code: 
  
  
  
  """simple docstring"""
  import csv
  from collections import defaultdict
  from dataclasses import dataclass, field
  from typing import List, Optional
  
  import matplotlib.pyplot as plt
  import numpy as np
  from matplotlib.ticker import ScalarFormatter
  
  from transformers import HfArgumentParser
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Optional[int]=None  , UpperCamelCase__	:	Any=None							):
  				return field(default_factory=lambda: default  , metadata=lowerCamelCase__							)
  @dataclass
  class 							_UpperCAmelCase							:
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					a__             =field(
  					    metadata={'''help''': '''The csv file to plot.'''}	,)
  					a__             =field(
  					    default=_a	,metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}	,)
  					a__             =field(
  					    default=_a	,metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}	,)
  					a__             =field(
  					    default=_a	,metadata={'''help''': '''Disable logarithmic scale when plotting'''}	,)
  					a__             =field(
  					    default=_a	,metadata={
  					        '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
  					    }	,)
  					a__             =field(
  					    default=_a	,metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}	,)
  					a__             =list_field(
  					    default=_a	,metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}   )
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Union[str, Any]							):
  
  
  
  
  				try:
  								int(lowerCamelCase__							)
  								return True
  				except ValueError:
  								return False
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Optional[Any]							):
  
  
  
  
  				try:
  								float(lowerCamelCase__							)
  								return True
  				except ValueError:
  								return False
  class 							_UpperCAmelCase							:
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def __init__(      self ,			A	)							->      Optional[Any]:
  									_UpperCAmelCase							:						Optional[Any]       							=					args
  									_UpperCAmelCase							:						Tuple       							=					defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)
  
  									with open(self.args.csv_file ,			newline=''''''	) as csv_file:
  													_UpperCAmelCase							:						str       							=					csv.DictReader(A	)
  													for row in reader:
  																	_UpperCAmelCase							:						List[str]       							=					row['''model''']
  																	self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']	)	)
  																	self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']	)	)
  																	if can_convert_to_int(row['''result''']	):
  																					# value is not None
  																					_UpperCAmelCase							:						List[str]       							=					int(row['''result''']	)
  																	elif can_convert_to_float(row['''result''']	):
  																					# value is not None
  																					_UpperCAmelCase							:						List[str]       							=					float(row['''result''']	)
  
  
  
  
  
  					def 				__lowerCAmelCase			(      self	)							->      str:
  									_UpperCAmelCase							,       _UpperCAmelCase							:						Optional[int]       							=					plt.subplots()
  									_UpperCAmelCase							:						int       							=					'''Time usage''' if self.args.is_time else '''Memory usage'''
  									_UpperCAmelCase							:						List[Any]       							=					title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''
  
  									if not self.args.no_log_scale:
  													# set logarithm scales
  													ax.set_xscale('''log'''	)
  													ax.set_yscale('''log'''	)
  
  									for axis in [ax.xaxis, ax.yaxis]:
  													axis.set_major_formatter(ScalarFormatter()	)
  
  									for model_name_idx, model_name in enumerate(self.result_dict.keys()	):
  													_UpperCAmelCase							:						Dict       							=					sorted(set(self.result_dict[model_name]['''bsz''']	)	)
  													_UpperCAmelCase							:						str       							=					sorted(set(self.result_dict[model_name]['''seq_len''']	)	)
  													_UpperCAmelCase							:						Tuple       							=					self.result_dict[model_name]['''result''']
  
  													((_UpperCAmelCase)							,       (_UpperCAmelCase))							:						str       							=					(
  													    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
  													)
  
  													_UpperCAmelCase							:						List[Any]       							=					(
  													    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
  													)
  
  													for inner_loop_value in inner_loop_array:
  																	if self.args.plot_along_batch:
  																					_UpperCAmelCase							:						Optional[Any]       							=					np.asarray(
  																					    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,			dtype=A ,			)
  																	else:
  																					_UpperCAmelCase							:						int       							=					np.asarray(
  																					    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] ,			dtype=np.floataa ,			)
  
