

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__							=			collections.namedtuple(
    "CheckpointState", ["model_dict", "optimizer_dict", "scheduler_dict", "offset", "epoch", "encoder_params"]
)
def   _lowerCAmelCase(						__A      ):
  print(F"Reading saved model from {model_file}"      )
  UpperCAmelCase          =	torch.load(__A ,							map_location=lambda __A ,							__A      : default_restore_location(__A ,							"cpu"      )      )
  return CheckpointState(**__A      )






class     __magic_name__     :


 def __init__(    self		:			Union[str, Any]      ,    lowerCAmelCase__		:			Path     )     ->   List[Any]:
   UpperCAmelCase          =	src_file


 def _UpperCamelCase						(    self		:			str     )     ->   Optional[Any]:
   raise NotImplementedError


 @staticmethod
 def _UpperCamelCase						(    lowerCAmelCase__		:			str      ,    *lowerCAmelCase__		:			Dict      ,    **lowerCAmelCase__		:			str     )     ->   "DPRState":
   if comp_type.startswith("c"     ):
     return DPRContextEncoderState(*lowerCAmelCase__      ,    **lowerCAmelCase__     )
   if comp_type.startswith("q"     ):
     return DPRQuestionEncoderState(*lowerCAmelCase__      ,    **lowerCAmelCase__     )
   if comp_type.startswith("r"     ):
     return DPRReaderState(*lowerCAmelCase__      ,    **lowerCAmelCase__     )
   else:
     raise ValueError("Component type must be either 'ctx_encoder', 'question_encoder' or 'reader'."     )






class     __magic_name__     (							_snake_case     ):


 def _UpperCamelCase						(    self		:			Optional[Any]     )     ->   List[str]:
   UpperCAmelCase          =	DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"     )[0]     )     )
   print(f"Loading DPR biencoder from {self.src_file}"     )
   UpperCAmelCase          =	load_states_from_checkpoint(self.src_file     )
   UpperCAmelCase   ,						UpperCAmelCase          =	model.ctx_encoder, "ctx_model."
   # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
   UpperCAmelCase          =	{"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(lowerCAmelCase__     ):
       UpperCAmelCase          =	key[len(lowerCAmelCase__     ) :]
       if not key.startswith("encode_proj."     ):
         UpperCAmelCase          =	"bert_model." + key
       UpperCAmelCase          =	value
   encoder.load_state_dict(lowerCAmelCase__     )
   return model






class     __magic_name__     (							_snake_case     ):


 def _UpperCamelCase						(    self		:			Any     )     ->   Any:
   UpperCAmelCase          =	DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"     )[0]     )     )
   print(f"Loading DPR biencoder from {self.src_file}"     )
   UpperCAmelCase          =	load_states_from_checkpoint(self.src_file     )
   UpperCAmelCase   ,						UpperCAmelCase          =	model.question_encoder, "question_model."
   # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
   UpperCAmelCase          =	{"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(lowerCAmelCase__     ):
       UpperCAmelCase          =	key[len(lowerCAmelCase__     ) :]
       if not key.startswith("encode_proj."     ):
         UpperCAmelCase          =	"bert_model." + key
       UpperCAmelCase          =	value
   encoder.load_state_dict(lowerCAmelCase__     )
   return model






class     __magic_name__     (							_snake_case     ):


 def _UpperCamelCase						(    self		:			Any     )     ->   Optional[int]:
   UpperCAmelCase          =	DPRReader(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"     )[0]     )     )
   print(f"Loading DPR reader from {self.src_file}"     )
   UpperCAmelCase          =	load_states_from_checkpoint(self.src_file     )
   # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
   UpperCAmelCase          =	{
       "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"     ):
       UpperCAmelCase          =	"encoder.bert_model." + key[len("encoder."     ) :]
     UpperCAmelCase          =	value
   model.span_predictor.load_state_dict(lowerCAmelCase__     )
   return model

def   _lowerCAmelCase(						__A ,							__A ,							__A      ):
  UpperCAmelCase          =	Path(__A      )
  dest_dir.mkdir(exist_ok=__A      )

