


"""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


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







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					str:
							print(f"""Reading saved model from {model_file}"""					)
							snake_case_     							=		torch.load(_SCREAMING_SNAKE_CASE					,			map_location=lambda _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					: default_restore_location(_SCREAMING_SNAKE_CASE					,			"""cpu"""					)					)
							return CheckpointState(**_SCREAMING_SNAKE_CASE					)





class   __A  :





							'''simple docstring'''

							def __init__(					self				:   Dict		,							UpperCAmelCase_				:   Path							)       ->List[Any]:



														"""simple docstring"""



														snake_case_     							=		src_file



							def 		lowerCAmelCase							(					self				:   Optional[Any]							)       ->Optional[int]:



														"""simple docstring"""



														raise NotImplementedError



							@staticmethod
							def 		lowerCAmelCase							(					UpperCAmelCase_				:   str		,							*UpperCAmelCase_				:   Union[str, Any]		,							**UpperCAmelCase_				:   List[str]							)       ->Any:



														"""simple docstring"""



														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  (lowerCAmelCase_):





							'''simple docstring'''

							def 		lowerCAmelCase							(					self				:   int							)       ->Tuple:



														"""simple docstring"""



														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  (lowerCAmelCase_):





							'''simple docstring'''

							def 		lowerCAmelCase							(					self				:   int							)       ->Union[str, Any]:



														"""simple docstring"""



														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  (lowerCAmelCase_):





							'''simple docstring'''

							def 		lowerCAmelCase							(					self				:   Dict							)       ->List[str]:



														"""simple docstring"""



														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     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					Union[str, Any]:
							snake_case_     							=		Path(_SCREAMING_SNAKE_CASE					)
							dest_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE					)

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE				:     Optional[int]		=     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.')
				__SCREAMING_SNAKE_CASE				:     Union[str, Any]		=     parser.parse_args()

				__SCREAMING_SNAKE_CASE				:     Union[str, Any]		=     Path(args.src)
				__SCREAMING_SNAKE_CASE				:     Dict		=     f"""converted-{src_file.name}""" if args.dest is None else args.dest
				__SCREAMING_SNAKE_CASE				:     List[str]		=     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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowercase	=    {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''GPTJForCausalLM''',
							    '''GPTJForQuestionAnswering''',
							    '''GPTJForSequenceClassification''',
							    '''GPTJModel''',
							    '''GPTJPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''TFGPTJForCausalLM''',
							    '''TFGPTJForQuestionAnswering''',
							    '''TFGPTJForSequenceClassification''',
							    '''TFGPTJModel''',
							    '''TFGPTJPreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''FlaxGPTJForCausalLM''',
							    '''FlaxGPTJModel''',
							    '''FlaxGPTJPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gptj import (
														    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTJForCausalLM,
														    GPTJForQuestionAnswering,
														    GPTJForSequenceClassification,
														    GPTJModel,
														    GPTJPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_gptj import (
														    TFGPTJForCausalLM,
														    TFGPTJForQuestionAnswering,
														    TFGPTJForSequenceClassification,
														    TFGPTJModel,
														    TFGPTJPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
							import sys

							__lowercase	=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






