



'''simple docstring'''
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_					:      Tuple    			=    logging.get_logger(__name__)

snake_case_					:      Union[str, Any]    			=    {
    '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_					:      List[str]    			=    [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						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
																								_UpperCamelCase   :						str												=  'lm_head'

												_UpperCamelCase   :						Any												=  getattr(UpperCAmelCase_    ,    UpperCAmelCase_				)

						if weight_type is not None:
												_UpperCamelCase   :						Optional[int]												=  getattr(UpperCAmelCase_    ,    UpperCAmelCase_				).shape
						else:
												_UpperCamelCase   :						Optional[int]												=  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":
												_UpperCamelCase   :						Union[str, Any]												=  value
						elif weight_type == "weight_g":
												_UpperCamelCase   :						Union[str, Any]												=  value
						elif weight_type == "weight_v":
												_UpperCamelCase   :						List[str]												=  value
						elif weight_type == "bias":
												_UpperCamelCase   :						str												=  value
						else:
												_UpperCamelCase   :						Union[str, Any]												=  value

						logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						str												=  []
						_UpperCamelCase   :						Dict												=  fairseq_model.state_dict()

						_UpperCamelCase   :						List[Any]												=  hf_model.unispeech.feature_extractor

						for name, value in fairseq_dict.items():
												_UpperCamelCase   :						int												=  False
												if "conv_layers" in name:
																		load_conv_layer(
																		    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    hf_model.config.feat_extract_norm == 'group'    ,    )
																		_UpperCamelCase   :						Dict												=  True
												else:
																		for key, mapped_key in MAPPING.items():
																								_UpperCamelCase   :						List[str]												=  '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]:
																														_UpperCamelCase   :						List[str]												=  True
																														if "*" in mapped_key:
																																				_UpperCamelCase   :						Any												=  name.split(UpperCAmelCase_				)[0].split('.'				)[-2]
																																				_UpperCamelCase   :						Optional[int]												=  mapped_key.replace('*'    ,    UpperCAmelCase_				)
																														if "weight_g" in name:
																																				_UpperCamelCase   :						Optional[Any]												=  'weight_g'
																														elif "weight_v" in name:
																																				_UpperCamelCase   :						str												=  'weight_v'
																														elif "bias" in name:
																																				_UpperCamelCase   :						List[Any]												=  'bias'
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				_UpperCamelCase   :						Tuple												=  'weight'
																														else:
																																				_UpperCamelCase   :						Union[str, Any]												=  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 A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Optional[int]												=  full_name.split('conv_layers.'				)[-1]
						_UpperCamelCase   :						Dict												=  name.split('.'				)
						_UpperCamelCase   :						Optional[Any]												=  int(items[0]				)
						_UpperCamelCase   :						int												=  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.'
																		)
																		_UpperCamelCase   :						Optional[int]												=  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.'
																		)
																		_UpperCamelCase   :						Any												=  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."
																		)
																		_UpperCamelCase   :						Union[str, Any]												=  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.'
																		)
																		_UpperCamelCase   :						str												=  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 A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_=None    ,    UpperCAmelCase_=None    ,    UpperCAmelCase_=True				):
						if config_path is not None:
												_UpperCamelCase   :						Optional[int]												=  UniSpeechConfig.from_pretrained(UpperCAmelCase_				)
						else:
												_UpperCamelCase   :						Dict												=  UniSpeechConfig()

						if is_finetuned:
												if dict_path:
																		_UpperCamelCase   :						int												=  Dictionary.load_from_json(UpperCAmelCase_				)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		_UpperCamelCase   :						Tuple												=  target_dict.pad_index
																		_UpperCamelCase   :						Union[str, Any]												=  target_dict.bos_index
																		_UpperCamelCase   :						Any												=  target_dict.eos_index
																		_UpperCamelCase   :						int												=  len(target_dict.symbols				)
																		_UpperCamelCase   :						Union[str, Any]												=  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_				)
																		_UpperCamelCase   :						Tuple												=  target_dict.indices

