
import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


__lowerCAmelCase      :					Any   	=       collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)

def a__   (	A_ ):






		'''simple docstring'''





		print(f'''Reading saved model from {model_file}''' )
		__magic_name__    					=	torch.load(_UpperCamelCase,					map_location=lambda A_,					A_ : default_restore_location(_UpperCamelCase,					"""cpu""" ) )
		return CheckpointState(**_UpperCamelCase )




class 	UpperCAmelCase_      :


		'''simple docstring'''







		def __init__(			self      :			int      ,							UpperCamelCase__      :			Path   )      ->     Optional[int]:







				"""simple docstring"""






				__magic_name__    					=	src_file
		def 							_lowercase							(			self      :			Optional[Any]   )      ->     int:







				"""simple docstring"""






				raise NotImplementedError





		@staticmethod
		def 							_lowercase							(			UpperCamelCase__      :			str      ,							*UpperCamelCase__      :			Optional[int]      ,							**UpperCamelCase__      :			List[str]   )      ->     Dict:







				"""simple docstring"""






				if comp_type.startswith("""c"""   ):
						return DPRContextEncoderState(*UpperCamelCase__      ,							**UpperCamelCase__   )
				if comp_type.startswith("""q"""   ):
						return DPRQuestionEncoderState(*UpperCamelCase__      ,							**UpperCamelCase__   )
				if comp_type.startswith("""r"""   ):
						return DPRReaderState(*UpperCamelCase__      ,							**UpperCamelCase__   )
				else:
						raise ValueError("""Component type must be either 'ctx_encoder', 'question_encoder' or 'reader'."""   )







class 	UpperCAmelCase_      (			_A					):


		'''simple docstring'''







		def 							_lowercase							(			self      :			Optional[Any]   )      ->     Optional[int]:







				"""simple docstring"""






				__magic_name__    					=	DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("""bert-base-uncased"""   )[0]   )   )
				print(F'''Loading DPR biencoder from {self.src_file}'''   )
				__magic_name__    					=	load_states_from_checkpoint(self.src_file   )
				__magic_name__    					=	model.ctx_encoder, "ctx_model."
				# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
				__magic_name__    					=	{"bert_model.embeddings.position_ids": model.ctx_encoder.bert_model.embeddings.position_ids}
				for key, value in saved_state.model_dict.items():
						if key.startswith(UpperCamelCase__   ):
								__magic_name__    					=	key[len(UpperCamelCase__   ) :]
								if not key.startswith("""encode_proj."""   ):
										__magic_name__    					=	"bert_model." + key
								__magic_name__    					=	value
				encoder.load_state_dict(UpperCamelCase__   )
				return model







class 	UpperCAmelCase_      (			_A					):


		'''simple docstring'''







		def 							_lowercase							(			self      :			Optional[int]   )      ->     str:







				"""simple docstring"""






				__magic_name__    					=	DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("""bert-base-uncased"""   )[0]   )   )
				print(F'''Loading DPR biencoder from {self.src_file}'''   )
				__magic_name__    					=	load_states_from_checkpoint(self.src_file   )
				__magic_name__    					=	model.question_encoder, "question_model."
				# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
				__magic_name__    					=	{"bert_model.embeddings.position_ids": model.question_encoder.bert_model.embeddings.position_ids}
				for key, value in saved_state.model_dict.items():
						if key.startswith(UpperCamelCase__   ):
								__magic_name__    					=	key[len(UpperCamelCase__   ) :]
								if not key.startswith("""encode_proj."""   ):
										__magic_name__    					=	"bert_model." + key
								__magic_name__    					=	value
				encoder.load_state_dict(UpperCamelCase__   )
				return model







class 	UpperCAmelCase_      (			_A					):


		'''simple docstring'''







		def 							_lowercase							(			self      :			Dict   )      ->     Dict:







				"""simple docstring"""






				__magic_name__    					=	DPRReader(DPRConfig(**BertConfig.get_config_dict("""bert-base-uncased"""   )[0]   )   )
				print(F'''Loading DPR reader from {self.src_file}'''   )
				__magic_name__    					=	load_states_from_checkpoint(self.src_file   )
				# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
				__magic_name__    					=	{
				    "encoder.bert_model.embeddings.position_ids": model.span_predictor.encoder.bert_model.embeddings.position_ids
				}
				for key, value in saved_state.model_dict.items():
						if key.startswith("""encoder."""   ) and not key.startswith("""encoder.encode_proj"""   ):
								__magic_name__    					=	"encoder.bert_model." + key[len("""encoder."""   ) :]
						__magic_name__    					=	value
				model.span_predictor.load_state_dict(UpperCamelCase__   )
				return model







def a__   (	A_,					A_,					A_ ):






		'''simple docstring'''





		__magic_name__    					=	Path(_UpperCamelCase )
		dest_dir.mkdir(exist_ok=_UpperCamelCase )

		__magic_name__    					=	DPRState.from_type(_UpperCamelCase,					src_file=_UpperCamelCase )
		__magic_name__    					=	dpr_state.load_dpr_model()
		model.save_pretrained(_UpperCamelCase )
		model.from_pretrained(_UpperCamelCase )  # sanity check


if __name__ == "__main__":
				__lowerCAmelCase      :					int   	=       argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--type', type=str, help='Type of the component to convert: \'ctx_encoder\', \'question_encoder\' or \'reader\'.'
				)
				parser.add_argument(
				    '--src',
				    type=str,
				    help=(
				        'Path to the dpr checkpoint file. They can be downloaded from the official DPR repo'
				        ' https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the'
				        ' \'retriever\' checkpoints.'
				    ),
				)
				parser.add_argument('--dest', type=str, default=None, help='Path to the output PyTorch model directory.')
				__lowerCAmelCase      :					Dict   	=       parser.parse_args()

