



'''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_				:					str							=      collections.namedtuple(
    """CheckpointState""", ["""model_dict""", """optimizer_dict""", """scheduler_dict""", """offset""", """epoch""", """encoder_params"""]
)




def        snake_case_   (	lowerCAmelCase_						)->						CheckpointState:





       '''simple docstring'''


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



class 				lowercase			:


 """simple docstring"""


 def __init__(				self    ,a_				)						->  str:
        _UpperCAmelCase    :		Dict     	=					src_file







 def _snake_case					(				self				)						->  str:
        raise NotImplementedError







 @staticmethod
 def _snake_case					(				a_    ,*a_    ,**a_				)						->  "DPRState":
        if comp_type.startswith("""c"""				):
               return DPRContextEncoderState(*a_    ,**a_				)
        if comp_type.startswith("""q"""				):
               return DPRQuestionEncoderState(*a_    ,**a_				)
        if comp_type.startswith("""r"""				):
               return DPRReaderState(*a_    ,**a_				)
        else:
               raise ValueError("""Component type must be either 'ctx_encoder', 'question_encoder' or 'reader'."""				)







class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


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







class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


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







class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


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







def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						str:





       '''simple docstring'''


       _UpperCAmelCase    :		List[Any]     	=					Path(lowerCAmelCase_						)
       dest_dir.mkdir(exist_ok=lowerCAmelCase_						)

       _UpperCAmelCase    :		int     	=					DPRState.from_type(lowerCAmelCase_			,  src_file=lowerCAmelCase_						)
       _UpperCAmelCase    :		str     	=					dpr_state.load_dpr_model()
       model.save_pretrained(lowerCAmelCase_						)
       model.from_pretrained(lowerCAmelCase_						)  # sanity check


if __name__ == "__main__":
   A_				:					Tuple							=      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_				:					Union[str, Any]							=      parser.parse_args()

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

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "facebook/musicgen-small": "https://huggingface.co/facebook/musicgen-small/resolve/main/config.json",
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}

class     a (					__magic_name__     ):
				_snake_case		     =			'''musicgen_decoder'''
				_snake_case		     =			['''past_key_values''']




				def __init__(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Dict=20_48,     SCREAMING_SNAKE_CASE_						:					List[str]=20_48,     SCREAMING_SNAKE_CASE_						:					int=24,     SCREAMING_SNAKE_CASE_						:					Dict=40_96,     SCREAMING_SNAKE_CASE_						:					str=16,     SCREAMING_SNAKE_CASE_						:					int=0.0,     SCREAMING_SNAKE_CASE_						:					Optional[Any]=True,     SCREAMING_SNAKE_CASE_						:					str="gelu",     SCREAMING_SNAKE_CASE_						:					List[Any]=10_24,     SCREAMING_SNAKE_CASE_						:					int=0.1,     SCREAMING_SNAKE_CASE_						:					List[Any]=0.0,     SCREAMING_SNAKE_CASE_						:					int=0.0,     SCREAMING_SNAKE_CASE_						:					str=0.02,     SCREAMING_SNAKE_CASE_						:					Any=False,     SCREAMING_SNAKE_CASE_						:					List[str]=4,     SCREAMING_SNAKE_CASE_						:					str=20_48,     SCREAMING_SNAKE_CASE_						:					int=20_48,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=False,     **SCREAMING_SNAKE_CASE_						:					Tuple,     ):
										snake_case							:  Optional[int]			=     vocab_size
										snake_case							:  Optional[int]			=     max_position_embeddings
										snake_case							:  Dict			=     hidden_size
										snake_case							:  Any			=     ffn_dim
										snake_case							:  Optional[Any]			=     num_hidden_layers
										snake_case							:  Tuple			=     num_attention_heads
										snake_case							:  int			=     dropout
										snake_case							:  Union[str, Any]			=     attention_dropout
										snake_case							:  Optional[int]			=     activation_dropout
										snake_case							:  Union[str, Any]			=     activation_function
										snake_case							:  Tuple			=     initializer_factor
										snake_case							:  List[Any]			=     layerdrop
										snake_case							:  Optional[int]			=     use_cache
										snake_case							:  Dict			=     scale_embedding  # scale factor will be sqrt(d_model) if True
										snake_case							:  Tuple			=     num_codebooks
										super().__init__(
										    pad_token_id=SCREAMING_SNAKE_CASE_,     bos_token_id=SCREAMING_SNAKE_CASE_,     eos_token_id=SCREAMING_SNAKE_CASE_,     tie_word_embeddings=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_,     )




class     a (					__magic_name__     ):
				_snake_case		     =			'''musicgen'''
				_snake_case		     =			True




				def __init__(				self						:					Dict,     **SCREAMING_SNAKE_CASE_						:					Optional[int]   ):
										super().__init__(**SCREAMING_SNAKE_CASE_   )
										if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
																raise ValueError('''Config has to be initialized with text_encoder, audio_encoder and decoder config'''   )

										snake_case							:  Any			=     kwargs.pop('''text_encoder'''   )
										snake_case							:  List[Any]			=     text_encoder_config.pop('''model_type'''   )

										snake_case							:  Union[str, Any]			=     kwargs.pop('''audio_encoder'''   )
										snake_case							:  Tuple			=     audio_encoder_config.pop('''model_type'''   )

										snake_case							:  List[str]			=     kwargs.pop('''decoder'''   )

										snake_case							:  List[str]			=     AutoConfig.for_model(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[Any]			=     AutoConfig.for_model(SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )
										snake_case							:  Optional[Any]			=     MusicgenDecoderConfig(**SCREAMING_SNAKE_CASE_   )
										snake_case							:  Any			=     True




				@classmethod
				def 						__snake_case				(				cls						:					Tuple,     SCREAMING_SNAKE_CASE_						:					PretrainedConfig,     SCREAMING_SNAKE_CASE_						:					PretrainedConfig,     SCREAMING_SNAKE_CASE_						:					MusicgenDecoderConfig,     **SCREAMING_SNAKE_CASE_						:					Optional[Any],     ):
										return cls(
										    text_encoder=text_encoder_config.to_dict(),     audio_encoder=audio_encoder_config.to_dict(),     decoder=decoder_config.to_dict(),     **SCREAMING_SNAKE_CASE_,     )




				def 						__snake_case				(				self						:					Optional[Any]   ):
										snake_case							:  Dict			=     copy.deepcopy(self.__dict__   )
										snake_case							:  Tuple			=     self.text_encoder.to_dict()
										snake_case							:  str			=     self.audio_encoder.to_dict()
										snake_case							:  Dict			=     self.decoder.to_dict()
										snake_case							:  Optional[int]			=     self.__class__.model_type
										return output

