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

lowercase__						       =      {
    "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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							Union[str, Any]:


		'''simple docstring'''


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

		if weight_type is not None:
				snake_case  :       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  :       int     			=						value
		elif weight_type == "weight_g":
				snake_case  :       Optional[Any]     			=						value
		elif weight_type == "weight_v":
				snake_case  :       Any     			=						value
		elif weight_type == "bias":
				snake_case  :       str     			=						value
		else:
				snake_case  :       Tuple     			=						value

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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							List[str]:


		'''simple docstring'''


		snake_case  :       Tuple     			=						[]
		snake_case  :       Union[str, Any]     			=						fairseq_model.state_dict()

		snake_case  :       Any     			=						hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				snake_case  :       List[str]     			=						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  :       Any     			=						True
				else:
						for key, mapped_key in MAPPING.items():
								snake_case  :       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  :       Any     			=						True
										if "*" in mapped_key:
												snake_case  :       Optional[int]     			=						name.split(SCREAMING_SNAKE_CASE__			)[0].split('''.'''			)[-2]
												snake_case  :       List[str]     			=						mapped_key.replace('''*''' ,       SCREAMING_SNAKE_CASE__			)
										if "weight_g" in name:
												snake_case  :       Any     			=						'''weight_g'''
										elif "weight_v" in name:
												snake_case  :       str     			=						'''weight_v'''
										elif "weight" in name:
												snake_case  :       int     			=						'''weight'''
										elif "bias" in name:
												snake_case  :       Any     			=						'''bias'''
										else:
												snake_case  :       List[str]     			=						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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							Dict:


		'''simple docstring'''


		snake_case  :       Optional[int]     			=						full_name.split('''conv_layers.'''			)[-1]
		snake_case  :       List[str]     			=						name.split('''.'''			)
		snake_case  :       str     			=						int(items[0]			)
		snake_case  :       Union[str, 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  :       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.'
						)
						snake_case  :       List[str]     			=						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  :       Union[str, 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.'
						)
						snake_case  :       Tuple     			=						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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							List[Any]:


		'''simple docstring'''


		snake_case  :       Dict     			=						SEWConfig()
		if is_finetuned:
				snake_case  :       List[str]     			=						model.wav_encoder.wav_model.cfg
		else:
				snake_case  :       List[str]     			=						model.cfg

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

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

		snake_case  :       Optional[int]     			=						'''Wav2Vec2FeatureExtractor'''
		snake_case  :       Optional[Any]     			=						'''Wav2Vec2CTCTokenizer'''

		return config




@torch.no_grad()
def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=None ,       SCREAMING_SNAKE_CASE__=None ,       SCREAMING_SNAKE_CASE__=True			)  ->							Tuple:


		'''simple docstring'''


		if is_finetuned:
				snake_case			,snake_case			,snake_case  :       List[str]     			=						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  :       List[str]     			=						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			)

		if config_path is not None:
				snake_case  :       Tuple     			=						SEWConfig.from_pretrained(SCREAMING_SNAKE_CASE__			)
		else:
				snake_case  :       List[str]     			=						convert_config(model[0] ,       SCREAMING_SNAKE_CASE__			)
		snake_case  :       Tuple     			=						model[0].eval()

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

		if is_finetuned:
				if dict_path:
						snake_case  :       List[Any]     			=						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  :       Dict     			=						target_dict.bos_index
						snake_case  :       int     			=						target_dict.pad_index
						snake_case  :       List[Any]     			=						target_dict.bos_index
						snake_case  :       Union[str, Any]     			=						target_dict.eos_index
						snake_case  :       Union[str, Any]     			=						len(target_dict.symbols			)
						snake_case  :       List[Any]     			=						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  :       str     			=						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  :       Union[str, Any]     			=						WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__ ,       tokenizer=SCREAMING_SNAKE_CASE__			)
						processor.save_pretrained(SCREAMING_SNAKE_CASE__			)

				snake_case  :       int     			=						SEWForCTC(SCREAMING_SNAKE_CASE__			)
		else:
				snake_case  :       str     			=						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__":
						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
						)




'''simple docstring'''
import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


lowercase__						       =      get_logger(__name__)






class 	snake_case__       (      enum.Enum					):







		"""simple docstring"""

		lowerCamelCase     							=						"""all_checks"""
		lowerCamelCase     							=						"""basic_checks"""
		lowerCamelCase     							=						"""no_checks"""







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=None			)  ->							str:


		'''simple docstring'''


		if expected_checksums is None:
				logger.info('''Unable to verify checksums.'''			)
				return
		if len(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			) > 0:
				raise ExpectedMoreDownloadedFiles(str(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			)			)
		if len(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			) > 0:
				raise UnexpectedDownloadedFile(str(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			)			)
		snake_case  :       Any     			=						[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
		snake_case  :       Union[str, Any]     			=						''' for ''' + verification_name if verification_name is not None else ''''''
		if len(SCREAMING_SNAKE_CASE__			) > 0:
				raise NonMatchingChecksumError(
				    F'Checksums didn\'t match{for_verification_name}:\n'
				    F'{bad_urls}\n'
				    '''Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error'''			)
		logger.info('''All the checksums matched successfully''' + for_verification_name			)






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""








def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							List[str]:


		'''simple docstring'''


		if expected_splits is None:
				logger.info('''Unable to verify splits sizes.'''			)
				return
		if len(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			) > 0:
				raise ExpectedMoreSplits(str(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			)			)
		if len(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			) > 0:
				raise UnexpectedSplits(str(set(SCREAMING_SNAKE_CASE__			) - set(SCREAMING_SNAKE_CASE__			)			)			)
		snake_case  :       Dict     			=						[
		    {'''expected''': expected_splits[name], '''recorded''': recorded_splits[name]}
		    for name in expected_splits
		    if expected_splits[name].num_examples != recorded_splits[name].num_examples
		]
		if len(SCREAMING_SNAKE_CASE__			) > 0:
				raise NonMatchingSplitsSizesError(str(SCREAMING_SNAKE_CASE__			)			)
		logger.info('''All the splits matched successfully.'''			)




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ = True			)  ->							dict:


		'''simple docstring'''


		if record_checksum:
				snake_case  :       Any     			=						shaaaa()
				with open(SCREAMING_SNAKE_CASE__ ,       '''rb'''			) as f:
						for chunk in iter(lambda: f.read(1 << 20			) ,       B''''''			):
								m.update(SCREAMING_SNAKE_CASE__			)
						snake_case  :       Any     			=						m.hexdigest()
		else:
				snake_case  :       Tuple     			=						None
		return {"num_bytes": os.path.getsize(SCREAMING_SNAKE_CASE__			), "checksum": checksum}




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__			)  ->							Dict:


		'''simple docstring'''


		if dataset_size and config.IN_MEMORY_MAX_SIZE:
				return dataset_size < config.IN_MEMORY_MAX_SIZE
		else:
				return False


