
"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_A     :			Optional[int]  						=			logging.get_logger(__name__)


def     __magic_name__      ( __snake_case							:			str    )					->							YolosConfig:
       lowercase       :							int									=				YolosConfig()

       # size of the architecture
       if "yolos_ti" in yolos_name:
              lowercase       :							Union[str, Any]									=				192
              lowercase       :							Tuple									=				768
              lowercase       :							Optional[Any]									=				12
              lowercase       :							List[str]									=				3
              lowercase       :							int									=				[800, 1333]
              lowercase       :							Union[str, Any]									=				False
       elif yolos_name == "yolos_s_dWr":
              lowercase       :							int									=				330
              lowercase       :							List[str]									=				14
              lowercase       :							Dict									=				6
              lowercase       :							Any									=				1320
       elif "yolos_s" in yolos_name:
              lowercase       :							str									=				384
              lowercase       :							int									=				1536
              lowercase       :							Any									=				12
              lowercase       :							int									=				6
       elif "yolos_b" in yolos_name:
              lowercase       :							str									=				[800, 1344]

       lowercase       :							Tuple									=				91
       lowercase       :							Tuple									=				"huggingface/label-files"
       lowercase       :							Any									=				"coco-detection-id2label.json"
       lowercase       :							str									=				json.load(open(hf_hub_download(__snake_case	,					__snake_case	,					repo_type="dataset"    )	,					"r"    )    )
       lowercase       :							List[str]									=				{int(__snake_case    ): v for k, v in idalabel.items()}
       lowercase       :							Union[str, Any]									=				idalabel
       lowercase       :							Union[str, Any]									=				{v: k for k, v in idalabel.items()}

       return config


def     __magic_name__      ( __snake_case							:			dict	,					__snake_case							:			YolosConfig	,					__snake_case							:			bool = False    )					->							int:
       for i in range(config.num_hidden_layers    ):
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              lowercase       :							Dict									=				state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""    )
              lowercase       :							int									=				state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""    )
              # next, add query, keys and values (in that order) to the state dict
              lowercase       :							Union[str, Any]									=				in_proj_weight[: config.hidden_size, :]
              lowercase       :							int									=				in_proj_bias[: config.hidden_size]
              lowercase       :							str									=				in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              lowercase       :							List[Any]									=				in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              lowercase       :							Dict									=				in_proj_weight[-config.hidden_size :, :]
              lowercase       :							List[Any]									=				in_proj_bias[-config.hidden_size :]


def     __magic_name__      ( __snake_case							:			str    )					->							str:
       if "backbone" in name:
              lowercase       :							Union[str, Any]									=				name.replace("backbone"	,					"vit"    )
       if "cls_token" in name:
              lowercase       :							Union[str, Any]									=				name.replace("cls_token"	,					"embeddings.cls_token"    )
       if "det_token" in name:
              lowercase       :							Dict									=				name.replace("det_token"	,					"embeddings.detection_tokens"    )
       if "mid_pos_embed" in name:
              lowercase       :							Optional[Any]									=				name.replace("mid_pos_embed"	,					"encoder.mid_position_embeddings"    )
       if "pos_embed" in name:
              lowercase       :							Any									=				name.replace("pos_embed"	,					"embeddings.position_embeddings"    )
       if "patch_embed.proj" in name:
              lowercase       :							Optional[int]									=				name.replace("patch_embed.proj"	,					"embeddings.patch_embeddings.projection"    )
       if "blocks" in name:
              lowercase       :							Union[str, Any]									=				name.replace("blocks"	,					"encoder.layer"    )
       if "attn.proj" in name:
              lowercase       :							Optional[int]									=				name.replace("attn.proj"	,					"attention.output.dense"    )
       if "attn" in name:
              lowercase       :							Dict									=				name.replace("attn"	,					"attention.self"    )
       if "norm1" in name:
              lowercase       :							List[str]									=				name.replace("norm1"	,					"layernorm_before"    )
       if "norm2" in name:
              lowercase       :							Union[str, Any]									=				name.replace("norm2"	,					"layernorm_after"    )
       if "mlp.fc1" in name:
              lowercase       :							Union[str, Any]									=				name.replace("mlp.fc1"	,					"intermediate.dense"    )
       if "mlp.fc2" in name:
              lowercase       :							List[str]									=				name.replace("mlp.fc2"	,					"output.dense"    )
       if "class_embed" in name:
              lowercase       :							List[Any]									=				name.replace("class_embed"	,					"class_labels_classifier"    )
       if "bbox_embed" in name:
              lowercase       :							Optional[Any]									=				name.replace("bbox_embed"	,					"bbox_predictor"    )
       if "vit.norm" in name:
              lowercase       :							Optional[int]									=				name.replace("vit.norm"	,					"vit.layernorm"    )

       return name


def     __magic_name__      ( __snake_case							:			dict	,					__snake_case							:			YolosForObjectDetection    )					->							dict:
       for key in orig_state_dict.copy().keys():
              lowercase       :							List[Any]									=				orig_state_dict.pop(__snake_case    )

