






from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


a_							=      {
    '''configuration_utils''': ['''GenerationConfig'''],
    '''streamers''': ['''TextIteratorStreamer''', '''TextStreamer'''],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=      [
	    '''Constraint''',
	    '''ConstraintListState''',
	    '''DisjunctiveConstraint''',
	    '''PhrasalConstraint''',
	]
	a_							=      [
	    '''BeamHypotheses''',
	    '''BeamScorer''',
	    '''BeamSearchScorer''',
	    '''ConstrainedBeamSearchScorer''',
	]
	a_							=      [
	    '''EpsilonLogitsWarper''',
	    '''EtaLogitsWarper''',
	    '''ForcedBOSTokenLogitsProcessor''',
	    '''ForcedEOSTokenLogitsProcessor''',
	    '''HammingDiversityLogitsProcessor''',
	    '''InfNanRemoveLogitsProcessor''',
	    '''LogitsProcessor''',
	    '''LogitsProcessorList''',
	    '''LogitsWarper''',
	    '''MinLengthLogitsProcessor''',
	    '''MinNewTokensLengthLogitsProcessor''',
	    '''NoBadWordsLogitsProcessor''',
	    '''NoRepeatNGramLogitsProcessor''',
	    '''PrefixConstrainedLogitsProcessor''',
	    '''RepetitionPenaltyLogitsProcessor''',
	    '''SequenceBiasLogitsProcessor''',
	    '''EncoderRepetitionPenaltyLogitsProcessor''',
	    '''TemperatureLogitsWarper''',
	    '''TopKLogitsWarper''',
	    '''TopPLogitsWarper''',
	    '''TypicalLogitsWarper''',
	    '''EncoderNoRepeatNGramLogitsProcessor''',
	    '''ExponentialDecayLengthPenalty''',
	    '''LogitNormalization''',
	]
	a_							=      [
	    '''MaxNewTokensCriteria''',
	    '''MaxLengthCriteria''',
	    '''MaxTimeCriteria''',
	    '''StoppingCriteria''',
	    '''StoppingCriteriaList''',
	    '''validate_stopping_criteria''',
	]
	a_							=      [
	    '''GenerationMixin''',
	    '''top_k_top_p_filtering''',
	    '''GreedySearchEncoderDecoderOutput''',
	    '''GreedySearchDecoderOnlyOutput''',
	    '''SampleEncoderDecoderOutput''',
	    '''SampleDecoderOnlyOutput''',
	    '''BeamSearchEncoderDecoderOutput''',
	    '''BeamSearchDecoderOnlyOutput''',
	    '''BeamSampleEncoderDecoderOutput''',
	    '''BeamSampleDecoderOnlyOutput''',
	    '''ContrastiveSearchEncoderDecoderOutput''',
	    '''ContrastiveSearchDecoderOnlyOutput''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=      [
	    '''TFForcedBOSTokenLogitsProcessor''',
	    '''TFForcedEOSTokenLogitsProcessor''',
	    '''TFLogitsProcessor''',
	    '''TFLogitsProcessorList''',
	    '''TFLogitsWarper''',
	    '''TFMinLengthLogitsProcessor''',
	    '''TFNoBadWordsLogitsProcessor''',
	    '''TFNoRepeatNGramLogitsProcessor''',
	    '''TFRepetitionPenaltyLogitsProcessor''',
	    '''TFTemperatureLogitsWarper''',
	    '''TFTopKLogitsWarper''',
	    '''TFTopPLogitsWarper''',
	    '''TFForceTokensLogitsProcessor''',
	    '''TFSuppressTokensAtBeginLogitsProcessor''',
	    '''TFSuppressTokensLogitsProcessor''',
	]
	a_							=      [
	    '''TFGenerationMixin''',
	    '''tf_top_k_top_p_filtering''',
	    '''TFGreedySearchDecoderOnlyOutput''',
	    '''TFGreedySearchEncoderDecoderOutput''',
	    '''TFSampleEncoderDecoderOutput''',
	    '''TFSampleDecoderOnlyOutput''',
	    '''TFBeamSearchEncoderDecoderOutput''',
	    '''TFBeamSearchDecoderOnlyOutput''',
	    '''TFBeamSampleEncoderDecoderOutput''',
	    '''TFBeamSampleDecoderOnlyOutput''',
	    '''TFContrastiveSearchEncoderDecoderOutput''',
	    '''TFContrastiveSearchDecoderOnlyOutput''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=      [
	    '''FlaxForcedBOSTokenLogitsProcessor''',
	    '''FlaxForcedEOSTokenLogitsProcessor''',
	    '''FlaxLogitsProcessor''',
	    '''FlaxLogitsProcessorList''',
	    '''FlaxLogitsWarper''',
	    '''FlaxMinLengthLogitsProcessor''',
	    '''FlaxTemperatureLogitsWarper''',
	    '''FlaxTopKLogitsWarper''',
	    '''FlaxTopPLogitsWarper''',
	]
	a_							=      [
	    '''FlaxGenerationMixin''',
	    '''FlaxGreedySearchOutput''',
	    '''FlaxSampleOutput''',
	    '''FlaxBeamSearchOutput''',
	]

