


'''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()
__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  _snake_case       (					A			,   A			,   A			,   A			,   A			,   A	)						->   Union[str, Any]:
							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(A			,   A	)

							if weight_type is not None:
														lowerCAmelCase__	       =     getattr(A			,   A	).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  _snake_case       (					A			,   A			,   A	)						->   List[str]:
							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(
																					    A			,   A			,   A			,   A			,   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(A	)[0].split('''.'''	)[-2]
																																										lowerCAmelCase__	       =     mapped_key.replace('''*'''			,   A	)
																																			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(A			,   A			,   A			,   A			,   A			,   A	)
																												continue
														if not is_used:
																					unused_weights.append(A	)

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


def  _snake_case       (					A			,   A			,   A			,   A			,   A	)						->   Union[str, Any]:
							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(A	)


@torch.no_grad()
def  _snake_case       (					A			,   A			,   A=None			,   A=None			,   A=True	)						->   List[str]:
							if config_path is not None:
														lowerCAmelCase__	       =     UniSpeechConfig.from_pretrained(A	)
							else:
														lowerCAmelCase__	       =     UniSpeechConfig()

							if is_finetuned:
														if dict_path:
																					lowerCAmelCase__	       =     Dictionary.load_from_json(A	)

																					# 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(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	)
																					lowerCAmelCase__	       =     target_dict.indices

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

														lowerCAmelCase__	       =     UniSpeechForCTC(A	)
							else:
														lowerCAmelCase__	       =     UniSpeechForPreTraining(A	)

							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(A			,   A			,   A	)

							hf_unispeech.save_pretrained(A	)


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
		)



'''simple docstring'''






from __future__ import annotations




class 		a__    :





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_       )	->							None:
														lowerCAmelCase__	       =     order

														# a_{0} ... a_{k}
														lowerCAmelCase__	       =     [1.0] + [0.0] * order
														# b_{0} ... b_{k}
														lowerCAmelCase__	       =     [1.0] + [0.0] * order

														# x[n-1] ... x[n-k]
														lowerCAmelCase__	       =     [0.0] * self.order
														# y[n-1] ... y[n-k]
														lowerCAmelCase__	       =     [0.0] * self.order







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							None:
														if len(lowerCamelCase_       ) < self.order:
																					lowerCAmelCase__	       =     [1.0, *a_coeffs]

														if len(lowerCamelCase_       ) != self.order + 1:
																					lowerCAmelCase__	       =     (
																					    F"""Expected a_coeffs to have {self.order + 1} elements """
																					    F"""for {self.order}-order filter, got {len(lowerCamelCase_       )}"""
																					)
																					raise ValueError(lowerCamelCase_       )

														if len(lowerCamelCase_       ) != self.order + 1:
																					lowerCAmelCase__	       =     (
																					    F"""Expected b_coeffs to have {self.order + 1} elements """
																					    F"""for {self.order}-order filter, got {len(lowerCamelCase_       )}"""
																					)
																					raise ValueError(lowerCamelCase_       )

														lowerCAmelCase__	       =     a_coeffs
														lowerCAmelCase__	       =     b_coeffs







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							float:
														lowerCAmelCase__	       =     0.0

														# Start at index 1 and do index 0 at the end.
														for i in range(1 ,   self.order + 1       ):
																					result += (
																					    self.b_coeffs[i] * self.input_history[i - 1]
																					    - self.a_coeffs[i] * self.output_history[i - 1]
																					)

														lowerCAmelCase__	       =     (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

														lowerCAmelCase__	       =     self.input_history[:-1]
														lowerCAmelCase__	       =     self.output_history[:-1]

														lowerCAmelCase__	       =     sample
														lowerCAmelCase__	       =     result

														return result