


'''simple docstring'''



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__lowercase	:						Any       =	logging.get_logger(__name__)

__lowercase	:						List[Any]       =	{
    'post_extract_proj': 'feature_projection.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.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
__lowercase	:						Optional[Any]       =	[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[int]						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   Dict						,      _SCREAMING_SNAKE_CASE      :   Dict						,      _SCREAMING_SNAKE_CASE      :   Any   ):
					for attribute in key.split('.'   ):
										if is_finetuned:
															if attribute in ["quantizer", "project_q", "project_hid"]:
																				# those layers are only relevant for pretraining and should be dropped
																				return

															if attribute == "ctc_proj":
																				# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
																				__a			:       Optional[Any]					    =  'lm_head'

										__a			:       Any					    =  getattr(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

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

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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       List[str]					    =  []
					__a			:       Optional[Any]					    =  fairseq_model.state_dict()

					__a			:       Dict					    =  hf_model.unispeech.feature_extractor

					for name, value in fairseq_dict.items():
										__a			:       Union[str, Any]					    =  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'						,      )
															__a			:       Tuple					    =  True
										else:
															for key, mapped_key in MAPPING.items():
																				__a			:       int					    =  'unispeech.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																				if key in name or key.split('w2v_model.'   )[-1] == name.split('.'   )[0]:
																									__a			:       str					    =  True
																									if "*" in mapped_key:
																														__a			:       Dict					    =  name.split(_SCREAMING_SNAKE_CASE   )[0].split('.'   )[-2]
																														__a			:       Optional[int]					    =  mapped_key.replace('*'						,      _SCREAMING_SNAKE_CASE   )
																									if "weight_g" in name:
																														__a			:       Any					    =  'weight_g'
																									elif "weight_v" in name:
																														__a			:       List[str]					    =  'weight_v'
																									elif "bias" in name:
																														__a			:       Dict					    =  'bias'
																									elif "weight" in name:
																														# TODO: don't match quantizer.weight_proj
																														__a			:       List[Any]					    =  'weight'
																									else:
																														__a			:       str					    =  None
																									set_recursively(_SCREAMING_SNAKE_CASE						,      _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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   List[Any]						,      _SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   List[Any]   ):
					__a			:       Union[str, Any]					    =  full_name.split('conv_layers.'   )[-1]
					__a			:       Dict					    =  name.split('.'   )
					__a			:       Any					    =  int(items[0]   )
					__a			:       str					    =  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."""
															)
															__a			:       List[Any]					    =  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."""
															)
															__a			:       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."
															)
															__a			:       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."""
															)
															__a			:       Dict					    =  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   )






@torch.no_grad()
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      _SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]=None						,      _SCREAMING_SNAKE_CASE      :   List[Any]=None						,      _SCREAMING_SNAKE_CASE      :   List[str]=True   ):
					if config_path is not None:
										__a			:       str					    =  UniSpeechConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )
					else:
										__a			:       Optional[Any]					    =  UniSpeechConfig()

					if is_finetuned:
										if dict_path:
															__a			:       List[str]					    =  Dictionary.load_from_json(_SCREAMING_SNAKE_CASE   )

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															__a			:       int					    =  target_dict.pad_index
															__a			:       Dict					    =  target_dict.bos_index
															__a			:       Tuple					    =  target_dict.eos_index
															__a			:       int					    =  len(target_dict.symbols   )
															__a			:       Union[str, 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   )
															__a			:       Optional[Any]					    =  target_dict.indices

															# fairseq has the <pad> and <s> switched
															__a			:       List[str]					    =  42
															__a			:       int					    =  43
															with open(_SCREAMING_SNAKE_CASE						,      'w'						,      encoding='utf-8'   ) as vocab_handle:
																				json.dump(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
															__a			:       Any					    =  WavaVecaPhonemeCTCTokenizer(
															    _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						,      )
															__a			:       List[str]					    =  True if config.feat_extract_norm == 'layer' else False
															__a			:       List[Any]					    =  WavaVecaFeatureExtractor(
															    feature_size=1						,      sampling_rate=16_000						,      padding_value=0						,      do_normalize=_SCREAMING_SNAKE_CASE						,      return_attention_mask=_SCREAMING_SNAKE_CASE						,      )
															__a			:       Dict					    =  WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE						,      tokenizer=_SCREAMING_SNAKE_CASE   )
															processor.save_pretrained(_SCREAMING_SNAKE_CASE   )

