
"""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			:			str    ,      _lowerCamelCase			:			Any    ,      _lowerCamelCase			:			str			):
       _snake_case					     =       question_encoder
       _snake_case					     =       generator
       _snake_case					     =       self.question_encoder







     def      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			Optional[Any]			):







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







     @classmethod
     def      lowercase	(							cls			:			Any    ,      _lowerCamelCase			:			List[str]    ,      **_lowerCamelCase			:			Dict			):
       from ..auto.tokenization_auto import AutoTokenizer

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

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

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







     def __call__(							self			:			str    ,      *_lowerCamelCase			:			Union[str, Any]    ,      **_lowerCamelCase			:			Union[str, Any]			):
       return self.current_tokenizer(*UpperCamelCase_    ,      **UpperCamelCase_			)







     def      lowercase	(							self			:			Any    ,      *_lowerCamelCase			:			List[Any]    ,      **_lowerCamelCase			:			str			):
       return self.generator.batch_decode(*UpperCamelCase_    ,      **UpperCamelCase_			)







     def      lowercase	(							self			:			Any    ,      *_lowerCamelCase			:			str    ,      **_lowerCamelCase			:			Tuple			):
       return self.generator.decode(*UpperCamelCase_    ,      **UpperCamelCase_			)







     def      lowercase	(							self			:			Tuple			):
       _snake_case					     =       self.question_encoder







     def      lowercase	(							self			:			str			):
       _snake_case					     =       self.generator







     def      lowercase	(							self			:			Union[str, 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]    ,      ):
       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'''    ,      UpperCamelCase_    ,      )
       if max_length is None:
         _snake_case					     =       self.current_tokenizer.model_max_length
       _snake_case					     =       self(
           UpperCamelCase_    ,      add_special_tokens=UpperCamelCase_    ,      return_tensors=UpperCamelCase_    ,      max_length=UpperCamelCase_    ,      padding=UpperCamelCase_    ,      truncation=UpperCamelCase_    ,      **UpperCamelCase_    ,      )
       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=UpperCamelCase_    ,      add_special_tokens=UpperCamelCase_    ,      return_tensors=UpperCamelCase_    ,      padding=UpperCamelCase_    ,      max_length=UpperCamelCase_    ,      truncation=UpperCamelCase_    ,      **UpperCamelCase_    ,      )
       _snake_case					     =       labels['''input_ids''']
       return model_inputs







from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase       =							{'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							['DPTFeatureExtractor']
			lowerCAmelCase       =							['DPTImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase       =							[
			    'DPT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'DPTForDepthEstimation',
			    'DPTForSemanticSegmentation',
			    'DPTModel',
			    'DPTPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_dpt import DPTFeatureExtractor
						from .image_processing_dpt import DPTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_dpt import (
						    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DPTForDepthEstimation,
						    DPTForSemanticSegmentation,
						    DPTModel,
						    DPTPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase       =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


