"""simple docstring"""





import argparse
import os

import evaluate
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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_UpperCamelCase     :		Tuple											=    16
_UpperCamelCase     :		List[Any]											=    32





def        a_  (   _lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				Tuple = 16  ):





       '''simple docstring'''

       lowercase__    :	Tuple     					=			AutoTokenizer.from_pretrained('bert-base-cased'  )
       lowercase__    :	List[Any]     					=			load_dataset('glue'     ,				'mrpc'  )

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

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              lowercase__    :	List[Any]     					=			datasets.map(
                  lowercase__     ,				batched=lowercase__     ,				remove_columns=['idx', 'sentence1', 'sentence2']     ,				)

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

       def collate_fn(_lowerCAmelCase						:				Any  ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              lowercase__    :	Dict     					=			128 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     lowercase__    :	List[Any]     					=			16
              elif accelerator.mixed_precision != "no":
                     lowercase__    :	Union[str, Any]     					=			8
              else:
                     lowercase__    :	Optional[Any]     					=			None

              return tokenizer.pad(
                  lowercase__     ,				padding='longest'     ,				max_length=lowercase__     ,				pad_to_multiple_of=lowercase__     ,				return_tensors='pt'     ,				)

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

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
       from accelerate.test_utils.training import mocked_dataloaders

       _UpperCamelCase     :		int											=    mocked_dataloaders  # noqa: F811





def        a_  (   _lowerCAmelCase						:				Tuple     ,				_lowerCAmelCase						:				Union[str, Any]  ):





       '''simple docstring'''

       if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,				lowercase__  ) == "1":
              lowercase__    :	Dict     					=			2
       # New Code #
       lowercase__    :	Any     					=			int(args.gradient_accumulation_steps  )
       # Initialize accelerator
       lowercase__    :	Any     					=			Accelerator(
           cpu=args.cpu     ,				mixed_precision=args.mixed_precision     ,				gradient_accumulation_steps=lowercase__  )
       if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
              raise NotImplementedError(
                  'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'  )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowercase__    :	Optional[int]     					=			config['lr']
       lowercase__    :	Optional[Any]     					=			int(config['num_epochs']  )
       lowercase__    :	List[str]     					=			int(config['seed']  )
       lowercase__    :	Union[str, Any]     					=			int(config['batch_size']  )

       lowercase__    :	List[str]     					=			evaluate.load('glue'     ,				'mrpc'  )

       set_seed(lowercase__  )
       lowercase__					,   lowercase__    :	Union[str, Any]     					=			get_dataloaders(lowercase__     ,				lowercase__  )
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowercase__    :	Optional[int]     					=			AutoModelForSequenceClassification.from_pretrained('bert-base-cased'     ,				return_dict=lowercase__  )

       # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
       # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
       # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
       lowercase__    :	Any     					=			model.to(accelerator.device  )

       # Instantiate optimizer
       lowercase__    :	Union[str, Any]     					=			AdamW(params=model.parameters()     ,				lr=lowercase__  )

       # Instantiate scheduler
       lowercase__    :	Dict     					=			get_linear_schedule_with_warmup(
           optimizer=lowercase__     ,				num_warmup_steps=100     ,				num_training_steps=(len(lowercase__  ) * num_epochs)     ,				)

       # 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.
       lowercase__					,   lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	List[str]     					=			accelerator.prepare(
           lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__  )

       # Now we train the model
       for epoch in range(lowercase__  ):
              model.train()
              for step, batch in enumerate(lowercase__  ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device  )
                     # New code #
                     # We use the new `accumulate` context manager to perform gradient accumulation
                     # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                     with accelerator.accumulate(lowercase__  ):
                            lowercase__    :	Optional[Any]     					=			model(**lowercase__  )
                            lowercase__    :	List[str]     					=			output.loss
                            accelerator.backward(lowercase__  )
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

              model.eval()
              for step, batch in enumerate(lowercase__  ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device  )
                     with torch.no_grad():
                            lowercase__    :	Optional[Any]     					=			model(**lowercase__  )
                     lowercase__    :	List[Any]     					=			outputs.logits.argmax(dim=-1  )
                     lowercase__					,   lowercase__    :	List[str]     					=			accelerator.gather_for_metrics((predictions, batch['labels'])  )
                     metric.add_batch(
                         predictions=lowercase__     ,				references=lowercase__     ,				)

              lowercase__    :	int     					=			metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"""epoch {epoch}:"""     ,				lowercase__  )





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Dict     					=			argparse.ArgumentParser(description='Simple example of training script.'  )
       parser.add_argument(
           '--mixed_precision'     ,				type=lowercase__     ,				default=lowercase__     ,				choices=['no', 'fp16', 'bf16', 'fp8']     ,				help='Whether to use mixed precision. Choose'
           'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
           'and an Nvidia Ampere GPU.'     ,				)
       # New Code #
       parser.add_argument(
           '--gradient_accumulation_steps'     ,				type=lowercase__     ,				default=1     ,				help='The number of minibatches to be ran before gradients are accumulated.'     ,				)
       parser.add_argument('--cpu'     ,				action='store_true'     ,				help='If passed, will train on the CPU.'  )
       lowercase__    :	Any     					=			parser.parse_args()
       lowercase__    :	int     					=			{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
       training_function(lowercase__     ,				lowercase__  )


if __name__ == "__main__":
       main()


"""simple docstring"""





import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_UpperCamelCase     :		Dict											=					logging.get_logger(__name__)

