=True						, __lowerCAmelCase					:						Tuple=True						, __lowerCAmelCase					:						str=2						, __lowerCAmelCase					:						Dict=1						, __lowerCAmelCase					:						Dict=0						, __lowerCAmelCase					:						List[Any]=2						, **__lowerCAmelCase					:						Optional[Any]						, ):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			vocab_size
         _lowerCamelCase						:    List[Any]         =			max_position_embeddings
         _lowerCamelCase						:    int         =			d_model
         _lowerCamelCase						:    Optional[Any]         =			ffn_dim
         _lowerCamelCase						:    Any         =			num_layers
         _lowerCamelCase						:    Union[str, Any]         =			attention_heads
         _lowerCamelCase						:    List[str]         =			activation_function
         _lowerCamelCase						:    Union[str, Any]         =			dropout
         _lowerCamelCase						:    int         =			attention_dropout
         _lowerCamelCase						:    Optional[int]         =			activation_dropout
         _lowerCamelCase						:    Any         =			layerdrop
         _lowerCamelCase						:    List[str]         =			init_std
         _lowerCamelCase						:    Union[str, Any]         =			scale_embedding  # scale factor will be sqrt(d_model) if True
         _lowerCamelCase						:    str         =			use_cache

         super().__init__(
             pad_token_id=__lowerCAmelCase						, bos_token_id=__lowerCAmelCase						, eos_token_id=__lowerCAmelCase						, decoder_start_token_id=__lowerCAmelCase						, **__lowerCAmelCase						, )


