






from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCAmelCase_							=      logging.get_logger(__name__)


class        lowercase__       (     _UpperCAmelCase		):
	a_             ="""mt5"""
	a_             =["""past_key_values"""]

	def __init__(							self						,      __UpperCAmelCase=250112						,      __UpperCAmelCase=512						,      __UpperCAmelCase=64						,      __UpperCAmelCase=1024						,      __UpperCAmelCase=8						,      __UpperCAmelCase=None						,      __UpperCAmelCase=6						,      __UpperCAmelCase=32						,      __UpperCAmelCase=128						,      __UpperCAmelCase=0.1						,      __UpperCAmelCase=1E-6						,      __UpperCAmelCase=1.0						,      __UpperCAmelCase="gated-gelu"						,      __UpperCAmelCase=True						,      __UpperCAmelCase=True						,      __UpperCAmelCase="T5Tokenizer"						,      __UpperCAmelCase=False						,      __UpperCAmelCase=0						,      __UpperCAmelCase=1						,      __UpperCAmelCase=0						,      **__UpperCAmelCase						,      )->   Union[str, Any]:







							'''simple docstring'''





							super().__init__(
							    is_encoder_decoder=__UpperCAmelCase						,      tokenizer_class=__UpperCAmelCase						,      tie_word_embeddings=__UpperCAmelCase						,      pad_token_id=__UpperCAmelCase						,      eos_token_id=__UpperCAmelCase						,      decoder_start_token_id=__UpperCAmelCase						,      **__UpperCAmelCase						,      )
							lowerCAmelCase__	 =  vocab_size
							lowerCAmelCase__	 =  d_model
							lowerCAmelCase__	 =  d_kv
							lowerCAmelCase__	 =  d_ff
							lowerCAmelCase__	 =  num_layers
							lowerCAmelCase__	 =  (
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							lowerCAmelCase__	 =  num_heads
							lowerCAmelCase__	 =  relative_attention_num_buckets
							lowerCAmelCase__	 =  relative_attention_max_distance
							lowerCAmelCase__	 =  dropout_rate
							lowerCAmelCase__	 =  layer_norm_epsilon
							lowerCAmelCase__	 =  initializer_factor
							lowerCAmelCase__	 =  feed_forward_proj
							lowerCAmelCase__	 =  use_cache

							lowerCAmelCase__	 =  self.feed_forward_proj.split("-"    )
							lowerCAmelCase__	 =  act_info[-1]
							lowerCAmelCase__	 =  act_info[0] == "gated"

							if len(__UpperCAmelCase    ) > 1 and act_info[0] != "gated" or len(__UpperCAmelCase    ) > 2:
													raise ValueError(
													    F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
													    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
													    "'gated-gelu' or 'relu'"    )

							# for backwards compatibility
							if feed_forward_proj == "gated-gelu":
													lowerCAmelCase__	 =  "gelu_new"

	@property
	def 						UpperCAmelCase					(							self    )->   Any:







							'''simple docstring'''





							return self.d_model

	@property
	def 						UpperCAmelCase					(							self    )->   Dict:







							'''simple docstring'''





							return self.num_heads


	@property
	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							return self.num_layers





class        lowercase__       (     _UpperCAmelCase		):

	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
	def 						UpperCAmelCase					(							self    )->   Mapping[str, Mapping[int, str]]:







							'''simple docstring'''





							lowerCAmelCase__	 =  {
							    "input_ids": {0: "batch", 1: "encoder_sequence"},
							    "attention_mask": {0: "batch", 1: "encoder_sequence"},
							}
							if self.use_past:
													lowerCAmelCase__	 =  "past_encoder_sequence + sequence"
													lowerCAmelCase__	 =  {0: "batch"}
													lowerCAmelCase__	 =  {0: "batch", 1: "past_decoder_sequence + sequence"}
							else:
													lowerCAmelCase__	 =  {0: "batch", 1: "decoder_sequence"}
													lowerCAmelCase__	 =  {0: "batch", 1: "decoder_sequence"}

							if self.use_past:
													self.fill_with_past_key_values_(__UpperCAmelCase						,      direction="inputs"    )

							return common_inputs

	@property
	# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							return 13


	@property
	def 						UpperCAmelCase					(							self    )->   float:







							'''simple docstring'''





							return 5E-4














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


def     _a					(       UpperCamelCase_   :			str				)	->				CheckpointState:
						"""simple docstring"""







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


class        lowercase__       :

	def __init__(							self						,      __UpperCAmelCase    )->   Dict:







							'''simple docstring'''





							lowerCAmelCase__	 =  src_file

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







							'''simple docstring'''





							raise NotImplementedError


	@staticmethod
	def 						UpperCAmelCase					(							__UpperCAmelCase						,      *__UpperCAmelCase						,      **__UpperCAmelCase    )->   "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        lowercase__       (     _UpperCAmelCase		):

	def 						UpperCAmelCase					(							self    )->   str:







							'''simple docstring'''





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





class        lowercase__       (     _UpperCAmelCase		):

	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





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





class        lowercase__       (     _UpperCAmelCase		):

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







							'''simple docstring'''





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





def     _a					(       UpperCamelCase_   :			str      ,				UpperCamelCase_   :			Path      ,				UpperCamelCase_   :			Path				)	->				Any:
						"""simple docstring"""







						lowerCAmelCase__	 =  Path(UpperCamelCase_				)
						dest_dir.mkdir(exist_ok=UpperCamelCase_				)

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


if __name__ == "__main__":
	a_							=      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_							=      parser.parse_args()

	a_							=      Path(args.src)
	a_							=      F"converted-{src_file.name}" if args.dest is None else args.dest
	a_							=      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)






