



"""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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a_			   =					logging.get_logger(__name__)

a_			   =					{
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''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__      (		SCREAMING_SNAKE_CASE__ :  Optional[int]				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  Optional[int]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					for attribute in key.split("""."""			):
										snake_case_					:		Optional[int] 					=					getattr(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)

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

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







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Optional[Any]				,    SCREAMING_SNAKE_CASE__ :  str				,    SCREAMING_SNAKE_CASE__ :  List[Any]			):



					"""simple docstring"""





					snake_case_					:		List[Any] 					=					[]
					snake_case_					:		List[Any] 					=					fairseq_model.state_dict()

					snake_case_					:		Tuple 					=					hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										snake_case_					:		Tuple 					=					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_					:		int 					=					True
										else:
															for key, mapped_key in MAPPING.items():
																				snake_case_					:		List[str] 					=					"""sew.""" + 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_					:		List[str] 					=					True
																									if "*" in mapped_key:
																														snake_case_					:		List[str] 					=					name.split(SCREAMING_SNAKE_CASE__			)[0].split("""."""			)[-2]
																														snake_case_					:		Tuple 					=					mapped_key.replace("""*"""				,    SCREAMING_SNAKE_CASE__			)
																									if "weight_g" in name:
																														snake_case_					:		int 					=					"""weight_g"""
																									elif "weight_v" in name:
																														snake_case_					:		Dict 					=					"""weight_v"""
																									elif "weight" in name:
																														snake_case_					:		Dict 					=					"""weight"""
																									elif "bias" in name:
																														snake_case_					:		Tuple 					=					"""bias"""
																									else:
																														snake_case_					:		Tuple 					=					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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  List[str]				,    SCREAMING_SNAKE_CASE__ :  List[str]			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					full_name.split("""conv_layers."""			)[-1]
					snake_case_					:		List[Any] 					=					name.split("""."""			)
					snake_case_					:		str 					=					int(items[0]			)
					snake_case_					:		List[Any] 					=					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_					:		Dict 					=					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_					:		Dict 					=					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_					:		int 					=					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_					:		Optional[Any] 					=					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 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Any			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					SEWConfig()
					if is_finetuned:
										snake_case_					:		List[Any] 					=					model.wav_encoder.wav_model.cfg
					else:
										snake_case_					:		Dict 					=					model.cfg

					snake_case_					:		List[str] 					=					fs_config.conv_bias
					snake_case_					:		Tuple 					=					eval(fs_config.conv_feature_layers			)
					snake_case_					:		str 					=					[x[0] for x in conv_layers]
					snake_case_					:		Tuple 					=					[x[1] for x in conv_layers]
					snake_case_					:		List[str] 					=					[x[2] for x in conv_layers]
					snake_case_					:		Tuple 					=					"""gelu"""
					snake_case_					:		Optional[int] 					=					"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
					snake_case_					:		Optional[Any] 					=					0.0
					snake_case_					:		Dict 					=					fs_config.activation_fn.name
					snake_case_					:		Optional[int] 					=					fs_config.encoder_embed_dim
					snake_case_					:		Optional[Any] 					=					0.02
					snake_case_					:		Optional[int] 					=					fs_config.encoder_ffn_embed_dim
					snake_case_					:		List[Any] 					=					1E-5
					snake_case_					:		int 					=					fs_config.encoder_layerdrop
					snake_case_					:		Union[str, Any] 					=					fs_config.encoder_attention_heads
					snake_case_					:		Union[str, Any] 					=					fs_config.conv_pos_groups
					snake_case_					:		List[Any] 					=					fs_config.conv_pos
					snake_case_					:		str 					=					len(SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Optional[int] 					=					fs_config.encoder_layers
					snake_case_					:		List[Any] 					=					fs_config.squeeze_factor

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

					snake_case_					:		Optional[int] 					=					"""Wav2Vec2FeatureExtractor"""
					snake_case_					:		List[str] 					=					"""Wav2Vec2CTCTokenizer"""

					return config







@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Any				,    SCREAMING_SNAKE_CASE__ :  Optional[Any]=None				,    SCREAMING_SNAKE_CASE__ :  Tuple=None				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]=True			):



					"""simple docstring"""





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

					if config_path is not None:
										snake_case_					:		int 					=					SEWConfig.from_pretrained(SCREAMING_SNAKE_CASE__			)
					else:
										snake_case_					:		int 					=					convert_config(model[0]				,    SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Optional[int] 					=					model[0].eval()

					snake_case_					:		Optional[Any] 					=					True if config.feat_extract_norm == """layer""" else False
					snake_case_					:		Optional[Any] 					=					WavaVecaFeatureExtractor(
					    feature_size=1				,    sampling_rate=1_6_0_0_0				,    padding_value=0				,    do_normalize=SCREAMING_SNAKE_CASE__				,    return_attention_mask=SCREAMING_SNAKE_CASE__				,    )

					if is_finetuned:
										if dict_path:
															snake_case_					:		Tuple 					=					Dictionary.load(SCREAMING_SNAKE_CASE__			)

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

										snake_case_					:		List[str] 					=					SEWForCTC(SCREAMING_SNAKE_CASE__			)
					else:
										snake_case_					:		Dict 					=					SEWModel(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__":
			a_			   =					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_			   =					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 logging
import pickle
from collections import Counter


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
a_			   =					logging.getLogger(__name__)

if __name__ == "__main__":
			a_			   =					argparse.ArgumentParser(
			    description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
			)
			parser.add_argument(
			    '''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
			)
			parser.add_argument(
			    '''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
			)
			parser.add_argument('''--vocab_size''', default=30522, type=int)
			a_			   =					parser.parse_args()

			logger.info(F'''Loading data from {args.data_file}''')
			with open(args.data_file, '''rb''') as fp:
						a_			   =					pickle.load(fp)

			logger.info('''Counting occurrences for MLM.''')
			a_			   =					Counter()
			for tk_ids in data:
						counter.update(tk_ids)
			a_			   =					[0] * args.vocab_size
			for k, v in counter.items():
						a_			   =					v

			logger.info(F'''Dump to {args.token_counts_dump}''')
			with open(args.token_counts_dump, '''wb''') as handle:
						pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)




