


'''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()
lowerCAmelCase   : int             =				logging.get_logger(__name__)

lowerCAmelCase   : 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""",
}
lowerCAmelCase   : Optional[Any]             =				[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]




def 	_A  (       A							,A							,A							,A							,A							)  ->				Tuple:
	for attribute in key.split("."							):
		lowercase						:		Dict   					=		getattr(A							,A							)

	if weight_type is not None:
		lowercase						:		List[str]   					=		getattr(A							,A							).shape
	else:
		lowercase						:		Any   					=		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":
		lowercase						:		List[Any]   					=		value
	elif weight_type == "weight_g":
		lowercase						:		List[Any]   					=		value
	elif weight_type == "weight_v":
		lowercase						:		int   					=		value
	elif weight_type == "bias":
		lowercase						:		Any   					=		value
	elif weight_type == "running_mean":
		lowercase						:		Tuple   					=		value
	elif weight_type == "running_var":
		lowercase						:		Dict   					=		value
	elif weight_type == "num_batches_tracked":
		lowercase						:		Optional[int]   					=		value
	elif weight_type == "inv_freq":
		lowercase						:		List[Any]   					=		value
	else:
		lowercase						:		Dict   					=		value

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




def 	_A  (       A							,A							,A							)  ->				int:
	lowercase						:		Optional[int]   					=		[]
	lowercase						:		Tuple   					=		fairseq_model.state_dict()

	lowercase						:		Optional[Any]   					=		hf_model.wavaveca_conformer.feature_extractor

	for name, value in fairseq_dict.items():
		lowercase						:		str   					=		False
		if "conv_layers" in name:
			load_conv_layer(
			    A							,A							,A							,A							,hf_model.config.feat_extract_norm == "group"							,)
			lowercase						:		Dict   					=		True
		else:
			for key, mapped_key in MAPPING.items():
				lowercase						:		List[str]   					=		"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]:
					lowercase						:		List[str]   					=		True
					if "*" in mapped_key:
						lowercase						:		int   					=		name.split(A							)[0].split("."							)[-2]
						lowercase						:		Optional[Any]   					=		mapped_key.replace("*"							,A							)
					if "pos_bias_u" in name:
						lowercase						:		str   					=		None
					elif "pos_bias_v" in name:
						lowercase						:		Optional[int]   					=		None
					elif "weight_g" in name:
						lowercase						:		int   					=		"weight_g"
					elif "weight_v" in name:
						lowercase						:		Dict   					=		"weight_v"
					elif "bias" in name:
						lowercase						:		List[str]   					=		"bias"
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						lowercase						:		Optional[int]   					=		"weight"
					elif "running_mean" in name:
						lowercase						:		Optional[int]   					=		"running_mean"
					elif "inv_freq" in name:
						lowercase						:		Dict   					=		"inv_freq"
					elif "running_var" in name:
						lowercase						:		int   					=		"running_var"
					elif "num_batches_tracked" in name:
						lowercase						:		Optional[Any]   					=		"num_batches_tracked"
					else:
						lowercase						:		int   					=		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  (       A							,A							,A							,A							,A							)  ->				List[str]:
	lowercase						:		Tuple   					=		full_name.split("conv_layers."							)[-1]
	lowercase						:		Optional[Any]   					=		name.split("."							)
	lowercase						:		str   					=		int(items[0]							)
	lowercase						:		List[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.'''							)
			lowercase						:		Union[str, Any]   					=		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.'''							)
			lowercase						:		str   					=		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.'''							)
			lowercase						:		int   					=		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.'''							)
			lowercase						:		str   					=		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  (       A							,A							,A=None							,A=None							,A=True							)  ->				Optional[Any]:
	if config_path is not None:
		lowercase						:		Tuple   					=		WavaVecaConformerConfig.from_pretrained(A							,hidden_act="swish"							)
	else:
		lowercase						:		Any   					=		WavaVecaConformerConfig()

	if "rope" in checkpoint_path:
		lowercase						:		str   					=		"rotary"

	if is_finetuned:
		if dict_path:
			lowercase						:		List[str]   					=		Dictionary.load(A							)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			lowercase						:		Optional[int]   					=		target_dict.pad_index
			lowercase						:		Optional[int]   					=		target_dict.bos_index
			lowercase						:		Optional[Any]   					=		target_dict.eos_index
			lowercase						:		str   					=		len(target_dict.symbols							)
			lowercase						:		List[Any]   					=		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							)
			lowercase						:		Any   					=		target_dict.indices

			# fairseq has the <pad> and <s> switched
			lowercase						:		Any   					=		0
			lowercase						:		Tuple   					=		1
			with open(A							,"w"							,encoding="utf-8"							) as vocab_handle:
				json.dump(A							,A							)
			lowercase						:		Tuple   					=		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							,)
			lowercase						:		Dict   					=		True if config.feat_extract_norm == "layer" else False
			lowercase						:		List[str]   					=		WavaVecaFeatureExtractor(
			    feature_size=1							,sampling_rate=1_6_0_0_0							,padding_value=0							,do_normalize=A							,return_attention_mask=A							,)
			lowercase						:		List[str]   					=		WavaVecaProcessor(feature_extractor=A							,tokenizer=A							)
			processor.save_pretrained(A							)

		lowercase						:		Any   					=		WavaVecaConformerForCTC(A							)
	else:
		lowercase						:		str   					=		WavaVecaConformerForPreTraining(A							)

	if is_finetuned:
		lowercase					,		lowercase					,		lowercase						:		List[str]   					=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]							,arg_overrides={"data": "/".join(dict_path.split("/"							)[:-1]							)}							)
	else:
		lowercase						:		List[str]   					=		argparse.Namespace(task="audio_pretraining"							)
		lowercase						:		Union[str, Any]   					=		fairseq.tasks.setup_task(A							)

		lowercase					,		lowercase					,		lowercase						:		Optional[Any]   					=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,task=A							)

	lowercase						:		List[str]   					=		model[0].eval()

	recursively_load_weights(A							,A							,not is_finetuned							)

	hf_wavavec.save_pretrained(A							)


if __name__ == "__main__":
					lowerCAmelCase   : str             =				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"""
					)
					lowerCAmelCase   : int             =				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'''
import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


lowerCAmelCase   : Any             =				[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["""memory_attention""", """encoder_attn"""],
    ["""attention""", """attn"""],
    ["""/""", """."""],
    [""".LayerNorm.gamma""", """_layer_norm.weight"""],
    [""".LayerNorm.beta""", """_layer_norm.bias"""],
    ["""r.layer_""", """r.layers."""],
    ["""output_proj""", """out_proj"""],
    ["""ffn.dense_1.""", """fc2."""],
    ["""ffn.dense.""", """fc1."""],
    ["""ffn_layer_norm""", """final_layer_norm"""],
    ["""kernel""", """weight"""],
    ["""encoder_layer_norm.""", """encoder.layer_norm."""],
    ["""decoder_layer_norm.""", """decoder.layer_norm."""],
    ["""embeddings.weights""", """shared.weight"""],
]




def 	_A  (       A							)  ->				List[str]:
	for pegasus_name, hf_name in PATTERNS:
		lowercase						:		int   					=		k.replace(A							,A							)
	return k




def 	_A  (       A							,A							)  ->				PegasusForConditionalGeneration:
	lowercase						:		int   					=		DEFAULTS.copy()
	cfg_kwargs.update(A							)
	lowercase						:		Any   					=		PegasusConfig(**A							)
	lowercase						:		Optional[Any]   					=		PegasusForConditionalGeneration(A							)
	lowercase						:		Dict   					=		torch_model.model.state_dict()
	lowercase						:		List[str]   					=		{}
	for k, v in tf_weights.items():
		lowercase						:		Optional[Any]   					=		rename_state_dict_key(A							)
		if new_k not in sd:
			raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''							)

		if "dense" in k or "proj" in new_k:
			lowercase						:		Tuple   					=		v.T
		lowercase						:		str   					=		torch.tensor(A							,dtype=sd[new_k].dtype							)
		assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
	# make sure embedding.padding_idx is respected
	lowercase						:		Union[str, Any]   					=		torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]							)
	lowercase						:		Optional[int]   					=		mapping["shared.weight"]
	lowercase						:		int   					=		mapping["shared.weight"]
	lowercase						:		Optional[Any]   					=		{k: torch.zeros_like(A							) for k, v in sd.items() if k.endswith("bias"							) and k not in mapping}
	mapping.update(**A							)
	lowercase					,		lowercase						:		Union[str, Any]   					=		torch_model.model.load_state_dict(A							,strict=A							)
	lowercase						:		List[str]   					=		[
	    k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
	]
	assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
	assert extra == [], F'''no matches found for the following tf keys {extra}'''
	return torch_model




def 	_A  (       A="./ckpt/aeslc/model.ckpt-32000"							)  ->				Dict:
	lowercase						:		Optional[int]   					=		tf.train.list_variables(A							)
	lowercase						:		List[str]   					=		{}
	lowercase						:		List[str]   					=		["Adafactor", "global_step"]
	for name, shape in tqdm(A							,desc="converting tf checkpoint to dict"							):
		lowercase						:		Any   					=		any(pat in name for pat in ignore_name							)
		if skip_key:
			continue
		lowercase						:		Optional[Any]   					=		tf.train.load_variable(A							,A							)
		lowercase						:		Dict   					=		array
	return tf_weights




def 	_A  (       A							,A							)  ->				Union[str, Any]:
	# save tokenizer first
	lowercase						:		int   					=		Path(A							).parent.name
	lowercase						:		str   					=		task_specific_params[F'''summarization_{dataset}''']["max_position_embeddings"]
	lowercase						:		int   					=		PegasusTokenizer.from_pretrained("sshleifer/pegasus"							,model_max_length=A							)
	assert tok.model_max_length == desired_max_model_length
	tok.save_pretrained(A							)

	# convert model
	lowercase						:		Dict   					=		get_tf_weights_as_numpy(A							)
	lowercase						:		str   					=		task_specific_params[F'''summarization_{dataset}''']
	if dataset == "large":
		lowercase						:		str   					=		task_specific_params
	lowercase						:		List[str]   					=		convert_pegasus(A							,A							)
	torch_model.save_pretrained(A							)
	lowercase						:		Union[str, Any]   					=		torch_model.state_dict()
	sd.pop("model.decoder.embed_positions.weight"							)
	sd.pop("model.encoder.embed_positions.weight"							)
	torch.save(A							,Path(A							) / "pytorch_model.bin"							)


if __name__ == "__main__":
					lowerCAmelCase   : Optional[int]             =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("""tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
					parser.add_argument("""save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
					lowerCAmelCase   : Dict             =				parser.parse_args()
					if args.save_dir is None:
										lowerCAmelCase   : Dict             =				Path(args.tf_ckpt_path).parent.name
										lowerCAmelCase   : Optional[int]             =				os.path.join("""pegasus""", dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)


