






"""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


__snake_case      :      Union[str, Any]             =       logging.get_logger(__name__)


class       __SCREAMING_SNAKE_CASE   :






						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  question_encoder
												lowerCAmelCase__        =  generator
												lowerCAmelCase__        =  self.question_encoder






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""


												if os.path.isfile(_snake_case				):
																		raise ValueError(F"Provided path ({save_directory}) should be a directory, not a file"				)
												os.makedirs(_snake_case       ,  exist_ok=_snake_case				)
												lowerCAmelCase__        =  os.path.join(_snake_case       ,  'question_encoder_tokenizer'				)
												lowerCAmelCase__        =  os.path.join(_snake_case       ,  'generator_tokenizer'				)
												self.question_encoder.save_pretrained(_snake_case				)
												self.generator.save_pretrained(_snake_case				)






						@classmethod
						def  UpperCamelCase__ (		cls       ,  _UpperCamelCase       ,  **_UpperCamelCase				):






												"""simple docstring"""
												from ..auto.tokenization_auto import AutoTokenizer

												lowerCAmelCase__        =  kwargs.pop('config'       ,  _snake_case				)

												if config is None:
																		lowerCAmelCase__        =  RagConfig.from_pretrained(_snake_case				)

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






						def __call__(		self       ,  *_UpperCamelCase       ,  **_UpperCamelCase				):






												"""simple docstring"""
												return self.current_tokenizer(*_snake_case       ,  **_snake_case				)






						def  UpperCamelCase__ (		self       ,  *_UpperCamelCase       ,  **_UpperCamelCase				):






												"""simple docstring"""
												return self.generator.batch_decode(*_snake_case       ,  **_snake_case				)






						def  UpperCamelCase__ (		self       ,  *_UpperCamelCase       ,  **_UpperCamelCase				):






												"""simple docstring"""
												return self.generator.decode(*_snake_case       ,  **_snake_case				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.question_encoder






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.generator







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = "longest"       ,  _UpperCamelCase = None       ,  _UpperCamelCase = True       ,  **_UpperCamelCase       ,  ):






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













from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case      :      Dict             =       {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case      :      str             =       [
					    """FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FocalNetForImageClassification""",
					    """FocalNetForMaskedImageModeling""",
					    """FocalNetBackbone""",
					    """FocalNetModel""",
					    """FocalNetPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_focalnet import (
										    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FocalNetBackbone,
										    FocalNetForImageClassification,
										    FocalNetForMaskedImageModeling,
										    FocalNetModel,
										    FocalNetPreTrainedModel,
										)

else:
					import sys

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





