



"""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


A_	:						List[str]				=			collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)
def    __snake_case       (  __A			:	List[Any]    )    ->					CheckpointState:






				'''simple docstring'''
				print(F"""Reading saved model from {model_file}"""    )
				SCREAMING_SNAKE_CASE	:       Optional[int] 							=    torch.load(__A       ,		map_location=lambda __A       ,		__A    : default_restore_location(__A       ,		'cpu'    )    )
				return CheckpointState(**__A    )
class 						lowerCAmelCase__	:


	'''simple docstring'''







	def __init__(			self							:					Any    ,					_SCREAMING_SNAKE_CASE							:					Path						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    src_file





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





					"""simple docstring"""


					raise NotImplementedError







	@staticmethod
	def  _lowerCAmelCase    (			_SCREAMING_SNAKE_CASE							:					str    ,					*_SCREAMING_SNAKE_CASE							:					Dict    ,					**_SCREAMING_SNAKE_CASE							:					int						)	->  Tuple:





					"""simple docstring"""


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




class 						lowerCAmelCase__	(  lowercase_   ):


	'''simple docstring'''







	def  _lowerCAmelCase    (			self							:					Any						)	->  int:





					"""simple docstring"""


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




class 						lowerCAmelCase__	(  lowercase_   ):


	'''simple docstring'''







	def  _lowerCAmelCase    (			self							:					Union[str, Any]						)	->  Tuple:





					"""simple docstring"""


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




class 						lowerCAmelCase__	(  lowercase_   ):


	'''simple docstring'''







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





					"""simple docstring"""


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




def    __snake_case       (  __A			:	Optional[Any]       ,		__A			:	Dict       ,		__A			:	Optional[int]    )    ->					Optional[Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    Path(__A    )
				dest_dir.mkdir(exist_ok=__A    )

				SCREAMING_SNAKE_CASE	:       int 							=    DPRState.from_type(__A       ,		src_file=__A    )
				SCREAMING_SNAKE_CASE	:       List[str] 							=    dpr_state.load_dpr_model()
				model.save_pretrained(__A    )
				model.from_pretrained(__A    )  # sanity check


if __name__ == "__main__":
							A_	:						List[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.')
							A_	:						Optional[int]				=			parser.parse_args()

							A_	:						List[str]				=			Path(args.src)
							A_	:						List[Any]				=			f'''converted-{src_file.name}''' if args.dest is None else args.dest
							A_	:						Optional[int]				=			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 torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Dict     	=   (VQDiffusionScheduler,)





	def  _lowerCAmelCase    (			self							:					Tuple    ,					**_SCREAMING_SNAKE_CASE							:					Optional[int]						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    {
					    'num_vec_classes': 4_097,
					    'num_train_timesteps': 100,
					}

					config.update(**_SCREAMING_SNAKE_CASE						)
					return config





	def  _lowerCAmelCase    (			self							:					Dict    ,					_SCREAMING_SNAKE_CASE							:					Tuple						)	->  List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    4
					SCREAMING_SNAKE_CASE	:       List[str] 							=    8
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    8

					SCREAMING_SNAKE_CASE	:       Tuple 							=    torch.randint(0    ,					_SCREAMING_SNAKE_CASE    ,					(batch_size, height * width)						)

					return sample





	@property
	def  _lowerCAmelCase    (			self							:					str						)	->  int:





					"""simple docstring"""


					assert False





	def  _lowerCAmelCase    (			self							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]						)	->  Optional[int]:





					"""simple docstring"""


					def model(_SCREAMING_SNAKE_CASE							:					Optional[Any]    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]    ,					*_SCREAMING_SNAKE_CASE							:					str						):
									SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE	:       Any 							=    sample.shape
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)						)
									SCREAMING_SNAKE_CASE	:       List[str] 							=    F.log_softmax(logits.double()    ,					dim=1						).float()
									return return_value

					return model





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





					"""simple docstring"""


					for timesteps in [2, 5, 100, 1_000]:
									self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE						)





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





					"""simple docstring"""


					for num_vec_classes in [5, 100, 1_000, 4_000]:
									self.check_over_configs(num_vec_classes=_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Dict						)	->  List[Any]:





					"""simple docstring"""


					for t in [0, 50, 99]:
									self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE						)







	def  _lowerCAmelCase    (			self							:					Any						)	->  Dict:





					"""simple docstring"""


					pass