_UpperCamelCase     :		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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "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": "lm_head",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
_UpperCamelCase     :		List[str]											=					[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				Optional[Any]     ,				_lowerCAmelCase						:				Tuple  ):





       '''simple docstring'''

       for attribute in key.split('.'  ):
              lowercase__    :	Dict     					=			getattr(_lowerCAmelCase     ,				_lowerCAmelCase  )

       if weight_type is not None:
              lowercase__    :	Optional[int]     					=			getattr(_lowerCAmelCase     ,				_lowerCAmelCase  ).shape
       else:
              lowercase__    :	Optional[int]     					=			hf_pointer.shape

       if hf_shape != value.shape:
              raise ValueError(
                  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":
              lowercase__    :	Optional[Any]     					=			value
       elif weight_type == "weight_g":
              lowercase__    :	Dict     					=			value
       elif weight_type == "weight_v":
              lowercase__    :	List[str]     					=			value
       elif weight_type == "bias":
              lowercase__    :	Optional[Any]     					=			value
       else:
              lowercase__    :	List[str]     					=			value

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





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				Optional[Any]  ):





       '''simple docstring'''

       lowercase__    :	Tuple     					=			[]
       lowercase__    :	List[str]     					=			fairseq_model.state_dict()

       lowercase__    :	Union[str, Any]     					=			hf_model.unispeech_sat.feature_extractor

       for name, value in fairseq_dict.items():
              lowercase__    :	Optional[int]     					=			False
              if "conv_layers" in name:
                     load_conv_layer(
                         _lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				hf_model.config.feat_extract_norm == 'group'     ,				)
                     lowercase__    :	Optional[Any]     					=			True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowercase__    :	List[Any]     					=			'unispeech_sat.' + 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]:
                                   if "layer_norm_for_extract" in name and (".".join(name.split('.'  )[:-1]  ) != key):
                                          # special case since naming is very similar
                                          continue
                                   lowercase__    :	int     					=			True
                                   if "*" in mapped_key:
                                          lowercase__    :	Optional[int]     					=			name.split(_lowerCAmelCase  )[0].split('.'  )[-2]
                                          lowercase__    :	List[str]     					=			mapped_key.replace('*'     ,				_lowerCAmelCase  )
                                   if "weight_g" in name:
                                          lowercase__    :	List[Any]     					=			'weight_g'
                                   elif "weight_v" in name:
                                          lowercase__    :	int     					=			'weight_v'
                                   elif "bias" in name:
                                          lowercase__    :	Dict     					=			'bias'
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          lowercase__    :	Union[str, Any]     					=			'weight'
                                   else:
                                          lowercase__    :	int     					=			None
                                   set_recursively(_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )
                            continue
              if not is_used:
                     unused_weights.append(_lowerCAmelCase  )

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





def        a_  (   _lowerCAmelCase						:				List[Any]     ,				_lowerCAmelCase						:				Union[str, Any]     ,				_lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				int     ,				_lowerCAmelCase						:				Dict  ):





       '''simple docstring'''

       lowercase__    :	int     					=			full_name.split('conv_layers.'  )[-1]
       lowercase__    :	int     					=			name.split('.'  )
       lowercase__    :	int     					=			int(items[0]  )
       lowercase__    :	Dict     					=			int(items[1]  )

       if type_id == 0:
              if "bias" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                            raise ValueError(
                                f"""{full_name} has size {value.shape}, but"""
                                f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""  )
                     lowercase__    :	Union[str, Any]     					=			value
                     logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""  )
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                            raise ValueError(
                                f"""{full_name} has size {value.shape}, but"""
                                f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""  )
                     lowercase__    :	Optional[int]     					=			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:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                            raise ValueError(
                                f"""{full_name} has size {value.shape}, but"""
                                f""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."""  )
                     lowercase__    :	List[Any]     					=			value
                     logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
              elif "weight" in name:
                     if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                            raise ValueError(
                                f"""{full_name} has size {value.shape}, but"""
                                f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""  )
                     lowercase__    :	int     					=			value
                     logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
       else:
              unused_weights.append(_lowerCAmelCase  )





@torch.no_grad()
def        a_  (   _lowerCAmelCase						:				Union[str, Any]     ,				_lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				List[Any]=None     ,				_lowerCAmelCase						:				str=None     ,				_lowerCAmelCase						:				Tuple=True  ):





       '''simple docstring'''

       if config_path is not None:
              lowercase__    :	Any     					=			UniSpeechSatConfig.from_pretrained(_lowerCAmelCase  )
       else:
              lowercase__    :	Any     					=			UniSpeechSatConfig()

       lowercase__    :	Union[str, Any]     					=			''

       if is_finetuned:
              lowercase__    :	Optional[Any]     					=			UniSpeechSatForCTC(_lowerCAmelCase  )
       else:
              lowercase__    :	List[Any]     					=			UniSpeechSatForPreTraining(_lowerCAmelCase  )

       lowercase__					,   lowercase__					,   lowercase__    :	int     					=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
           [checkpoint_path]     ,				arg_overrides={'data': '/'.join(dict_path.split('/'  )[:-1]  )}  )
       lowercase__    :	Union[str, Any]     					=			model[0].eval()

       recursively_load_weights(_lowerCAmelCase     ,				_lowerCAmelCase  )

       hf_wavavec.save_pretrained(_lowerCAmelCase  )


if __name__ == "__main__":
       _UpperCamelCase     :		Tuple											=					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"
       )
       _UpperCamelCase     :		str											=					parser.parse_args()
       convert_unispeech_sat_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )

