




'''simple docstring'''

import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


a__         =     logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

a__         =     {
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
a__         =     {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
a__         =     sorted(arg_to_scheduler.keys())
a__         =     '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''





class __magic_name__(						pl.LightningModule      ):






						def __init__(   self						:						Union[str, Any]					,   __UpperCamelCase						:						argparse.Namespace					,   __UpperCamelCase						:						Dict=None					,   __UpperCamelCase						:						List[str]="base"					,   __UpperCamelCase						:						int=None					,   __UpperCamelCase						:						List[Any]=None					,   __UpperCamelCase						:						Tuple=None					,   **__UpperCamelCase						:						List[Any]					,   ):



									'''simple docstring'''






									super().__init__()
									# TODO: move to self.save_hyperparameters()
									# self.save_hyperparameters()
									# can also expand arguments into trainer signature for easier reading

									self.save_hyperparameters(__UpperCamelCase       )
									snake_case__          =						0
									snake_case__          =						Path(self.hparams.output_dir       )
									snake_case__          =						self.hparams.cache_dir if self.hparams.cache_dir else None
									if config is None:
												snake_case__          =						AutoConfig.from_pretrained(
												    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path					,   **({"""num_labels""": num_labels} if num_labels is not None else {})					,   cache_dir=__UpperCamelCase					,   **__UpperCamelCase					,   )
									else:
												snake_case__          =						config

									snake_case__          =						("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
									for p in extra_model_params:
												if getattr(self.hparams					,   __UpperCamelCase					,   __UpperCamelCase       ):
															assert hasattr(self.config					,   __UpperCamelCase       ), f"""model config doesn't have a `{p}` attribute"""
															setattr(self.config					,   __UpperCamelCase					,   getattr(self.hparams					,   __UpperCamelCase       )       )

									if tokenizer is None:
												snake_case__          =						AutoTokenizer.from_pretrained(
												    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,   cache_dir=__UpperCamelCase					,   )
									else:
												snake_case__          =						tokenizer
									snake_case__          =						MODEL_MODES[mode]
									if model is None:
												snake_case__          =						self.model_type.from_pretrained(
												    self.hparams.model_name_or_path					,   from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path       )					,   config=self.config					,   cache_dir=__UpperCamelCase					,   )
									else:
												snake_case__          =						model






						def       __lowerCAmelCase(   self						:						List[str]					,   *__UpperCamelCase						:						Dict					,   **__UpperCamelCase						:						Tuple       ):



									'''simple docstring'''






									snake_case__          =						self.model_type.from_pretrained(*__UpperCamelCase					,   **__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									snake_case__          =						arg_to_scheduler[self.hparams.lr_scheduler]
									snake_case__          =						get_schedule_func(
									    self.opt					,   num_warmup_steps=self.hparams.warmup_steps					,   num_training_steps=self.total_steps()       )
									snake_case__          =						{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
									return scheduler






						def       __lowerCAmelCase(   self						:						Union[str, Any]       ):



									'''simple docstring'''






									snake_case__          =						self.model
									snake_case__          =						["""bias""", """LayerNorm.weight"""]
									snake_case__          =						[
									    {
									        """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay       )],
									        """weight_decay""": self.hparams.weight_decay,
									    },
									    {
									        """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay       )],
									        """weight_decay""": 0.0,
									    },
									]
									if self.hparams.adafactor:
												snake_case__          =						Adafactor(
												    __UpperCamelCase					,   lr=self.hparams.learning_rate					,   scale_parameter=__UpperCamelCase					,   relative_step=__UpperCamelCase       )

									else:
												snake_case__          =						AdamW(
												    __UpperCamelCase					,   lr=self.hparams.learning_rate					,   eps=self.hparams.adam_epsilon       )
									snake_case__          =						optimizer

									snake_case__          =						self.get_lr_scheduler()

									return [optimizer], [scheduler]






						def       __lowerCAmelCase(   self						:						Optional[Any]					,   __UpperCamelCase						:						Any					,   __UpperCamelCase						:						Dict       ):



									'''simple docstring'''






									return self.validation_step(__UpperCamelCase					,   __UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						Union[str, Any]					,   __UpperCamelCase						:						Optional[Any]       ):



									'''simple docstring'''






									return self.validation_end(__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									snake_case__          =						max(1					,   self.hparams.gpus       )  # TODO: consider num_tpu_cores
									snake_case__          =						self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
									return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs






						def       __lowerCAmelCase(   self						:						Optional[Any]					,   __UpperCamelCase						:						List[Any]       ):



