



"""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()
__A   :							Dict										=   logging.get_logger(__name__)

__A   :							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",
}
__A   :							Dict										=   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						Tuple					,     _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						List[Any]				):






				'''simple docstring'''



				for attribute in key.split('''.'''				):
								_UpperCAmelCase					=    getattr(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

				if weight_type is not None:
								_UpperCAmelCase					=    getattr(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				).shape
				else:
								_UpperCAmelCase					=    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":
								_UpperCAmelCase					=    value
				elif weight_type == "weight_g":
								_UpperCAmelCase					=    value
				elif weight_type == "weight_v":
								_UpperCAmelCase					=    value
				elif weight_type == "bias":
								_UpperCAmelCase					=    value
				elif weight_type == "running_mean":
								_UpperCAmelCase					=    value
				elif weight_type == "running_var":
								_UpperCAmelCase					=    value
				elif weight_type == "num_batches_tracked":
								_UpperCAmelCase					=    value
				elif weight_type == "inv_freq":
								_UpperCAmelCase					=    value
				else:
								_UpperCAmelCase					=    value

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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''simple docstring'''



				_UpperCAmelCase					=    []
				_UpperCAmelCase					=    fairseq_model.state_dict()

				_UpperCAmelCase					=    hf_model.wavaveca_conformer.feature_extractor

				for name, value in fairseq_dict.items():
								_UpperCAmelCase					=    False
								if "conv_layers" in name:
												load_conv_layer(
												    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     hf_model.config.feat_extract_norm == '''group'''					,     )
												_UpperCAmelCase					=    True
								else:
												for key, mapped_key in MAPPING.items():
																_UpperCAmelCase					=    '''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]:
																				_UpperCAmelCase					=    True
																				if "*" in mapped_key:
																								_UpperCAmelCase					=    name.split(_SCREAMING_SNAKE_CASE				)[0].split('''.'''				)[-2]
																								_UpperCAmelCase					=    mapped_key.replace('''*'''					,     _SCREAMING_SNAKE_CASE				)
																				if "pos_bias_u" in name:
																								_UpperCAmelCase					=    None
																				elif "pos_bias_v" in name:
																								_UpperCAmelCase					=    None
																				elif "weight_g" in name:
																								_UpperCAmelCase					=    '''weight_g'''
																				elif "weight_v" in name:
																								_UpperCAmelCase					=    '''weight_v'''
																				elif "bias" in name:
																								_UpperCAmelCase					=    '''bias'''
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								_UpperCAmelCase					=    '''weight'''
																				elif "running_mean" in name:
																								_UpperCAmelCase					=    '''running_mean'''
																				elif "inv_freq" in name:
																								_UpperCAmelCase					=    '''inv_freq'''
																				elif "running_var" in name:
																								_UpperCAmelCase					=    '''running_var'''
																				elif "num_batches_tracked" in name:
																								_UpperCAmelCase					=    '''num_batches_tracked'''
																				else:
																								_UpperCAmelCase					=    None
																				set_recursively(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
																continue
								if not is_used:
												unused_weights.append(_SCREAMING_SNAKE_CASE				)

				logger.warning(f'Unused weights: {unused_weights}'				)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Tuple					,     _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						Optional[int]				):






				'''simple docstring'''



				_UpperCAmelCase					=    full_name.split('''conv_layers.'''				)[-1]
				_UpperCAmelCase					=    name.split('''.'''				)
				_UpperCAmelCase					=    int(items[0]				)
				_UpperCAmelCase					=    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.'				)
												_UpperCAmelCase					=    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.'				)
												_UpperCAmelCase					=    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.'				)
												_UpperCAmelCase					=    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.'				)
												_UpperCAmelCase					=    value
												logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
				else:
								unused_weights.append(_SCREAMING_SNAKE_CASE				)







@torch.no_grad()
def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						int=None					,     _SCREAMING_SNAKE_CASE   :						Dict=None					,     _SCREAMING_SNAKE_CASE   :						Any=True				):






