




'''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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_lowerCamelCase							:						Union[str, Any]							     =  logging.get_logger(__name__)

_lowerCamelCase							:						Optional[int]							     =  {
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
    'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj',
    'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
    'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense',
    'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
    'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense',
    'output.dense': 'encoder.encoder.layer.*.output.dense',
    'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
    'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings',
    'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm',
    '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 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		for attribute in key.split("."  ):
				_snake_case     	=	getattr(SCREAMING_SNAKE_CASE__						,							SCREAMING_SNAKE_CASE__  )

		if weight_type is not None:
				_snake_case     	=	getattr(SCREAMING_SNAKE_CASE__						,							SCREAMING_SNAKE_CASE__  ).shape
		else:
				_snake_case     	=	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":
				_snake_case     	=	value
		elif weight_type == "weight_g":
				_snake_case     	=	value
		elif weight_type == "weight_v":
				_snake_case     	=	value
		elif weight_type == "bias":
				_snake_case     	=	value
		else:
				_snake_case     	=	value

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






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	[]
		_snake_case     	=	fairseq_model.state_dict()

		_snake_case     	=	hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				_snake_case     	=	False
				if "conv_layers" in name:
						load_conv_layer(
						    SCREAMING_SNAKE_CASE__						,							SCREAMING_SNAKE_CASE__						,							SCREAMING_SNAKE_CASE__						,							SCREAMING_SNAKE_CASE__						,							hf_model.config.feat_extract_norm == "group"						,							)
						_snake_case     	=	True
				else:
						for key, mapped_key in MAPPING.items():
								_snake_case     	=	'sew_d.' + 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]:
										_snake_case     	=	True
										if "*" in mapped_key:
												_snake_case     	=	name.split(SCREAMING_SNAKE_CASE__  )[0].split("."  )[-2]
												if not layer_index.isnumeric():
														continue
												_snake_case     	=	mapped_key.replace("*"						,							SCREAMING_SNAKE_CASE__  )
										if "weight_g" in name:
												_snake_case     	=	'weight_g'
										elif "weight_v" in name:
												_snake_case     	=	'weight_v'
										elif "weight" in name:
												_snake_case     	=	'weight'
										elif "bias" in name:
												_snake_case     	=	'bias'
										else:
												_snake_case     	=	None
										set_recursively(SCREAMING_SNAKE_CASE__						,							SCREAMING_SNAKE_CASE__						,							SCREAMING_SNAKE_CASE__						,							SCREAMING_SNAKE_CASE__						,							SCREAMING_SNAKE_CASE__  )
								continue
				if not is_used:
						unused_weights.append(SCREAMING_SNAKE_CASE__  )

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






def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_						,							lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	full_name.split("conv_layers."  )[-1]
		_snake_case     	=	name.split("."  )
		_snake_case     	=	int(items[0]  )
		_snake_case     	=	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.'''
						)
						_snake_case     	=	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.'''
						)
						_snake_case     	=	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."
						)
						_snake_case     	=	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.'''
						)
						_snake_case     	=	value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
		else:
				unused_weights.append(SCREAMING_SNAKE_CASE__  )






def 		_lowercase		(     lowercase_						,							lowercase_  ):



		'''simple docstring'''







		_snake_case     	=	SEWDConfig()
		if is_finetuned:
				_snake_case     	=	model.wav_encoder.wav_model.cfg
		else:
				_snake_case     	=	model.cfg