if TYPE_CHECKING:
	from .configuration_utils import GenerationConfig
	from .streamers import TextIteratorStreamer, TextStreamer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
		from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
		from .logits_process import (
		    EncoderNoRepeatNGramLogitsProcessor,
		    EncoderRepetitionPenaltyLogitsProcessor,
		    EpsilonLogitsWarper,
		    EtaLogitsWarper,
		    ExponentialDecayLengthPenalty,
		    ForcedBOSTokenLogitsProcessor,
		    ForcedEOSTokenLogitsProcessor,
		    HammingDiversityLogitsProcessor,
		    InfNanRemoveLogitsProcessor,
		    LogitNormalization,
		    LogitsProcessor,
		    LogitsProcessorList,
		    LogitsWarper,
		    MinLengthLogitsProcessor,
		    MinNewTokensLengthLogitsProcessor,
		    NoBadWordsLogitsProcessor,
		    NoRepeatNGramLogitsProcessor,
		    PrefixConstrainedLogitsProcessor,
		    RepetitionPenaltyLogitsProcessor,
		    SequenceBiasLogitsProcessor,
		    TemperatureLogitsWarper,
		    TopKLogitsWarper,
		    TopPLogitsWarper,
		    TypicalLogitsWarper,
		)
		from .stopping_criteria import (
		    MaxLengthCriteria,
		    MaxNewTokensCriteria,
		    MaxTimeCriteria,
		    StoppingCriteria,
		    StoppingCriteriaList,
		    validate_stopping_criteria,
		)
		from .utils import (
		    BeamSampleDecoderOnlyOutput,
		    BeamSampleEncoderDecoderOutput,
		    BeamSearchDecoderOnlyOutput,
		    BeamSearchEncoderDecoderOutput,
		    ContrastiveSearchDecoderOnlyOutput,
		    ContrastiveSearchEncoderDecoderOutput,
		    GenerationMixin,
		    GreedySearchDecoderOnlyOutput,
		    GreedySearchEncoderDecoderOutput,
		    SampleDecoderOnlyOutput,
		    SampleEncoderDecoderOutput,
		    top_k_top_p_filtering,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tf_logits_process import (
		    TFForcedBOSTokenLogitsProcessor,
		    TFForcedEOSTokenLogitsProcessor,
		    TFForceTokensLogitsProcessor,
		    TFLogitsProcessor,
		    TFLogitsProcessorList,
		    TFLogitsWarper,
		    TFMinLengthLogitsProcessor,
		    TFNoBadWordsLogitsProcessor,
		    TFNoRepeatNGramLogitsProcessor,
		    TFRepetitionPenaltyLogitsProcessor,
		    TFSuppressTokensAtBeginLogitsProcessor,
		    TFSuppressTokensLogitsProcessor,
		    TFTemperatureLogitsWarper,
		    TFTopKLogitsWarper,
		    TFTopPLogitsWarper,
		)
		from .tf_utils import (
		    TFBeamSampleDecoderOnlyOutput,
		    TFBeamSampleEncoderDecoderOutput,
		    TFBeamSearchDecoderOnlyOutput,
		    TFBeamSearchEncoderDecoderOutput,
		    TFContrastiveSearchDecoderOnlyOutput,
		    TFContrastiveSearchEncoderDecoderOutput,
		    TFGenerationMixin,
		    TFGreedySearchDecoderOnlyOutput,
		    TFGreedySearchEncoderDecoderOutput,
		    TFSampleDecoderOnlyOutput,
		    TFSampleEncoderDecoderOutput,
		    tf_top_k_top_p_filtering,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .flax_logits_process import (
		    FlaxForcedBOSTokenLogitsProcessor,
		    FlaxForcedEOSTokenLogitsProcessor,
		    FlaxLogitsProcessor,
		    FlaxLogitsProcessorList,
		    FlaxLogitsWarper,
		    FlaxMinLengthLogitsProcessor,
		    FlaxTemperatureLogitsWarper,
		    FlaxTopKLogitsWarper,
		    FlaxTopPLogitsWarper,
		)
		from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
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)






