





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[Any]		  =      collections.namedtuple(
    '''CheckpointState''', ['''model_dict''', '''optimizer_dict''', '''scheduler_dict''', '''offset''', '''epoch''', '''encoder_params''']
)



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						CheckpointState:
		print(F"Reading saved model from {model_file}"       )
		__lowercase					=    torch.load(snake_case_	,      map_location=lambda snake_case	,      snake_case       : default_restore_location(snake_case_	,      'cpu'       )       )
		return CheckpointState(**snake_case_       )

class 		snake_case_      :







	'''simple docstring'''




	def __init__(	self       :    List[str]				,			__lowerCamelCase       :    Any      )					->   Union[str, Any]:

			'''simple docstring'''




			__lowercase					=    src_file




	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   Optional[Any]:

			'''simple docstring'''




			raise NotImplementedError






	@staticmethod
	def       UpperCAmelCase (	__lowerCamelCase       :    List[Any]				,			*__lowerCamelCase       :    Tuple				,			**__lowerCamelCase       :    Union[str, Any]      )					->   "DPRState":

			'''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 		snake_case_      (       UpperCamelCase_	):







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    str      )					->   Optional[Any]:

			'''simple docstring'''




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





class 		snake_case_      (       UpperCamelCase_	):







	'''simple docstring'''




	def       UpperCAmelCase (	self       :    Union[str, Any]      )					->   Dict:

			'''simple docstring'''




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





class 		snake_case_      (       UpperCamelCase_	):







	'''simple docstring'''




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

			'''simple docstring'''




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





def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case       )		->						Optional[int]:
		__lowercase					=    Path(snake_case_       )
		dest_dir.mkdir(exist_ok=snake_case_       )

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


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_      :			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.''')
		SCREAMING_SNAKE_CASE_      :			Tuple		  =      parser.parse_args()

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












import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		(VQDiffusionScheduler,)




	def       UpperCAmelCase (	self       :    int				,			**__lowerCamelCase       :    List[Any]      )					->   Tuple:

			'''simple docstring'''




			__lowercase					=    {
			    'num_vec_classes': 4_097,
			    'num_train_timesteps': 100,
			}

			config.update(**__lowerCamelCase      )
			return config




	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    List[str]      )					->   Tuple:

			'''simple docstring'''




			__lowercase					=    4
			__lowercase					=    8
			__lowercase					=    8

			__lowercase					=    torch.randint(0				,			__lowerCamelCase				,			(batch_size, height * width)      )

			return sample




	@property
	def       UpperCAmelCase (	self       :    Any      )					->   List[Any]:

			'''simple docstring'''




			assert False




	def       UpperCAmelCase (	self       :    Union[str, Any]				,			__lowerCamelCase       :    str      )					->   Dict:

			'''simple docstring'''




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

			return model




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

			'''simple docstring'''




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




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

			'''simple docstring'''




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




	def       UpperCAmelCase (	self       :    Any      )					->   List[Any]:

			'''simple docstring'''




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






	def       UpperCAmelCase (	self       :    str      )					->   Optional[int]:

			'''simple docstring'''




			pass




