

import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       []
							if isinstance(__snake_case  ,				__snake_case ):
														for v in tree.values():
																					shapes.extend(_fetch_dims(__snake_case ) )
							elif isinstance(__snake_case  ,				(list, tuple) ):
														for t in tree:
																					shapes.extend(_fetch_dims(__snake_case ) )
							elif isinstance(__snake_case  ,				torch.Tensor ):
														shapes.append(tree.shape )
							else:
														raise ValueError('Not supported' )

							return shapes





@torch.jit.ignore
def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       []
							for d in reversed(__snake_case ):
														idx.append(flat_idx % d )
														snake_case_     							=       flat_idx // d

							return tuple(reversed(__snake_case ) )





@torch.jit.ignore
def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ = None  ,				UpperCamelCase__ = None  ,				):







							'''simple docstring'''






							def reduce_edge_list(UpperCamelCase__ ) -> None:
														snake_case_     							=       True
														for i in range(len(__snake_case ) ):
																					snake_case_     							=       -1 * (i + 1)
																					l[reversed_idx] &= tally
																					snake_case_     							=       l[reversed_idx]

							if start_edges is None:
														snake_case_     							=       [s == 0 for s in start]
														reduce_edge_list(__snake_case )
							if end_edges is None:
														snake_case_     							=       [e == (d - 1) for e, d in zip(__snake_case  ,				__snake_case )]
														reduce_edge_list(__snake_case )

							# Base cases. Either start/end are empty and we're done, or the final,
							# one-dimensional tensor can be simply sliced
							if len(__snake_case ) == 0:
														return [()]
							elif len(__snake_case ) == 1:
														return [(slice(start[0]  ,				end[0] + 1 ),)]

							snake_case_     							=       []
							snake_case_     							=       []

							# Dimensions common to start and end can be selected directly
							for s, e in zip(__snake_case  ,				__snake_case ):
														if s == e:
																					path_list.append(slice(__snake_case  ,				s + 1 ) )
														else:
																					break

							snake_case_     							=       tuple(__snake_case )
							snake_case_     							=       len(__snake_case )

							# start == end, and we're done
							if divergence_idx == len(__snake_case ):
														return [path]

							def upper() -> Tuple[Tuple[slice, ...], ...]:
														assert start_edges is not None
														assert end_edges is not None

														snake_case_     							=       start[divergence_idx]
														return tuple(
														    path + (slice(__snake_case  ,				sdi + 1 ),) + s
														    for s in _get_minimal_slice_set(
														        start[divergence_idx + 1 :]  ,				[d - 1 for d in dims[divergence_idx + 1 :]]  ,				dims[divergence_idx + 1 :]  ,				start_edges=start_edges[divergence_idx + 1 :]  ,				end_edges=[True for _ in end_edges[divergence_idx + 1 :]]  ,				) )

							def lower() -> Tuple[Tuple[slice, ...], ...]:
														assert start_edges is not None
														assert end_edges is not None

														snake_case_     							=       end[divergence_idx]
														return tuple(
														    path + (slice(__snake_case  ,				edi + 1 ),) + s
														    for s in _get_minimal_slice_set(
														        [0 for _ in start[divergence_idx + 1 :]]  ,				end[divergence_idx + 1 :]  ,				dims[divergence_idx + 1 :]  ,				start_edges=[True for _ in start_edges[divergence_idx + 1 :]]  ,				end_edges=end_edges[divergence_idx + 1 :]  ,				) )

							# If both start and end are at the edges of the subtree rooted at
							# divergence_idx, we can just select the whole subtree at once
							if start_edges[divergence_idx] and end_edges[divergence_idx]:
														slices.append(path + (slice(start[divergence_idx]  ,				end[divergence_idx] + 1 ),) )
							# If just start is at the edge, we can grab almost all of the subtree,
							# treating only the ragged bottom edge as an edge case
							elif start_edges[divergence_idx]:
														slices.append(path + (slice(start[divergence_idx]  ,				end[divergence_idx] ),) )
														slices.extend(lower() )
							# Analogous to the previous case, but the top is ragged this time
							elif end_edges[divergence_idx]:
														slices.extend(upper() )
														slices.append(path + (slice(start[divergence_idx] + 1  ,				end[divergence_idx] + 1 ),) )
							# If both sides of the range are ragged, we need to handle both sides
							# separately. If there's contiguous meat in between them, we can index it
							# in one big chunk
							else:
														slices.extend(upper() )
														snake_case_     							=       end[divergence_idx] - start[divergence_idx]
														if middle_ground > 1:
																					slices.append(path + (slice(start[divergence_idx] + 1  ,				end[divergence_idx] ),) )
														slices.extend(lower() )

							return slices





@torch.jit.ignore
def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       t.shape[:no_batch_dims]
							snake_case_     							=       list(_flat_idx_to_idx(__snake_case  ,				__snake_case ) )
							# _get_minimal_slice_set is inclusive
							snake_case_     							=       list(_flat_idx_to_idx(flat_end - 1  ,				__snake_case ) )

