



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()
UpperCAmelCase_						=logging.get_logger(__name__)

UpperCAmelCase_						={
    """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""",
}
UpperCAmelCase_						=[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case						, _snake_case						, _snake_case						, _snake_case							):
							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(_snake_case						, _snake_case							)

							if weight_type is not None:
														lowerCAmelCase   =		getattr(_snake_case						, _snake_case							).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       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case							):
							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(
																					    _snake_case						, _snake_case						, _snake_case						, _snake_case						, 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(_snake_case							)[0].split('''.'''							)[-2]
																																										lowerCAmelCase   =		mapped_key.replace('''*'''						, _snake_case							)
																																			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(_snake_case						, _snake_case						, _snake_case						, _snake_case						, _snake_case						, _snake_case							)
																												continue
														if not is_used:
																					unused_weights.append(_snake_case							)

							logger.warning(F"""Unused weights: {unused_weights}"""							)



def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case						, _snake_case						, _snake_case							):
							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(_snake_case							)



@torch.no_grad()
def       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case=None						, _snake_case=None						, _snake_case=True							):
							if config_path is not None:
														lowerCAmelCase   =		UniSpeechConfig.from_pretrained(_snake_case							)
							else:
														lowerCAmelCase   =		UniSpeechConfig()

							if is_finetuned:
														if dict_path:
																					lowerCAmelCase   =		Dictionary.load_from_json(_snake_case							)

																					# 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(_snake_case						, '''vocab.json'''							)
																					if not os.path.isdir(_snake_case							):
																												logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_snake_case							)							)
																												return
																					os.makedirs(_snake_case						, exist_ok=_snake_case							)
																					lowerCAmelCase   =		target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					lowerCAmelCase   =		42
																					lowerCAmelCase   =		43
																					with open(_snake_case						, '''w'''						, encoding='''utf-8'''							) as vocab_handle:
																												json.dump(_snake_case						, _snake_case							)
																					lowerCAmelCase   =		WavaVecaPhonemeCTCTokenizer(
																					    _snake_case						, 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=_snake_case						, )
																					lowerCAmelCase   =		True if config.feat_extract_norm == '''layer''' else False
																					lowerCAmelCase   =		WavaVecaFeatureExtractor(
																					    feature_size=1						, sampling_rate=16000						, padding_value=0						, do_normalize=_snake_case						, return_attention_mask=_snake_case						, )
																					lowerCAmelCase   =		WavaVecaProcessor(feature_extractor=_snake_case						, tokenizer=_snake_case							)
																					processor.save_pretrained(_snake_case							)

														lowerCAmelCase   =		UniSpeechForCTC(_snake_case							)
							else:
														lowerCAmelCase   =		UniSpeechForPreTraining(_snake_case							)

							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(_snake_case						, _snake_case						, _snake_case							)

							hf_unispeech.save_pretrained(_snake_case							)


if __name__ == "__main__":
		UpperCAmelCase_						=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"""
		)
		UpperCAmelCase_						=parser.parse_args()
		convert_unispeech_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)









from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = True       , UpperCAmelCase_ = None       , UpperCAmelCase_ = False       , UpperCAmelCase_ = None       , UpperCAmelCase_ = True       , UpperCAmelCase_ = "arrow"       , **UpperCAmelCase_       , ):
											super().__init__(
											    split=UpperCAmelCase_       , features=UpperCAmelCase_       , cache_dir=UpperCAmelCase_       , keep_in_memory=UpperCAmelCase_       , streaming=UpperCAmelCase_       , **UpperCAmelCase_       , )
											lowerCAmelCase   =		load_from_cache_file
											lowerCAmelCase   =		file_format
											lowerCAmelCase   =		Spark(
											    df=UpperCAmelCase_       , features=UpperCAmelCase_       , cache_dir=UpperCAmelCase_       , working_dir=UpperCAmelCase_       , **UpperCAmelCase_       , )






				def       __snake_case		( self						):
											if self.streaming:
																		return self.builder.as_streaming_dataset(split=self.split						)
											lowerCAmelCase   =		None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
											self.builder.download_and_prepare(
											    download_mode=UpperCAmelCase_       , file_format=self._file_format       , )
											return self.builder.as_dataset(split=self.split						)