		_snake_case     	=	fs_config.conv_bias
		_snake_case     	=	eval(fs_config.conv_feature_layers  )
		_snake_case     	=	[x[0] for x in conv_layers]
		_snake_case     	=	[x[1] for x in conv_layers]
		_snake_case     	=	[x[2] for x in conv_layers]
		_snake_case     	=	'gelu'
		_snake_case     	=	'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
		_snake_case     	=	0.0
		_snake_case     	=	fs_config.activation_fn.name
		_snake_case     	=	fs_config.encoder_embed_dim
		_snake_case     	=	0.02
		_snake_case     	=	fs_config.encoder_ffn_embed_dim
		_snake_case     	=	1e-5
		_snake_case     	=	fs_config.encoder_layerdrop
		_snake_case     	=	fs_config.encoder_attention_heads
		_snake_case     	=	fs_config.conv_pos_groups
		_snake_case     	=	fs_config.conv_pos
		_snake_case     	=	len(SCREAMING_SNAKE_CASE__  )
		_snake_case     	=	fs_config.encoder_layers
		_snake_case     	=	fs_config.squeeze_factor
		# DeBERTa-specific parameters:
		_snake_case     	=	fs_config.max_position_embeddings
		_snake_case     	=	fs_config.position_buckets
		_snake_case     	=	fs_config.share_att_key
		_snake_case     	=	fs_config.relative_attention
		_snake_case     	=	fs_config.position_biased_input
		_snake_case     	=	tuple(fs_config.pos_att_type.split("|"  )  )
		_snake_case     	=	fs_config.norm_rel_ebd

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

		_snake_case     	=	'Wav2Vec2FeatureExtractor'
		_snake_case     	=	'Wav2Vec2CTCTokenizer'

		return config






@torch.no_grad()
def 		_lowercase		(     lowercase_						,							lowercase_						,							lowercase_=None						,							lowercase_=None						,							lowercase_=True  ):



		'''simple docstring'''







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

		if config_path is not None:
				_snake_case     	=	SEWDConfig.from_pretrained(SCREAMING_SNAKE_CASE__  )
		else:
				_snake_case     	=	convert_config(model[0]						,							SCREAMING_SNAKE_CASE__  )
		_snake_case     	=	model[0].eval()

		_snake_case     	=	True if config.feat_extract_norm == 'layer' else False
		_snake_case     	=	WavaVecaFeatureExtractor(
		    feature_size=1						,							sampling_rate=16000						,							padding_value=0						,							do_normalize=SCREAMING_SNAKE_CASE__						,							return_attention_mask=SCREAMING_SNAKE_CASE__						,							)

		if is_finetuned:
				if dict_path:
						_snake_case     	=	Dictionary.load(SCREAMING_SNAKE_CASE__  )

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						_snake_case     	=	target_dict.pad_index
						_snake_case     	=	target_dict.bos_index
						_snake_case     	=	target_dict.pad_index
						_snake_case     	=	target_dict.bos_index
						_snake_case     	=	target_dict.eos_index
						_snake_case     	=	len(target_dict.symbols  )
						_snake_case     	=	os.path.join(SCREAMING_SNAKE_CASE__						,							"vocab.json"  )
						if not os.path.isdir(SCREAMING_SNAKE_CASE__  ):
								logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(SCREAMING_SNAKE_CASE__  )  )
								return
						os.makedirs(SCREAMING_SNAKE_CASE__						,							exist_ok=SCREAMING_SNAKE_CASE__  )
						with open(SCREAMING_SNAKE_CASE__						,							"w"						,							encoding="utf-8"  ) as vocab_handle:
								json.dump(target_dict.indices						,							SCREAMING_SNAKE_CASE__  )
						_snake_case     	=	WavaVecaCTCTokenizer(
						    SCREAMING_SNAKE_CASE__						,							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=SCREAMING_SNAKE_CASE__						,							)
						_snake_case     	=	WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__						,							tokenizer=SCREAMING_SNAKE_CASE__  )
						processor.save_pretrained(SCREAMING_SNAKE_CASE__  )

				_snake_case     	=	SEWDForCTC(SCREAMING_SNAKE_CASE__  )
		else:
				_snake_case     	=	SEWDModel(SCREAMING_SNAKE_CASE__  )
				feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__  )

		recursively_load_weights(SCREAMING_SNAKE_CASE__						,							SCREAMING_SNAKE_CASE__						,							SCREAMING_SNAKE_CASE__  )

		hf_model.save_pretrained(SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
						_lowerCamelCase							:						Union[str, Any]							     =  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'
						)
						_lowerCamelCase							:						Optional[int]							     =  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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase							:						Optional[int]							     =  logging.get_logger(__name__)

