




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
		)









import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


UpperCAmelCase_						=datasets.utils.logging.get_logger(__name__)


@dataclass
class 							__UpperCamelCase			( datasets.BuilderConfig  ):





				'''simple docstring'''



				__a							: Optional[datasets.Features]										=None
				__a							: str										="utf-8"
				__a							: Optional[str]										=None
				__a							: Optional[str]										=None
				__a							: bool										=True  # deprecated
				__a							: Optional[int]										=None  # deprecated
				__a							: int										=1_0 << 2_0  # 10MB
				__a							: Optional[bool]										=None




class 							__UpperCamelCase			( datasets.ArrowBasedBuilder  ):





				'''simple docstring'''



				__a							: str										=JsonConfig



				def       __snake_case		( self						):
											if self.config.block_size is not None:
																		logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''						)
																		lowerCAmelCase   =		self.config.block_size
											if self.config.use_threads is not True:
																		logger.warning(
																		    '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'''						)
											if self.config.newlines_in_values is not None:
																		raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported'''						)
											return datasets.DatasetInfo(features=self.config.features						)



				def       __snake_case		( self       , UpperCAmelCase_						):
											if not self.config.data_files:
																		raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""						)
											lowerCAmelCase   =		dl_manager.download_and_extract(self.config.data_files						)
											if isinstance(UpperCAmelCase_       , (str, list, tuple)						):
																		lowerCAmelCase   =		data_files
																		if isinstance(UpperCAmelCase_       , UpperCAmelCase_						):
																									lowerCAmelCase   =		[files]
																		lowerCAmelCase   =		[dl_manager.iter_files(UpperCAmelCase_						) for file in files]
																		return [datasets.SplitGenerator(name=datasets.Split.TRAIN       , gen_kwargs={'''files''': files}						)]
											lowerCAmelCase   =		[]
											for split_name, files in data_files.items():
																		if isinstance(UpperCAmelCase_       , UpperCAmelCase_						):
																									lowerCAmelCase   =		[files]
																		lowerCAmelCase   =		[dl_manager.iter_files(UpperCAmelCase_						) for file in files]
																		splits.append(datasets.SplitGenerator(name=UpperCAmelCase_       , gen_kwargs={'''files''': files}						)						)
											return splits



				def       __snake_case		( self       , UpperCAmelCase_						):
											if self.config.features is not None:
																		# adding missing columns
																		for column_name in set(self.config.features						) - set(pa_table.column_names						):
																									lowerCAmelCase   =		self.config.features.arrow_schema.field(UpperCAmelCase_						).type
																									lowerCAmelCase   =		pa_table.append_column(UpperCAmelCase_       , pa.array([None] * len(UpperCAmelCase_						)       , type=UpperCAmelCase_						)						)
																		# more expensive cast to support nested structures with keys in a different order
																		# allows str <-> int/float or str to Audio for example
																		lowerCAmelCase   =		table_cast(UpperCAmelCase_       , self.config.features.arrow_schema						)
											return pa_table






				def       __snake_case		( self       , UpperCAmelCase_						):
											for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase_						)						):
																		# If the file is one json object and if we need to look at the list of items in one specific field
																		if self.config.field is not None:
																									with open(UpperCAmelCase_       , encoding=self.config.encoding       , errors=self.config.encoding_errors						) as f:
																																lowerCAmelCase   =		json.load(UpperCAmelCase_						)

																									# We keep only the field we are interested in
																									lowerCAmelCase   =		dataset[self.config.field]

																									# We accept two format: a list of dicts or a dict of lists
																									if isinstance(UpperCAmelCase_       , (list, tuple)						):
																																lowerCAmelCase   =		set().union(*[row.keys() for row in dataset]						)
																																lowerCAmelCase   =		{col: [row.get(UpperCAmelCase_						) for row in dataset] for col in keys}
																									else:
																																lowerCAmelCase   =		dataset
																									lowerCAmelCase   =		pa.Table.from_pydict(UpperCAmelCase_						)
																									yield file_idx, self._cast_table(UpperCAmelCase_						)

																		# If the file has one json object per line
																		else:
																									with open(UpperCAmelCase_       , '''rb'''						) as f:
																																lowerCAmelCase   =		0
																																# Use block_size equal to the chunk size divided by 32 to leverage multithreading
																																# Set a default minimum value of 16kB if the chunk size is really small
																																lowerCAmelCase   =		max(self.config.chunksize // 32       , 16 << 10						)
																																lowerCAmelCase   =		(
																																    self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
																																)
																																while True:
																																							lowerCAmelCase   =		f.read(self.config.chunksize						)
																																							if not batch:
																																														break
																																							# Finish current line
																																							try:
																																														batch += f.readline()
																																							except (AttributeError, io.UnsupportedOperation):
																																														batch += readline(UpperCAmelCase_						)
																																							# PyArrow only accepts utf-8 encoded bytes
																																							if self.config.encoding != "utf-8":
																																														lowerCAmelCase   =		batch.decode(self.config.encoding       , errors=UpperCAmelCase_						).encode('''utf-8'''						)
																																							try:
																																														while True:
																																																					try:
																																																												lowerCAmelCase   =		paj.read_json(
																																																												    io.BytesIO(UpperCAmelCase_						)       , read_options=paj.ReadOptions(block_size=UpperCAmelCase_						)						)
																																																												break
																																																					except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																																																												if (
																																																												    isinstance(UpperCAmelCase_       , pa.ArrowInvalid						)
																																																												    and "straddling" not in str(UpperCAmelCase_						)
																																																												    or block_size > len(UpperCAmelCase_						)
																																																												):
																																																																			raise
																																																												else:
																																																																			# Increase the block size in case it was too small.
																																																																			# The block size will be reset for the next file.
																																																																			logger.debug(
																																																																			    F"""Batch of {len(UpperCAmelCase_						)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""						)
																																																																			block_size *= 2
																																							except pa.ArrowInvalid as e:
																																														try:
																																																					with open(
																																																					    UpperCAmelCase_       , encoding=self.config.encoding       , errors=self.config.encoding_errors						) as f:
																																																												lowerCAmelCase   =		json.load(UpperCAmelCase_						)
																																														except json.JSONDecodeError:
																																																					logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase_						)}: {e}"""						)
																																																					raise e
																																														# If possible, parse the file as a list of json objects and exit the loop
																																														if isinstance(UpperCAmelCase_       , UpperCAmelCase_						):  # list is the only sequence type supported in JSON
																																																					try:
																																																												lowerCAmelCase   =		set().union(*[row.keys() for row in dataset]						)
																																																												lowerCAmelCase   =		{col: [row.get(UpperCAmelCase_						) for row in dataset] for col in keys}
																																																												lowerCAmelCase   =		pa.Table.from_pydict(UpperCAmelCase_						)
																																																					except (pa.ArrowInvalid, AttributeError) as e:
																																																												logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase_						)}: {e}"""						)
																																																												raise ValueError(F"""Not able to read records in the JSON file at {file}."""						) from None
																																																					yield file_idx, self._cast_table(UpperCAmelCase_						)
																																																					break
																																														else:
																																																					logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase_						)}: {e}"""						)
																																																					raise ValueError(
																																																					    F"""Not able to read records in the JSON file at {file}. """
																																																					    F"""You should probably indicate the field of the JSON file containing your records. """
																																																					    F"""This JSON file contain the following fields: {str(list(dataset.keys()						)						)}. """
																																																					    F"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """						) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																							yield (file_idx, batch_idx), self._cast_table(UpperCAmelCase_						)
																																							batch_idx += 1