				'''simple docstring'''



				if config_path is not None:
								_UpperCAmelCase					=    WavaVecaConformerConfig.from_pretrained(_SCREAMING_SNAKE_CASE					,     hidden_act='''swish'''				)
				else:
								_UpperCAmelCase					=    WavaVecaConformerConfig()

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

				if is_finetuned:
								if dict_path:
												_UpperCAmelCase					=    Dictionary.load(_SCREAMING_SNAKE_CASE				)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												_UpperCAmelCase					=    target_dict.pad_index
												_UpperCAmelCase					=    target_dict.bos_index
												_UpperCAmelCase					=    target_dict.eos_index
												_UpperCAmelCase					=    len(target_dict.symbols				)
												_UpperCAmelCase					=    os.path.join(_SCREAMING_SNAKE_CASE					,     '''vocab.json'''				)
												if not os.path.isdir(_SCREAMING_SNAKE_CASE				):
																logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_SCREAMING_SNAKE_CASE				)				)
																return
												os.makedirs(_SCREAMING_SNAKE_CASE					,     exist_ok=_SCREAMING_SNAKE_CASE				)
												_UpperCAmelCase					=    target_dict.indices

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

								_UpperCAmelCase					=    WavaVecaConformerForCTC(_SCREAMING_SNAKE_CASE				)
				else:
								_UpperCAmelCase					=    WavaVecaConformerForPreTraining(_SCREAMING_SNAKE_CASE				)

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

								_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,     task=_SCREAMING_SNAKE_CASE				)

				_UpperCAmelCase					=    model[0].eval()

				recursively_load_weights(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     not is_finetuned				)

				hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
					__A   :							Dict										=   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   :							Optional[Any]										=   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"""


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				def is_in_circle(_SCREAMING_SNAKE_CASE   :						float					,     _SCREAMING_SNAKE_CASE   :						float				) -> bool:
								_UpperCAmelCase					=    sqrt((x**2) + (y**2)				)
								# Our circle has a radius of 1, so a distance
								# greater than 1 would land outside the circle.
								return distance_from_centre <= 1

				# The proportion of guesses that landed in the circle
				_UpperCAmelCase					=    mean(
				    int(is_in_circle(uniform(-1.0					,     1.0				)					,     uniform(-1.0					,     1.0				)				)				)
				    for _ in range(_SCREAMING_SNAKE_CASE				)				)
				# The ratio of the area for circle to square is pi/4.
				_UpperCAmelCase					=    proportion * 4
				print(f'The estimated value of pi is {pi_estimate}'				)
				print(f'The numpy value of pi is {pi}'				)
				print(f'The total error is {abs(pi - pi_estimate				)}'				)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Callable[[float], float]					,     _SCREAMING_SNAKE_CASE   :						float = 0.0					,     _SCREAMING_SNAKE_CASE   :						float = 1.0					,     ):






				'''simple docstring'''



				return mean(
				    function_to_integrate(uniform(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)				) for _ in range(_SCREAMING_SNAKE_CASE				)				) * (max_value - min_value)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						float = 0.0					,     _SCREAMING_SNAKE_CASE   :						float = 1.0				):






				'''simple docstring'''



				def identity_function(_SCREAMING_SNAKE_CASE   :						float				) -> float:
								return x

				_UpperCAmelCase					=    area_under_curve_estimator(
				    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    (max_value * max_value - min_value * min_value) / 2

				print('''******************'''				)
				print(f'Estimating area under y=x where x varies from {min_value} to {max_value}'				)
				print(f'Estimated value is {estimated_value}'				)
				print(f'Expected value is {expected_value}'				)
				print(f'Total error is {abs(estimated_value - expected_value				)}'				)
				print('''******************'''				)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				def function_to_integrate(_SCREAMING_SNAKE_CASE   :						float				) -> float:
								return sqrt(4.0 - x * x				)

				_UpperCAmelCase					=    area_under_curve_estimator(
				    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     0.0					,     2.0				)

				print('''******************'''				)
				print('''Estimating pi using area_under_curve_estimator'''				)
				print(f'Estimated value is {estimated_value}'				)
				print(f'Expected value is {pi}'				)
				print(f'Total error is {abs(estimated_value - pi				)}'				)
				print('''******************'''				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