				__lowerCAmelCase      :					Any   	=       Path(args.src)
				__lowerCAmelCase      :					List[str]   	=       F'''converted-{src_file.name}''' if args.dest is None else args.dest
				__lowerCAmelCase      :					Union[str, Any]   	=       Path(dest_dir)
				assert src_file.exists()
				assert (
				    args.type is not None
				), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
				convert(args.type, src_file, dest_dir)



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  a__   	=		["""image_processor""", """tokenizer"""]
  a__   	=		"""LayoutLMv2ImageProcessor"""
  a__   	=		("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")
  def __init__(			self      :			int      ,							UpperCamelCase__      :			Dict=None      ,							UpperCamelCase__      :			int=None      ,							**UpperCamelCase__      :			str   )      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__    					=	None
    if "feature_extractor" in kwargs:
      warnings.warn(
          """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
          """ instead."""      ,							UpperCamelCase__      ,							)
      __magic_name__    					=	kwargs.pop("""feature_extractor"""   )

    __magic_name__    					=	image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
      raise ValueError("""You need to specify an `image_processor`."""   )
    if tokenizer is None:
      raise ValueError("""You need to specify a `tokenizer`."""   )

    super().__init__(UpperCamelCase__      ,							UpperCamelCase__   )
  def __call__(			self      :			List[str]      ,							UpperCamelCase__      :			Union[str, Any]      ,							UpperCamelCase__      :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,							UpperCamelCase__      :			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,							UpperCamelCase__      :			Union[List[List[int]], List[List[List[int]]]] = None      ,							UpperCamelCase__      :			Optional[Union[List[int], List[List[int]]]] = None      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Union[bool, str, PaddingStrategy] = False      ,							UpperCamelCase__      :			Union[bool, str, TruncationStrategy] = False      ,							UpperCamelCase__      :			Optional[int] = None      ,							UpperCamelCase__      :			int = 0      ,							UpperCamelCase__      :			Optional[int] = None      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Optional[Union[str, TensorType]] = None      ,							**UpperCamelCase__      :			Union[str, Any]      ,							)      ->     BatchEncoding:







    """simple docstring"""






    if self.image_processor.apply_ocr and (boxes is not None):
      raise ValueError(
          """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""   )

    if self.image_processor.apply_ocr and (word_labels is not None):
      raise ValueError(
          """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""   )

    if return_overflowing_tokens is True and return_offsets_mapping is False:
      raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""   )

    # first, apply the image processor
    __magic_name__    					=	self.image_processor(images=UpperCamelCase__      ,							return_tensors=UpperCamelCase__   )

    # second, apply the tokenizer
    if text is not None and self.image_processor.apply_ocr and text_pair is None:
      if isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
        __magic_name__    					=	[text]  # add batch dimension (as the image processor always adds a batch dimension)
      __magic_name__    					=	features["""words"""]

    __magic_name__    					=	self.tokenizer(
        text=text if text is not None else features["""words"""]      ,							text_pair=text_pair if text_pair is not None else None      ,							boxes=boxes if boxes is not None else features["""boxes"""]      ,							word_labels=UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__      ,							padding=UpperCamelCase__      ,							truncation=UpperCamelCase__      ,							max_length=UpperCamelCase__      ,							stride=UpperCamelCase__      ,							pad_to_multiple_of=UpperCamelCase__      ,							return_token_type_ids=UpperCamelCase__      ,							return_attention_mask=UpperCamelCase__      ,							return_overflowing_tokens=UpperCamelCase__      ,							return_special_tokens_mask=UpperCamelCase__      ,							return_offsets_mapping=UpperCamelCase__      ,							return_length=UpperCamelCase__      ,							verbose=UpperCamelCase__      ,							return_tensors=UpperCamelCase__      ,							**UpperCamelCase__      ,							)

    # add pixel values
    __magic_name__    					=	features.pop("""pixel_values"""   )
    if return_overflowing_tokens is True:
      __magic_name__    					=	self.get_overflowing_images(UpperCamelCase__      ,							encoded_inputs["""overflow_to_sample_mapping"""]   )
    __magic_name__    					=	images

    return encoded_inputs
  def 							_lowercase							(			self      :			Optional[int]      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			Optional[int]   )      ->     Any:







    """simple docstring"""






    __magic_name__    					=	[]
    for sample_idx in overflow_to_sample_mapping:
      images_with_overflow.append(images[sample_idx]   )

    if len(UpperCamelCase__   ) != len(UpperCamelCase__   ):
      raise ValueError(
          """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
          F''' {len(UpperCamelCase__   )} and {len(UpperCamelCase__   )}'''   )

    return images_with_overflow
  def 							_lowercase							(			self      :			Optional[int]      ,							*UpperCamelCase__      :			str      ,							**UpperCamelCase__      :			int   )      ->     Union[str, Any]:







    """simple docstring"""






    return self.tokenizer.batch_decode(*UpperCamelCase__      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			Dict      ,							*UpperCamelCase__      :			Optional[int]      ,							**UpperCamelCase__      :			str   )      ->     str:







    """simple docstring"""






    return self.tokenizer.decode(*UpperCamelCase__      ,							**UpperCamelCase__   )
  @property
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     Optional[int]:







    """simple docstring"""






    return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
  @property
  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     Optional[int]:







    """simple docstring"""






    warnings.warn(
        """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      ,							UpperCamelCase__      ,							)
    return self.image_processor_class





  @property
  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     Dict:







    """simple docstring"""






    warnings.warn(
        """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,							UpperCamelCase__      ,							)
    return self.image_processor