							# Get an ordered list of slices to perform
							snake_case_     							=       _get_minimal_slice_set(
							    __snake_case  ,				__snake_case  ,				__snake_case  ,				)

							snake_case_     							=       [t[s] for s in slices]

							return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ = False  ,				UpperCamelCase__ = None  ,				UpperCamelCase__ = False  ,				):







							'''simple docstring'''






							if not (len(__snake_case ) > 0):
														raise ValueError('Must provide at least one input' )

							snake_case_     							=       [shape[:no_batch_dims] for shape in _fetch_dims(__snake_case )]
							snake_case_     							=       tuple([max(__snake_case ) for s in zip(*__snake_case )] )

							def _prep_inputs(UpperCamelCase__ ) -> torch.Tensor:
														if not low_mem:
																					if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
																												snake_case_     							=       t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
																					snake_case_     							=       t.reshape(-1  ,				*t.shape[no_batch_dims:] )
														else:
																					snake_case_     							=       t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
														return t

							snake_case_     							=       tensor_tree_map(_prep_inputs  ,				__snake_case )
							snake_case_     							=       None
							if _out is not None:
														snake_case_     							=       tensor_tree_map(lambda UpperCamelCase__ : t.view([-1] + list(t.shape[no_batch_dims:] ) )  ,				_out )

							snake_case_     							=       1
							for d in orig_batch_dims:
														flat_batch_dim *= d

							snake_case_     							=       flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

							def _select_chunk(UpperCamelCase__ ) -> torch.Tensor:
														return t[i : i + chunk_size] if t.shape[0] != 1 else t

							snake_case_     							=       0
							snake_case_     							=       prepped_outputs
							for _ in range(__snake_case ):
														# Chunk the input
														if not low_mem:
																					snake_case_     							=       _select_chunk
														else:
																					snake_case_     							=       partial(
																					    _chunk_slice  ,				flat_start=__snake_case  ,				flat_end=min(__snake_case  ,				i + chunk_size )  ,				no_batch_dims=len(__snake_case )  ,				)

														snake_case_     							=       tensor_tree_map(__snake_case  ,				__snake_case )

														# Run the layer on the chunk
														snake_case_     							=       layer(**__snake_case )

														# Allocate space for the output
														if out is None:
																					snake_case_     							=       tensor_tree_map(lambda UpperCamelCase__ : t.new_zeros((flat_batch_dim,) + t.shape[1:] )  ,				__snake_case )

														# Put the chunk in its pre-allocated space
														if isinstance(__snake_case  ,				__snake_case ):

																					def assign(UpperCamelCase__  ,				UpperCamelCase__ ) -> None:
																												for k, v in da.items():
																																			if isinstance(__snake_case  ,				__snake_case ):
																																										assign(__snake_case  ,				da[k] )
																																			else:
																																										if _add_into_out:
																																																	v[i : i + chunk_size] += da[k]
																																										else:
																																																	snake_case_     							=       da[k]

																					assign(__snake_case  ,				__snake_case )
														elif isinstance(__snake_case  ,				__snake_case ):
																					for xa, xa in zip(__snake_case  ,				__snake_case ):
																												if _add_into_out:
																																			xa[i : i + chunk_size] += xa
																												else:
																																			snake_case_     							=       xa
														elif isinstance(__snake_case  ,				torch.Tensor ):
																					if _add_into_out:
																												out[i : i + chunk_size] += output_chunk
																					else:
																												snake_case_     							=       output_chunk
														else:
																					raise ValueError('Not supported' )

														i += chunk_size

							snake_case_     							=       tensor_tree_map(lambda UpperCamelCase__ : t.view(orig_batch_dims + t.shape[1:] )  ,				__snake_case )

							return out







class 					lowercase							:




				def __init__(   self ,  snake_case = 512 ,  ):
											snake_case_     							=       max_chunk_size
											snake_case_     							=       None
											snake_case_     							=       None




