





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()
_lowerCamelCase 					=      logging.get_logger(__name__)

_lowerCamelCase 					=      {
    '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',
}
_lowerCamelCase 					=      [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				List[Any]       , UpperCamelCase__:				List[Any]       , UpperCamelCase__:				Dict       , UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				Dict     ):
		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
								SCREAMING_SNAKE_CASE__								=   """lm_head"""

				SCREAMING_SNAKE_CASE__								=   getattr(UpperCamelCase__       , UpperCamelCase__     )

		if weight_type is not None:
				SCREAMING_SNAKE_CASE__								=   getattr(UpperCamelCase__       , UpperCamelCase__     ).shape
		else:
				SCREAMING_SNAKE_CASE__								=   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":
				SCREAMING_SNAKE_CASE__								=   value
		elif weight_type == "weight_g":
				SCREAMING_SNAKE_CASE__								=   value
		elif weight_type == "weight_v":
				SCREAMING_SNAKE_CASE__								=   value
		elif weight_type == "bias":
				SCREAMING_SNAKE_CASE__								=   value
		else:
				SCREAMING_SNAKE_CASE__								=   value

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






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any       , UpperCamelCase__:				int       , UpperCamelCase__:				str     ):
		SCREAMING_SNAKE_CASE__								=   []
		SCREAMING_SNAKE_CASE__								=   fairseq_model.state_dict()

		SCREAMING_SNAKE_CASE__								=   hf_model.unispeech.feature_extractor

		for name, value in fairseq_dict.items():
				SCREAMING_SNAKE_CASE__								=   False
				if "conv_layers" in name:
						load_conv_layer(
						    UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , hf_model.config.feat_extract_norm == """group"""       , )
						SCREAMING_SNAKE_CASE__								=   True
				else:
						for key, mapped_key in MAPPING.items():
								SCREAMING_SNAKE_CASE__								=   """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]:
										SCREAMING_SNAKE_CASE__								=   True
										if "*" in mapped_key:
												SCREAMING_SNAKE_CASE__								=   name.split(UpperCamelCase__     )[0].split("""."""     )[-2]
												SCREAMING_SNAKE_CASE__								=   mapped_key.replace("""*"""       , UpperCamelCase__     )
										if "weight_g" in name:
												SCREAMING_SNAKE_CASE__								=   """weight_g"""
										elif "weight_v" in name:
												SCREAMING_SNAKE_CASE__								=   """weight_v"""
										elif "bias" in name:
												SCREAMING_SNAKE_CASE__								=   """bias"""
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												SCREAMING_SNAKE_CASE__								=   """weight"""
										else:
												SCREAMING_SNAKE_CASE__								=   None
										set_recursively(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
								continue
				if not is_used:
						unused_weights.append(UpperCamelCase__     )

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






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]       , UpperCamelCase__:				Optional[int]       , UpperCamelCase__:				int       , UpperCamelCase__:				Optional[int]       , UpperCamelCase__:				Optional[int]     ):
		SCREAMING_SNAKE_CASE__								=   full_name.split("""conv_layers."""     )[-1]
		SCREAMING_SNAKE_CASE__								=   name.split("""."""     )
		SCREAMING_SNAKE_CASE__								=   int(items[0]     )
		SCREAMING_SNAKE_CASE__								=   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.'''
						)
						SCREAMING_SNAKE_CASE__								=   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.'''
						)
						SCREAMING_SNAKE_CASE__								=   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."
						)
						SCREAMING_SNAKE_CASE__								=   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.'''
						)
						SCREAMING_SNAKE_CASE__								=   value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
		else:
				unused_weights.append(UpperCamelCase__     )






