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






 '''simple docstring'''






 def __init__(     self   :    List[str]     ,       lowercase__   :    List[str]):







        '''simple docstring'''



        lowerCAmelCase__	      =   src_file




 def 						__snake_case		(     self   :    int):







        '''simple docstring'''



        raise NotImplementedError




 @staticmethod
 def 						__snake_case		(     lowercase__   :    Optional[Any]     ,       *lowercase__   :    Dict     ,       **lowercase__   :    str):







        '''simple docstring'''



        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 							a_					(		_a		):






 '''simple docstring'''






 def 						__snake_case		(     self   :    Dict):







        '''simple docstring'''



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






class 							a_					(		_a		):






 '''simple docstring'''






 def 						__snake_case		(     self   :    Optional[int]):







        '''simple docstring'''



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






class 							a_					(		_a		):






 '''simple docstring'''






 def 						__snake_case		(     self   :    Any):







        '''simple docstring'''



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






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__	      =   Path(lowerCAmelCase_      )
       dest_dir.mkdir(exist_ok=lowerCAmelCase_      )

       lowerCAmelCase__	      =   DPRState.from_type(lowerCAmelCase_      ,    src_file=lowerCAmelCase_      )
       lowerCAmelCase__	      =   dpr_state.load_dpr_model()
       model.save_pretrained(lowerCAmelCase_      )
       model.from_pretrained(lowerCAmelCase_      )  # 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 os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch





class   lowerCAmelCase    (       unittest.TestCase      ):



     @patch('sys.argv'					,   ['fakeprogrampath', 'env']							)
     def       a__	(       self							):
            # test transformers-cli env
            import transformers.commands.transformers_cli

            with CaptureStd() as cs:
                   transformers.commands.transformers_cli.main()
            self.assertIn('Python version'					,   cs.out							)
            self.assertIn('Platform'					,   cs.out							)
            self.assertIn('Using distributed or parallel set-up in script?'					,   cs.out							)



     @is_pt_tf_cross_test
     @patch(
         'sys.argv'					,   ['fakeprogrampath', 'pt-to-tf', '--model-name', 'hf-internal-testing/tiny-random-gptj', '--no-pr']							)
     def       a__	(       self							):
            import transformers.commands.transformers_cli

            shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'					,   ignore_errors=lowerCAmelCase__							)  # cleans potential past runs
            transformers.commands.transformers_cli.main()

            # The original repo has no TF weights -- if they exist, they were created by the CLI
            self.assertTrue(os.path.exists('/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5'							)							)



     @require_torch
     @patch('sys.argv'					,   ['fakeprogrampath', 'download', 'hf-internal-testing/tiny-random-gptj', '--cache-dir', '/tmp']							)
     def       a__	(       self							):
            import transformers.commands.transformers_cli

            # # remove any previously downloaded model to start clean
            shutil.rmtree('/tmp/models--hf-internal-testing--tiny-random-gptj'					,   ignore_errors=lowerCAmelCase__							)

            # run the command
            transformers.commands.transformers_cli.main()

            # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/blobs'							)							)
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/refs'							)							)
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots'							)							)





     @require_torch
     @patch(
         'sys.argv'					,   [
             'fakeprogrampath',
             'download',
             'hf-internal-testing/test_dynamic_model_with_tokenizer',
             '--trust-remote-code',
             '--cache-dir',
             '/tmp',
         ]					,   )
     def       a__	(       self							):
            import transformers.commands.transformers_cli

            # # remove any previously downloaded model to start clean
            shutil.rmtree('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer'					,   ignore_errors=lowerCAmelCase__							)

            # run the command
            transformers.commands.transformers_cli.main()

            # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs'							)							)
            self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs'							)							)
            self.assertTrue(
                os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots'							)							)