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case			):
											logging.info('Tuning chunk size...'			)

											if min_chunk_size >= self.max_chunk_size:
																		return min_chunk_size

											snake_case_     							=       [2**l for l in range(int(math.log(self.max_chunk_size ,  2			)			) + 1			)]
											snake_case_     							=       [c for c in candidates if c > min_chunk_size]
											snake_case_     							=       [min_chunk_size] + candidates
											candidates[-1] += 4

											def test_chunk_size(snake_case			) -> bool:
																		try:
																									with torch.no_grad():
																																fn(*_a ,  chunk_size=_a			)
																									return True
																		except RuntimeError:
																									return False

											snake_case_     							=       0
											snake_case_     							=       len(_a			) - 1
											while i > min_viable_chunk_size_index:
																		snake_case_     							=       test_chunk_size(candidates[i]			)
																		if not viable:
																									snake_case_     							=       (min_viable_chunk_size_index + i) // 2
																		else:
																									snake_case_     							=       i
																									snake_case_     							=       (i + len(_a			) - 1) // 2

											return candidates[min_viable_chunk_size_index]




				def        a				(   self ,  snake_case ,  snake_case			):
											snake_case_     							=       True
											for aa, aa in zip(_a ,  _a			):
																		assert type(_a			) == type(_a			)
																		if isinstance(_a ,  (list, tuple)			):
																									consistent &= self._compare_arg_caches(_a ,  _a			)
																		elif isinstance(_a ,  _a			):
																									snake_case_     							=       [v for _, v in sorted(aa.items() ,  key=lambda snake_case			: x[0]			)]
																									snake_case_     							=       [v for _, v in sorted(aa.items() ,  key=lambda snake_case			: x[0]			)]
																									consistent &= self._compare_arg_caches(_a ,  _a			)
																		else:
																									consistent &= aa == aa

											return consistent




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case ,  ):
											snake_case_     							=       True
											snake_case_     							=       tree_map(lambda snake_case			: a.shape if isinstance(_a ,  torch.Tensor			) else a ,  _a ,  _a			)
											if self.cached_arg_data is not None:
																		# If args have changed shape/value, we need to re-tune
																		assert len(self.cached_arg_data			) == len(_a			)
																		snake_case_     							=       self._compare_arg_caches(self.cached_arg_data ,  _a			)
											else:
																		# Otherwise, we can reuse the precomputed value
																		snake_case_     							=       False

											if not consistent:
																		snake_case_     							=       self._determine_favorable_chunk_size(
																		    _a ,  _a ,  _a ,  )
																		snake_case_     							=       arg_data

											assert self.cached_chunk_size is not None

											return self.cached_chunk_size






"""simple docstring"""




import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
   from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
   _A     :			List[str]  						=			True
   from torch.cuda.amp import autocast


_A     :			Any  						=			logging.getLogger(__name__)

@dataclass
class 						a__		:
   __lowerCAmelCase				=							field(
       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}		)
   __lowerCAmelCase				=							field(
       default=a_,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""},     )
   __lowerCAmelCase				=							field(
       default=a_,     metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}		)
   __lowerCAmelCase				=							field(
       default=a_,     metadata={"""help""": """Whether to log verbose messages or not."""},     )
   __lowerCAmelCase				=							field(
       default=2.0,     metadata={"""help""": """Maximum temperature for gumbel softmax."""}		)
   __lowerCAmelCase				=							field(
       default=0.5,     metadata={"""help""": """Minimum temperature for gumbel softmax."""}		)
   __lowerCAmelCase				=							field(
       default=0.999995,     metadata={"""help""": """Decay of gumbel temperature during training."""}		)


def     __magic_name__      ( __snake_case							:			ModelArguments	,					__snake_case							:			TrainingArguments    )					->							Any:
       logging.basicConfig(
           format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"	,					datefmt="%m/%d/%Y %H:%M:%S"	,					handlers=[logging.StreamHandler(sys.stdout    )]	,					)
       lowercase       :							Optional[int]									=				logging.WARNING
       if model_args.verbose_logging:
              lowercase       :							Union[str, Any]									=				logging.DEBUG
       elif trainer_utils.is_main_process(training_args.local_rank    ):
              lowercase       :							Union[str, Any]									=				logging.INFO
       logger.setLevel(__snake_case    )

