



'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase_     :       Dict								=						logging.get_logger(__name__)

lowerCAmelCase_     :       int								=						{
    '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',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
def 					UpperCAmelCase   (						A  :				int					,		A  :				List[Any]					,		A  :				Any					,		A  :				List[Any]					,		A  :				List[str]				):
							for attribute in key.split('''.'''				):
														SCREAMING_SNAKE_CASE					:      Tuple     					=			getattr(A					,		A				)

							if weight_type is not None:
														SCREAMING_SNAKE_CASE					:      str     					=			getattr(A					,		A				).shape
							else:
														SCREAMING_SNAKE_CASE					:      str     					=			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":
														SCREAMING_SNAKE_CASE					:      Tuple     					=			value
							elif weight_type == "weight_g":
														SCREAMING_SNAKE_CASE					:      Tuple     					=			value
							elif weight_type == "weight_v":
														SCREAMING_SNAKE_CASE					:      Tuple     					=			value
							elif weight_type == "bias":
														SCREAMING_SNAKE_CASE					:      str     					=			value
							else:
														SCREAMING_SNAKE_CASE					:      Dict     					=			value

							logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""				)
def 					UpperCAmelCase   (						A  :				List[str]					,		A  :				List[str]					,		A  :				str				):
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[]
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			fairseq_model.state_dict()

							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														SCREAMING_SNAKE_CASE					:      Dict     					=			False
														if "conv_layers" in name:
																					load_conv_layer(
																					    A					,		A					,		A					,		A					,		hf_model.config.feat_extract_norm == '''group'''					,		)
																					SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			True
														else:
																					for key, mapped_key in MAPPING.items():
																												SCREAMING_SNAKE_CASE					:      List[str]     					=			'''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

																												if key in name or (key.split('''w2v_model.'''				)[-1] == name.split('''.'''				)[0] and not is_finetuned):
																																			SCREAMING_SNAKE_CASE					:      Any     					=			True
																																			if "*" in mapped_key:
																																										SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			name.split(A				)[0].split('''.'''				)[-2]
																																										SCREAMING_SNAKE_CASE					:      Any     					=			mapped_key.replace('''*'''					,		A				)
																																			if "weight_g" in name:
																																										SCREAMING_SNAKE_CASE					:      Dict     					=			'''weight_g'''
																																			elif "weight_v" in name:
																																										SCREAMING_SNAKE_CASE					:      List[Any]     					=			'''weight_v'''
																																			elif "weight" in name:
																																										SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			'''weight'''
																																			elif "bias" in name:
																																										SCREAMING_SNAKE_CASE					:      Tuple     					=			'''bias'''
																																			else:
																																										SCREAMING_SNAKE_CASE					:      Optional[int]     					=			None
																																			set_recursively(A					,		A					,		A					,		A					,		A				)
																												continue
														if not is_used:
																					unused_weights.append(A				)

							logger.warning(F"""Unused weights: {unused_weights}"""				)
def 					UpperCAmelCase   (						A  :				Dict					,		A  :				List[Any]					,		A  :				Any					,		A  :				Dict					,		A  :				Optional[Any]				):
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			full_name.split('''conv_layers.'''				)[-1]
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			name.split('''.'''				)
							SCREAMING_SNAKE_CASE					:      int     					=			int(items[0]				)
							SCREAMING_SNAKE_CASE					:      Any     					=			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."""
																					)
																					SCREAMING_SNAKE_CASE					:      Dict     					=			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."""
																					)
																					SCREAMING_SNAKE_CASE					:      Tuple     					=			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."
																					)
																					SCREAMING_SNAKE_CASE					:      Optional[int]     					=			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."""
																					)
																					SCREAMING_SNAKE_CASE					:      str     					=			value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""				)
							else:
														unused_weights.append(A				)
@torch.no_grad()
def 					UpperCAmelCase   (						A  :				Tuple					,		A  :				Any					,		A  :				Dict=None					,		A  :				str=None					,		A  :				Any=True				):
							if config_path is not None:
														SCREAMING_SNAKE_CASE					:      Tuple     					=			HubertConfig.from_pretrained(A				)
							else:
														SCREAMING_SNAKE_CASE					:      List[str]     					=			HubertConfig()

							if is_finetuned:
														if dict_path:
																					SCREAMING_SNAKE_CASE					:      Dict     					=			Dictionary.load(A				)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					SCREAMING_SNAKE_CASE					:      List[str]     					=			target_dict.pad_index
																					SCREAMING_SNAKE_CASE					:      List[str]     					=			target_dict.bos_index
																					SCREAMING_SNAKE_CASE					:      Optional[int]     					=			target_dict.eos_index
																					SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			len(target_dict.symbols				)
																					SCREAMING_SNAKE_CASE					:      Tuple     					=			os.path.join(A					,		'''vocab.json'''				)
																					if not os.path.isdir(A				):
																												logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(A				)				)
																												return
																					os.makedirs(A					,		exist_ok=A				)
																					with open(A					,		'''w'''					,		encoding='''utf-8'''				) as vocab_handle:
																												json.dump(target_dict.indices					,		A				)
																					SCREAMING_SNAKE_CASE					:      Any     					=			WavaVecaCTCTokenizer(
																					    A					,		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=A					,		)
																					SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			True if config.feat_extract_norm == '''layer''' else False
																					SCREAMING_SNAKE_CASE					:      Any     					=			WavaVecaFeatureExtractor(
																					    feature_size=1					,		sampling_rate=16000					,		padding_value=0					,		do_normalize=A					,		return_attention_mask=A					,		)
																					SCREAMING_SNAKE_CASE					:      str     					=			WavaVecaProcessor(feature_extractor=A					,		tokenizer=A				)
																					processor.save_pretrained(A				)

														SCREAMING_SNAKE_CASE					:      Any     					=			HubertForCTC(A				)
							else:
														SCREAMING_SNAKE_CASE					:      List[Any]     					=			HubertModel(A				)

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

							SCREAMING_SNAKE_CASE					:      Dict     					=			model[0].eval()

							recursively_load_weights(A					,		A					,		A				)

							hf_wavavec.save_pretrained(A				)


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






'''simple docstring'''



from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id'''					,		['''canonical_dataset_name''', '''org-name/dataset-name''']				)
@pytest.mark.parametrize('''path'''					,		['''filename.csv''', '''filename with blanks.csv''']				)
@pytest.mark.parametrize('''revision'''					,		[None, '''v2''']				)
def 					UpperCAmelCase   (						A  :				int					,		A  :				Tuple					,		A  :				Any				):
							SCREAMING_SNAKE_CASE					:      int     					=			hf_hub_url(repo_id=A					,		path=A					,		revision=A				)
							assert url == F"""https://huggingface.co/datasets/{repo_id}/resolve/{revision or "main"}/{quote(A				)}"""




