



"""simple docstring"""
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE  :						str    							=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE  :						Dict    							=      {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''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''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]



def        lowerCAmelCase_(   lowercase_     :							str						,   lowercase_     :							Any						,   lowercase_     :							int						,   lowercase_     :							Union[str, Any]						,   lowercase_     :							Tuple							)	->       List[Any]:
				for attribute in key.split('''.'''							):
								_lowerCamelCase        =				getattr(lowercase_						,   lowercase_							)

				if weight_type is not None:
								_lowerCamelCase        =				getattr(lowercase_						,   lowercase_							).shape
				else:
								_lowerCamelCase        =				hf_pointer.shape

				if hf_shape != value.shape:
								raise ValueError(
								    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
				elif weight_type == "running_mean":
								_lowerCamelCase        =				value
				elif weight_type == "running_var":
								_lowerCamelCase        =				value
				elif weight_type == "num_batches_tracked":
								_lowerCamelCase        =				value
				elif weight_type == "inv_freq":
								_lowerCamelCase        =				value
				else:
								_lowerCamelCase        =				value

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



def        lowerCAmelCase_(   lowercase_     :							Optional[Any]						,   lowercase_     :							str						,   lowercase_     :							Optional[int]							)	->       Tuple:
				_lowerCamelCase        =				[]
				_lowerCamelCase        =				fairseq_model.state_dict()

				_lowerCamelCase        =				hf_model.wavaveca_conformer.feature_extractor

				for name, value in fairseq_dict.items():
								_lowerCamelCase        =				False
								if "conv_layers" in name:
												load_conv_layer(
												    lowercase_						,   lowercase_						,   lowercase_						,   lowercase_						,   hf_model.config.feat_extract_norm == '''group'''						,   )
												_lowerCamelCase        =				True
								else:
												for key, mapped_key in MAPPING.items():
																_lowerCamelCase        =				'''wav2vec2_conformer.''' + 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(lowercase_							)[0].split('''.'''							)[-2]
																								_lowerCamelCase        =				mapped_key.replace('''*'''						,   lowercase_							)
																				if "pos_bias_u" in name:
																								_lowerCamelCase        =				None
																				elif "pos_bias_v" in name:
																								_lowerCamelCase        =				None
																				elif "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'''
																				elif "running_mean" in name:
																								_lowerCamelCase        =				'''running_mean'''
																				elif "inv_freq" in name:
																								_lowerCamelCase        =				'''inv_freq'''
																				elif "running_var" in name:
																								_lowerCamelCase        =				'''running_var'''
																				elif "num_batches_tracked" in name:
																								_lowerCamelCase        =				'''num_batches_tracked'''
																				else:
																								_lowerCamelCase        =				None
																				set_recursively(lowercase_						,   lowercase_						,   lowercase_						,   lowercase_						,   lowercase_							)
																continue
								if not is_used:
												unused_weights.append(lowercase_							)

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



def        lowerCAmelCase_(   lowercase_     :							Tuple						,   lowercase_     :							int						,   lowercase_     :							Optional[Any]						,   lowercase_     :							List[Any]						,   lowercase_     :							Dict							)	->       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:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																raise ValueError(
																    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:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																raise ValueError(
																    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:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																raise ValueError(
																    F"""{full_name} has size {value.shape}, but"""
																    F""" {feature_extractor.conv_layers[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:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																raise ValueError(
																    F"""{full_name} has size {value.shape}, but"""
																    F""" {feature_extractor.conv_layers[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(lowercase_							)



@torch.no_grad()
def        lowerCAmelCase_(   lowercase_     :							int						,   lowercase_     :							Tuple						,   lowercase_     :							Any=None						,   lowercase_     :							int=None						,   lowercase_     :							Union[str, Any]=True							)	->       int:
				if config_path is not None:
								_lowerCamelCase        =				WavaVecaConformerConfig.from_pretrained(lowercase_						,   hidden_act='''swish'''							)
				else:
								_lowerCamelCase        =				WavaVecaConformerConfig()

				if "rope" in checkpoint_path:
								_lowerCamelCase        =				'''rotary'''

				if is_finetuned:
								if dict_path:
												_lowerCamelCase        =				Dictionary.load(lowercase_							)

												# 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(lowercase_						,   '''vocab.json'''							)
												if not os.path.isdir(lowercase_							):
																logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowercase_							)							)
																return
												os.makedirs(lowercase_						,   exist_ok=lowercase_							)
												_lowerCamelCase        =				target_dict.indices

												# fairseq has the <pad> and <s> switched
												_lowerCamelCase        =				0
												_lowerCamelCase        =				1
												with open(lowercase_						,   '''w'''						,   encoding='''utf-8'''							) as vocab_handle:
																json.dump(lowercase_						,   lowercase_							)
												_lowerCamelCase        =				WavaVecaCTCTokenizer(
												    lowercase_						,   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=lowercase_						,   )
												_lowerCamelCase        =				True if config.feat_extract_norm == '''layer''' else False
												_lowerCamelCase        =				WavaVecaFeatureExtractor(
												    feature_size=1						,   sampling_rate=1_60_00						,   padding_value=0						,   do_normalize=lowercase_						,   return_attention_mask=lowercase_						,   )
												_lowerCamelCase        =				WavaVecaProcessor(feature_extractor=lowercase_						,   tokenizer=lowercase_							)
												processor.save_pretrained(lowercase_							)

								_lowerCamelCase        =				WavaVecaConformerForCTC(lowercase_							)
				else:
								_lowerCamelCase        =				WavaVecaConformerForPreTraining(lowercase_							)

				if is_finetuned:
								_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]						,   arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							)}							)
				else:
								_lowerCamelCase        =				argparse.Namespace(task='''audio_pretraining'''							)
								_lowerCamelCase        =				fairseq.tasks.setup_task(lowercase_							)

								_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						,   task=lowercase_							)

				_lowerCamelCase        =				model[0].eval()

				recursively_load_weights(lowercase_						,   lowercase_						,   not is_finetuned							)

				hf_wavavec.save_pretrained(lowercase_							)


if __name__ == "__main__":
					__SCREAMING_SNAKE_CASE  :						Tuple    							=      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'''
					)
					__SCREAMING_SNAKE_CASE  :						Tuple    							=      parser.parse_args()
					convert_wavaveca_conformer_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)





"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)



def        lowerCAmelCase_(   lowercase_     :							List[Any]							)	->       Optional[Any]:
				from diffusers.utils.testing_utils import pytest_addoption_shared

				pytest_addoption_shared(lowercase_							)




def        lowerCAmelCase_(   lowercase_     :							List[str]							)	->       List[str]:
				from diffusers.utils.testing_utils import pytest_terminal_summary_main

				_lowerCamelCase        =				terminalreporter.config.getoption('''--make-reports'''							)
				if make_reports:
								pytest_terminal_summary_main(lowercase_						,   id=lowercase_							)
