




"""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


_lowerCamelCase					:     Optional[int]				       =    collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)

def 						lowercase_ (						_UpperCAmelCase  ):




	"""simple docstring"""






	print(f"""Reading saved model from {model_file}"""  )
	A_					:			int						=						torch.load(_UpperCAmelCase					, map_location=lambda _UpperCAmelCase					, _UpperCAmelCase  : default_restore_location(_UpperCAmelCase					, '''cpu'''  )  )
	return CheckpointState(**_UpperCAmelCase  )

class     lowercase :







				def __init__(							self				:   str ,       _lowerCamelCase				:   Path    ):



					"""simple docstring"""

					A_					:			Optional[Any]						=						src_file







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					raise NotImplementedError







				@staticmethod
				def    a_					(							_lowerCamelCase				:   str ,       *_lowerCamelCase				:   int ,       **_lowerCamelCase				:   Optional[Any]    ):



					"""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     lowercase (      __UpperCAmelCase):







				def    a_					(							self				:   Union[str, Any]    ):



					"""simple docstring"""

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

class     lowercase (      __UpperCAmelCase):







				def    a_					(							self				:   Union[str, Any]    ):



					"""simple docstring"""

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

class     lowercase (      __UpperCAmelCase):







				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

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

def 						lowercase_ (						_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase  ):




	"""simple docstring"""






	A_					:			Union[str, Any]						=						Path(_UpperCAmelCase  )
	dest_dir.mkdir(exist_ok=_UpperCAmelCase  )

	A_					:			Optional[Any]						=						DPRState.from_type(_UpperCAmelCase					, src_file=_UpperCAmelCase  )
	A_					:			List[Any]						=						dpr_state.load_dpr_model()
	model.save_pretrained(_UpperCAmelCase  )
	model.from_pretrained(_UpperCAmelCase  )  # sanity check


if __name__ == "__main__":
		_lowerCamelCase					:     List[str]				       =    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					:     int				       =    parser.parse_args()

		_lowerCamelCase					:     Optional[int]				       =    Path(args.src)
		_lowerCamelCase					:     Union[str, Any]				       =    f'converted-{src_file.name}' if args.dest is None else args.dest
		_lowerCamelCase					:     Union[str, Any]				       =    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     lowercase (      unittest.TestCase):







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			List[str]						=						tempfile.mkdtemp()

					A_					:			List[str]						=						CLIPImageProcessor()
					A_					:			Any						=						BertTokenizer.from_pretrained(
					    '''hf-internal-testing/tiny-random-BertModel''' ,       model_input_names=['''input_ids''', '''attention_mask''']    )

					A_					:			str						=						GitProcessor(_lowerCamelCase ,       _lowerCamelCase    )

					processor.save_pretrained(self.tmpdirname    )







				def    a_					(							self				:   Tuple ,       **_lowerCamelCase				:   List[str]    ):



					"""simple docstring"""

					return AutoProcessor.from_pretrained(self.tmpdirname ,       **_lowerCamelCase    ).tokenizer







				def    a_					(							self				:   List[Any] ,       **_lowerCamelCase				:   int    ):



					"""simple docstring"""

					return AutoProcessor.from_pretrained(self.tmpdirname ,       **_lowerCamelCase    ).image_processor







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					shutil.rmtree(self.tmpdirname    )







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			List[str]						=						[np.random.randint(2_55 ,       size=(3, 30, 4_00) ,       dtype=np.uinta    )]

					A_					:			Any						=						[Image.fromarray(np.moveaxis(_lowerCamelCase ,       0 ,       -1    )    ) for x in image_inputs]

					return image_inputs







				def    a_					(							self				:   str    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						GitProcessor(tokenizer=self.get_tokenizer() ,       image_processor=self.get_image_processor()    )
					processor.save_pretrained(self.tmpdirname    )

					A_					:			Union[str, Any]						=						self.get_tokenizer(bos_token='''(BOS)''' ,       eos_token='''(EOS)'''    )
					A_					:			List[Any]						=						self.get_image_processor(do_normalize=_lowerCamelCase ,       padding_value=1.0    )

					A_					:			List[str]						=						GitProcessor.from_pretrained(
					    self.tmpdirname ,       bos_token='''(BOS)''' ,       eos_token='''(EOS)''' ,       do_normalize=_lowerCamelCase ,       padding_value=1.0    )

					self.assertEqual(processor.tokenizer.get_vocab() ,       tokenizer_add_kwargs.get_vocab()    )
					self.assertIsInstance(processor.tokenizer ,       _lowerCamelCase    )

					self.assertEqual(processor.image_processor.to_json_string() ,       image_processor_add_kwargs.to_json_string()    )
					self.assertIsInstance(processor.image_processor ,       _lowerCamelCase    )







				def    a_					(							self				:   Optional[Any]    ):



					"""simple docstring"""

					A_					:			Tuple						=						self.get_image_processor()
					A_					:			Optional[Any]						=						self.get_tokenizer()

					A_					:			int						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Tuple						=						self.prepare_image_inputs()

					A_					:			Any						=						image_processor(_lowerCamelCase ,       return_tensors='''np'''    )
					A_					:			Optional[Any]						=						processor(images=_lowerCamelCase ,       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    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					A_					:			Union[str, Any]						=						self.get_image_processor()
					A_					:			Union[str, Any]						=						self.get_tokenizer()

					A_					:			int						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			List[str]						=						'''lower newer'''

					A_					:			Tuple						=						processor(text=_lowerCamelCase    )

					A_					:			List[str]						=						tokenizer(_lowerCamelCase ,       return_token_type_ids=_lowerCamelCase    )

					for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key] ,       encoded_processor[key]    )







				def    a_					(							self				:   Tuple    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						self.get_image_processor()
					A_					:			Optional[Any]						=						self.get_tokenizer()

					A_					:			Optional[Any]						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Tuple						=						'''lower newer'''
					A_					:			str						=						self.prepare_image_inputs()

					A_					:			Optional[int]						=						processor(text=_lowerCamelCase ,       images=_lowerCamelCase    )

					self.assertListEqual(list(inputs.keys()    ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']    )

					# test if it raises when no input is passed
					with pytest.raises(_lowerCamelCase    ):
						processor()







				def    a_					(							self				:   Optional[int]    ):



					"""simple docstring"""

					A_					:			Optional[int]						=						self.get_image_processor()
					A_					:			List[Any]						=						self.get_tokenizer()

					A_					:			Optional[Any]						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			Dict						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					A_					:			str						=						processor.batch_decode(_lowerCamelCase    )
					A_					:			int						=						tokenizer.batch_decode(_lowerCamelCase    )

					self.assertListEqual(_lowerCamelCase ,       _lowerCamelCase    )







				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					A_					:			Dict						=						self.get_image_processor()
					A_					:			List[str]						=						self.get_tokenizer()

					A_					:			Any						=						GitProcessor(tokenizer=_lowerCamelCase ,       image_processor=_lowerCamelCase    )

					A_					:			int						=						'''lower newer'''
					A_					:			Union[str, Any]						=						self.prepare_image_inputs()

					A_					:			List[str]						=						processor(text=_lowerCamelCase ,       images=_lowerCamelCase    )

					# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
					self.assertListEqual(list(inputs.keys()    ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']    )