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


if __name__ == "__main__":
      lowerCAmelCase__							=			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__							=			parser.parse_args()

      lowerCAmelCase__							=			Path(args.src)
      lowerCAmelCase__							=			f"converted-{src_file.name}" if args.dest is None else args.dest
      lowerCAmelCase__							=			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     __magic_name__     (							_snake_case     ):
 UpperCAmelCase				     =    ["""image_processor""", """tokenizer"""]
 UpperCAmelCase				     =    """LayoutLMv2ImageProcessor"""
 UpperCAmelCase				     =    ("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")


 def __init__(    self		:			Any      ,    lowerCAmelCase__		:			Any=None      ,    lowerCAmelCase__		:			Optional[Any]=None      ,    **lowerCAmelCase__		:			int     )     ->   Union[str, Any]:
   UpperCAmelCase          =	None
   if "feature_extractor" in kwargs:
     warnings.warn(
         "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
         " instead."      ,    lowerCAmelCase__      ,    )
     UpperCAmelCase          =	kwargs.pop("feature_extractor"     )

   UpperCAmelCase          =	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__(lowerCAmelCase__      ,    lowerCAmelCase__     )


 def __call__(    self		:			int      ,    lowerCAmelCase__		:			Tuple      ,    lowerCAmelCase__		:			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,    lowerCAmelCase__		:			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,    lowerCAmelCase__		:			Union[List[List[int]], List[List[List[int]]]] = None      ,    lowerCAmelCase__		:			Optional[Union[List[int], List[List[int]]]] = None      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Union[bool, str, PaddingStrategy] = False      ,    lowerCAmelCase__		:			Union[bool, str, TruncationStrategy] = False      ,    lowerCAmelCase__		:			Optional[int] = None      ,    lowerCAmelCase__		:			int = 0      ,    lowerCAmelCase__		:			Optional[int] = None      ,    lowerCAmelCase__		:			Optional[bool] = None      ,    lowerCAmelCase__		:			Optional[bool] = None      ,    lowerCAmelCase__		:			bool = False      ,    lowerCAmelCase__		:			bool = False      ,    lowerCAmelCase__		:			bool = False      ,    lowerCAmelCase__		:			bool = False      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Optional[Union[str, TensorType]] = None      ,    **lowerCAmelCase__		:			Any      ,    )     ->   BatchEncoding:
   # verify input
   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
   UpperCAmelCase          =	self.image_processor(images=lowerCAmelCase__      ,    return_tensors=lowerCAmelCase__     )

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

   UpperCAmelCase          =	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=lowerCAmelCase__      ,    add_special_tokens=lowerCAmelCase__      ,    padding=lowerCAmelCase__      ,    truncation=lowerCAmelCase__      ,    max_length=lowerCAmelCase__      ,    stride=lowerCAmelCase__      ,    pad_to_multiple_of=lowerCAmelCase__      ,    return_token_type_ids=lowerCAmelCase__      ,    return_attention_mask=lowerCAmelCase__      ,    return_overflowing_tokens=lowerCAmelCase__      ,    return_special_tokens_mask=lowerCAmelCase__      ,    return_offsets_mapping=lowerCAmelCase__      ,    return_length=lowerCAmelCase__      ,    verbose=lowerCAmelCase__      ,    return_tensors=lowerCAmelCase__      ,    **lowerCAmelCase__      ,    )

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

   return encoded_inputs


 def _UpperCamelCase						(    self		:			str      ,    lowerCAmelCase__		:			Union[str, Any]      ,    lowerCAmelCase__		:			Union[str, Any]     )     ->   int:
   # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
   UpperCAmelCase          =	[]
   for sample_idx in overflow_to_sample_mapping:
     images_with_overflow.append(images[sample_idx]     )

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

   return images_with_overflow


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    *lowerCAmelCase__		:			int      ,    **lowerCAmelCase__		:			Union[str, Any]     )     ->   List[str]:
   return self.tokenizer.batch_decode(*lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    *lowerCAmelCase__		:			Tuple      ,    **lowerCAmelCase__		:			Union[str, Any]     )     ->   List[Any]:
   return self.tokenizer.decode(*lowerCAmelCase__      ,    **lowerCAmelCase__     )


 @property
 def _UpperCamelCase						(    self		:			int     )     ->   List[str]:
   return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]


 @property
 def _UpperCamelCase						(    self		:			Dict     )     ->   List[str]:
   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."      ,    lowerCAmelCase__      ,    )
   return self.image_processor_class


 @property
 def _UpperCamelCase						(    self		:			List[Any]     )     ->   int:
   warnings.warn(
       "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."      ,    lowerCAmelCase__      ,    )
   return self.image_processor



