





"""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()
A	     =					logging.get_logger(__name__)

A	     =					{
    '''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''',
}
A	     =					[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]

def       __A (     a_  :Tuple				,    a_  :int				,    a_  :List[Any]				,    a_  :str				,    a_  :List[Any]				,    a_  :str)						->						Optional[int]:
				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
																__a	:  str         =					'''lm_head'''

								__a	:  Any         =					getattr(a_				,    a_)

				if weight_type is not None:
								__a	:  Any         =					getattr(a_				,    a_).shape
				else:
								__a	:  Tuple         =					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":
								__a	:  int         =					value
				elif weight_type == "weight_g":
								__a	:  Optional[int]         =					value
				elif weight_type == "weight_v":
								__a	:  int         =					value
				elif weight_type == "bias":
								__a	:  Optional[Any]         =					value
				else:
								__a	:  Any         =					value

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

def       __A (     a_  :List[Any]				,    a_  :List[Any]				,    a_  :Any)						->						Optional[Any]:
				__a	:  Tuple         =					[]
				__a	:  int         =					fairseq_model.state_dict()

				__a	:  int         =					hf_model.unispeech.feature_extractor

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

				if is_finetuned:
								if dict_path:
												__a	:  Any         =					Dictionary.load_from_json(a_)

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

												# fairseq has the <pad> and <s> switched
												__a	:  Tuple         =					42
												__a	:  Tuple         =					43
												with open(a_				,    '''w'''				,    encoding='''utf-8''') as vocab_handle:
																json.dump(a_				,    a_)
												__a	:  Any         =					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_				,    )
												__a	:  List[str]         =					True if config.feat_extract_norm == '''layer''' else False
												__a	:  List[str]         =					WavaVecaFeatureExtractor(
												    feature_size=1				,    sampling_rate=1_60_00				,    padding_value=0				,    do_normalize=a_				,    return_attention_mask=a_				,    )
												__a	:  Optional[Any]         =					WavaVecaProcessor(feature_extractor=a_				,    tokenizer=a_)
												processor.save_pretrained(a_)

								__a	:  Optional[Any]         =					UniSpeechForCTC(a_)
				else:
								__a	:  List[Any]         =					UniSpeechForPreTraining(a_)

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

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

				recursively_load_weights(a_				,    a_				,    a_)

				hf_unispeech.save_pretrained(a_)


if __name__ == "__main__":
		A	     =					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'''
		)
		A	     =					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

import sys
from collections import deque
from typing import Generic, TypeVar

A	     =					TypeVar('''T''')




class 			__lowercase     (     Generic[T]							):







					'''simple docstring'''




					__lowerCAmelCase					=					42  # Cache store of keys
					__lowerCAmelCase					=					42  # References of the keys in cache
					__lowerCAmelCase					=					10  # Maximum capacity of cache







					def __init__(  self		,     _UpperCAmelCase       ):
									__a	:  Optional[int]         =					deque()
									__a	:  Dict         =					set()
									if not n:
													__a	:  List[Any]         =					sys.maxsize
									elif n < 0:
													raise ValueError('''n should be an integer greater than 0.'''       )
									else:
													__a	:  str         =					n







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									if x not in self.key_reference:
													if len(self.dq_store       ) == LRUCache._MAX_CAPACITY:
																	__a	:  int         =					self.dq_store.pop()
																	self.key_reference.remove(_UpperCAmelCase       )
									else:
													self.dq_store.remove(_UpperCAmelCase       )

									self.dq_store.appendleft(_UpperCAmelCase       )
									self.key_reference.add(_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self       ):
									for k in self.dq_store:
													print(_UpperCAmelCase       )







					def __repr__(  self       ):
									return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store       )}"""


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		A	     =					LRUCache(4)
		lru_cache.refer('''A''')
		lru_cache.refer(2)
		lru_cache.refer(3)
		lru_cache.refer('''A''')
		lru_cache.refer(4)
		lru_cache.refer(5)
		lru_cache.display()

		print(lru_cache)
		assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"