






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


a_							=      {
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=      [
	    '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''MobileViTV2ForImageClassification''',
	    '''MobileViTV2ForSemanticSegmentation''',
	    '''MobileViTV2Model''',
	    '''MobileViTV2PreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_mobilevitva import (
	    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    MobileViTVaConfig,
	    MobileViTVaOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mobilevitva import (
		    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MobileViTVaForImageClassification,
		    MobileViTVaForSemanticSegmentation,
		    MobileViTVaModel,
		    MobileViTVaPreTrainedModel,
		)

else:
	import sys

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














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)






