





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   UpperCamelCase_       :


							def __init__(							self    :Any							,				__A    :int							,				__A    :str		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      question_encoder
									SCREAMING_SNAKE_CASE__			=      generator
									SCREAMING_SNAKE_CASE__			=      self.question_encoder


							def      _snake_case  (							self    :Optional[Any]							,				__A    :Optional[Any]		)   ->   Optional[int]:



									"""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		)
									SCREAMING_SNAKE_CASE__			=      os.path.join(__A							,				"""question_encoder_tokenizer"""		)
									SCREAMING_SNAKE_CASE__			=      os.path.join(__A							,				"""generator_tokenizer"""		)
									self.question_encoder.save_pretrained(__A		)
									self.generator.save_pretrained(__A		)


							@classmethod
							def      _snake_case  (							cls    :List[str]							,				__A    :Optional[int]							,				**__A    :str		)   ->   Optional[int]:



									"""simple docstring"""



									from ..auto.tokenization_auto import AutoTokenizer

									SCREAMING_SNAKE_CASE__			=      kwargs.pop("""config"""							,				__A		)

									if config is None:
											SCREAMING_SNAKE_CASE__			=      RagConfig.from_pretrained(__A		)

									SCREAMING_SNAKE_CASE__			=      AutoTokenizer.from_pretrained(
									    __A							,				config=config.question_encoder							,				subfolder="""question_encoder_tokenizer"""		)
									SCREAMING_SNAKE_CASE__			=      AutoTokenizer.from_pretrained(
									    __A							,				config=config.generator							,				subfolder="""generator_tokenizer"""		)
									return cls(question_encoder=__A							,				generator=__A		)


							def __call__(							self    :Optional[int]							,				*__A    :List[Any]							,				**__A    :str		)   ->   Optional[Any]:



									"""simple docstring"""



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


							def      _snake_case  (							self    :Optional[int]							,				*__A    :Optional[Any]							,				**__A    :List[Any]		)   ->   Optional[int]:



									"""simple docstring"""



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


							def      _snake_case  (							self    :Union[str, Any]							,				*__A    :int							,				**__A    :List[Any]		)   ->   List[Any]:



									"""simple docstring"""



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


							def      _snake_case  (							self    :int		)   ->   Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.question_encoder


							def      _snake_case  (							self    :int		)   ->   Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.generator


							def      _snake_case  (							self    :str							,				__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							,				)   ->   BatchEncoding:



									"""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:
											SCREAMING_SNAKE_CASE__			=      self.current_tokenizer.model_max_length
									SCREAMING_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:
											SCREAMING_SNAKE_CASE__			=      self.current_tokenizer.model_max_length
									SCREAMING_SNAKE_CASE__			=      self(
									    text_target=__A							,				add_special_tokens=__A							,				return_tensors=__A							,				padding=__A							,				max_length=__A							,				truncation=__A							,				**__A							,				)
									SCREAMING_SNAKE_CASE__			=      labels["""input_ids"""]
									return model_inputs






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowerCamelCase   				=			{
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			['LayoutLMv3TokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			[
	    'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'LayoutLMv3ForQuestionAnswering',
	    'LayoutLMv3ForSequenceClassification',
	    'LayoutLMv3ForTokenClassification',
	    'LayoutLMv3Model',
	    'LayoutLMv3PreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			[
	    'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFLayoutLMv3ForQuestionAnswering',
	    'TFLayoutLMv3ForSequenceClassification',
	    'TFLayoutLMv3ForTokenClassification',
	    'TFLayoutLMv3Model',
	    'TFLayoutLMv3PreTrainedModel',
	]

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			['LayoutLMv3FeatureExtractor']
	_lowerCamelCase   				=			['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
	from .configuration_layoutlmva import (
	    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    LayoutLMvaConfig,
	    LayoutLMvaOnnxConfig,
	)
	from .processing_layoutlmva import LayoutLMvaProcessor
	from .tokenization_layoutlmva import LayoutLMvaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_layoutlmva import (
		    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LayoutLMvaForQuestionAnswering,
		    LayoutLMvaForSequenceClassification,
		    LayoutLMvaForTokenClassification,
		    LayoutLMvaModel,
		    LayoutLMvaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_layoutlmva import (
		    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFLayoutLMvaForQuestionAnswering,
		    TFLayoutLMvaForSequenceClassification,
		    TFLayoutLMvaForTokenClassification,
		    TFLayoutLMvaModel,
		    TFLayoutLMvaPreTrainedModel,
		)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
		from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
	import sys

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