

"""simple docstring"""




def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					return (input_a, input_a).count(0 ) % 2
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					assert xor_gate(0     ,				0 ) == 0
					assert xor_gate(0     ,				1 ) == 1
					assert xor_gate(1     ,				0 ) == 1
					assert xor_gate(1     ,				1 ) == 0


if __name__ == "__main__":
	print(xor_gate(0, 0))
	print(xor_gate(0, 1))








"""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()
__A	:       str        =						logging.get_logger(__name__)

__A	:       str        =						{
    '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   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					for attribute in key.split("." ):
										A     =   getattr(lowercase__     ,				lowercase__ )

					if weight_type is not None:
										A     =   getattr(lowercase__     ,				lowercase__ ).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		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   []
					A     =   fairseq_model.state_dict()

					A     =   hf_model.sew_d.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(
															    lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				hf_model.config.feat_extract_norm == "group"     ,				)
															A     =   True
										else:
															for key, mapped_key in MAPPING.items():
																				A     =   "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]:
																									A     =   True
																									if "*" in mapped_key:
																														A     =   name.split(lowercase__ )[0].split("." )[-2]
																														if not layer_index.isnumeric():
																																			continue
																														A     =   mapped_key.replace("*"     ,				lowercase__ )
																									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(lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__ )
																				continue
										if not is_used:
															unused_weights.append(lowercase__ )

					logger.warning(F"""Unused weights: {unused_weights}""" )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					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(lowercase__ )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   SEWDConfig()
					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(lowercase__ )
					A     =   fs_config.encoder_layers
					A     =   fs_config.squeeze_factor
					# DeBERTa-specific parameters:
					A     =   fs_config.max_position_embeddings
					A     =   fs_config.position_buckets
					A     =   fs_config.share_att_key
					A     =   fs_config.relative_attention
					A     =   fs_config.position_biased_input
					A     =   tuple(fs_config.pos_att_type.split("|" ) )
					A     =   fs_config.norm_rel_ebd

					# 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		(		lowercase__     ,				lowercase__     ,				lowercase__=None     ,				lowercase__=None     ,				lowercase__=True ):
					"""simple docstring"""

					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     =   SEWDConfig.from_pretrained(lowercase__ )
					else:
										A     =   convert_config(model[0]     ,				lowercase__ )
					A     =   model[0].eval()

					A     =   True if config.feat_extract_norm == "layer" else False
					A     =   WavaVecaFeatureExtractor(
					    feature_size=1     ,				sampling_rate=16_000     ,				padding_value=0     ,				do_normalize=lowercase__     ,				return_attention_mask=lowercase__     ,				)

					if is_finetuned:
										if dict_path:
															A     =   Dictionary.load(lowercase__ )

															# 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(lowercase__     ,				"vocab.json" )
															if not os.path.isdir(lowercase__ ):
																				logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(lowercase__ ) )
																				return
															os.makedirs(lowercase__     ,				exist_ok=lowercase__ )
															with open(lowercase__     ,				"w"     ,				encoding="utf-8" ) as vocab_handle:
																				json.dump(target_dict.indices     ,				lowercase__ )
															A     =   WavaVecaCTCTokenizer(
															    lowercase__     ,				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=lowercase__     ,				)
															A     =   WavaVecaProcessor(feature_extractor=lowercase__     ,				tokenizer=lowercase__ )
															processor.save_pretrained(lowercase__ )

										A     =   SEWDForCTC(lowercase__ )
					else:
										A     =   SEWDModel(lowercase__ )
										feature_extractor.save_pretrained(lowercase__ )

					recursively_load_weights(lowercase__     ,				lowercase__     ,				lowercase__ )

					hf_model.save_pretrained(lowercase__ )


if __name__ == "__main__":
	__A	:       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'
	)
	__A	:       Tuple        =						parser.parse_args()
	convert_sew_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
	)





