
"""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()
SCREAMING_SNAKE_CASE_			      =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_			      =					{
    "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      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		):



						for attribute in key.split("""."""		):
												__lowerCAmelCase        =       getattr(UpperCamelCase_				,  UpperCamelCase_		)

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

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

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		):
						__lowerCAmelCase        =       []
						__lowerCAmelCase        =       fairseq_model.state_dict()

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

						for name, value in fairseq_dict.items():
												__lowerCAmelCase        =       False
												if "conv_layers" in name:
																		load_conv_layer(
																		    UpperCamelCase_				,  UpperCamelCase_				,  UpperCamelCase_				,  UpperCamelCase_				,  hf_model.config.feat_extract_norm == """group"""				,  )
																		__lowerCAmelCase        =       True
												else:
																		for key, mapped_key in MAPPING.items():
																								__lowerCAmelCase        =       """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]:
																														__lowerCAmelCase        =       True
																														if "*" in mapped_key:
																																				__lowerCAmelCase        =       name.split(UpperCamelCase_		)[0].split("""."""		)[-2]
																																				if not layer_index.isnumeric():
																																										continue
																																				__lowerCAmelCase        =       mapped_key.replace("""*"""				,  UpperCamelCase_		)
																														if "weight_g" in name:
																																				__lowerCAmelCase        =       """weight_g"""
																														elif "weight_v" in name:
																																				__lowerCAmelCase        =       """weight_v"""
																														elif "weight" in name:
																																				__lowerCAmelCase        =       """weight"""
																														elif "bias" in name:
																																				__lowerCAmelCase        =       """bias"""
																														else:
																																				__lowerCAmelCase        =       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 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		):
						__lowerCAmelCase        =       full_name.split("""conv_layers."""		)[-1]
						__lowerCAmelCase        =       name.split("""."""		)
						__lowerCAmelCase        =       int(items[0]		)
						__lowerCAmelCase        =       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."""
																		)
																		__lowerCAmelCase        =       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."""
																		)
																		__lowerCAmelCase        =       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."
																		)
																		__lowerCAmelCase        =       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."""
																		)
																		__lowerCAmelCase        =       value
																		logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
						else:
												unused_weights.append(UpperCamelCase_		)

def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase		):
						__lowerCAmelCase        =       SEWDConfig()
						if is_finetuned:
												__lowerCAmelCase        =       model.wav_encoder.wav_model.cfg
						else:
												__lowerCAmelCase        =       model.cfg

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

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

						__lowerCAmelCase        =       """Wav2Vec2FeatureExtractor"""
						__lowerCAmelCase        =       """Wav2Vec2CTCTokenizer"""

						return config

@torch.no_grad()
def 				lowercase      (_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase=None				,  _lowerCAmelCase=None				,  _lowerCAmelCase=True		):



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

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

						__lowerCAmelCase        =       True if config.feat_extract_norm == """layer""" else False
						__lowerCAmelCase        =       WavaVecaFeatureExtractor(
						    feature_size=1				,  sampling_rate=1_6000				,  padding_value=0				,  do_normalize=UpperCamelCase_				,  return_attention_mask=UpperCamelCase_				,  )

						if is_finetuned:
												if dict_path:
																		__lowerCAmelCase        =       Dictionary.load(UpperCamelCase_		)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		__lowerCAmelCase        =       target_dict.pad_index
																		__lowerCAmelCase        =       target_dict.bos_index
																		__lowerCAmelCase        =       target_dict.pad_index
																		__lowerCAmelCase        =       target_dict.bos_index
																		__lowerCAmelCase        =       target_dict.eos_index
																		__lowerCAmelCase        =       len(target_dict.symbols		)
																		__lowerCAmelCase        =       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_		)
																		__lowerCAmelCase        =       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_				,  )
																		__lowerCAmelCase        =       WavaVecaProcessor(feature_extractor=UpperCamelCase_				,  tokenizer=UpperCamelCase_		)
																		processor.save_pretrained(UpperCamelCase_		)

												__lowerCAmelCase        =       SEWDForCTC(UpperCamelCase_		)
						else:
												__lowerCAmelCase        =       SEWDModel(UpperCamelCase_		)
												feature_extractor.save_pretrained(UpperCamelCase_		)

						recursively_load_weights(UpperCamelCase_				,  UpperCamelCase_				,  UpperCamelCase_		)

						hf_model.save_pretrained(UpperCamelCase_		)


if __name__ == "__main__":
	SCREAMING_SNAKE_CASE_			      =					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'''
	)
	SCREAMING_SNAKE_CASE_			      =					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 argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def 				lowercase      (_lowerCAmelCase		):
						__lowerCAmelCase        =       args.serialization_dir
						__lowerCAmelCase        =       args.pruning_method
						__lowerCAmelCase        =       args.threshold

						__lowerCAmelCase        =       torch.load(os.path.join(_lowerCAmelCase				,  """pytorch_model.bin"""		)				,  map_location="""cpu"""		)

						__lowerCAmelCase        =       0  # Number of remaining (not pruned) params in the encoder
						__lowerCAmelCase        =       0  # Number of params in the encoder

						print("""name""".ljust(60				,  """ """		)				,  """Remaining Weights %"""				,  """Remaining Weight"""		)
						for name, param in st.items():
												if "encoder" not in name:
																		continue

												if "mask_scores" in name:
																		if pruning_method == "topK":
																								__lowerCAmelCase        =       TopKBinarizer.apply(_lowerCAmelCase				,  _lowerCAmelCase		).sum().item()
																		elif pruning_method == "sigmoied_threshold":
																								__lowerCAmelCase        =       ThresholdBinarizer.apply(_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase		).sum().item()
																		elif pruning_method == "l0":
																								__lowerCAmelCase ,      __lowerCAmelCase        =       -0.1, 1.1
																								__lowerCAmelCase        =       torch.sigmoid(_lowerCAmelCase		)
																								__lowerCAmelCase        =       s * (r - l) + l
																								__lowerCAmelCase        =       s_bar.clamp(min=0.0				,  max=1.0		)
																								__lowerCAmelCase        =       (mask > 0.0).sum().item()
																		else:
																								raise ValueError("""Unknown pruning method"""		)
																		remaining_count += mask_ones
																		print(name.ljust(60				,  """ """		)				,  str(round(100 * mask_ones / param.numel()				,  3		)		).ljust(20				,  """ """		)				,  str(_lowerCAmelCase		)		)
												else:
																		encoder_count += param.numel()
																		if "bias" in name or "LayerNorm" in name:
																								remaining_count += param.numel()

						print(""""""		)
						print("""Remaining Weights (global) %: """				,  100 * remaining_count / encoder_count		)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_			      =					argparse.ArgumentParser()

			parser.add_argument(
			    '''--pruning_method''',
			    choices=['''l0''', '''topK''', '''sigmoied_threshold'''],
			    type=str,
			    required=True,
			    help=(
			        '''Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'''
			        ''' pruning)'''
			    ),
			)
			parser.add_argument(
			    '''--threshold''',
			    type=float,
			    required=False,
			    help=(
			        '''For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
			        '''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
			        '''Not needed for `l0`'''
			    ),
			)
			parser.add_argument(
			    '''--serialization_dir''',
			    type=str,
			    required=True,
			    help='''Folder containing the model that was previously fine-pruned''',
			)

			SCREAMING_SNAKE_CASE_			      =					parser.parse_args()

			main(args)