_lowerCamelCase							:						int							     =  {
    'flaubert/flaubert_small_cased': 'https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json',
    'flaubert/flaubert_base_uncased': 'https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json',
    'flaubert/flaubert_base_cased': 'https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json',
    'flaubert/flaubert_large_cased': 'https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json',
}





class 					lowerCamelCase_							(	UpperCAmelCase_							):




					'''simple docstring'''






					lowercase__   =			"""flaubert"""
					lowercase__   =			{
					    """hidden_size""": """emb_dim""",
					    """num_attention_heads""": """n_heads""",
					    """num_hidden_layers""": """n_layers""",
					    """n_words""": """vocab_size""",  # For backward compatibility
					}






					def __init__(   self,  __UpperCamelCase=False,  __UpperCamelCase=0.0,  __UpperCamelCase=30_145,  __UpperCamelCase=2_048,  __UpperCamelCase=12,  __UpperCamelCase=16,  __UpperCamelCase=0.1,  __UpperCamelCase=0.1,  __UpperCamelCase=True,  __UpperCamelCase=False,  __UpperCamelCase=False,  __UpperCamelCase=False,  __UpperCamelCase=1,  __UpperCamelCase=True,  __UpperCamelCase=512,  __UpperCamelCase=2_048**-0.5,  __UpperCamelCase=1E-12,  __UpperCamelCase=0.02,  __UpperCamelCase=0,  __UpperCamelCase=1,  __UpperCamelCase=2,  __UpperCamelCase=3,  __UpperCamelCase=5,  __UpperCamelCase=True,  __UpperCamelCase="first",  __UpperCamelCase=True,  __UpperCamelCase=None,  __UpperCamelCase=True,  __UpperCamelCase=0.1,  __UpperCamelCase=5,  __UpperCamelCase=5,  __UpperCamelCase=0,  __UpperCamelCase=0,  __UpperCamelCase=2,  __UpperCamelCase=0,  **__UpperCamelCase,  ):
							_snake_case     	=	pre_norm
							_snake_case     	=	layerdrop
							_snake_case     	=	vocab_size
							_snake_case     	=	emb_dim
							_snake_case     	=	n_layers
							_snake_case     	=	n_heads
							_snake_case     	=	dropout
							_snake_case     	=	attention_dropout
							_snake_case     	=	gelu_activation
							_snake_case     	=	sinusoidal_embeddings
							_snake_case     	=	causal
							_snake_case     	=	asm
							_snake_case     	=	n_langs
							_snake_case     	=	use_lang_emb
							_snake_case     	=	layer_norm_eps
							_snake_case     	=	bos_index
							_snake_case     	=	eos_index
							_snake_case     	=	pad_index
							_snake_case     	=	unk_index
							_snake_case     	=	mask_index
							_snake_case     	=	is_encoder
							_snake_case     	=	max_position_embeddings
							_snake_case     	=	embed_init_std
							_snake_case     	=	init_std
							_snake_case     	=	summary_type
							_snake_case     	=	summary_use_proj
							_snake_case     	=	summary_activation
							_snake_case     	=	summary_proj_to_labels
							_snake_case     	=	summary_first_dropout
							_snake_case     	=	start_n_top
							_snake_case     	=	end_n_top
							_snake_case     	=	mask_token_id
							_snake_case     	=	lang_id

							if "n_words" in kwargs:
									_snake_case     	=	kwargs["n_words"]

							super().__init__(pad_token_id=__UpperCamelCase,  bos_token_id=__UpperCamelCase,  **__UpperCamelCase			)





class 					lowerCamelCase_							(	UpperCAmelCase_							):




					'''simple docstring'''






					@property
					def     __SCREAMING_SNAKE_CASE     (   self			):
							if self.task == "multiple-choice":
									_snake_case     	=	{0: "batch", 1: "choice", 2: "sequence"}
							else:
									_snake_case     	=	{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]			)





