




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()
lowercase_		=							logging.get_logger(__name__)

lowercase_		=							{
    '''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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->List[Any]:






					"""simple docstring"""






					for attribute in key.split('''.'''       ):
										__magic_name__					:							List[Any]		   =							getattr(UpperCAmelCase,	UpperCAmelCase       )

					if weight_type is not None:
										__magic_name__					:							List[Any]		   =							getattr(UpperCAmelCase,	UpperCAmelCase       ).shape
					else:
										__magic_name__					:							Tuple		   =							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":
										__magic_name__					:							Any		   =							value
					elif weight_type == "weight_g":
										__magic_name__					:							Tuple		   =							value
					elif weight_type == "weight_v":
										__magic_name__					:							Any		   =							value
					elif weight_type == "bias":
										__magic_name__					:							Union[str, Any]		   =							value
					else:
										__magic_name__					:							str		   =							value

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







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->Any:






					"""simple docstring"""






					__magic_name__					:							Dict		   =							[]
					__magic_name__					:							Any		   =							fairseq_model.state_dict()

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

					for name, value in fairseq_dict.items():
										__magic_name__					:							str		   =							False
										if "conv_layers" in name:
															load_conv_layer(
															    UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase,	hf_model.config.feat_extract_norm == '''group''',	)
															__magic_name__					:							Optional[int]		   =							True
										else:
															for key, mapped_key in MAPPING.items():
																				__magic_name__					:							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]:
																									__magic_name__					:							Any		   =							True
																									if "*" in mapped_key:
																														__magic_name__					:							Tuple		   =							name.split(UpperCAmelCase       )[0].split('''.'''       )[-2]
																														if not layer_index.isnumeric():
																																			continue
																														__magic_name__					:							Optional[int]		   =							mapped_key.replace('''*''',	UpperCAmelCase       )
																									if "weight_g" in name:
																														__magic_name__					:							str		   =							'''weight_g'''
																									elif "weight_v" in name:
																														__magic_name__					:							str		   =							'''weight_v'''
																									elif "weight" in name:
																														__magic_name__					:							Optional[Any]		   =							'''weight'''
																									elif "bias" in name:
																														__magic_name__					:							Optional[Any]		   =							'''bias'''
																									else:
																														__magic_name__					:							Tuple		   =							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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->Tuple:






					"""simple docstring"""






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







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Dict		   =							SEWDConfig()
					if is_finetuned:
										__magic_name__					:							str		   =							model.wav_encoder.wav_model.cfg
					else:
										__magic_name__					:							Optional[int]		   =							model.cfg

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

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

					__magic_name__					:							List[str]		   =							'''Wav2Vec2FeatureExtractor'''
					__magic_name__					:							Optional[Any]		   =							'''Wav2Vec2CTCTokenizer'''

					return config







@torch.no_grad()
def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase=None,	UpperCAmelCase=None,	UpperCAmelCase=True       )  ->List[str]:






					"""simple docstring"""






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

					if config_path is not None:
										__magic_name__					:							Dict		   =							SEWDConfig.from_pretrained(UpperCAmelCase       )
					else:
										__magic_name__					:							Optional[int]		   =							convert_config(model[0],	UpperCAmelCase       )
					__magic_name__					:							List[str]		   =							model[0].eval()

					__magic_name__					:							List[str]		   =							True if config.feat_extract_norm == '''layer''' else False
					__magic_name__					:							Optional[Any]		   =							WavaVecaFeatureExtractor(
					    feature_size=1,	sampling_rate=1_6000,	padding_value=0,	do_normalize=UpperCAmelCase,	return_attention_mask=UpperCAmelCase,	)

					if is_finetuned:
										if dict_path:
															__magic_name__					:							Union[str, Any]		   =							Dictionary.load(UpperCAmelCase       )

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															__magic_name__					:							Tuple		   =							target_dict.pad_index
															__magic_name__					:							Optional[Any]		   =							target_dict.bos_index
															__magic_name__					:							Any		   =							target_dict.pad_index
															__magic_name__					:							Tuple		   =							target_dict.bos_index
															__magic_name__					:							Tuple		   =							target_dict.eos_index
															__magic_name__					:							List[str]		   =							len(target_dict.symbols       )
															__magic_name__					:							List[str]		   =							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       )
															__magic_name__					:							str		   =							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,	)
															__magic_name__					:							Dict		   =							WavaVecaProcessor(feature_extractor=UpperCAmelCase,	tokenizer=UpperCAmelCase       )
															processor.save_pretrained(UpperCAmelCase       )

										__magic_name__					:							Union[str, Any]		   =							SEWDForCTC(UpperCAmelCase       )
					else:
										__magic_name__					:							List[str]		   =							SEWDModel(UpperCAmelCase       )
										feature_extractor.save_pretrained(UpperCAmelCase       )

					recursively_load_weights(UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )

					hf_model.save_pretrained(UpperCAmelCase       )


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









import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
				import tensorflow as tf

				from transformers.activations_tf import get_tf_activation





@require_tf
class 		A__				(   unittest.TestCase ):






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						__magic_name__					:							int		   =							tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 1_0_0.0]						)
						__magic_name__					:							List[str]		   =							get_tf_activation('''gelu'''						)
						__magic_name__					:							str		   =							get_tf_activation('''gelu_10'''						)

						__magic_name__					:							List[str]		   =							gelu(lowerCamelCase						)
						__magic_name__					:							Optional[Any]		   =							geluaa(lowerCamelCase						)

						__magic_name__					:							Optional[int]		   =							tf.where(y_gelu_aa < 1_0.0				,     1.0				,     0.0						)

						self.assertEqual(tf.math.reduce_max(lowerCamelCase						).numpy().item()				,     1_0.0						)
						self.assertTrue(np.allclose(y_gelu * clipped_mask				,     y_gelu_aa * clipped_mask						)						)






	def  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						get_tf_activation('''gelu'''						)
						get_tf_activation('''gelu_10'''						)
						get_tf_activation('''gelu_fast'''						)
						get_tf_activation('''gelu_new'''						)
						get_tf_activation('''glu'''						)
						get_tf_activation('''mish'''						)
						get_tf_activation('''quick_gelu'''						)
						get_tf_activation('''relu'''						)
						get_tf_activation('''sigmoid'''						)
						get_tf_activation('''silu'''						)
						get_tf_activation('''swish'''						)
						get_tf_activation('''tanh'''						)
						with self.assertRaises(lowerCamelCase						):
											get_tf_activation('''bogus'''						)
						with self.assertRaises(lowerCamelCase						):
											get_tf_activation(lowerCamelCase						)



