import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
snake_case__				= logging.get_logger(__name__)

snake_case__				= {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
snake_case__				= [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    Optional[Any]:



						'''simple docstring'''

						for attribute in key.split('''.'''		):
												if is_finetuned:
																		if attribute in ["quantizer", "project_q", "project_hid"]:
																								# those layers are only relevant for pretraining and should be dropped
																								return

																		if attribute == "ctc_proj":
																								# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
																								_lowerCamelCase			    =				'''lm_head'''

												_lowerCamelCase			    =				getattr(__UpperCAmelCase ,						__UpperCAmelCase		)

						if weight_type is not None:
												_lowerCamelCase			    =				getattr(__UpperCAmelCase ,						__UpperCAmelCase		).shape
						else:
												_lowerCamelCase			    =				hf_pointer.shape

						assert hf_shape == value.shape, (
						    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
						    F' {value.shape} for {full_name}'
						)

						if weight_type == "weight":
												_lowerCamelCase			    =				value
						elif weight_type == "weight_g":
												_lowerCamelCase			    =				value
						elif weight_type == "weight_v":
												_lowerCamelCase			    =				value
						elif weight_type == "bias":
												_lowerCamelCase			    =				value
						else:
												_lowerCamelCase			    =				value

						logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    List[str]:



						'''simple docstring'''

						_lowerCamelCase			    =				[]
						_lowerCamelCase			    =				fairseq_model.state_dict()

						_lowerCamelCase			    =				hf_model.unispeech.feature_extractor

						for name, value in fairseq_dict.items():
												_lowerCamelCase			    =				False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						hf_model.config.feat_extract_norm == '''group''' ,						)
																		_lowerCamelCase			    =				True
												else:
																		for key, mapped_key in MAPPING.items():
																								_lowerCamelCase			    =				'''unispeech.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																								if key in name or key.split('''w2v_model.'''		)[-1] == name.split('''.'''		)[0]:
																														_lowerCamelCase			    =				True
																														if "*" in mapped_key:
																																				_lowerCamelCase			    =				name.split(__UpperCAmelCase		)[0].split('''.'''		)[-2]
																																				_lowerCamelCase			    =				mapped_key.replace('''*''' ,						__UpperCAmelCase		)
																														if "weight_g" in name:
																																				_lowerCamelCase			    =				'''weight_g'''
																														elif "weight_v" in name:
																																				_lowerCamelCase			    =				'''weight_v'''
																														elif "bias" in name:
																																				_lowerCamelCase			    =				'''bias'''
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				_lowerCamelCase			    =				'''weight'''
																														else:
																																				_lowerCamelCase			    =				None
																														set_recursively(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)
																								continue
												if not is_used:
																		unused_weights.append(__UpperCAmelCase		)

						logger.warning(F'Unused weights: {unused_weights}'		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    int:



						'''simple docstring'''

						_lowerCamelCase			    =				full_name.split('''conv_layers.'''		)[-1]
						_lowerCamelCase			    =				name.split('''.'''		)
						_lowerCamelCase			    =				int(items[0]		)
						_lowerCamelCase			    =				int(items[1]		)

						if type_id == 0:
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																		    F'{full_name} has size {value.shape}, but'
																		    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
																		)
																		_lowerCamelCase			    =				value
																		logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'		)
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																		    F'{full_name} has size {value.shape}, but'
																		    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
																		)
																		_lowerCamelCase			    =				value
																		logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'		)
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																		    F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
																		    " found."
																		)
																		_lowerCamelCase			    =				value
																		logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																		    F'{full_name} has size {value.shape}, but'
																		    F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
																		)
																		_lowerCamelCase			    =				value
																		logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
						else:
												unused_weights.append(__UpperCAmelCase		)
@torch.no_grad()
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=None ,						__UpperCAmelCase=None ,						__UpperCAmelCase=True		)						->    Union[str, Any]:



						'''simple docstring'''

						if config_path is not None:
												_lowerCamelCase			    =				UniSpeechConfig.from_pretrained(__UpperCAmelCase		)
						else:
												_lowerCamelCase			    =				UniSpeechConfig()

						if is_finetuned:
												if dict_path:
																		_lowerCamelCase			    =				Dictionary.load_from_json(__UpperCAmelCase		)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		_lowerCamelCase			    =				target_dict.pad_index
																		_lowerCamelCase			    =				target_dict.bos_index
																		_lowerCamelCase			    =				target_dict.eos_index
																		_lowerCamelCase			    =				len(target_dict.symbols		)
																		_lowerCamelCase			    =				os.path.join(__UpperCAmelCase ,						'''vocab.json'''		)
																		if not os.path.isdir(__UpperCAmelCase		):
																								logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__UpperCAmelCase		)		)
																								return
																		os.makedirs(__UpperCAmelCase ,						exist_ok=__UpperCAmelCase		)
																		_lowerCamelCase			    =				target_dict.indices

																		# fairseq has the <pad> and <s> switched
																		_lowerCamelCase			    =				42
																		_lowerCamelCase			    =				43
																		with open(__UpperCAmelCase ,						'''w''' ,						encoding='''utf-8'''		) as vocab_handle:
																								json.dump(__UpperCAmelCase ,						__UpperCAmelCase		)
																		_lowerCamelCase			    =				WavaVecaPhonemeCTCTokenizer(
																		    __UpperCAmelCase ,						unk_token=target_dict.unk_word ,						pad_token=target_dict.pad_word ,						bos_token=target_dict.bos_word ,						eos_token=target_dict.eos_word ,						word_delimiter_token='''|''' ,						do_lower_case=__UpperCAmelCase ,						)
																		_lowerCamelCase			    =				True if config.feat_extract_norm == '''layer''' else False
																		_lowerCamelCase			    =				WavaVecaFeatureExtractor(
																		    feature_size=1 ,						sampling_rate=1_6000 ,						padding_value=0 ,						do_normalize=__UpperCAmelCase ,						return_attention_mask=__UpperCAmelCase ,						)
																		_lowerCamelCase			    =				WavaVecaProcessor(feature_extractor=__UpperCAmelCase ,						tokenizer=__UpperCAmelCase		)
																		processor.save_pretrained(__UpperCAmelCase		)

												_lowerCamelCase			    =				UniSpeechForCTC(__UpperCAmelCase		)
						else:
												_lowerCamelCase			    =				UniSpeechForPreTraining(__UpperCAmelCase		)

						if is_finetuned:
												_lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path] ,						arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''		)[:-1]		), '''w2v_path''': checkpoint_path}		)
						else:
												_lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

						_lowerCamelCase			    =				model[0].eval()

						recursively_load_weights(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)

						hf_unispeech.save_pretrained(__UpperCAmelCase		)


if __name__ == "__main__":
					snake_case__				= argparse.ArgumentParser()
					parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
					parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
					parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
					parser.add_argument(
					    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
					)
					snake_case__				= parser.parse_args()
					convert_unispeech_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)
import argparse
import json

from tqdm import tqdm
def       __magic_name__(  )						->    List[str]:



      '''simple docstring'''

      _lowerCamelCase			    =				argparse.ArgumentParser()

      # Required parameters
      parser.add_argument(
          '''--src_path''' ,						type=__UpperCAmelCase ,						default='''biencoder-nq-dev.json''' ,						help='''Path to raw DPR training data''' ,						)
      parser.add_argument(
          '''--evaluation_set''' ,						type=__UpperCAmelCase ,						help='''where to store parsed evaluation_set file''' ,						)
      parser.add_argument(
          '''--gold_data_path''' ,						type=__UpperCAmelCase ,						help='''where to store parsed gold_data_path file''' ,						)
      _lowerCamelCase			    =				parser.parse_args()

      with open(args.src_path ,						'''r'''		) as src_file, open(args.evaluation_set ,						'''w'''		) as eval_file, open(
          args.gold_data_path ,						'''w'''		) as gold_file:
            _lowerCamelCase			    =				json.load(__UpperCAmelCase		)
            for dpr_record in tqdm(__UpperCAmelCase		):
                  _lowerCamelCase			    =				dpr_record['''question''']
                  _lowerCamelCase			    =				[context['''title'''] for context in dpr_record['''positive_ctxs''']]
                  eval_file.write(question + '''\n'''		)
                  gold_file.write('''\t'''.join(__UpperCAmelCase		) + '''\n'''		)


if __name__ == "__main__":
     main()