
"""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)


"""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"]						)