@torch.no_grad()
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]       , UpperCamelCase__:				Tuple       , UpperCamelCase__:				Optional[Any]=None       , UpperCamelCase__:				Any=None       , UpperCamelCase__:				Optional[int]=True     ):
		if config_path is not None:
				SCREAMING_SNAKE_CASE__								=   UniSpeechConfig.from_pretrained(UpperCamelCase__     )
		else:
				SCREAMING_SNAKE_CASE__								=   UniSpeechConfig()

		if is_finetuned:
				if dict_path:
						SCREAMING_SNAKE_CASE__								=   Dictionary.load_from_json(UpperCamelCase__     )

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						SCREAMING_SNAKE_CASE__								=   target_dict.pad_index
						SCREAMING_SNAKE_CASE__								=   target_dict.bos_index
						SCREAMING_SNAKE_CASE__								=   target_dict.eos_index
						SCREAMING_SNAKE_CASE__								=   len(target_dict.symbols     )
						SCREAMING_SNAKE_CASE__								=   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__     )
						SCREAMING_SNAKE_CASE__								=   target_dict.indices

						# fairseq has the <pad> and <s> switched
						SCREAMING_SNAKE_CASE__								=   42
						SCREAMING_SNAKE_CASE__								=   43
						with open(UpperCamelCase__       , """w"""       , encoding="""utf-8"""     ) as vocab_handle:
								json.dump(UpperCamelCase__       , UpperCamelCase__     )
						SCREAMING_SNAKE_CASE__								=   WavaVecaPhonemeCTCTokenizer(
						    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__       , )
						SCREAMING_SNAKE_CASE__								=   True if config.feat_extract_norm == """layer""" else False
						SCREAMING_SNAKE_CASE__								=   WavaVecaFeatureExtractor(
						    feature_size=1       , sampling_rate=16_000       , padding_value=0       , do_normalize=UpperCamelCase__       , return_attention_mask=UpperCamelCase__       , )
						SCREAMING_SNAKE_CASE__								=   WavaVecaProcessor(feature_extractor=UpperCamelCase__       , tokenizer=UpperCamelCase__     )
						processor.save_pretrained(UpperCamelCase__     )

				SCREAMING_SNAKE_CASE__								=   UniSpeechForCTC(UpperCamelCase__     )
		else:
				SCREAMING_SNAKE_CASE__								=   UniSpeechForPreTraining(UpperCamelCase__     )

		if is_finetuned:
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]       , arg_overrides={"""data""": """/""".join(dict_path.split("""/"""     )[:-1]     ), """w2v_path""": checkpoint_path}     )
		else:
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     )

		SCREAMING_SNAKE_CASE__								=   model[0].eval()

		recursively_load_weights(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )

		hf_unispeech.save_pretrained(UpperCamelCase__     )


if __name__ == "__main__":
	_lowerCamelCase 					=      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'
	)
	_lowerCamelCase 					=      parser.parse_args()
	convert_unispeech_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)






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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################


a__      :     Any         =		16
a__      :     str         =		32

def      UpperCAmelCase_    (    _UpperCAmelCase							:Accelerator  ,						_UpperCAmelCase							:int = 16				)	->				int:
     '''simple docstring'''


     A_          =       AutoTokenizer.from_pretrained('''bert-base-cased'''				)
     A_          =       load_dataset('''glue'''  ,						'''mrpc'''				)

     def tokenize_function(_UpperCAmelCase							:Optional[Any]				):
          # max_length=None => use the model max length (it's actually the default)
          A_          =       tokenizer(examples['''sentence1''']  ,						examples['''sentence2''']  ,						truncation=_UpperCAmelCase  ,						max_length=_UpperCAmelCase				)
          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():
          A_          =       datasets.map(
              _UpperCAmelCase  ,						batched=_UpperCAmelCase  ,						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
     A_          =       tokenized_datasets.rename_column('''label'''  ,						'''labels'''				)

     def collate_fn(_UpperCAmelCase							:List[Any]				):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          A_          =       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":
               A_          =       16
          elif accelerator.mixed_precision != "no":
               A_          =       8
          else:
               A_          =       None