									'''simple docstring'''






									if mode == "test":
												snake_case__          =						len(self.test_dataloader().dataset       )
									else:
												snake_case__          =						self.get_dataloader("""train"""					,   self.hparams.train_batch_size					,   shuffle=__UpperCamelCase       )
												snake_case__          =						len(self.train_dataloader().dataset       )






						def       __lowerCAmelCase(   self						:						Any					,   __UpperCamelCase						:						str					,   __UpperCamelCase						:						int					,   __UpperCamelCase						:						bool = False       ):



									'''simple docstring'''






									raise NotImplementedError("""You must implement this for your task"""       )






						def       __lowerCAmelCase(   self						:						Union[str, Any]       ):



									'''simple docstring'''






									return self.train_loader






						def       __lowerCAmelCase(   self						:						Any       ):



									'''simple docstring'''






									return self.get_dataloader("""dev"""					,   self.hparams.eval_batch_size					,   shuffle=__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									return self.get_dataloader("""test"""					,   self.hparams.eval_batch_size					,   shuffle=__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						List[Any]					,   __UpperCamelCase						:						str       ):



									'''simple docstring'''






									return os.path.join(
									    self.hparams.data_dir					,   """cached_{}_{}_{}""".format(
									        __UpperCamelCase					,   list(filter(__UpperCamelCase					,   self.hparams.model_name_or_path.split("""/"""       )       )       ).pop()					,   str(self.hparams.max_seq_length       )					,   )					,   )






						@pl.utilities.rank_zero_only
						def       __lowerCAmelCase(   self						:						List[str]					,   __UpperCamelCase						:						Dict[str, Any]       ):



									'''simple docstring'''






									snake_case__          =						self.output_dir.joinpath("""best_tfmr"""       )
									snake_case__          =						self.step_count
									self.model.save_pretrained(__UpperCamelCase       )
									self.tokenizer.save_pretrained(__UpperCamelCase       )







						@staticmethod
						def       __lowerCAmelCase(   __UpperCamelCase						:						Union[str, Any]					,   __UpperCamelCase						:						str       ):



									'''simple docstring'''






									parser.add_argument(
									    """--model_name_or_path"""					,   default=__UpperCamelCase					,   type=__UpperCamelCase					,   required=__UpperCamelCase					,   help="""Path to pretrained model or model identifier from huggingface.co/models"""					,   )
									parser.add_argument(
									    """--config_name"""					,   default=""""""					,   type=__UpperCamelCase					,   help="""Pretrained config name or path if not the same as model_name"""       )
									parser.add_argument(
									    """--tokenizer_name"""					,   default=__UpperCamelCase					,   type=__UpperCamelCase					,   help="""Pretrained tokenizer name or path if not the same as model_name"""					,   )
									parser.add_argument(
									    """--cache_dir"""					,   default=""""""					,   type=__UpperCamelCase					,   help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""					,   )
									parser.add_argument(
									    """--encoder_layerdrop"""					,   type=__UpperCamelCase					,   help="""Encoder layer dropout probability (Optional). Goes into model.config"""					,   )
									parser.add_argument(
									    """--decoder_layerdrop"""					,   type=__UpperCamelCase					,   help="""Decoder layer dropout probability (Optional). Goes into model.config"""					,   )
									parser.add_argument(
									    """--dropout"""					,   type=__UpperCamelCase					,   help="""Dropout probability (Optional). Goes into model.config"""					,   )
									parser.add_argument(
									    """--attention_dropout"""					,   type=__UpperCamelCase					,   help="""Attention dropout probability (Optional). Goes into model.config"""					,   )
									parser.add_argument("""--learning_rate"""					,   default=5E-5					,   type=__UpperCamelCase					,   help="""The initial learning rate for Adam."""       )
									parser.add_argument(
									    """--lr_scheduler"""					,   default="""linear"""					,   choices=__UpperCamelCase					,   metavar=__UpperCamelCase					,   type=__UpperCamelCase					,   help="""Learning rate scheduler"""					,   )
									parser.add_argument("""--weight_decay"""					,   default=0.0					,   type=__UpperCamelCase					,   help="""Weight decay if we apply some."""       )
									parser.add_argument("""--adam_epsilon"""					,   default=1E-8					,   type=__UpperCamelCase					,   help="""Epsilon for Adam optimizer."""       )
									parser.add_argument("""--warmup_steps"""					,   default=0					,   type=__UpperCamelCase					,   help="""Linear warmup over warmup_steps."""       )
									parser.add_argument("""--num_workers"""					,   default=4					,   type=__UpperCamelCase					,   help="""kwarg passed to DataLoader"""       )
									parser.add_argument("""--num_train_epochs"""					,   dest="""max_epochs"""					,   default=3					,   type=__UpperCamelCase       )
									parser.add_argument("""--train_batch_size"""					,   default=3_2					,   type=__UpperCamelCase       )
									parser.add_argument("""--eval_batch_size"""					,   default=3_2					,   type=__UpperCamelCase       )
									parser.add_argument("""--adafactor"""					,   action="""store_true"""       )







class __magic_name__(						pl.Callback      ):






						def       __lowerCAmelCase(   self						:						Optional[int]					,   __UpperCamelCase						:						Optional[int]					,   __UpperCamelCase						:						int       ):



