code: 
  """simple docstring"""
  
  
  
  
  
  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
  
  
  _a	 =							collections.namedtuple(
      'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
  )
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase						):
     print(f"""Reading saved model from {model_file}"""						)
     UpperCAmelCase_      :      Optional[int]      =					torch.load(__lowerCamelCase,	map_location=lambda __lowerCamelCase,	__lowerCamelCase						: default_restore_location(__lowerCamelCase,	"cpu"						)						)
     return CheckpointState(**__lowerCamelCase						)
  
  
  class A_			:
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[str]      =					src_file
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       raise NotImplementedError
  
  
  
  
  
  
  
    @staticmethod
    def  UpperCamelCase__      (	lowercase_       ,     *lowercase_       ,     **lowercase_						):
  
       """simple docstring"""
  
  
  
  
  
  
  
       if comp_type.startswith("c"						):
          return DPRContextEncoderState(*lowercase_       ,     **lowercase_						)
       if comp_type.startswith("q"						):
          return DPRQuestionEncoderState(*lowercase_       ,     **lowercase_						)
       if comp_type.startswith("r"						):
          return DPRReaderState(*lowercase_       ,     **lowercase_						)
       else:
          raise ValueError("Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'."						)
  
  
  
  
  class A_			(lowercase_   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      str      =					DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"						)[0]						)						)
       print(F"""Loading DPR biencoder from {self.src_file}"""						)
       UpperCAmelCase_      :      Optional[Any]      =					load_states_from_checkpoint(self.src_file						)
       UpperCAmelCase_  ,     UpperCAmelCase_      :      Union[str, Any]      =					model.ctx_encoder, "ctx_model."
       # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
       UpperCAmelCase_      :      str      =					{"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(lowercase_						):
             UpperCAmelCase_      :      Dict      =					key[len(lowercase_						) :]
             if not key.startswith("encode_proj."						):
                UpperCAmelCase_      :      Optional[int]      =					"bert_model." + key
             UpperCAmelCase_      :      List[str]      =					value
       encoder.load_state_dict(lowercase_						)
       return model
  
  
  
  
  class A_			(lowercase_   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      int      =					DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"						)[0]						)						)
       print(F"""Loading DPR biencoder from {self.src_file}"""						)
       UpperCAmelCase_      :      Optional[int]      =					load_states_from_checkpoint(self.src_file						)
       UpperCAmelCase_  ,     UpperCAmelCase_      :      Tuple      =					model.question_encoder, "question_model."
       # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
       UpperCAmelCase_      :      Any      =					{"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(lowercase_						):
             UpperCAmelCase_      :      List[str]      =					key[len(lowercase_						) :]
             if not key.startswith("encode_proj."						):
                UpperCAmelCase_      :      Union[str, Any]      =					"bert_model." + key
             UpperCAmelCase_      :      int      =					value
       encoder.load_state_dict(lowercase_						)
       return model
  
  
  
  
  class A_			(lowercase_   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[int]      =					DPRReader(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"						)[0]						)						)
       print(F"""Loading DPR reader from {self.src_file}"""						)
       UpperCAmelCase_      :      Tuple      =					load_states_from_checkpoint(self.src_file						)
       # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
       UpperCAmelCase_      :      Tuple      =					{
           "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_      :      Union[str, Any]      =					"encoder.bert_model." + key[len("encoder."						) :]
          UpperCAmelCase_      :      Optional[int]      =					value
       model.span_predictor.load_state_dict(lowercase_						)
       return model
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						):
     UpperCAmelCase_      :      List[Any]      =					Path(__lowerCamelCase						)
     dest_dir.mkdir(exist_ok=__lowerCamelCase						)
  
     UpperCAmelCase_      :      Any      =					DPRState.from_type(__lowerCamelCase,	src_file=__lowerCamelCase						)
     UpperCAmelCase_      :      Union[str, Any]      =					dpr_state.load_dpr_model()
     model.save_pretrained(__lowerCamelCase						)
     model.from_pretrained(__lowerCamelCase						)  # sanity check
  
  
  if __name__ == "__main__":
      _a	 =							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.')
      _a	 =							parser.parse_args()
  
      _a	 =							Path(args.src)
      _a	 =							f"""converted-{src_file.name}""" if args.dest is None else args.dest
      _a	 =							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)
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers.testing_utils import require_vision
  from transformers.utils import is_vision_available
  
  
  if is_vision_available():
      from PIL import Image
  
      from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
  
  
  @require_vision
  class A_			(unittest.TestCase   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[int]      =					tempfile.mkdtemp()
  
       UpperCAmelCase_      :      List[Any]      =					CLIPImageProcessor()
       UpperCAmelCase_      :      Any      =					BertTokenizer.from_pretrained(
           "hf-internal-testing/tiny-random-BertModel"       ,     model_input_names=["input_ids", "attention_mask"]						)
  
       UpperCAmelCase_      :      Tuple      =					GitProcessor(lowercase_       ,     lowercase_						)
  
       processor.save_pretrained(self.tmpdirname						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     **lowercase_						):
  
       """simple docstring"""
       return AutoProcessor.from_pretrained(self.tmpdirname       ,     **lowercase_						).tokenizer
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     **lowercase_						):
  
