




"""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


UpperCamelCase__ :    Optional[Any] 					=							collections.namedtuple(
    "CheckpointState", ["model_dict", "optimizer_dict", "scheduler_dict", "offset", "epoch", "encoder_params"]
)




def       snake_case__	(       lowerCAmelCase   :		Tuple		):
					print(F'''Reading saved model from {model_file}'''		)
					__snake_case          =	torch.load(snake_case_				,					map_location=lambda lowerCAmelCase				,					lowerCAmelCase		: default_restore_location(snake_case_				,					"""cpu"""		)		)
					return CheckpointState(**snake_case_		)






class     A__      :




	def __init__(   self  :	Optional[int]      ,    lowercase  :	Dict	)					-> Union[str, Any]:
						__snake_case          =	src_file




	def 							a_		(   self  :	Optional[Any]	)					-> Optional[Any]:
						raise NotImplementedError




	@staticmethod
	def 							a_		(   lowercase  :	Union[str, Any]      ,    *lowercase  :	str      ,    **lowercase  :	Union[str, Any]	)					-> "DPRState":


						if comp_type.startswith("""c"""	):
											return DPRContextEncoderState(*__UpperCamelCase      ,    **__UpperCamelCase	)
						if comp_type.startswith("""q"""	):
											return DPRQuestionEncoderState(*__UpperCamelCase      ,    **__UpperCamelCase	)
						if comp_type.startswith("""r"""	):
											return DPRReaderState(*__UpperCamelCase      ,    **__UpperCamelCase	)
						else:
											raise ValueError("""Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'."""	)






class     A__      (      UpperCamelCase_     ):




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






class     A__      (      UpperCamelCase_     ):




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






class     A__      (      UpperCamelCase_     ):




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




def       snake_case__	(       lowerCAmelCase   :		int				,					lowerCAmelCase   :		str				,					lowerCAmelCase   :		Optional[int]		):
					__snake_case          =	Path(snake_case_		)
					dest_dir.mkdir(exist_ok=snake_case_		)

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


if __name__ == "__main__":
		UpperCamelCase__ :    Optional[Any] 					=							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__ :    Tuple 					=							parser.parse_args()

		UpperCamelCase__ :    Optional[Any] 					=							Path(args.src)
		UpperCamelCase__ :    Optional[int] 					=							f'converted-{src_file.name}' if args.dest is None else args.dest
		UpperCamelCase__ :    Optional[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"""

from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


UpperCamelCase__ :    Union[str, Any] 					=							{
    "id": "5733be284776f41900661182",
    "title": "University_of_Notre_Dame",
    "context": "Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.",
    "question": "To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?",
    "answers": {"text": ["Saint Bernadette Soubirous"], "answer_start": [5_15]},
}




@pytest.mark.parametrize(
    """task_cls"""				,					[
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]				,					)
def       snake_case__	(       lowerCAmelCase   :		Optional[int]		):
					__snake_case          =	task_cls()
					__snake_case          =	asdict(lowerCAmelCase		)
					__snake_case          =	task_template_from_dict(lowerCAmelCase		)
					assert task == reloaded
class     A__      :




	def 							a_		(   self  :	Tuple	)					-> int:
						__snake_case          =	LanguageModeling(text_column="""input_text"""	)
						assert {"input_text": "text"} == task.column_mapping




	def 							a_		(   self  :	Union[str, Any]	)					-> List[Any]:
						__snake_case          =	Features({"""text""": Value("""string"""	)}	)
						__snake_case          =	{"""text_column""": """input_text"""}
						__snake_case          =	LanguageModeling.from_dict(lowercase	)
						assert "language-modeling" == task.task
						assert input_schema == task.input_schema