              if "qkv" in key:
                     lowercase       :							Union[str, Any]									=				key.split("."    )
                     lowercase       :							List[Any]									=				int(key_split[2]    )
                     lowercase       :							Union[str, Any]									=				model.vit.encoder.layer[layer_num].attention.attention.all_head_size
                     if "weight" in key:
                            lowercase       :							int									=				val[:dim, :]
                            lowercase       :							List[Any]									=				val[
                                dim : dim * 2, :
                            ]
                            lowercase       :							Any									=				val[-dim:, :]
                     else:
                            lowercase       :							Dict									=				val[:dim]
                            lowercase       :							Dict									=				val[dim : dim * 2]
                            lowercase       :							str									=				val[-dim:]
              else:
                     lowercase       :							List[str]									=				val

       return orig_state_dict


def     __magic_name__      ( )					->							torch.Tensor:
       lowercase       :							Optional[int]									=				"http://images.cocodataset.org/val2017/000000039769.jpg"
       lowercase       :							Tuple									=				Image.open(requests.get(__snake_case	,					stream=__snake_case    ).raw    )
       return im


@torch.no_grad()
def     __magic_name__      ( __snake_case							:			str	,					__snake_case							:			str	,					__snake_case							:			str	,					__snake_case							:			bool = False    )					->							Dict:
       lowercase       :							Optional[int]									=				get_yolos_config(__snake_case    )

       # load original state_dict
       lowercase       :							str									=				torch.load(__snake_case	,					map_location="cpu"    )["model"]

       # load 🤗 model
       lowercase       :							str									=				YolosForObjectDetection(__snake_case    )
       model.eval()
       lowercase       :							Dict									=				convert_state_dict(__snake_case	,					__snake_case    )
       model.load_state_dict(__snake_case    )

       # Check outputs on an image, prepared by YolosImageProcessor
       lowercase       :							Any									=				800 if yolos_name != "yolos_ti" else 512
       lowercase       :							List[Any]									=				YolosImageProcessor(format="coco_detection"	,					size=__snake_case    )
       lowercase       :							Any									=				image_processor(images=prepare_img()	,					return_tensors="pt"    )
       lowercase       :							Any									=				model(**__snake_case    )
       lowercase						,       lowercase       :							Tuple									=				outputs.logits, outputs.pred_boxes

       lowercase						,       lowercase       :							int									=				None, None
       if yolos_name == "yolos_ti":
              lowercase       :							int									=				torch.tensor(
                  [[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]]    )
              lowercase       :							List[Any]									=				torch.tensor(
                  [[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]]    )
       elif yolos_name == "yolos_s_200_pre":
              lowercase       :							List[Any]									=				torch.tensor(
                  [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]    )
              lowercase       :							List[Any]									=				torch.tensor(
                  [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]    )
       elif yolos_name == "yolos_s_300_pre":
              lowercase       :							Optional[int]									=				torch.tensor(
                  [[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]]    )
              lowercase       :							Tuple									=				torch.tensor(
                  [[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]]    )
       elif yolos_name == "yolos_s_dWr":
              lowercase       :							Tuple									=				torch.tensor(
                  [[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]]    )
              lowercase       :							List[str]									=				torch.tensor(
                  [[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]]    )
       elif yolos_name == "yolos_base":
              lowercase       :							Optional[Any]									=				torch.tensor(
                  [[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]]    )
              lowercase       :							List[str]									=				torch.tensor(
                  [[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]]    )
       else:
              raise ValueError(f"""Unknown yolos_name: {yolos_name}"""    )

       assert torch.allclose(logits[0, :3, :3]	,					__snake_case	,					atol=1E-4    )
       assert torch.allclose(pred_boxes[0, :3, :3]	,					__snake_case	,					atol=1E-4    )

       Path(__snake_case    ).mkdir(exist_ok=__snake_case    )
       print(f"""Saving model {yolos_name} to {pytorch_dump_folder_path}"""    )
       model.save_pretrained(__snake_case    )
       print(f"""Saving image processor to {pytorch_dump_folder_path}"""    )
       image_processor.save_pretrained(__snake_case    )

       if push_to_hub:
              lowercase       :							Optional[int]									=				{
                  "yolos_ti": "yolos-tiny",
                  "yolos_s_200_pre": "yolos-small",
                  "yolos_s_300_pre": "yolos-small-300",
                  "yolos_s_dWr": "yolos-small-dwr",
                  "yolos_base": "yolos-base",
              }

              print("Pushing to the hub..."    )
              lowercase       :							Optional[Any]									=				model_mapping[yolos_name]
              image_processor.push_to_hub(__snake_case	,					organization="hustvl"    )
              model.push_to_hub(__snake_case	,					organization="hustvl"    )


if __name__ == "__main__":
   _A     :			List[Any]  						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--yolos_name""",
       default="""yolos_s_200_pre""",
       type=str,
       help=(
           """Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"""
           """ 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."""
       ),
   )
   parser.add_argument(
       """--checkpoint_path""", default=None, type=str, help="""Path to the original state dict (.pth file)."""
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
   )
   parser.add_argument(
       """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
   )

   _A     :			Any  						=			parser.parse_args()
   convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




"""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()


