






import os
import warnings
from typing import List, Optional

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


lowercase    				=   logging.get_logger(__name__)




class 					UpperCamelCase_ :



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       str:
								snake_case_           =				question_encoder
								snake_case_           =				generator
								snake_case_           =				self.question_encoder




	def 				_UpperCamelCase     (			self			,     a   )			->       Optional[Any]:
								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   )
								snake_case_           =				os.path.join(a			,     'question_encoder_tokenizer'   )
								snake_case_           =				os.path.join(a			,     'generator_tokenizer'   )
								self.question_encoder.save_pretrained(a   )
								self.generator.save_pretrained(a   )




	@classmethod
	def 				_UpperCamelCase     (			cls			,     a			,     **a   )			->       Any:
								# dynamically import AutoTokenizer
								from ..auto.tokenization_auto import AutoTokenizer

								snake_case_           =				kwargs.pop('config'			,     a   )

								if config is None:
															snake_case_           =				RagConfig.from_pretrained(a   )

								snake_case_           =				AutoTokenizer.from_pretrained(
								    a			,     config=config.question_encoder			,     subfolder='question_encoder_tokenizer'   )
								snake_case_           =				AutoTokenizer.from_pretrained(
								    a			,     config=config.generator			,     subfolder='generator_tokenizer'   )
								return cls(question_encoder=a			,     generator=a   )




	def __call__(			self			,     *a			,     **a   )			->       Optional[Any]:
								return self.current_tokenizer(*a			,     **a   )




	def 				_UpperCamelCase     (			self			,     *a			,     **a   )			->       List[str]:
								return self.generator.batch_decode(*a			,     **a   )




	def 				_UpperCamelCase     (			self			,     *a			,     **a   )			->       Any:
								return self.generator.decode(*a			,     **a   )




	def 				_UpperCamelCase     (			self   )			->       str:
								snake_case_           =				self.question_encoder




	def 				_UpperCamelCase     (			self   )			->       List[str]:
								snake_case_           =				self.generator






	def 				_UpperCamelCase     (			self			,     a			,     a = None			,     a = None			,     a = None			,     a = "longest"			,     a = None			,     a = True			,     **a			,     )			->       BatchEncoding:
								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:
															snake_case_           =				self.current_tokenizer.model_max_length
								snake_case_           =				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:
															snake_case_           =				self.current_tokenizer.model_max_length
								snake_case_           =				self(
								    text_target=a			,     add_special_tokens=a			,     return_tensors=a			,     padding=a			,     max_length=a			,     truncation=a			,     **a			,     )
								snake_case_           =				labels['input_ids']
								return model_inputs













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


lowercase    				=   logging.get_logger(__name__)

lowercase    				=   {
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					'''transfo-xl'''
	lowerCAmelCase						=					['''mems''']
	lowerCAmelCase						=					{
	    '''n_token''': '''vocab_size''',
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''n_head''',
	    '''num_hidden_layers''': '''n_layer''',
	}




	def __init__(			self			,     a=26_77_35			,     a=[2_00_00, 4_00_00, 20_00_00]			,     a=10_24			,     a=10_24			,     a=16			,     a=64			,     a=40_96			,     a=4			,     a=False			,     a=18			,     a=16_00			,     a=10_00			,     a=True			,     a=True			,     a=0			,     a=-1			,     a=True			,     a=0.1			,     a=0.0			,     a=True			,     a="normal"			,     a=0.01			,     a=0.01			,     a=0.02			,     a=1E-5			,     a=0			,     **a			,     )			->       Union[str, Any]:
								snake_case_           =				vocab_size
								snake_case_           =				[]
								self.cutoffs.extend(a   )
								if proj_share_all_but_first:
															snake_case_           =				[False] + [True] * len(self.cutoffs   )
								else:
															snake_case_           =				[False] + [False] * len(self.cutoffs   )
								snake_case_           =				d_model
								snake_case_           =				d_embed
								snake_case_           =				d_head
								snake_case_           =				d_inner
								snake_case_           =				div_val
								snake_case_           =				pre_lnorm
								snake_case_           =				n_layer
								snake_case_           =				n_head
								snake_case_           =				mem_len
								snake_case_           =				same_length
								snake_case_           =				attn_type
								snake_case_           =				clamp_len
								snake_case_           =				sample_softmax
								snake_case_           =				adaptive
								snake_case_           =				dropout
								snake_case_           =				dropatt
								snake_case_           =				untie_r
								snake_case_           =				init
								snake_case_           =				init_range
								snake_case_           =				proj_init_std
								snake_case_           =				init_std
								snake_case_           =				layer_norm_epsilon
								super().__init__(eos_token_id=a			,     **a   )




	@property
	def 				_UpperCamelCase     (			self   )			->       int:
								# Message copied from Transformer-XL documentation
								logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )
								return -1






	@max_position_embeddings.setter
	def 				_UpperCamelCase     (			self			,     a   )			->       List[str]:
								# Message copied from Transformer-XL documentation
								raise NotImplementedError(
								    F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )





