






'''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  (				A  :       Union[str, Any]  ,		A  :       Tuple  ,		A  :       Union[str, Any]  ,		A  :       List[str]  ,		A  :       Any  )    -> Any:







			"""simple docstring"""
			for attribute in key.split('.'  ):
						__snake_case				:      Optional[int]     							= getattr(A  ,		A  )

			if weight_type is not None:
						__snake_case				:      Any     							= getattr(A  ,		A  ).shape
			else:
						__snake_case				:      Any     							= 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				:      str     							= value
			elif weight_type == "weight_v":
						__snake_case				:      List[Any]     							= value
			elif weight_type == "bias":
						__snake_case				:      List[str]     							= value
			else:
						__snake_case				:      List[Any]     							= value

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




def        _SCREAMING_SNAKE_CASE  (				A  :       List[Any]  ,		A  :       List[str]  ,		A  :       Dict  )    -> int:







			"""simple docstring"""
			__snake_case				:      Optional[Any]     							= []
			__snake_case				:      int     							= fairseq_model.state_dict()

			__snake_case				:      Optional[int]     							= hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						__snake_case				:      str     							= False
						if "conv_layers" in name:
									load_conv_layer(
									    A  ,		A  ,		A  ,		A  ,		hf_model.config.feat_extract_norm == 'group'  ,		)
									__snake_case				:      List[Any]     							= True
						else:
									for key, mapped_key in MAPPING.items():
												__snake_case				:      Tuple     							= '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				:      int     							= name.split(A  )[0].split('.'  )[-2]
																		__snake_case				:      List[str]     							= mapped_key.replace('*'  ,		A  )
															if "weight_g" in name:
																		__snake_case				:      str     							= 'weight_g'
															elif "weight_v" in name:
																		__snake_case				:      Tuple     							= 'weight_v'
															elif "weight" in name:
																		__snake_case				:      Optional[int]     							= 'weight'
															elif "bias" in name:
																		__snake_case				:      str     							= 'bias'
															else:
																		__snake_case				:      List[str]     							= None
															set_recursively(A  ,		A  ,		A  ,		A  ,		A  )
												continue
						if not is_used:
									unused_weights.append(A  )

			logger.warning(F"""Unused weights: {unused_weights}"""  )




def        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  ,		A  :       Optional[int]  ,		A  :       List[str]  ,		A  :       int  ,		A  :       int  )    -> Dict:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       Dict  ,		A  :       int  )    -> Optional[Any]:







			"""simple docstring"""
			__snake_case				:      Optional[Any]     							= SEWConfig()
			if is_finetuned:
						__snake_case				:      List[str]     							= model.wav_encoder.wav_model.cfg
			else:
						__snake_case				:      Dict     							= model.cfg

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

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

			__snake_case				:      Tuple     							= 'Wav2Vec2FeatureExtractor'
			__snake_case				:      List[str]     							= 'Wav2Vec2CTCTokenizer'

			return config




@torch.no_grad()
def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       List[str]  ,		A  :       List[str]=None  ,		A  :       List[Any]=None  ,		A  :       Optional[int]=True  )    -> List[Any]:







			"""simple docstring"""

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

			if config_path is not None:
						__snake_case				:      List[str]     							= SEWConfig.from_pretrained(A  )
			else:
						__snake_case				:      Optional[Any]     							= convert_config(model[0]  ,		A  )
			__snake_case				:      Optional[int]     							= model[0].eval()

			__snake_case				:      Tuple     							= True if config.feat_extract_norm == 'layer' else False
			__snake_case				:      str     							= WavaVecaFeatureExtractor(
			    feature_size=1  ,		sampling_rate=1_60_00  ,		padding_value=0  ,		do_normalize=A  ,		return_attention_mask=A  ,		)

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

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									__snake_case				:      str     							= target_dict.pad_index
									__snake_case				:      Union[str, Any]     							= target_dict.bos_index
									__snake_case				:      List[str]     							= target_dict.pad_index
									__snake_case				:      Tuple     							= target_dict.bos_index
									__snake_case				:      List[str]     							= target_dict.eos_index
									__snake_case				:      Union[str, Any]     							= len(target_dict.symbols  )
									__snake_case				:      int     							= os.path.join(A  ,		'vocab.json'  )
									if not os.path.isdir(A  ):
												logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(A  )  )
												return
									os.makedirs(A  ,		exist_ok=A  )
									with open(A  ,		'w'  ,		encoding='utf-8'  ) as vocab_handle:
												json.dump(target_dict.indices  ,		A  )
									__snake_case				:      List[str]     							= WavaVecaCTCTokenizer(
									    A  ,		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=A  ,		)
									__snake_case				:      Optional[int]     							= WavaVecaProcessor(feature_extractor=A  ,		tokenizer=A  )
									processor.save_pretrained(A  )

						__snake_case				:      Optional[int]     							= SEWForCTC(A  )
			else:
						__snake_case				:      Any     							= SEWModel(A  )
						feature_extractor.save_pretrained(A  )

			recursively_load_weights(A  ,		A  ,		A  )

			hf_model.save_pretrained(A  )


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'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A    	=   {'''configuration_timm_backbone''': ['''TimmBackboneConfig''']}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   ['''TimmBackbone''']


if TYPE_CHECKING:
      from .configuration_timm_backbone import TimmBackboneConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_timm_backbone import TimmBackbone

else:
      import sys

      __A    	=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)