       """simple docstring"""
       return AutoProcessor.from_pretrained(self.tmpdirname       ,     **lowercase_						).image_processor
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       shutil.rmtree(self.tmpdirname						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[int]      =					[np.random.randint(255       ,     size=(3, 30, 400)       ,     dtype=np.uinta						)]
  
       UpperCAmelCase_      :      Optional[Any]      =					[Image.fromarray(np.moveaxis(lowercase_       ,     0       ,     -1						)						) for x in image_inputs]
  
       return image_inputs
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[Any]      =					GitProcessor(tokenizer=self.get_tokenizer()       ,     image_processor=self.get_image_processor()						)
       processor.save_pretrained(self.tmpdirname						)
  
       UpperCAmelCase_      :      Optional[int]      =					self.get_tokenizer(bos_token="(BOS)"       ,     eos_token="(EOS)"						)
       UpperCAmelCase_      :      Union[str, Any]      =					self.get_image_processor(do_normalize=lowercase_       ,     padding_value=1.0						)
  
       UpperCAmelCase_      :      str      =					GitProcessor.from_pretrained(
           self.tmpdirname       ,     bos_token="(BOS)"       ,     eos_token="(EOS)"       ,     do_normalize=lowercase_       ,     padding_value=1.0						)
  
       self.assertEqual(processor.tokenizer.get_vocab()       ,     tokenizer_add_kwargs.get_vocab()						)
       self.assertIsInstance(processor.tokenizer       ,     lowercase_						)
  
       self.assertEqual(processor.image_processor.to_json_string()       ,     image_processor_add_kwargs.to_json_string()						)
       self.assertIsInstance(processor.image_processor       ,     lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Dict      =					self.get_image_processor()
       UpperCAmelCase_      :      Union[str, Any]      =					self.get_tokenizer()
  
       UpperCAmelCase_      :      Union[str, Any]      =					GitProcessor(tokenizer=lowercase_       ,     image_processor=lowercase_						)
  
       UpperCAmelCase_      :      List[Any]      =					self.prepare_image_inputs()
  
       UpperCAmelCase_      :      Optional[Any]      =					image_processor(lowercase_       ,     return_tensors="np"						)
       UpperCAmelCase_      :      int      =					processor(images=lowercase_       ,     return_tensors="np"						)
  
       for key in input_feat_extract.keys():
          self.assertAlmostEqual(input_feat_extract[key].sum()       ,     input_processor[key].sum()       ,     delta=1E-2						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Union[str, Any]      =					self.get_image_processor()
       UpperCAmelCase_      :      Tuple      =					self.get_tokenizer()
  
       UpperCAmelCase_      :      int      =					GitProcessor(tokenizer=lowercase_       ,     image_processor=lowercase_						)
  
       UpperCAmelCase_      :      Optional[Any]      =					"lower newer"
  
       UpperCAmelCase_      :      Any      =					processor(text=lowercase_						)
  
       UpperCAmelCase_      :      int      =					tokenizer(lowercase_       ,     return_token_type_ids=lowercase_						)
  
       for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]       ,     encoded_processor[key]						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[int]      =					self.get_image_processor()
       UpperCAmelCase_      :      Union[str, Any]      =					self.get_tokenizer()
  
       UpperCAmelCase_      :      Optional[int]      =					GitProcessor(tokenizer=lowercase_       ,     image_processor=lowercase_						)
  
       UpperCAmelCase_      :      List[str]      =					"lower newer"
       UpperCAmelCase_      :      Dict      =					self.prepare_image_inputs()
  
       UpperCAmelCase_      :      Any      =					processor(text=lowercase_       ,     images=lowercase_						)
  
       self.assertListEqual(list(inputs.keys()						)       ,     ["input_ids", "attention_mask", "pixel_values"]						)
  
       # test if it raises when no input is passed
       with pytest.raises(lowercase_						):
          processor()
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[Any]      =					self.get_image_processor()
       UpperCAmelCase_      :      str      =					self.get_tokenizer()
  
       UpperCAmelCase_      :      int      =					GitProcessor(tokenizer=lowercase_       ,     image_processor=lowercase_						)
  
       UpperCAmelCase_      :      Tuple      =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
       UpperCAmelCase_      :      Any      =					processor.batch_decode(lowercase_						)
       UpperCAmelCase_      :      Optional[Any]      =					tokenizer.batch_decode(lowercase_						)
  
       self.assertListEqual(lowercase_       ,     lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Union[str, Any]      =					self.get_image_processor()
       UpperCAmelCase_      :      Optional[Any]      =					self.get_tokenizer()
  
       UpperCAmelCase_      :      str      =					GitProcessor(tokenizer=lowercase_       ,     image_processor=lowercase_						)
  
       UpperCAmelCase_      :      int      =					"lower newer"
       UpperCAmelCase_      :      Tuple      =					self.prepare_image_inputs()
  
       UpperCAmelCase_      :      Optional[Any]      =					processor(text=lowercase_       ,     images=lowercase_						)
  
       # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
       self.assertListEqual(list(inputs.keys()						)       ,     ["input_ids", "attention_mask", "pixel_values"]						)
  
  
  