										__a			:       Optional[int]					    =  UniSpeechForCTC(_SCREAMING_SNAKE_CASE   )
					else:
										__a			:       Tuple					    =  UniSpeechForPreTraining(_SCREAMING_SNAKE_CASE   )

					if is_finetuned:
										__a    ,				__a    ,				__a			:       Dict					    =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
										    [checkpoint_path]						,      arg_overrides={'data': '/'.join(dict_path.split('/'   )[:-1]   ), 'w2v_path': checkpoint_path}   )
					else:
										__a    ,				__a    ,				__a			:       Union[str, Any]					    =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

					__a			:       int					    =  model[0].eval()

					recursively_load_weights(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					hf_unispeech.save_pretrained(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	__lowercase	:						Optional[int]       =	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(
	    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
	)
	__lowercase	:						str       =	parser.parse_args()
	convert_unispeech_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)







'''simple docstring'''



import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__lowercase	:						Dict       =	16
__lowercase	:						List[Any]       =	32






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					return int(x / 2**20   )





class 							__UpperCamelCase				:




	def __enter__(   self							):

						'''simple docstring'''





						gc.collect()
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
						__a			:       Optional[int]					    =  torch.cuda.memory_allocated()
						return self





	def __exit__(   self						,				*__a							):

						'''simple docstring'''





						gc.collect()
						torch.cuda.empty_cache()
						__a			:       Dict					    =  torch.cuda.memory_allocated()
						__a			:       List[Any]					    =  torch.cuda.max_memory_allocated()
						__a			:       Tuple					    =  bamb(self.end - self.begin							)
						__a			:       Tuple					    =  bamb(self.peak - self.begin							)
						# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Accelerator						,      _SCREAMING_SNAKE_CASE      :   int = 16						,      _SCREAMING_SNAKE_CASE      :   str = "bert-base-cased"						,      _SCREAMING_SNAKE_CASE      :   int = 320						,      _SCREAMING_SNAKE_CASE      :   int = 160						,      ):
					__a			:       int					    =  AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   )
					__a			:       List[Any]					    =  load_dataset(
					    'glue'						,      'mrpc'						,      split={'train': F"""train[:{n_train}]""", 'validation': F"""validation[:{n_val}]"""}   )

					def tokenize_function(_SCREAMING_SNAKE_CASE      :   Tuple   ):
										# max_length=None => use the model max length (it's actually the default)
										__a			:       Any					    =  tokenizer(examples['sentence1']						,      examples['sentence2']						,      truncation=_SCREAMING_SNAKE_CASE						,      max_length=_SCREAMING_SNAKE_CASE   )
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					__a			:       List[str]					    =  datasets.map(
					    _SCREAMING_SNAKE_CASE						,      batched=_SCREAMING_SNAKE_CASE						,      remove_columns=['idx', 'sentence1', 'sentence2']						,      load_from_cache_file=_SCREAMING_SNAKE_CASE   )

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					__a			:       Tuple					    =  tokenized_datasets.rename_column('label'						,      'labels'   )

					def collate_fn(_SCREAMING_SNAKE_CASE      :   Tuple   ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										if accelerator.distributed_type == DistributedType.TPU:
															return tokenizer.pad(_SCREAMING_SNAKE_CASE						,      padding='max_length'						,      max_length=128						,      return_tensors='pt'   )
										return tokenizer.pad(_SCREAMING_SNAKE_CASE						,      padding='longest'						,      return_tensors='pt'   )

					# Instantiate dataloaders.
					__a			:       int					    =  DataLoader(
					    tokenized_datasets['train']						,      shuffle=_SCREAMING_SNAKE_CASE						,      collate_fn=_SCREAMING_SNAKE_CASE						,      batch_size=_SCREAMING_SNAKE_CASE   )
					__a			:       Tuple					    =  DataLoader(
					    tokenized_datasets['validation']						,      shuffle=_SCREAMING_SNAKE_CASE						,      collate_fn=_SCREAMING_SNAKE_CASE						,      batch_size=_SCREAMING_SNAKE_CASE   )

					return train_dataloader, eval_dataloader






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int   ):
					# Initialize accelerator
					__a			:       str					    =  Accelerator()

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__a			:       Dict					    =  config['lr']
					__a			:       str					    =  int(config['num_epochs']   )
					__a			:       Optional[int]					    =  int(config['seed']   )
					__a			:       Any					    =  int(config['batch_size']   )
					__a			:       List[str]					    =  args.model_name_or_path

					set_seed(_SCREAMING_SNAKE_CASE   )
					__a    ,				__a			:       int					    =  get_dataloaders(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      args.n_train						,      args.n_val   )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					__a			:       Optional[int]					    =  AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE						,      return_dict=_SCREAMING_SNAKE_CASE   )

