





'''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()
__UpperCAmelCase         =   logging.get_logger(__name__)

__UpperCAmelCase         =   {
    """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 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		for attribute in key.split("""."""					):
				SCREAMING_SNAKE_CASE				:							Optional[Any]						=	getattr(__UpperCamelCase	,	__UpperCamelCase					)

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

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



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							List[str]						=	[]
		SCREAMING_SNAKE_CASE				:							str						=	fairseq_model.state_dict()

		SCREAMING_SNAKE_CASE				:							Tuple						=	hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				SCREAMING_SNAKE_CASE				:							List[Any]						=	False
				if "conv_layers" in name:
						load_conv_layer(
						    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	hf_model.config.feat_extract_norm == """group"""	,	)
						SCREAMING_SNAKE_CASE				:							int						=	True
				else:
						for key, mapped_key in MAPPING.items():
								SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	"""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]:
										SCREAMING_SNAKE_CASE				:							Tuple						=	True
										if "*" in mapped_key:
												SCREAMING_SNAKE_CASE				:							List[str]						=	name.split(__UpperCamelCase					)[0].split("""."""					)[-2]
												if not layer_index.isnumeric():
														continue
												SCREAMING_SNAKE_CASE				:							str						=	mapped_key.replace("""*"""	,	__UpperCamelCase					)
										if "weight_g" in name:
												SCREAMING_SNAKE_CASE				:							Dict						=	"""weight_g"""
										elif "weight_v" in name:
												SCREAMING_SNAKE_CASE				:							Tuple						=	"""weight_v"""
										elif "weight" in name:
												SCREAMING_SNAKE_CASE				:							Any						=	"""weight"""
										elif "bias" in name:
												SCREAMING_SNAKE_CASE				:							int						=	"""bias"""
										else:
												SCREAMING_SNAKE_CASE				:							Optional[int]						=	None
										set_recursively(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase					)
								continue
				if not is_used:
						unused_weights.append(__UpperCamelCase					)

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



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Optional[int]						=	full_name.split("""conv_layers."""					)[-1]
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	name.split("""."""					)
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	int(items[0]					)
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	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.'''
						)
						SCREAMING_SNAKE_CASE				:							str						=	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.'''
						)
						SCREAMING_SNAKE_CASE				:							List[Any]						=	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."
						)
						SCREAMING_SNAKE_CASE				:							List[Any]						=	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.'''
						)
						SCREAMING_SNAKE_CASE				:							Dict						=	value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
		else:
				unused_weights.append(__UpperCamelCase					)



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Any						=	SEWDConfig()
		if is_finetuned:
				SCREAMING_SNAKE_CASE				:							Optional[int]						=	model.wav_encoder.wav_model.cfg
		else:
				SCREAMING_SNAKE_CASE				:							Any						=	model.cfg

		SCREAMING_SNAKE_CASE				:							Any						=	fs_config.conv_bias
		SCREAMING_SNAKE_CASE				:							Dict						=	eval(fs_config.conv_feature_layers					)
		SCREAMING_SNAKE_CASE				:							List[str]						=	[x[0] for x in conv_layers]
		SCREAMING_SNAKE_CASE				:							List[Any]						=	[x[1] for x in conv_layers]
		SCREAMING_SNAKE_CASE				:							List[Any]						=	[x[2] for x in conv_layers]
		SCREAMING_SNAKE_CASE				:							Dict						=	"""gelu"""
		SCREAMING_SNAKE_CASE				:							Any						=	"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
		SCREAMING_SNAKE_CASE				:							List[Any]						=	0.0
		SCREAMING_SNAKE_CASE				:							Dict						=	fs_config.activation_fn.name
		SCREAMING_SNAKE_CASE				:							str						=	fs_config.encoder_embed_dim
		SCREAMING_SNAKE_CASE				:							Any						=	0.02
		SCREAMING_SNAKE_CASE				:							int						=	fs_config.encoder_ffn_embed_dim
		SCREAMING_SNAKE_CASE				:							Tuple						=	1E-5
		SCREAMING_SNAKE_CASE				:							int						=	fs_config.encoder_layerdrop
		SCREAMING_SNAKE_CASE				:							Tuple						=	fs_config.encoder_attention_heads
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	fs_config.conv_pos_groups
		SCREAMING_SNAKE_CASE				:							List[str]						=	fs_config.conv_pos
		SCREAMING_SNAKE_CASE				:							Dict						=	len(__UpperCamelCase					)
		SCREAMING_SNAKE_CASE				:							Tuple						=	fs_config.encoder_layers
		SCREAMING_SNAKE_CASE				:							str						=	fs_config.squeeze_factor
		# DeBERTa-specific parameters:
		SCREAMING_SNAKE_CASE				:							Any						=	fs_config.max_position_embeddings
		SCREAMING_SNAKE_CASE				:							int						=	fs_config.position_buckets
		SCREAMING_SNAKE_CASE				:							Dict						=	fs_config.share_att_key
		SCREAMING_SNAKE_CASE				:							List[str]						=	fs_config.relative_attention
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	fs_config.position_biased_input
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	tuple(fs_config.pos_att_type.split("""|"""					)					)
		SCREAMING_SNAKE_CASE				:							str						=	fs_config.norm_rel_ebd

