



"""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()







"""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
  )


