




'''simple docstring'''


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

UpperCAmelCase_ 		=      {
    "post_extract_proj": "feature_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.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}


def 						SCREAMING_SNAKE_CASE       (      a_	:							Union[str, Any]				,					a_	:							List[str]				,					a_	:							List[str]				,					a_	:							Optional[Any]				,					a_	:							Any			):
		for attribute in key.split('.'			):
				__a			=					getattr(a_				,					a_			)

		if weight_type is not None:
				__a			=					getattr(a_				,					a_			).shape
		else:
				__a			=					hf_pointer.shape

		assert hf_shape == value.shape, (
		    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":
				__a			=					value
		elif weight_type == "weight_g":
				__a			=					value
		elif weight_type == "weight_v":
				__a			=					value
		elif weight_type == "bias":
				__a			=					value
		else:
				__a			=					value

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


def 						SCREAMING_SNAKE_CASE       (      a_	:							Dict				,					a_	:							Optional[int]				,					a_	:							Optional[Any]			):
		__a			=					[]
		__a			=					fairseq_model.state_dict()

		__a			=					hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				__a			=					False
				if "conv_layers" in name:
						load_conv_layer(
						    a_				,					a_				,					a_				,					a_				,					hf_model.config.feat_extract_norm == 'group'				,					)
						__a			=					True
				else:
						for key, mapped_key in MAPPING.items():
								__a			=					'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

								if key in name or key.split('w2v_model.'			)[-1] == name.split('.'			)[0]:
										__a			=					True
										if "*" in mapped_key:
												__a			=					name.split(a_			)[0].split('.'			)[-2]
												__a			=					mapped_key.replace('*'				,					a_			)
										if "weight_g" in name:
												__a			=					'weight_g'
										elif "weight_v" in name:
												__a			=					'weight_v'
										elif "weight" in name:
												__a			=					'weight'
										elif "bias" in name:
												__a			=					'bias'
										else:
												__a			=					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 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							str				,					a_	:							Tuple				,					a_	:							Union[str, Any]				,					a_	:							Optional[int]			):
		__a			=					full_name.split('conv_layers.'			)[-1]
		__a			=					name.split('.'			)
		__a			=					int(items[0]			)
		__a			=					int(items[1]			)

		if type_id == 0:
				if "bias" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
						    f"{full_name} has size {value.shape}, but"
						    f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
						)
						__a			=					value
						logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."			)
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
						    f"{full_name} has size {value.shape}, but"
						    f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
						)
						__a			=					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:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
						    f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
						    " found."
						)
						__a			=					value
						logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."			)
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
						    f"{full_name} has size {value.shape}, but"
						    f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
						)
						__a			=					value
						logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."			)
		else:
				unused_weights.append(a_			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple				,					a_	:							List[str]			):
		__a			=					SEWConfig()
		if is_finetuned:
				__a			=					model.wav_encoder.wav_model.cfg
		else:
				__a			=					model.cfg

		__a			=					fs_config.conv_bias
		__a			=					eval(fs_config.conv_feature_layers			)
		__a			=					[x[0] for x in conv_layers]
		__a			=					[x[1] for x in conv_layers]
		__a			=					[x[2] for x in conv_layers]
		__a			=					'gelu'
		__a			=					'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
		__a			=					0.0
		__a			=					fs_config.activation_fn.name
		__a			=					fs_config.encoder_embed_dim
		__a			=					0.02
		__a			=					fs_config.encoder_ffn_embed_dim
		__a			=					1e-5
		__a			=					fs_config.encoder_layerdrop
		__a			=					fs_config.encoder_attention_heads
		__a			=					fs_config.conv_pos_groups
		__a			=					fs_config.conv_pos
		__a			=					len(a_			)
		__a			=					fs_config.encoder_layers
		__a			=					fs_config.squeeze_factor

		# take care of any params that are overridden by the Wav2VecCtc model
		if is_finetuned:
				__a			=					model.cfg
				__a			=					fs_config.final_dropout
				__a			=					fs_config.layerdrop
		__a			=					fs_config.activation_dropout
		__a			=					fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
		__a			=					fs_config.attention_dropout
		__a			=					fs_config.dropout_input
		__a			=					fs_config.dropout
		__a			=					fs_config.mask_channel_length
		__a			=					fs_config.mask_channel_prob
		__a			=					fs_config.mask_length
		__a			=					fs_config.mask_prob

		__a			=					'Wav2Vec2FeatureExtractor'
		__a			=					'Wav2Vec2CTCTokenizer'

		return config


@torch.no_grad()
def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[Any]				,					a_	:							Any				,					a_	:							List[Any]=None				,					a_	:							Tuple=None				,					a_	:							Dict=True			):

		if is_finetuned:
				__a	,       __a	,       __a			=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]				,					arg_overrides={'data': '/'.join(dict_path.split('/'			)[:-1]			)}			)
		else:
				__a	,       __a	,       __a			=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			)

		if config_path is not None:
				__a			=					SEWConfig.from_pretrained(a_			)
		else:
				__a			=					convert_config(model[0]				,					a_			)
		__a			=					model[0].eval()

		__a			=					True if config.feat_extract_norm == 'layer' else False
		__a			=					WavaVecaFeatureExtractor(
		    feature_size=1				,					sampling_rate=1_6000				,					padding_value=0				,					do_normalize=a_				,					return_attention_mask=a_				,					)

		if is_finetuned:
				if dict_path:
						__a			=					Dictionary.load(a_			)

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						__a			=					target_dict.pad_index
						__a			=					target_dict.bos_index
						__a			=					target_dict.pad_index
						__a			=					target_dict.bos_index
						__a			=					target_dict.eos_index
						__a			=					len(target_dict.symbols			)
						__a			=					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_			)
						with open(a_				,					'w'				,					encoding='utf-8'			) as vocab_handle:
								json.dump(target_dict.indices				,					a_			)
						__a			=					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_				,					)
						__a			=					WavaVecaProcessor(feature_extractor=a_				,					tokenizer=a_			)
						processor.save_pretrained(a_			)

				__a			=					SEWForCTC(a_			)
		else:
				__a			=					SEWModel(a_			)
				feature_extractor.save_pretrained(a_			)

		recursively_load_weights(a_				,					a_				,					a_			)

		hf_model.save_pretrained(a_			)


if __name__ == "__main__":
		UpperCAmelCase_ 		=      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(
		    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
		)
		UpperCAmelCase_ 		=      parser.parse_args()
		convert_sew_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
		)









'''simple docstring'''


from ..utils import DummyObject, requires_backends





class    __lowercase						(  metaclass=__magic_name__ ):
   _a							=      ["""onnx"""]







   def __init__(	self      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    str:
     requires_backends(self      ,							['onnx']      )







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    Tuple:
     requires_backends(cls      ,							['onnx']      )







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    Tuple:
     requires_backends(cls      ,							['onnx']      )