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	str	)					-> Any:
						__snake_case          =	sorted(["""pos""", """neg"""]	)




	def 							a_		(   self  :	str	)					-> Union[str, Any]:
						__snake_case          =	TextClassification(text_column="""input_text"""      ,    label_column="""input_label"""	)
						self.assertDictEqual({"""input_text""": """text""", """input_label""": """labels"""}      ,    task.column_mapping	)




	def 							a_		(   self  :	int	)					-> List[Any]:
						__snake_case          =	Features({"""text""": Value("""string"""	)}	)
						# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
						__snake_case          =	Features({"""labels""": ClassLabel}	)
						__snake_case          =	{"""text_column""": """input_text""", """label_column""": """input_labels"""}
						__snake_case          =	TextClassification.from_dict(lowercase	)
						self.assertEqual("""text-classification"""      ,    task.task	)
						self.assertEqual(lowercase      ,    task.input_schema	)
						self.assertEqual(lowercase      ,    task.label_schema	)




	def 							a_		(   self  :	Union[str, Any]	)					-> List[Any]:
						__snake_case          =	TextClassification(text_column="""input_text"""      ,    label_column="""input_label"""	)
						self.assertEqual(task.label_schema["""labels"""]      ,    lowercase	)
						__snake_case          =	task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels	)}	)	)
						self.assertEqual(task.label_schema["""labels"""]      ,    ClassLabel(names=self.labels	)	)

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	Any	)					-> Tuple:
						__snake_case          =	QuestionAnsweringExtractive(
						    context_column="""input_context"""      ,    question_column="""input_question"""      ,    answers_column="""input_answers"""	)
						self.assertDictEqual(
						    {"""input_context""": """context""", """input_question""": """question""", """input_answers""": """answers"""}      ,    task.column_mapping	)




	def 							a_		(   self  :	int	)					-> Tuple:
						__snake_case          =	Features({"""question""": Value("""string"""	), """context""": Value("""string"""	)}	)
						__snake_case          =	Features(
						    {
						        """answers""": Sequence(
						            {
						                """text""": Value("""string"""	),
						                """answer_start""": Value("""int32"""	),
						            }	)
						    }	)
						__snake_case          =	{
						    """context_column""": """input_input_context""",
						    """question_column""": """input_question""",
						    """answers_column""": """input_answers""",
						}
						__snake_case          =	QuestionAnsweringExtractive.from_dict(lowercase	)
						self.assertEqual("""question-answering-extractive"""      ,    task.task	)
						self.assertEqual(lowercase      ,    task.input_schema	)
						self.assertEqual(lowercase      ,    task.label_schema	)

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	Any	)					-> Union[str, Any]:
						__snake_case          =	Summarization(text_column="""input_text"""      ,    summary_column="""input_summary"""	)
						self.assertDictEqual({"""input_text""": """text""", """input_summary""": """summary"""}      ,    task.column_mapping	)




	def 							a_		(   self  :	List[Any]	)					-> Dict:
						__snake_case          =	Features({"""text""": Value("""string"""	)}	)
						__snake_case          =	Features({"""summary""": Value("""string"""	)}	)
						__snake_case          =	{"""text_column""": """input_text""", """summary_column""": """input_summary"""}
						__snake_case          =	Summarization.from_dict(lowercase	)
						self.assertEqual("""summarization"""      ,    task.task	)
						self.assertEqual(lowercase      ,    task.input_schema	)
						self.assertEqual(lowercase      ,    task.label_schema	)

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	Any	)					-> int:
						__snake_case          =	AutomaticSpeechRecognition(audio_column="""input_audio"""      ,    transcription_column="""input_transcription"""	)
						self.assertDictEqual({"""input_audio""": """audio""", """input_transcription""": """transcription"""}      ,    task.column_mapping	)




	def 							a_		(   self  :	Dict	)					-> List[str]:
						__snake_case          =	Features({"""audio""": Audio()}	)
						__snake_case          =	Features({"""transcription""": Value("""string"""	)}	)
						__snake_case          =	{
						    """audio_column""": """input_audio""",
						    """transcription_column""": """input_transcription""",
						}
						__snake_case          =	AutomaticSpeechRecognition.from_dict(lowercase	)
						self.assertEqual("""automatic-speech-recognition"""      ,    task.task	)
						self.assertEqual(lowercase      ,    task.input_schema	)
						self.assertEqual(lowercase      ,    task.label_schema	)