																		# fairseq has the <pad> and <s> switched
																		_UpperCamelCase   :						str												=  4_2
																		_UpperCamelCase   :						List[Any]												=  4_3
																		with open(UpperCAmelCase_    ,    'w'    ,    encoding='utf-8'				) as vocab_handle:
																								json.dump(UpperCAmelCase_    ,    UpperCAmelCase_				)
																		_UpperCamelCase   :						Optional[int]												=  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_    ,    )
																		_UpperCamelCase   :						Union[str, Any]												=  True if config.feat_extract_norm == 'layer' else False
																		_UpperCamelCase   :						Tuple												=  WavaVecaFeatureExtractor(
																		    feature_size=1    ,    sampling_rate=1_6_0_0_0    ,    padding_value=0    ,    do_normalize=UpperCAmelCase_    ,    return_attention_mask=UpperCAmelCase_    ,    )
																		_UpperCamelCase   :						Dict												=  WavaVecaProcessor(feature_extractor=UpperCAmelCase_    ,    tokenizer=UpperCAmelCase_				)
																		processor.save_pretrained(UpperCAmelCase_				)

												_UpperCamelCase   :						List[Any]												=  UniSpeechForCTC(UpperCAmelCase_				)
						else:
												_UpperCamelCase   :						List[Any]												=  UniSpeechForPreTraining(UpperCAmelCase_				)

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

						_UpperCamelCase   :						Optional[int]												=  model[0].eval()

						recursively_load_weights(UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				)

						hf_unispeech.save_pretrained(UpperCAmelCase_				)


if __name__ == "__main__":
					snake_case_					:      Union[str, Any]    			=    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_					:      Optional[int]    			=    parser.parse_args()
					convert_unispeech_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)







'''simple docstring'''
from typing import List

import numpy as np






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						Any												=  {key: len(UpperCAmelCase_				) for key, value in gen_kwargs.items() if isinstance(UpperCAmelCase_    ,    UpperCAmelCase_				)}
						if len(set(lists_lengths.values()				)				) > 1:
												raise RuntimeError(
												    (
												        'Sharding is ambiguous for this dataset: '
												        + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
												        + '\n'.join(f'\t- key {key} has length {length}' for key, length in lists_lengths.items()				)
												        + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
												        + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
												    )				)
						_UpperCamelCase   :						Tuple												=  max(lists_lengths.values()    ,    default=0				)
						return max(1    ,    UpperCAmelCase_				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Dict												=  []
						for group_idx in range(UpperCAmelCase_				):
												_UpperCamelCase   :						int												=  num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
												if num_shards_to_add == 0:
																		break
												_UpperCamelCase   :						Tuple												=  shards_indices_per_group[-1].stop if shards_indices_per_group else 0
												_UpperCamelCase   :						Tuple												=  range(UpperCAmelCase_    ,    start + num_shards_to_add				)
												shards_indices_per_group.append(UpperCAmelCase_				)
						return shards_indices_per_group






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Tuple												=  _number_of_shards_in_gen_kwargs(UpperCAmelCase_				)
						if num_shards == 1:
												return [dict(UpperCAmelCase_				)]
						else:
												_UpperCamelCase   :						str												=  _distribute_shards(num_shards=UpperCAmelCase_    ,    max_num_jobs=UpperCAmelCase_				)
												return [
												    {
												        key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
												        if isinstance(UpperCAmelCase_    ,    UpperCAmelCase_				)
												        else value
												        for key, value in gen_kwargs.items()
												    }
												    for group_idx in range(len(UpperCAmelCase_				)				)
												]






def A__       (	UpperCAmelCase_				):
						return {
						    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
						    if isinstance(gen_kwargs_list[0][key]    ,    UpperCAmelCase_				)
						    else gen_kwargs_list[0][key]
						    for key in gen_kwargs_list[0]
						}






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						int												=  {len(UpperCAmelCase_				) for value in gen_kwargs.values() if isinstance(UpperCAmelCase_    ,    UpperCAmelCase_				)}
						_UpperCamelCase   :						Union[str, Any]												=  {}
						for size in list_sizes:
												_UpperCamelCase   :						str												=  list(range(UpperCAmelCase_				)				)
												rng.shuffle(indices_per_size[size]				)
						# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
						_UpperCamelCase   :						Union[str, Any]												=  dict(UpperCAmelCase_				)
						for key, value in shuffled_kwargs.items():
												if isinstance(UpperCAmelCase_    ,    UpperCAmelCase_				):
																		_UpperCamelCase   :						Dict												=  [value[i] for i in indices_per_size[len(UpperCAmelCase_				)]]
						return shuffled_kwargs