  																	((_UpperCAmelCase)							,       (_UpperCAmelCase))							:						List[str]       							=					(
  																	    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
  																	)
  
  																	_UpperCAmelCase							:						List[str]       							=					np.asarray(A ,			A	)[: len(A	)]
  																	plt.scatter(
  																	    A ,			A ,			label=f'{label_model_name} - {inner_loop_label}: {inner_loop_value}'	)
  																	plt.plot(A ,			A ,			'''--'''	)
  
  													title_str += f' {label_model_name} vs.'
  
  									_UpperCAmelCase							:						int       							=					title_str[:-4]
  									_UpperCAmelCase							:						List[Any]       							=					'''Time in s''' if self.args.is_time else '''Memory in MB'''
  
  									# plot
  									plt.title(A	)
  									plt.xlabel(A	)
  									plt.ylabel(A	)
  									plt.legend()
  
  									if self.args.figure_png_file is not None:
  													plt.savefig(self.args.figure_png_file	)
  									else:
  													plt.show()
  
  
  
  
  
  def 				lowerCamelCase_		():
  				_UpperCAmelCase							:						Union[str, Any]       							=					HfArgumentParser(lowerCamelCase__							)
  				_UpperCAmelCase							:						List[str]       							=					parser.parse_args_into_dataclasses()[0]
  				_UpperCAmelCase							:						Dict       							=					Plot(args=lowerCamelCase__							)
  				plot.plot()
  
  
  if __name__ == "__main__":
  		main()
  
  
  
style_context: 
  
  
  
  """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()
  _lowerCAmelCase					:int  =      logging.get_logger(__name__)
  
  _lowerCAmelCase					:Dict  =      {
      '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__	:	str  , UpperCamelCase__	:	Union[str, Any]  , UpperCamelCase__	:	Optional[int]  , UpperCamelCase__	:	Any  , UpperCamelCase__	:	int							):
      for attribute in key.split('''.'''							):
          _UpperCAmelCase							:						Optional[Any]       							=					getattr(UpperCamelCase__  , UpperCamelCase__							)
  
      if weight_type is not None:
          _UpperCAmelCase							:						Union[str, Any]       							=					getattr(UpperCamelCase__  , UpperCamelCase__							).shape
      else:
          _UpperCAmelCase							:						Union[str, 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":
          _UpperCAmelCase							:						Dict       							=					value
      elif weight_type == "weight_g":
          _UpperCAmelCase							:						Optional[int]       							=					value
      elif weight_type == "weight_v":
          _UpperCAmelCase							:						List[Any]       							=					value
      elif weight_type == "bias":
          _UpperCAmelCase							:						Union[str, Any]       							=					value
      else:
          _UpperCAmelCase							:						Optional[int]       							=					value
  
      logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'							)
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	Optional[Any]  , UpperCamelCase__	:	int							):
      _UpperCAmelCase							:						Dict       							=					[]
      _UpperCAmelCase							:						Optional[int]       							=					fairseq_model.state_dict()
  
      _UpperCAmelCase							:						Any       							=					hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor
  
      for name, value in fairseq_dict.items():
          _UpperCAmelCase							:						str       							=					False
          if "conv_layers" in name:
              load_conv_layer(
                  UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__  , hf_model.config.feat_extract_norm == '''group'''  , )
              _UpperCAmelCase							:						Any       							=					True
          else:
              for key, mapped_key in MAPPING.items():
                  _UpperCAmelCase							:						str       							=					'''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]:
                      _UpperCAmelCase							:						Dict       							=					True
                      if "*" in mapped_key:
                          _UpperCAmelCase							:						List[str]       							=					name.split(UpperCamelCase__							)[0].split('''.'''							)[-2]
                          if not layer_index.isnumeric():
                              continue
                          _UpperCAmelCase							:						str       							=					mapped_key.replace('''*'''  , UpperCamelCase__							)
                      if "weight_g" in name:
                          _UpperCAmelCase							:						int       							=					'''weight_g'''
                      elif "weight_v" in name:
                          _UpperCAmelCase							:						Optional[int]       							=					'''weight_v'''
                      elif "weight" in name:
                          _UpperCAmelCase							:						int       							=					'''weight'''
                      elif "bias" in name:
                          _UpperCAmelCase							:						int       							=					'''bias'''
                      else:
                          _UpperCAmelCase							:						Dict       							=					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__	:	Tuple  , UpperCamelCase__	:	Optional[Any]  , UpperCamelCase__	:	Tuple  , UpperCamelCase__	:	Any  , UpperCamelCase__	:	str							):
      _UpperCAmelCase							:						List[Any]       							=					full_name.split('''conv_layers.'''							)[-1]
      _UpperCAmelCase							:						Any       							=					name.split('''.'''							)
      _UpperCAmelCase							:						Union[str, Any]       							=					int(items[0]							)
      _UpperCAmelCase							:						int       							=					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.'
              )
              _UpperCAmelCase							:						Tuple       							=					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.'
              )
              _UpperCAmelCase							:						Union[str, 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."
              )
              _UpperCAmelCase							:						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.'
              )
              _UpperCAmelCase							:						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__	:	List[Any]  , UpperCamelCase__	:	Any							):
      _UpperCAmelCase							:						Any       							=					SEWDConfig()
      if is_finetuned:
          _UpperCAmelCase							:						str       							=					model.wav_encoder.wav_model.cfg
      else:
          _UpperCAmelCase							:						Optional[Any]       							=					model.cfg
  
      _UpperCAmelCase							:						Optional[Any]       							=					fs_config.conv_bias
      _UpperCAmelCase							:						Optional[int]       							=					eval(fs_config.conv_feature_layers							)
      _UpperCAmelCase							:						Dict       							=					[x[0] for x in conv_layers]
      _UpperCAmelCase							:						int       							=					[x[1] for x in conv_layers]
      _UpperCAmelCase							:						Optional[int]       							=					[x[2] for x in conv_layers]
      _UpperCAmelCase							:						int       							=					'''gelu'''
      _UpperCAmelCase							:						int       							=					'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
      _UpperCAmelCase							:						Tuple       							=					0.0
      _UpperCAmelCase							:						List[Any]       							=					fs_config.activation_fn.name
      _UpperCAmelCase							:						Optional[Any]       							=					fs_config.encoder_embed_dim
      _UpperCAmelCase							:						Optional[int]       							=					0.02
      _UpperCAmelCase							:						List[Any]       							=					fs_config.encoder_ffn_embed_dim
      _UpperCAmelCase							:						Dict       							=					1E-5
      _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.encoder_layerdrop
      _UpperCAmelCase							:						Tuple       							=					fs_config.encoder_attention_heads
      _UpperCAmelCase							:						Optional[Any]       							=					fs_config.conv_pos_groups
      _UpperCAmelCase							:						Any       							=					fs_config.conv_pos
      _UpperCAmelCase							:						Optional[Any]       							=					len(UpperCamelCase__							)
      _UpperCAmelCase							:						Any       							=					fs_config.encoder_layers
      _UpperCAmelCase							:						Dict       							=					fs_config.squeeze_factor
      # DeBERTa-specific parameters:
      _UpperCAmelCase							:						int       							=					fs_config.max_position_embeddings
      _UpperCAmelCase							:						Any       							=					fs_config.position_buckets
      _UpperCAmelCase							:						Optional[int]       							=					fs_config.share_att_key
      _UpperCAmelCase							:						Dict       							=					fs_config.relative_attention
      _UpperCAmelCase							:						List[Any]       							=					fs_config.position_biased_input
      _UpperCAmelCase							:						str       							=					tuple(fs_config.pos_att_type.split('''|'''							)							)
      _UpperCAmelCase							:						List[Any]       							=					fs_config.norm_rel_ebd
  
      # take care of any params that are overridden by the Wav2VecCtc model
      if is_finetuned:
          _UpperCAmelCase							:						Any       							=					model.cfg
          _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.final_dropout
          _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.layerdrop
      _UpperCAmelCase							:						str       							=					fs_config.activation_dropout
      _UpperCAmelCase							:						List[Any]       							=					fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
      _UpperCAmelCase							:						List[Any]       							=					fs_config.attention_dropout
      _UpperCAmelCase							:						List[Any]       							=					fs_config.dropout_input
      _UpperCAmelCase							:						str       							=					fs_config.dropout
      _UpperCAmelCase							:						List[Any]       							=					fs_config.mask_channel_length
      _UpperCAmelCase							:						Tuple       							=					fs_config.mask_channel_prob
      _UpperCAmelCase							:						List[str]       							=					fs_config.mask_length
      _UpperCAmelCase							:						Union[str, Any]       							=					fs_config.mask_prob
  
      _UpperCAmelCase							:						int       							=					'''Wav2Vec2FeatureExtractor'''
      _UpperCAmelCase							:						str       							=					'''Wav2Vec2CTCTokenizer'''
  
      return config
  
  
  
  
  
  @torch.no_grad()
  def 				lowerCamelCase_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	int  , UpperCamelCase__	:	Tuple=None  , UpperCamelCase__	:	Any=None  , UpperCamelCase__	:	Union[str, Any]=True							):
  
      if is_finetuned:
          _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							:						int       							=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]  , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							)}							)
      else:
          _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							:						Tuple       							=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)
  
      if config_path is not None:
          _UpperCAmelCase							:						List[str]       							=					SEWDConfig.from_pretrained(UpperCamelCase__							)
      else:
          _UpperCAmelCase							:						Optional[Any]       							=					convert_config(model[0]  , UpperCamelCase__							)
      _UpperCAmelCase							:						Dict       							=					model[0].eval()
  
      _UpperCAmelCase							:						List[str]       							=					True if config.feat_extract_norm == '''layer''' else False
      _UpperCAmelCase							:						Optional[int]       							=					WavaVecaFeatureExtractor(
          feature_size=1  , sampling_rate=1_6000  , padding_value=0  , do_normalize=UpperCamelCase__  , return_attention_mask=UpperCamelCase__  , )
  
      if is_finetuned:
          if dict_path:
              _UpperCAmelCase							:						List[str]       							=					Dictionary.load(UpperCamelCase__							)
  
              # important change bos & pad token id since CTC symbol is <pad> and
              # not <s> as in fairseq
              _UpperCAmelCase							:						Union[str, Any]       							=					target_dict.pad_index
              _UpperCAmelCase							:						Union[str, Any]       							=					target_dict.bos_index
              _UpperCAmelCase							:						int       							=					target_dict.pad_index
              _UpperCAmelCase							:						str       							=					target_dict.bos_index
              _UpperCAmelCase							:						Tuple       							=					target_dict.eos_index
              _UpperCAmelCase							:						List[str]       							=					len(target_dict.symbols							)
              _UpperCAmelCase							:						Optional[Any]       							=					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__							)
              _UpperCAmelCase							:						Any       							=					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__  , )
              _UpperCAmelCase							:						Dict       							=					WavaVecaProcessor(feature_extractor=UpperCamelCase__  , tokenizer=UpperCamelCase__							)
              processor.save_pretrained(UpperCamelCase__							)
  
          _UpperCAmelCase							:						Tuple       							=					SEWDForCTC(UpperCamelCase__							)
      else:
          _UpperCAmelCase							:						Any       							=					SEWDModel(UpperCamelCase__							)
          feature_extractor.save_pretrained(UpperCamelCase__							)
  
      recursively_load_weights(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)
  
      hf_model.save_pretrained(UpperCamelCase__							)
  
  
  if __name__ == "__main__":
    _lowerCAmelCase					: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'
    )
    _lowerCAmelCase					:int  =      parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )
  
  
  