									'''simple docstring'''






									snake_case__          =						trainer.lr_schedulers[0]["""scheduler"""]
									snake_case__          =						{f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()       )}
									pl_module.logger.log_metrics(__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						Tuple					,   __UpperCamelCase						:						pl.Trainer					,   __UpperCamelCase						:						pl.LightningModule       ):



									'''simple docstring'''






									rank_zero_info("""***** Validation results *****"""       )
									snake_case__          =						trainer.callback_metrics
									# Log results
									for key in sorted(__UpperCamelCase       ):
												if key not in ["log", "progress_bar"]:
															rank_zero_info("""{} = {}\n""".format(__UpperCamelCase					,   str(metrics[key]       )       )       )







						def       __lowerCAmelCase(   self						:						Union[str, Any]					,   __UpperCamelCase						:						pl.Trainer					,   __UpperCamelCase						:						pl.LightningModule       ):



									'''simple docstring'''






									rank_zero_info("""***** Test results *****"""       )
									snake_case__          =						trainer.callback_metrics
									# Log and save results to file
									snake_case__          =						os.path.join(pl_module.hparams.output_dir					,   """test_results.txt"""       )
									with open(__UpperCamelCase					,   """w"""       ) as writer:
												for key in sorted(__UpperCamelCase       ):
															if key not in ["log", "progress_bar"]:
																		rank_zero_info("""{} = {}\n""".format(__UpperCamelCase					,   str(metrics[key]       )       )       )
																		writer.write("""{} = {}\n""".format(__UpperCamelCase					,   str(metrics[key]       )       )       )







def    snake_case__     (  a       ,      a	)			->       None:





			'''simple docstring'''






			parser.add_argument(
			    """--output_dir"""       ,      default=a       ,      type=a       ,      required=a       ,      help="""The output directory where the model predictions and checkpoints will be written."""       ,      )
			parser.add_argument(
			    """--fp16"""       ,      action="""store_true"""       ,      help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""       ,      )

			parser.add_argument(
			    """--fp16_opt_level"""       ,      type=a       ,      default="""O2"""       ,      help=(
			        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
			        """See details at https://nvidia.github.io/apex/amp.html"""
			    )       ,      )
			parser.add_argument("""--n_tpu_cores"""       ,      dest="""tpu_cores"""       ,      type=a	)
			parser.add_argument("""--max_grad_norm"""       ,      dest="""gradient_clip_val"""       ,      default=1.0       ,      type=a       ,      help="""Max gradient norm"""	)
			parser.add_argument("""--do_train"""       ,      action="""store_true"""       ,      help="""Whether to run training."""	)
			parser.add_argument("""--do_predict"""       ,      action="""store_true"""       ,      help="""Whether to run predictions on the test set."""	)
			parser.add_argument(
			    """--gradient_accumulation_steps"""       ,      dest="""accumulate_grad_batches"""       ,      type=a       ,      default=1       ,      help="""Number of updates steps to accumulate before performing a backward/update pass."""       ,      )
			parser.add_argument("""--seed"""       ,      type=a       ,      default=42       ,      help="""random seed for initialization"""	)
			parser.add_argument(
			    """--data_dir"""       ,      default=a       ,      type=a       ,      required=a       ,      help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""       ,      )




def    snake_case__     (  a       ,      a       ,      a=None       ,      a=True       ,      a=[]       ,      a=None       ,      a=None       ,      **a       ,      )			->       int:





			'''simple docstring'''






			pl.seed_everything(args.seed	)