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

     # Instantiate dataloaders.
     A_          =       DataLoader(
         tokenized_datasets['''train''']  ,						shuffle=_UpperCAmelCase  ,						collate_fn=_UpperCAmelCase  ,						batch_size=_UpperCAmelCase				)
     A_          =       DataLoader(
         tokenized_datasets['''validation''']  ,						shuffle=_UpperCAmelCase  ,						collate_fn=_UpperCAmelCase  ,						batch_size=_UpperCAmelCase				)

     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

      a__      :     Optional[Any]         =		mocked_dataloaders  # noqa: F811

def      UpperCAmelCase_    (    _UpperCAmelCase							:List[str]  ,						_UpperCAmelCase							:Dict				)	->				Dict:
     '''simple docstring'''


     if os.environ.get('''TESTING_MOCKED_DATALOADERS'''  ,						_UpperCAmelCase				) == "1":
          A_          =       2
     # New Code #
     A_          =       int(args.gradient_accumulation_steps				)
     A_          =       int(args.local_sgd_steps				)
     # Initialize accelerator
     A_          =       Accelerator(
         cpu=args.cpu  ,						mixed_precision=args.mixed_precision  ,						gradient_accumulation_steps=_UpperCAmelCase				)
     if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
          raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''				)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     A_          =       config['''lr''']
     A_          =       int(config['''num_epochs''']				)
     A_          =       int(config['''seed''']				)
     A_          =       int(config['''batch_size''']				)

     A_          =       evaluate.load('''glue'''  ,						'''mrpc'''				)

     set_seed(_UpperCAmelCase				)
     A_   ,    A_          =       get_dataloaders(_UpperCAmelCase  ,						_UpperCAmelCase				)
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     A_          =       AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''  ,						return_dict=_UpperCAmelCase				)

     # 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).
     A_          =       model.to(accelerator.device				)

     # Instantiate optimizer
     A_          =       AdamW(params=model.parameters()  ,						lr=_UpperCAmelCase				)

     # Instantiate scheduler
     A_          =       get_linear_schedule_with_warmup(
         optimizer=_UpperCAmelCase  ,						num_warmup_steps=100  ,						num_training_steps=(len(_UpperCAmelCase				) * 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.
     A_   ,    A_   ,    A_   ,    A_   ,    A_          =       accelerator.prepare(
         _UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase  ,						_UpperCAmelCase				)

     # Now we train the model
     for epoch in range(_UpperCAmelCase				):
          model.train()
          with LocalSGD(
              accelerator=_UpperCAmelCase  ,						model=_UpperCAmelCase  ,						local_sgd_steps=_UpperCAmelCase  ,						enabled=local_sgd_steps is not None				) as local_sgd:
               for step, batch in enumerate(_UpperCAmelCase				):
                    # 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(_UpperCAmelCase				):
                         A_          =       model(**_UpperCAmelCase				)
                         A_          =       output.loss
                         accelerator.backward(_UpperCAmelCase				)
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # LocalSGD-specific line
                         local_sgd.step()

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

          A_          =       metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'epoch {epoch}:'  ,						_UpperCAmelCase				)

def      UpperCAmelCase_    (    )	->				Union[str, Any]:
     '''simple docstring'''


     A_          =       argparse.ArgumentParser(description='''Simple example of training script.'''				)
     parser.add_argument(
         '''--mixed_precision'''  ,						type=_UpperCAmelCase  ,						default=_UpperCAmelCase  ,						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=_UpperCAmelCase  ,						default=1  ,						help='''The number of minibatches to be ran before gradients are accumulated.'''  ,						)
     parser.add_argument(
         '''--local_sgd_steps'''  ,						type=_UpperCAmelCase  ,						default=8  ,						help='''Number of local SGD steps or None to disable local SGD'''				)
     parser.add_argument('''--cpu'''  ,						action='''store_true'''  ,						help='''If passed, will train on the CPU.'''				)
     A_          =       parser.parse_args()
     A_          =       {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(_UpperCAmelCase  ,						_UpperCAmelCase				)


if __name__ == "__main__":
      main()