					# Instantiate optimizer
					__a			:       Optional[Any]					    =  (
					    AdamW
					    if accelerator.state.deepspeed_plugin is None
					    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
					    else DummyOptim
					)
					__a			:       Optional[Any]					    =  optimizer_cls(params=model.parameters()						,      lr=_SCREAMING_SNAKE_CASE   )

					if accelerator.state.deepspeed_plugin is not None:
										__a			:       int					    =  accelerator.state.deepspeed_plugin.deepspeed_config[
										    'gradient_accumulation_steps'
										]
					else:
										__a			:       Union[str, Any]					    =  1
					__a			:       Tuple					    =  (len(_SCREAMING_SNAKE_CASE   ) * num_epochs) // gradient_accumulation_steps

					# Instantiate scheduler
					if (
					    accelerator.state.deepspeed_plugin is None
					    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
					):
										__a			:       str					    =  get_linear_schedule_with_warmup(
										    optimizer=_SCREAMING_SNAKE_CASE						,      num_warmup_steps=0						,      num_training_steps=_SCREAMING_SNAKE_CASE						,      )
					else:
										__a			:       List[Any]					    =  DummyScheduler(_SCREAMING_SNAKE_CASE						,      total_num_steps=_SCREAMING_SNAKE_CASE						,      warmup_num_steps=0   )

					# Prepare everything
					# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
					# prepare method.
					__a    ,				__a    ,				__a    ,				__a    ,				__a			:       Optional[Any]					    =  accelerator.prepare(
					    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					# We need to keep track of how many total steps we have iterated over
					__a			:       Union[str, Any]					    =  0
					# We also need to keep track of the stating epoch so files are named properly
					__a			:       Dict					    =  0

					# Now we train the model
					__a			:       str					    =  {}
					for epoch in range(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ):
										with TorchTracemalloc() as tracemalloc:
															model.train()
															for step, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
																				__a			:       List[Any]					    =  model(**_SCREAMING_SNAKE_CASE   )
																				__a			:       str					    =  outputs.loss
																				__a			:       str					    =  loss / gradient_accumulation_steps
																				accelerator.backward(_SCREAMING_SNAKE_CASE   )
																				if step % gradient_accumulation_steps == 0:
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()

																				overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
										accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin   )   )   )
										accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used   )   )
										accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked   )   )
										accelerator.print(
										    'Total Peak Memory consumed during the train (max): {}'.format(
										        tracemalloc.peaked + bamb(tracemalloc.begin   )   )   )
										__a			:       List[Any]					    =  tracemalloc.peaked + bamb(tracemalloc.begin   )
										if args.peak_memory_upper_bound is not None:
															assert (
															    train_total_peak_memory[F"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
															), "Peak memory usage exceeded the upper bound"

					accelerator.wait_for_everyone()
					if accelerator.is_main_process:
										with open(os.path.join(args.output_dir						,      'peak_memory_utilization.json'   )						,      'w'   ) as f:
															json.dump(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )






def   lowerCamelCase							():
					__a			:       int					    =  argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'   )
					parser.add_argument(
					    '--model_name_or_path'						,      type=_SCREAMING_SNAKE_CASE						,      default='bert-base-cased'						,      help='Path to pretrained model or model identifier from huggingface.co/models.'						,      required=_SCREAMING_SNAKE_CASE						,      )
					parser.add_argument(
					    '--output_dir'						,      type=_SCREAMING_SNAKE_CASE						,      default='.'						,      help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'						,      )
					parser.add_argument(
					    '--peak_memory_upper_bound'						,      type=_SCREAMING_SNAKE_CASE						,      default=_SCREAMING_SNAKE_CASE						,      help='The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.'						,      )
					parser.add_argument(
					    '--n_train'						,      type=_SCREAMING_SNAKE_CASE						,      default=320						,      help='Number of training examples to use.'						,      )
					parser.add_argument(
					    '--n_val'						,      type=_SCREAMING_SNAKE_CASE						,      default=160						,      help='Number of validation examples to use.'						,      )
					parser.add_argument(
					    '--num_epochs'						,      type=_SCREAMING_SNAKE_CASE						,      default=1						,      help='Number of train epochs.'						,      )
					__a			:       List[str]					    =  parser.parse_args()
					__a			:       List[Any]					    =  {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
					training_function(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	main()



