
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()
__lowerCAmelCase					   =       logging.get_logger(__name__)

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


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




			'''simple docstring'''


			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
												SCREAMING_SNAKE_CASE     						= '''lm_head'''

						SCREAMING_SNAKE_CASE     						= getattr(a		,						a	)

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

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


def    a      ( a		,						a		,						a	)     ->Dict:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= []
			SCREAMING_SNAKE_CASE     						= fairseq_model.state_dict()

			SCREAMING_SNAKE_CASE     						= hf_model.unispeech.feature_extractor

			for name, value in fairseq_dict.items():
						SCREAMING_SNAKE_CASE     						= False
						if "conv_layers" in name:
									load_conv_layer(
									    a		,						a		,						a		,						a		,						hf_model.config.feat_extract_norm == '''group'''		,						)
									SCREAMING_SNAKE_CASE     						= True
						else:
									for key, mapped_key in MAPPING.items():
												SCREAMING_SNAKE_CASE     						= '''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]:
															SCREAMING_SNAKE_CASE     						= True
															if "*" in mapped_key:
																		SCREAMING_SNAKE_CASE     						= name.split(a	)[0].split('''.'''	)[-2]
																		SCREAMING_SNAKE_CASE     						= mapped_key.replace('''*'''		,						a	)
															if "weight_g" in name:
																		SCREAMING_SNAKE_CASE     						= '''weight_g'''
															elif "weight_v" in name:
																		SCREAMING_SNAKE_CASE     						= '''weight_v'''
															elif "bias" in name:
																		SCREAMING_SNAKE_CASE     						= '''bias'''
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		SCREAMING_SNAKE_CASE     						= '''weight'''
															else:
																		SCREAMING_SNAKE_CASE     						= 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		,						a		,						a		,						a		,						a	)     ->int:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= full_name.split('''conv_layers.'''	)[-1]
			SCREAMING_SNAKE_CASE     						= name.split('''.'''	)
			SCREAMING_SNAKE_CASE     						= int(items[0]	)
			SCREAMING_SNAKE_CASE     						= 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     						= 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     						= 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     						= 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     						= 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		,						a		,						a=None		,						a=None		,						a=True	)     ->List[Any]:




			'''simple docstring'''


			if config_path is not None:
						SCREAMING_SNAKE_CASE     						= UniSpeechConfig.from_pretrained(a	)
			else:
						SCREAMING_SNAKE_CASE     						= UniSpeechConfig()

			if is_finetuned:
						if dict_path:
									SCREAMING_SNAKE_CASE     						= Dictionary.load_from_json(a	)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									SCREAMING_SNAKE_CASE     						= target_dict.pad_index
									SCREAMING_SNAKE_CASE     						= target_dict.bos_index
									SCREAMING_SNAKE_CASE     						= target_dict.eos_index
									SCREAMING_SNAKE_CASE     						= len(target_dict.symbols	)
									SCREAMING_SNAKE_CASE     						= 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	)
									SCREAMING_SNAKE_CASE     						= target_dict.indices

									# fairseq has the <pad> and <s> switched
									SCREAMING_SNAKE_CASE     						= 42
									SCREAMING_SNAKE_CASE     						= 43
									with open(a		,						'''w'''		,						encoding='''utf-8'''	) as vocab_handle:
												json.dump(a		,						a	)
									SCREAMING_SNAKE_CASE     						= 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		,						)
									SCREAMING_SNAKE_CASE     						= True if config.feat_extract_norm == '''layer''' else False
									SCREAMING_SNAKE_CASE     						= WavaVecaFeatureExtractor(
									    feature_size=1		,						sampling_rate=1_6000		,						padding_value=0		,						do_normalize=a		,						return_attention_mask=a		,						)
									SCREAMING_SNAKE_CASE     						= WavaVecaProcessor(feature_extractor=a		,						tokenizer=a	)
									processor.save_pretrained(a	)

						SCREAMING_SNAKE_CASE     						= UniSpeechForCTC(a	)
			else:
						SCREAMING_SNAKE_CASE     						= UniSpeechForPreTraining(a	)

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

			SCREAMING_SNAKE_CASE     						= model[0].eval()

			recursively_load_weights(a		,						a		,						a	)

			hf_unispeech.save_pretrained(a	)


if __name__ == "__main__":
							__lowerCAmelCase					   =       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					   =       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 __future__ import annotations


def    a      ( a	)     ->float:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= 0.00
			SCREAMING_SNAKE_CASE     						= 0
			for resistor in resistors:
						if resistor <= 0:
									SCREAMING_SNAKE_CASE     						= F"""Resistor at index {index} has a negative or zero value!"""
									raise ValueError(a	)
						first_sum += 1 / float(a	)
						index += 1
			return 1 / first_sum


def    a      ( a	)     ->float:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= 0.00
			SCREAMING_SNAKE_CASE     						= 0
			for resistor in resistors:
						sum_r += resistor
						if resistor < 0:
									SCREAMING_SNAKE_CASE     						= F"""Resistor at index {index} has a negative value!"""
									raise ValueError(a	)
						index += 1
			return sum_r


if __name__ == "__main__":
							import doctest

							doctest.testmod()