
'''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				:Union[str, Any]			,     __lowerCamelCase				:str			,     __lowerCamelCase				:Tuple			,     __lowerCamelCase				:Any			,     __lowerCamelCase				:Union[str, Any]		):





						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				:Optional[Any]			,     __lowerCamelCase				:Any			,     __lowerCamelCase				:List[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				:Any			,     __lowerCamelCase				:List[Any]			,     __lowerCamelCase				:str			,     __lowerCamelCase				:Tuple		):
						_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				:Dict			,     __lowerCamelCase				:Dict			,     __lowerCamelCase				:Dict=None			,     __lowerCamelCase				:List[Any]=None			,     __lowerCamelCase				: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  						=			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  						=			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'''



def A				(__lowerCamelCase				:list[int]			,     __lowerCamelCase				:list[int]		):

						# Check if the input is valid
						if not len(__lowerCamelCase		) == len(__lowerCamelCase		) == 3:
												raise ValueError("""Please enter a valid equation."""		)
						if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
												raise ValueError("""Both a & b of two equations can't be zero."""		)

						# Extract the coefficients
						_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			equationa
						_lowerCAmelCase						,   _lowerCAmelCase						,   _lowerCAmelCase  						=			equationa

						# Calculate the determinants of the matrices
						_lowerCAmelCase  						=			aa * ba - aa * ba
						_lowerCAmelCase  						=			ca * ba - ca * ba
						_lowerCAmelCase  						=			aa * ca - aa * ca

						# Check if the system of linear equations has a solution (using Cramer's rule)
						if determinant == 0:
												if determinant_x == determinant_y == 0:
																		raise ValueError("""Infinite solutions. (Consistent system)"""		)
												else:
																		raise ValueError("""No solution. (Inconsistent system)"""		)
						else:
												if determinant_x == determinant_y == 0:
																		# Trivial solution (Inconsistent system)
																		return (0.0, 0.0)
												else:
																		_lowerCAmelCase  						=			determinant_x / determinant
																		_lowerCAmelCase  						=			determinant_y / determinant
																		# Non-Trivial Solution (Consistent system)
																		return (x, y)
