



import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 		_lowercase		(							UpperCAmelCase__, unittest.TestCase						):
   _UpperCAmelCase  						=      HerbertTokenizer
   _UpperCAmelCase  						=      HerbertTokenizerFast
   _UpperCAmelCase  						=      True

   def 		A				(   self      :							Any				)							->					List[str]:


    """simple docstring"""






    super().setUp()

    # Use a simpler test file without japanese/chinese characters
    with open(f"""{get_tests_dir()}/fixtures/sample_text_no_unicode.txt"""				, encoding="utf-8"				) as f_data:
     a							=		f_data.read().replace("\n\n"				, "\n"				).strip()

    a							=		[
        "<s>",
        "</s>",
        "l",
        "o",
        "w",
        "e",
        "r",
        "s",
        "t",
        "i",
        "d",
        "n",
        "w</w>",
        "r</w>",
        "t</w>",
        "lo",
        "low",
        "er</w>",
        "low</w>",
        "lowest</w>",
        "newer</w>",
        "wider</w>",
        ",</w>",
        "<unk>",
    ]
    a							=		dict(zip(__lowerCAmelCase				, range(len(__lowerCAmelCase				)				)				)				)
    a							=		["l o 123", "lo w 1456", "e r</w> 1789", ""]

    a							=		os.path.join(self.tmpdirname				, VOCAB_FILES_NAMES["vocab_file"]				)
    a							=		os.path.join(self.tmpdirname				, VOCAB_FILES_NAMES["merges_file"]				)
    with open(self.vocab_file				, "w"				) as fp:
     fp.write(json.dumps(__lowerCAmelCase				)				)
    with open(self.merges_file				, "w"				) as fp:
     fp.write("\n".join(__lowerCAmelCase				)				)

   def 		A				(   self      :							Optional[Any]				, __lowerCAmelCase      :							Union[str, Any]				)							->					int:


    """simple docstring"""






    a							=		"lower newer"
    a							=		"lower newer"
    return input_text, output_text

   def 		A				(   self      :							Tuple				)							->					int:


    """simple docstring"""






    a							=		self.tokenizer_class(vocab_file=self.vocab_file				, merges_file=self.merges_file				)

    a							=		"lower"
    a							=		["low", "er</w>"]
    a							=		tokenizer.tokenize(__lowerCAmelCase				)
    self.assertListEqual(__lowerCAmelCase				, __lowerCAmelCase				)

    a							=		tokens + ["<unk>"]
    a							=		[16, 17, 23]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase				)				, __lowerCAmelCase				)

   def 		A				(   self      :							Optional[Any]				)							->					List[str]:


    """simple docstring"""






    if not self.test_rust_tokenizer:
     return

    a							=		self.get_tokenizer()
    a							=		self.get_rust_tokenizer()

    a							=		"lower,newer"

    a							=		tokenizer.tokenize(__lowerCAmelCase				)
    a							=		rust_tokenizer.tokenize(__lowerCAmelCase				)
    self.assertListEqual(__lowerCAmelCase				, __lowerCAmelCase				)

    a							=		tokenizer.encode(__lowerCAmelCase				, add_special_tokens=__lowerCAmelCase				)
    a							=		rust_tokenizer.encode(__lowerCAmelCase				, add_special_tokens=__lowerCAmelCase				)
    self.assertListEqual(__lowerCAmelCase				, __lowerCAmelCase				)

    a							=		self.get_rust_tokenizer()
    a							=		tokenizer.encode(__lowerCAmelCase				)
    a							=		rust_tokenizer.encode(__lowerCAmelCase				)
    self.assertListEqual(__lowerCAmelCase				, __lowerCAmelCase				)




   @slow
   def 		A				(   self      :							Union[str, Any]				)							->					Union[str, Any]:


    """simple docstring"""






    a							=		self.tokenizer_class.from_pretrained("allegro/herbert-base-cased"				)

    a							=		tokenizer.encode("konstruowanie sekwencji"				, add_special_tokens=__lowerCAmelCase				)
    a							=		tokenizer.encode("konstruowanie wielu sekwencji"				, add_special_tokens=__lowerCAmelCase				)

    a							=		tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase				)
    a							=		tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase				, __lowerCAmelCase				)

    assert encoded_sentence == [0] + text + [2]
    assert encoded_pair == [0] + text + [2] + text_a + [2]


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


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

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    CheckpointState:




       '''simple docstring'''






       print(F"Reading saved model from {model_file}" )
       _A=						torch.load(lowerCAmelCase_							,       map_location=lambda lowerCAmelCase_							,       lowerCAmelCase_ : default_restore_location(lowerCAmelCase_							,       'cpu' ) )
       return CheckpointState(**lowerCAmelCase_ )





class   lowerCAmelCase    :



     def __init__(       self					,   lowerCAmelCase__							):
            _A=						src_file



     def       a__	(       self							):
            raise NotImplementedError





     @staticmethod
     def       a__	(       lowerCAmelCase__					,   *lowerCAmelCase__					,   **lowerCAmelCase__							):
            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   lowerCAmelCase    (       _a      ):



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





class   lowerCAmelCase    (       _a      ):



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





class   lowerCAmelCase    (       _a      ):



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


def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_ )		->    Tuple:




       '''simple docstring'''






       _A=						Path(lowerCAmelCase_ )
       dest_dir.mkdir(exist_ok=lowerCAmelCase_ )

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


if __name__ == "__main__":
      UpperCAmelCase_    		=				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.''')
      UpperCAmelCase_    		=				parser.parse_args()

      UpperCAmelCase_    		=				Path(args.src)
      UpperCAmelCase_    		=				F"converted-{src_file.name}" if args.dest is None else args.dest
      UpperCAmelCase_    		=				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)