@dataclass
class 						a__		:
   __lowerCAmelCase				=							field(
       default=a_,     metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}		)
   __lowerCAmelCase				=							field(
       default=a_,     metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}		)
   __lowerCAmelCase				=							field(
       default="""train""",     metadata={
           """help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
       },     )
   __lowerCAmelCase				=							field(
       default="""validation""",     metadata={
           """help""": (
               """The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"""
           )
       },     )
   __lowerCAmelCase				=							field(
       default="""file""",     metadata={"""help""": """Column in the dataset that contains speech file path. Defaults to 'file'"""},     )
   __lowerCAmelCase				=							field(
       default=a_,     metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}		)
   __lowerCAmelCase				=							field(
       default=1,     metadata={
           """help""": """The percentage of the train set used as validation set in case there's no validation split"""
       },     )
   __lowerCAmelCase				=							field(
       default=a_,     metadata={"""help""": """The number of processes to use for the preprocessing."""},     )
   __lowerCAmelCase				=							field(
       default=20.0,     metadata={"""help""": """Filter audio files that are longer than `max_duration_in_seconds` seconds"""}		)


@dataclass
class 						a__		:
   __lowerCAmelCase				=							42
   __lowerCAmelCase				=							42
   __lowerCAmelCase				=							"longest"
   __lowerCAmelCase				=							None
   __lowerCAmelCase				=							None
   def __call__(     self      ,       _a							):
          # reformat list to dict and set to pytorch format
          lowercase       :							Tuple									=				self.feature_extractor.pad(
              _a      ,       max_length=self.max_length      ,       padding=self.padding      ,       pad_to_multiple_of=self.pad_to_multiple_of      ,       return_tensors="pt"      ,       )
          lowercase       :							str									=				self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1]							)

          lowercase       :							Tuple									=				batch["input_values"].shape[0]

          # make sure that no loss is computed on padded inputs
          if batch["attention_mask"] is not None:
                 # compute real output lengths according to convolution formula
                 lowercase       :							Any									=				self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1							)							).to(
                     torch.long							)

                 lowercase       :							Dict									=				torch.zeros(
                     (batch_size, mask_indices_seq_length)      ,       dtype=torch.long      ,       device=batch["input_values"].device							)

                 # these two operations makes sure that all values
                 # before the output lengths indices are attended to
                 lowercase       :							int									=				1
                 lowercase       :							List[Any]									=				attention_mask.flip([-1]							).cumsum(-1							).flip([-1]							).bool()

          # sample randomly masked indices
          lowercase       :							int									=				_compute_mask_indices(
              (batch_size, mask_indices_seq_length)      ,       self.model.config.mask_time_prob      ,       self.model.config.mask_time_length      ,       attention_mask=_a      ,       min_masks=2      ,       )

          return batch


class 						a__		( a_		):
   def __init__(     self      ,       *_a      ,       _a=1      ,       _a=0      ,       _a=1.0      ,       **_a							):
          super().__init__(*_a      ,       **_a							)
          lowercase       :							Tuple									=				0
          lowercase       :							Optional[Any]									=				max_gumbel_temp
          lowercase       :							Union[str, Any]									=				min_gumbel_temp
          lowercase       :							str									=				gumbel_temp_decay

   def      __magic_name__     (     self      ,       _a      ,       _a							):
          model.train()
          lowercase       :							Union[str, Any]									=				self._prepare_inputs(_a							)

          if self.use_amp:
                 with autocast():
                        lowercase       :							Optional[Any]									=				self.compute_loss(_a      ,       _a							)
          else:
                 lowercase       :							List[str]									=				self.compute_loss(_a      ,       _a							)

          if self.args.n_gpu > 1 or self.deepspeed:
                 if model.module.config.ctc_loss_reduction == "mean":
                        lowercase       :							Tuple									=				loss.mean()
                 elif model.module.config.ctc_loss_reduction == "sum":
                        lowercase       :							Any									=				loss.sum() / (inputs["mask_time_indices"]).sum()
                 else:
                        raise ValueError(f"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"""							)

          if self.args.gradient_accumulation_steps > 1:
                 lowercase       :							List[str]									=				loss / self.args.gradient_accumulation_steps

          if self.use_amp:
                 self.scaler.scale(_a							).backward()
          elif self.use_apex:
                 with amp.scale_loss(_a      ,       self.optimizer							) as scaled_loss:
                        scaled_loss.backward()
          elif self.deepspeed:
                 self.deepspeed.backward(_a							)
          else:
                 loss.backward()

          self.num_update_step += 1
          # make sure gumbel softmax temperature is decayed
          if self.args.n_gpu > 1 or self.deepspeed:
                 model.module.set_gumbel_temperature(
                     max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step      ,       self.min_gumbel_temp							)							)
          else:
                 model.set_gumbel_temperature(
                     max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step      ,       self.min_gumbel_temp							)							)

          return loss.detach()


def     __magic_name__      ( )					->							List[Any]:
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.

