

"""simple docstring"""






import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


lowerCamelCase__				:  Optional[int]													=  get_logger(__name__)
def       UpperCamelCase					(			_lowerCAmelCase     :						str,  _lowerCAmelCase     :						str )				->			int:
	if text.startswith(_lowerCAmelCase ):
		return text[len(_lowerCAmelCase ) :]
	return text  # or whatever
def       UpperCamelCase					(			_lowerCAmelCase     :						int )				->			Tuple:
	return {remove_prefix(_lowerCAmelCase,  """model.""" ): v for k, v in sd.items()}
def       UpperCamelCase					(			_lowerCAmelCase     :						List[Dict[str, torch.Tensor]] )				->			str:
	_UpperCAmelCase    :							List[Any]									=					{}
	for k in state_dicts[0].keys():
		_UpperCAmelCase    :							Tuple									=					[sd[k] for sd in state_dicts]
		_UpperCAmelCase    :							str									=					sum(_lowerCAmelCase ) / len(_lowerCAmelCase )
		assert isinstance(_lowerCAmelCase,  torch.Tensor )
		_UpperCAmelCase    :							Any									=					new_t
	return new_sd
def       UpperCamelCase					(			_lowerCAmelCase     :						str,  _lowerCAmelCase     :						str,  _lowerCAmelCase     :						str )				->			None:
	_UpperCAmelCase    :							Tuple									=					AutoModelForSeqaSeqLM.from_pretrained(_lowerCAmelCase )
	if os.path.isfile(_lowerCAmelCase ):
		_UpperCAmelCase    :							Union[str, Any]									=					[pl_ckpt_path]
	else:
		assert os.path.isdir(_lowerCAmelCase )
		_UpperCAmelCase    :							Tuple									=					list(Path(_lowerCAmelCase ).glob("""*.ckpt""" ) )
		assert ckpt_files, f'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

	if len(_lowerCAmelCase ) > 1:
		logger.info(f'''averaging the weights of {ckpt_files}''' )

	_UpperCAmelCase    :							Any									=					[sanitize(torch.load(_lowerCAmelCase,  map_location="""cpu""" )["""state_dict"""] ) for x in ckpt_files]
	_UpperCAmelCase    :							Dict									=					average_state_dicts(_lowerCAmelCase )

	_UpperCAmelCase						,   _UpperCAmelCase    :							Optional[Any]									=					hf_model.load_state_dict(_lowerCAmelCase,  strict=_lowerCAmelCase )
	assert not missing, f'''missing keys: {missing}'''
	hf_model.save_pretrained(_lowerCAmelCase )
	try:
		_UpperCAmelCase    :							int									=					AutoTokenizer.from_pretrained(_lowerCAmelCase )
		tok.save_pretrained(_lowerCAmelCase )
	except Exception:
		pass
		# dont copy tokenizer if cant


if __name__ == "__main__":
						fire.Fire(convert_pl_to_hf)








"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__				:  Union[str, Any]													=  {
    '''configuration_dpr''': ['''DPR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPRConfig'''],
    '''tokenization_dpr''': [
        '''DPRContextEncoderTokenizer''',
        '''DPRQuestionEncoderTokenizer''',
        '''DPRReaderOutput''',
        '''DPRReaderTokenizer''',
    ],
}


try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  List[Any]													=  [
						    '''DPRContextEncoderTokenizerFast''',
						    '''DPRQuestionEncoderTokenizerFast''',
						    '''DPRReaderTokenizerFast''',
						]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Dict													=  [
						    '''DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DPRContextEncoder''',
						    '''DPRPretrainedContextEncoder''',
						    '''DPRPreTrainedModel''',
						    '''DPRPretrainedQuestionEncoder''',
						    '''DPRPretrainedReader''',
						    '''DPRQuestionEncoder''',
						    '''DPRReader''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Any													=  [
						    '''TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFDPRContextEncoder''',
						    '''TFDPRPretrainedContextEncoder''',
						    '''TFDPRPretrainedQuestionEncoder''',
						    '''TFDPRPretrainedReader''',
						    '''TFDPRQuestionEncoder''',
						    '''TFDPRReader''',
						]


if TYPE_CHECKING:
						from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
						from .tokenization_dpr import (
						    DPRContextEncoderTokenizer,
						    DPRQuestionEncoderTokenizer,
						    DPRReaderOutput,
						    DPRReaderTokenizer,
						)

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_dpr_fast import (
												    DPRContextEncoderTokenizerFast,
												    DPRQuestionEncoderTokenizerFast,
												    DPRReaderTokenizerFast,
												)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_dpr import (
												    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DPRContextEncoder,
												    DPRPretrainedContextEncoder,
												    DPRPreTrainedModel,
												    DPRPretrainedQuestionEncoder,
												    DPRPretrainedReader,
												    DPRQuestionEncoder,
												    DPRReader,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_dpr import (
												    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDPRContextEncoder,
												    TFDPRPretrainedContextEncoder,
												    TFDPRPretrainedQuestionEncoder,
												    TFDPRPretrainedReader,
												    TFDPRQuestionEncoder,
												    TFDPRReader,
												)

else:
						import sys

						lowerCamelCase__				:  Optional[Any]													=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





