


'''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			)		->       Optional[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			)		->       Any:
							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			)		->       List[str]:
							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			)		->       Union[str, Any]:
							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
		)




def        __a			(   __lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     list[list[float]]						=				[]
					for data in source_data:
										for i, el in enumerate(__lowerCAmelCase   ):
															if len(__lowerCAmelCase   ) < i + 1:
																				data_lists.append([]   )
															data_lists[i].append(float(__lowerCAmelCase   )   )
					return data_lists

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     list[list[float]]						=				[]
					for dlist, weight in zip(__lowerCAmelCase      ,	__lowerCAmelCase   ):
										SCREAMING_SNAKE_CASE							:     str						=				min(__lowerCAmelCase   )
										SCREAMING_SNAKE_CASE							:     int						=				max(__lowerCAmelCase   )

										SCREAMING_SNAKE_CASE							:     list[float]						=				[]
										# for weight 0 score is 1 - actual score
										if weight == 0:
															for item in dlist:
																				try:
																									score.append(1 - ((item - mind) / (maxd - mind))   )
																				except ZeroDivisionError:
																									score.append(1   )

										elif weight == 1:
															for item in dlist:
																				try:
																									score.append((item - mind) / (maxd - mind)   )
																				except ZeroDivisionError:
																									score.append(0   )

        # weight not 0 or 1
										else:
															SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				F'''Invalid weight of {weight:f} provided'''
															raise ValueError(__lowerCAmelCase   )

										score_lists.append(__lowerCAmelCase   )

					return score_lists

def        __a			(   __lowerCAmelCase   )     ->     list[float]:
					SCREAMING_SNAKE_CASE							:     list[float]						=				[0 for i in range(len(score_lists[0]   )   )]

					for slist in score_lists:
										for j, ele in enumerate(__lowerCAmelCase   ):
															SCREAMING_SNAKE_CASE							:     str						=				final_scores[j] + ele

					return final_scores



def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     list[list[float]]:
					SCREAMING_SNAKE_CASE							:     Tuple						=				get_data(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     List[str]						=				calculate_each_score(__lowerCAmelCase      ,	__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     str						=				generate_final_scores(__lowerCAmelCase   )

					# append scores to source data
					for i, ele in enumerate(__lowerCAmelCase   ):
										source_data[i].append(__lowerCAmelCase   )

					return source_data