       lowercase       :							Any									=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )

       lowercase						,       lowercase						,       lowercase       :							Union[str, Any]									=				parser.parse_args_into_dataclasses()
       configure_logger(__snake_case	,					__snake_case    )

       # Downloading and loading a dataset from the hub.
       lowercase       :							Any									=				load_dataset(data_args.dataset_name	,					data_args.dataset_config_name	,					cache_dir=model_args.cache_dir    )

       if "validation" not in datasets.keys():
              # make sure only "validation" and "train" keys remain"
              lowercase       :							Union[str, Any]									=				DatasetDict()
              lowercase       :							Dict									=				load_dataset(
                  data_args.dataset_name	,					data_args.dataset_config_name	,					split=f"""{data_args.train_split_name}[:{data_args.validation_split_percentage}%]"""	,					cache_dir=model_args.cache_dir	,					)
              lowercase       :							Any									=				load_dataset(
                  data_args.dataset_name	,					data_args.dataset_config_name	,					split=f"""{data_args.train_split_name}[{data_args.validation_split_percentage}%:]"""	,					cache_dir=model_args.cache_dir	,					)
       else:
              # make sure only "validation" and "train" keys remain"
              lowercase       :							Tuple									=				DatasetDict()
              lowercase       :							Dict									=				load_dataset(
                  data_args.dataset_name	,					data_args.dataset_config_name	,					split="validation"	,					cache_dir=model_args.cache_dir	,					)
              lowercase       :							int									=				load_dataset(
                  data_args.dataset_name	,					data_args.dataset_config_name	,					split=f"""{data_args.train_split_name}"""	,					cache_dir=model_args.cache_dir	,					)

       # only normalized-inputs-training is supported
       lowercase       :							Optional[int]									=				WavaVecaFeatureExtractor.from_pretrained(
           model_args.model_name_or_path	,					cache_dir=model_args.cache_dir	,					do_normalize=__snake_case    )

       def prepare_dataset(__snake_case							:			Tuple    ):
              # check that all files have the correct sampling rate
              lowercase						,       lowercase       :							Union[str, Any]									=				librosa.load(batch[data_args.speech_file_column]	,					sr=feature_extractor.sampling_rate    )
              return batch

       # load audio files into numpy arrays
       lowercase       :							str									=				datasets.map(
           __snake_case	,					num_proc=data_args.preprocessing_num_workers	,					remove_columns=datasets["train"].column_names    )

       # filter audio files that are too long
       lowercase       :							Optional[Any]									=				vectorized_datasets.filter(
           lambda __snake_case    : len(data["speech"]    ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate    )    )

       def normalize(__snake_case							:			Union[str, Any]    ):
              return feature_extractor(batch["speech"]	,					sampling_rate=feature_extractor.sampling_rate    )

       # normalize and transform to `BatchFeatures`
       lowercase       :							Any									=				vectorized_datasets.map(
           __snake_case	,					batched=__snake_case	,					num_proc=data_args.preprocessing_num_workers	,					load_from_cache_file=not data_args.overwrite_cache	,					remove_columns=vectorized_datasets["train"].column_names	,					)

       # pretraining is only supported for "newer" stable layer norm architecture
       # apply_spec_augment has to be True, mask_feature_prob has to be 0.0
       lowercase       :							Union[str, Any]									=				WavaVecaConfig.from_pretrained(
           model_args.model_name_or_path	,					cache_dir=model_args.cache_dir	,					gradient_checkpointing=training_args.gradient_checkpointing	,					)

       if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
              raise ValueError(
                  "PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"
                  " ``config.feat_extract_norm='layer'"    )

       lowercase       :							Optional[Any]									=				WavaVecaForPreTraining(__snake_case    )

       lowercase       :							Optional[int]									=				DataCollatorForWavaVecaPretraining(model=__snake_case	,					feature_extractor=__snake_case    )

       lowercase       :							List[Any]									=				WavaVecaPreTrainer(
           model=__snake_case	,					data_collator=__snake_case	,					args=__snake_case	,					train_dataset=vectorized_datasets["train"]	,					eval_dataset=vectorized_datasets["validation"]	,					tokenizer=__snake_case	,					max_gumbel_temp=model_args.max_gumbel_temperature	,					min_gumbel_temp=model_args.min_gumbel_temperature	,					gumbel_temp_decay=model_args.gumbel_temperature_decay	,					)
       trainer.train()


if __name__ == "__main__":
   main()