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	int	)					-> Union[str, Any]:
						__snake_case          =	sorted(["""pos""", """neg"""]	)




	def 							a_		(   self  :	Optional[int]	)					-> str:
						__snake_case          =	AudioClassification(audio_column="""input_audio"""      ,    label_column="""input_label"""	)
						self.assertDictEqual({"""input_audio""": """audio""", """input_label""": """labels"""}      ,    task.column_mapping	)




	def 							a_		(   self  :	Union[str, Any]	)					-> Any:
						__snake_case          =	Features({"""audio""": Audio()}	)
						__snake_case          =	Features({"""labels""": ClassLabel}	)
						__snake_case          =	{
						    """audio_column""": """input_image""",
						    """label_column""": """input_label""",
						}
						__snake_case          =	AudioClassification.from_dict(lowercase	)
						self.assertEqual("""audio-classification"""      ,    task.task	)
						self.assertEqual(lowercase      ,    task.input_schema	)
						self.assertEqual(lowercase      ,    task.label_schema	)




	def 							a_		(   self  :	Optional[Any]	)					-> Optional[int]:
						__snake_case          =	AudioClassification(audio_column="""input_audio"""      ,    label_column="""input_label"""	)
						self.assertEqual(task.label_schema["""labels"""]      ,    lowercase	)
						__snake_case          =	task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels	)}	)	)
						self.assertEqual(task.label_schema["""labels"""]      ,    ClassLabel(names=self.labels	)	)

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	Optional[int]	)					-> str:
						__snake_case          =	sorted(["""pos""", """neg"""]	)




	def 							a_		(   self  :	Optional[int]	)					-> List[str]:
						__snake_case          =	ImageClassification(image_column="""input_image"""      ,    label_column="""input_label"""	)
						self.assertDictEqual({"""input_image""": """image""", """input_label""": """labels"""}      ,    task.column_mapping	)




	def 							a_		(   self  :	Optional[Any]	)					-> int:
						__snake_case          =	Features({"""image""": Image()}	)
						__snake_case          =	Features({"""labels""": ClassLabel}	)
						__snake_case          =	{
						    """image_column""": """input_image""",
						    """label_column""": """input_label""",
						}
						__snake_case          =	ImageClassification.from_dict(lowercase	)
						self.assertEqual("""image-classification"""      ,    task.task	)
						self.assertEqual(lowercase      ,    task.input_schema	)
						self.assertEqual(lowercase      ,    task.label_schema	)




	def 							a_		(   self  :	Optional[int]	)					-> Any:
						__snake_case          =	ImageClassification(image_column="""input_image"""      ,    label_column="""input_label"""	)
						self.assertEqual(task.label_schema["""labels"""]      ,    lowercase	)
						__snake_case          =	task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels	)}	)	)
						self.assertEqual(task.label_schema["""labels"""]      ,    ClassLabel(names=self.labels	)	)

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	str	)					-> str:
						__snake_case          =	DatasetInfo(task_templates=QuestionAnsweringExtractive()	)
						__snake_case          =	Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}      ,    info=lowercase	)
						assert isinstance(dataset.info.task_templates      ,    lowercase	)
						assert len(dataset.info.task_templates	) == 1

						def keep_task(lowercase  :	Tuple	):
											return x

						def dont_keep_task(lowercase  :	List[str]	):
											__snake_case          =	deepcopy(lowercase	)
											__snake_case          =	0
											return out

						__snake_case          =	dataset.map(lowercase	)
						assert mapped_dataset.info.task_templates == dataset.info.task_templates
						# reload from cache
						__snake_case          =	dataset.map(lowercase	)
						assert mapped_dataset.info.task_templates == dataset.info.task_templates

						__snake_case          =	dataset.map(lowercase	)
						assert mapped_dataset.info.task_templates == []
						# reload from cache
						__snake_case          =	dataset.map(lowercase	)
						assert mapped_dataset.info.task_templates == []




	def 							a_		(   self  :	Any	)					-> int:
						__snake_case          =	Features({"""text""": Value("""string"""	), """label""": ClassLabel(names=("""pos""", """neg""")	)}	)
						__snake_case          =	TextClassification(text_column="""text"""      ,    label_column="""label"""	)
						__snake_case          =	DatasetInfo(features=lowercase      ,    task_templates=lowercase	)
						__snake_case          =	Dataset.from_dict({"""text""": ["""A sentence."""], """label""": ["""pos"""]}      ,    info=lowercase	)

						def process(lowercase  :	List[Any]	):
											return example

						__snake_case          =	dataset.remove_columns("""label"""	)
						__snake_case          =	modified_dataset.map(lowercase	)
						assert mapped_dataset.info.task_templates == []


