
'''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()
_lowercase    =		logging.get_logger(__name__)

_lowercase    =		{
    """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""",
}
_lowercase    =		[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]


def A				(__lowerCamelCase				:Optional[Any]			,     __lowerCamelCase				:Dict			,     __lowerCamelCase				:str			,     __lowerCamelCase				:Optional[int]			,     __lowerCamelCase				:str		):





						for attribute in key.split("""."""		):
												_lowerCAmelCase  						=			getattr(a__			,     a__		)

						if weight_type is not None:
												_lowerCAmelCase  						=			getattr(a__			,     a__		).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 A				(__lowerCamelCase				:Tuple			,     __lowerCamelCase				:Union[str, Any]			,     __lowerCamelCase				:Any		):
						_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(
																		    a__			,     a__			,     a__			,     a__			,     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(a__		)[0].split("""."""		)[-2]
																																				_lowerCAmelCase  						=			mapped_key.replace("""*"""			,     a__		)
																														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(a__			,     a__			,     a__			,     a__			,     a__		)
																								continue
												if not is_used:
																		unused_weights.append(a__		)

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


def A				(__lowerCamelCase				:Tuple			,     __lowerCamelCase				:Optional[int]			,     __lowerCamelCase				:Optional[Any]			,     __lowerCamelCase				:List[str]			,     __lowerCamelCase				:List[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(a__		)


@torch.no_grad()
def A				(__lowerCamelCase				:List[str]			,     __lowerCamelCase				:Optional[int]			,     __lowerCamelCase				:Optional[Any]=None			,     __lowerCamelCase				:List[Any]=None			,     __lowerCamelCase				:Optional[Any]=True		):





						if config_path is not None:
												_lowerCAmelCase  						=			WavaVecaConformerConfig.from_pretrained(a__			,     hidden_act="""swish"""		)
						else:
												_lowerCAmelCase  						=			WavaVecaConformerConfig()

						if "rope" in checkpoint_path:
												_lowerCAmelCase  						=			"""rotary"""

						if is_finetuned:
												if dict_path:
																		_lowerCAmelCase  						=			Dictionary.load(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  						=			0
																		_lowerCAmelCase  						=			1
																		with open(a__			,     """w"""			,     encoding="""utf-8"""		) as vocab_handle:
																								json.dump(a__			,     a__		)
																		_lowerCAmelCase  						=			WavaVecaCTCTokenizer(
																		    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  						=			WavaVecaConformerForCTC(a__		)
						else:
												_lowerCAmelCase  						=			WavaVecaConformerForPreTraining(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]		)}		)
						else:
												_lowerCAmelCase  						=			argparse.Namespace(task="""audio_pretraining"""		)
												_lowerCAmelCase  						=			fairseq.tasks.setup_task(a__		)

												_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,     task=a__		)

						_lowerCAmelCase  						=			model[0].eval()

						recursively_load_weights(a__			,     a__			,     not is_finetuned		)

						hf_wavavec.save_pretrained(a__		)


if __name__ == "__main__":
						_lowercase    =		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"""
						)
						_lowercase    =		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 __future__ import annotations

from typing import Any




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						pass






class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			data
												_lowerCAmelCase  						=			None




						def __iter__( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self
												_lowerCAmelCase  						=			[]
												while node:
																		if node in visited:
																								raise ContainsLoopError
																		visited.append(_lowercase				)
																		yield node.data
																		_lowerCAmelCase  						=			node.next_node




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												try:
																		list(self				)
																		return False
												except ContainsLoopError:
																		return True


if __name__ == "__main__":
						_lowercase    =		Node(1)
						_lowercase    =		Node(2)
						_lowercase    =		Node(3)
						_lowercase    =		Node(4)
						print(root_node.has_loop)  # False
						_lowercase    =		root_node.next_node
						print(root_node.has_loop)  # True

						_lowercase    =		Node(5)
						_lowercase    =		Node(6)
						_lowercase    =		Node(5)
						_lowercase    =		Node(6)
						print(root_node.has_loop)  # False

						_lowercase    =		Node(1)
						print(root_node.has_loop)  # False