			# init model
			snake_case__          =						Path(model.hparams.output_dir	)
			odir.mkdir(exist_ok=a	)

			# add custom checkpoints
			if checkpoint_callback is None:
						snake_case__          =						pl.callbacks.ModelCheckpoint(
						    filepath=args.output_dir       ,      prefix="""checkpoint"""       ,      monitor="""val_loss"""       ,      mode="""min"""       ,      save_top_k=1	)
			if early_stopping_callback:
						extra_callbacks.append(a	)
			if logging_callback is None:
						snake_case__          =						LoggingCallback()

			snake_case__          =						{}

			# TODO: remove with PyTorch 1.6 since pl uses native amp
			if args.fpaa:
						snake_case__          =						16
						snake_case__          =						args.fpaa_opt_level

			if args.gpus > 1:
						snake_case__          =						"""ddp"""

			snake_case__          =						args.accumulate_grad_batches
			snake_case__          =						extra_train_kwargs.get("""accelerator"""       ,      a	)
			snake_case__          =						extra_train_kwargs.get("""profiler"""       ,      a	)

			snake_case__          =						pl.Trainer.from_argparse_args(
			    a       ,      weights_summary=a       ,      callbacks=[logging_callback] + extra_callbacks       ,      logger=a       ,      checkpoint_callback=a       ,      **a       ,      )

			if args.do_train:
						trainer.fit(a	)

			return trainer





'''simple docstring'''


import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
UpperCamelCase			     =					logging.get_logger(__name__)

UpperCamelCase			     =					{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''models.0.layer_norm''': '''feature_projection.layer_norm''',
    '''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''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
UpperCamelCase			     =					[
    '''lm_head''',
]
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> Any:
     for attribute in key.split('''.'''	):
          A:		Union[str, Any]          =		getattr(__lowercase		,				__lowercase	)

     if weight_type is not None:
          A:		Tuple          =		getattr(__lowercase		,				__lowercase	).shape
     else:
          A:		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":
          A:		int          =		value
     elif weight_type == "weight_g":
          A:		Any          =		value
     elif weight_type == "weight_v":
          A:		str          =		value
     elif weight_type == "bias":
          A:		List[str]          =		value
     else:
          A:		str          =		value

     logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase	)			-> Dict:
     A:		Optional[int]          =		[]
     A:		Tuple          =		fairseq_model.state_dict()

     if not is_headless:
          A:		int          =		hf_model.dataavec_audio.feature_extractor
          A:		Dict          =		hf_model.dataavec_audio.encoder.pos_conv_embed

     else:
          A:		str          =		hf_model.feature_extractor
          A:		Tuple          =		hf_model.encoder.pos_conv_embed

     for name, value in fairseq_dict.items():
          A:		int          =		False
          if "conv_layers" in name:
               load_conv_layer(
                   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase		,				)
               A:		List[Any]          =		True
          elif "pos_conv" in name:
               load_pos_conv_layer(
                   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase		,				)
               A:		Union[str, Any]          =		True
          else:
               for key, mapped_key in MAPPING.items():
                    if not is_headless:
                         A:		str          =		'''data2vec_audio.''' + 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:		Optional[int]          =		True
                         if "*" in mapped_key:
                              A:		Any          =		name.split(__lowercase	)[0].split('''.'''	)[-2]
                              A:		Any          =		mapped_key.replace('''*'''		,				__lowercase	)
                         if "weight_g" in name:
                              A:		List[Any]          =		'''weight_g'''
                         elif "weight_v" in name:
                              A:		List[Any]          =		'''weight_v'''
                         elif "bias" in name:
                              A:		int          =		'''bias'''
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              A:		int          =		'''weight'''
                         else:
                              A:		List[Any]          =		None
                         set_recursively(__lowercase		,				__lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)
                    continue
          if not is_used:
               unused_weights.append(__lowercase	)

     logger.warning(F"""Unused weights: {unused_weights}"""	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase	)			-> str:
     A:		int          =		path.split('''.'''	)
     return reduce(__lowercase		,				__lowercase		,				__lowercase	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> Optional[Any]:
     A:		Tuple          =		access_by_string(__lowercase		,				__lowercase	)
     A:		Tuple          =		hf_weight.data

     if fsq_value.shape != hf_value.shape:
          raise ValueError(F"""{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found."""	)
     A:		List[Any]          =		fsq_value
     logger.info(F"""{full_name} was correctly initialized from {hf_weight_path}."""	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> List[Any]:
     A:		List[Any]          =		full_name.split('''conv_layers.'''	)[-1]
     A:		List[str]          =		name.split('''.'''	)
     A:		Tuple          =		int(items[0]	)
     A:		Optional[int]          =		int(items[1]	)

     A:		List[str]          =		name.split('''.'''	)[-1]
     if type_id == 0:
          A:		Optional[Any]          =		'''conv'''
     elif type_id == 2:
          A:		List[str]          =		'''layer_norm'''
     else:
          unused_weights.append(__lowercase	)
          return

     set_weights(__lowercase		,				__lowercase		,				__lowercase		,				F"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> Optional[Any]:
     A:		Any          =		full_name.split('''pos_conv.'''	)[-1]
     A:		Any          =		name.split('''.'''	)
     A:		int          =		int(items[0]	)
     A:		Optional[int]          =		int(items[1]	)

