

import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
a_      =     logging.get_logger(__name__)

a_      =     {
    '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',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    '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',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
a_      =     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]

def 			lowerCamelCase__			(       _a				,      _a				,      _a				,      _a				,      _a):
	for attribute in key.split("."):
		SCREAMING_SNAKE_CASE :   List[Any]					  =							getattr(_a				,      _a)

	if weight_type is not None:
		SCREAMING_SNAKE_CASE :   str					  =							getattr(_a				,      _a).shape
	else:
		SCREAMING_SNAKE_CASE :   Dict					  =							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":
		SCREAMING_SNAKE_CASE :   Any					  =							value
	elif weight_type == "weight_g":
		SCREAMING_SNAKE_CASE :   List[Any]					  =							value
	elif weight_type == "weight_v":
		SCREAMING_SNAKE_CASE :   List[Any]					  =							value
	elif weight_type == "bias":
		SCREAMING_SNAKE_CASE :   List[Any]					  =							value
	else:
		SCREAMING_SNAKE_CASE :   Any					  =							value

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

def 			lowerCamelCase__			(       _a				,      _a):
	SCREAMING_SNAKE_CASE :   int					  =							[]
	SCREAMING_SNAKE_CASE :   int					  =							fairseq_model.state_dict()

	SCREAMING_SNAKE_CASE :   int					  =							hf_model.unispeech_sat.feature_extractor

	for name, value in fairseq_dict.items():
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							False
		if "conv_layers" in name:
			load_conv_layer(
			    _a				,      _a				,      _a				,      _a				,      hf_model.config.feat_extract_norm == "group"				,      )
			SCREAMING_SNAKE_CASE :   Optional[int]					  =							True
		else:
			for key, mapped_key in MAPPING.items():
				SCREAMING_SNAKE_CASE :   str					  =							"unispeech_sat." + 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]:
					if "layer_norm_for_extract" in name and (".".join(name.split(".")[:-1]) != key):
						# special case since naming is very similar
						continue
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							True
					if "*" in mapped_key:
						SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							name.split(_a)[0].split(".")[-2]
						SCREAMING_SNAKE_CASE :   Dict					  =							mapped_key.replace("*"				,      _a)
					if "weight_g" in name:
						SCREAMING_SNAKE_CASE :   Any					  =							"weight_g"
					elif "weight_v" in name:
						SCREAMING_SNAKE_CASE :   List[str]					  =							"weight_v"
					elif "bias" in name:
						SCREAMING_SNAKE_CASE :   str					  =							"bias"
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						SCREAMING_SNAKE_CASE :   Any					  =							"weight"
					else:
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							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 			lowerCamelCase__			(       _a				,      _a				,      _a				,      _a				,      _a):
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							full_name.split("conv_layers.")[-1]
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							name.split(".")
	SCREAMING_SNAKE_CASE :   Any					  =							int(items[0])
	SCREAMING_SNAKE_CASE :   Any					  =							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.")
			SCREAMING_SNAKE_CASE :   Tuple					  =							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.")
			SCREAMING_SNAKE_CASE :   int					  =							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[layer_id].layer_norm.bias.data.shape} was found.")
			SCREAMING_SNAKE_CASE :   Tuple					  =							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[layer_id].layer_norm.weight.data.shape} was found.")
			SCREAMING_SNAKE_CASE :   Tuple					  =							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 			lowerCamelCase__			(       _a				,      _a				,      _a=None				,      _a=None				,      _a=True):
	if config_path is not None:
		SCREAMING_SNAKE_CASE :   Tuple					  =							UniSpeechSatConfig.from_pretrained(_a)
	else:
		SCREAMING_SNAKE_CASE :   List[Any]					  =							UniSpeechSatConfig()

	SCREAMING_SNAKE_CASE :   List[str]					  =							""

	if is_finetuned:
		SCREAMING_SNAKE_CASE :   Dict					  =							UniSpeechSatForCTC(_a)
	else:
		SCREAMING_SNAKE_CASE :   str					  =							UniSpeechSatForPreTraining(_a)

	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							fairseq.checkpoint_utils.load_model_ensemble_and_task(
	    [checkpoint_path]				,      arg_overrides={"data": "/".join(dict_path.split("/")[:-1])})
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							model[0].eval()

	recursively_load_weights(_a				,      _a)

	hf_wavavec.save_pretrained(_a)


if __name__ == "__main__":
			a_      =     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_      =     parser.parse_args()
			convert_unispeech_sat_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)






from __future__ import annotations

import numpy as np





def  __magic_name__  (				lowercase				)				-> Tuple:

							"""simple docstring"""



							return np.maximum(0				,       lowercase				)


if __name__ == "__main__":
							print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]