		# take care of any params that are overridden by the Wav2VecCtc model
		if is_finetuned:
				SCREAMING_SNAKE_CASE				:							List[Any]						=	model.cfg
				SCREAMING_SNAKE_CASE				:							Dict						=	fs_config.final_dropout
				SCREAMING_SNAKE_CASE				:							Optional[int]						=	fs_config.layerdrop
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	fs_config.activation_dropout
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
		SCREAMING_SNAKE_CASE				:							str						=	fs_config.attention_dropout
		SCREAMING_SNAKE_CASE				:							List[str]						=	fs_config.dropout_input
		SCREAMING_SNAKE_CASE				:							int						=	fs_config.dropout
		SCREAMING_SNAKE_CASE				:							Tuple						=	fs_config.mask_channel_length
		SCREAMING_SNAKE_CASE				:							List[Any]						=	fs_config.mask_channel_prob
		SCREAMING_SNAKE_CASE				:							Dict						=	fs_config.mask_length
		SCREAMING_SNAKE_CASE				:							List[str]						=	fs_config.mask_prob

		SCREAMING_SNAKE_CASE				:							Optional[int]						=	"""Wav2Vec2FeatureExtractor"""
		SCREAMING_SNAKE_CASE				:							Dict						=	"""Wav2Vec2CTCTokenizer"""

		return config



@torch.no_grad()
def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_=None	,	lowerCamelCase_=None	,	lowerCamelCase_=True					):


		"""simple docstring"""



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

		if config_path is not None:
				SCREAMING_SNAKE_CASE				:							int						=	SEWDConfig.from_pretrained(__UpperCamelCase					)
		else:
				SCREAMING_SNAKE_CASE				:							List[Any]						=	convert_config(model[0]	,	__UpperCamelCase					)
		SCREAMING_SNAKE_CASE				:							Dict						=	model[0].eval()

		SCREAMING_SNAKE_CASE				:							Any						=	True if config.feat_extract_norm == """layer""" else False
		SCREAMING_SNAKE_CASE				:							List[str]						=	WavaVecaFeatureExtractor(
		    feature_size=1	,	sampling_rate=1_60_00	,	padding_value=0	,	do_normalize=__UpperCamelCase	,	return_attention_mask=__UpperCamelCase	,	)

		if is_finetuned:
				if dict_path:
						SCREAMING_SNAKE_CASE				:							List[Any]						=	Dictionary.load(__UpperCamelCase					)

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

				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	SEWDForCTC(__UpperCamelCase					)
		else:
				SCREAMING_SNAKE_CASE				:							Dict						=	SEWDModel(__UpperCamelCase					)
				feature_extractor.save_pretrained(__UpperCamelCase					)

		recursively_load_weights(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase					)

		hf_model.save_pretrained(__UpperCamelCase					)


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'''




import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


__UpperCAmelCase         =   logging.get_logger(__name__)




class       UpperCamelCase__			(			lowercase_	):




					"""simple docstring"""


					def __init__(		self							:	Dict     ,		*lowerCamelCase_							:	Optional[int]     ,		**lowerCamelCase_							:	Optional[int]		):

							'''simple docstring'''



							warnings.warn(
							    """The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
							    """ Please use DetrImageProcessor instead."""     ,		lowerCamelCase_     ,		)
							super().__init__(*lowerCamelCase_     ,		**lowerCamelCase_		)