     A:		Optional[Any]          =		name.split('''.'''	)[-1]
     if type_id != 0:
          unused_weights.append(__lowercase	)
          return
     else:
          A:		List[str]          =		'''conv'''

     set_weights(__lowercase		,				__lowercase		,				__lowercase		,				F"""layers.{layer_id}.{layer_type}.{weight_type}"""	)
@torch.no_grad()
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase=None		,				__lowercase=None		,				__lowercase=True	)			-> Optional[Any]:
     if config_path is not None:
          A:		Tuple          =		DataaVecAudioConfig.from_pretrained(__lowercase	)
     else:
          A:		Optional[Any]          =		DataaVecAudioConfig()

     if not is_finetuned:
          # Modify final_proj layer name
          A:		Union[str, Any]          =		DataaVecAudioModel(__lowercase	)
          A:		List[Any]          =		os.path.dirname(__lowercase	)

          A:		Union[str, Any]          =		torch.load(__lowercase	)
          A:		int          =		state_dict['''model'''].pop('''final_proj.0.weight'''	)
          A:		str          =		state_dict['''model'''].pop('''final_proj.0.bias'''	)
          A:		Any          =		os.path.join(__lowercase		,				'''converted.pt'''	)
          torch.save(__lowercase		,				__lowercase	)
     else:
          A:		Tuple          =		DataaVecAudioForCTC(__lowercase	)
          A:		Optional[int]          =		checkpoint_path

     def load_dataavec(__lowercase	):
          A     ,    A     ,    A:		List[Any]          =		fairseq.checkpoint_utils.load_model_ensemble_and_task([path]	)
          return model[0].eval()

     A:		Any          =		load_dataavec(__lowercase	)

     recursively_load_weights(__lowercase		,				__lowercase		,				not is_finetuned	)

     A:		Optional[int]          =		WavaVecaProcessor.from_pretrained('''facebook/wav2vec2-large-lv60'''	)

     A:		Optional[Any]          =		load_dataset('''patrickvonplaten/librispeech_asr_dummy'''		,				'''clean'''		,				split='''validation'''	)
     A:		Optional[int]          =		[x['''array'''] for x in ds[:4]['''audio''']]

     A:		Dict          =		processor(__lowercase		,				return_tensors='''pt'''		,				padding=__lowercase	)

     A:		str          =		inputs.input_values
     A:		str          =		inputs.attention_mask
     #    input_values = inputs.input_values[:, :-1]
     #    attention_mask = inputs.attention_mask[:, :-1]

     hf_wavavec.eval()
     model.eval()
     if is_finetuned:
          A:		Any          =		model(source=__lowercase		,				padding_mask=(1 - attention_mask)		,				mask=__lowercase		,				features_only=__lowercase	)[
              '''encoder_out'''
          ].transpose(0		,				1	)
          A:		int          =		hf_wavavec(__lowercase		,				attention_mask=__lowercase	)['''logits''']

          A:		str          =		torch.argmax(__lowercase		,				dim=-1	)
          A:		Any          =		processor.batch_decode(__lowercase	)

          print(F"""Expected Output: {ds[:4]['text']}, Pred: {output_string}"""	)
     else:
          A:		int          =		model(source=__lowercase		,				padding_mask=(1 - attention_mask)		,				mask=__lowercase		,				features_only=__lowercase	)[
              '''layer_results'''
          ][-1][0].transpose(0		,				1	)
          A:		List[Any]          =		hf_wavavec(__lowercase		,				attention_mask=__lowercase	)['''last_hidden_state''']

     print(our_output.shape		,				their_output.shape	)
     A:		List[Any]          =		torch.max(torch.abs(our_output - their_output	)	).item()
     print(F"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
     A:		Optional[int]          =		torch.allclose(__lowercase		,				__lowercase		,				atol=1E-3	)
     print('''Do both models output the same tensors?'''		,				'''🔥''' if success else '''💩'''	)
     if not success:
          raise Exception('''Something went wRoNg'''	)

     hf_wavavec.save_pretrained(__lowercase	)

     if is_finetuned:
          processor.save_pretrained(__lowercase	)
     else:
          processor.feature_extractor.save_pretrained(__lowercase	)


if __name__ == "__main__":
     UpperCamelCase			     =					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			     =					parser.parse_args()
     convert_wavaveca_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )






