


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

import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


if is_apex_available():
  from apex import amp


if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
  SCREAMING_SNAKE_CASE							    =							True
  from torch.cuda.amp import autocast

SCREAMING_SNAKE_CASE							    =							logging.getLogger(__name__)




def  a   (lowerCAmelCase__=None		,				lowerCAmelCase__=None   ):
   return field(default_factory=lambda: default		,				metadata=lowerCAmelCase__   )


@dataclass
class     __UpperCAmelCase							:


     """simple docstring"""
     _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=0.1 ,					metadata={"""help""": """The dropout ratio for the attention probabilities."""}							)
     _lowerCamelCase   =      field(
         default=0.1 ,					metadata={"""help""": """The dropout ratio for activations inside the fully connected layer."""}							)
     _lowerCamelCase   =      field(
         default=0.1 ,					metadata={
             """help""": """The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."""
         } ,					)
     _lowerCamelCase   =      field(
         default=0.1 ,					metadata={"""help""": """The dropout probabilitiy for all 1D convolutional layers in feature extractor."""} ,					)
     _lowerCamelCase   =      field(
         default=0.05 ,					metadata={
             """help""": (
                 """Propability of each feature vector along the time axis to be chosen as the start of the vector"""
                 """span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"""
                 """vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."""
             )
         } ,					)
     _lowerCamelCase   =      field(default=0.0 ,					metadata={"""help""": """The LayerDrop probability."""}							)







@dataclass
class     __UpperCAmelCase							:


     """simple docstring"""
     _lowerCamelCase   =      field(
         default=__A ,					metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}							)
     _lowerCamelCase   =      field(
         default="""train+validation""" ,					metadata={
             """help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
         } ,					)
     _lowerCamelCase   =      field(
         default=__A ,					metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}							)
     _lowerCamelCase   =      field(
         default=__A ,					metadata={"""help""": """The number of processes to use for the preprocessing."""} ,					)
     _lowerCamelCase   =      field(
         default=__A ,					metadata={
             """help""": (
                 """For debugging purposes or quicker training, truncate the number of training examples to this """
                 """value if set."""
             )
         } ,					)
     _lowerCamelCase   =      field(
         default=__A ,					metadata={
             """help""": (
                 """For debugging purposes or quicker training, truncate the number of validation examples to this """
                 """value if set."""
             )
         } ,					)
     _lowerCamelCase   =      list_field(
         default=[""",""", """?""", """.""", """!""", """-""", """;""", """:""", """\"\"""", """%""", """'""", """\"""", """�"""] ,					metadata={"""help""": """A list of characters to remove from the transcripts."""} ,					)







@dataclass
class     __UpperCAmelCase							:


     """simple docstring"""
     _lowerCamelCase   =      42
     _lowerCamelCase   =      True
     _lowerCamelCase   =      None
     _lowerCamelCase   =      None
     _lowerCamelCase   =      None
     _lowerCamelCase   =      None


     def __call__(	self  ,       __A    ):
        # split inputs and labels since they have to be of different lenghts and need
        # different padding methods
        __a					=  [{"""input_values""": feature["""input_values"""]} for feature in features]
        __a					=  [{"""input_ids""": feature["""labels"""]} for feature in features]

        __a					=  self.processor.pad(
            __A  ,       padding=self.padding  ,       max_length=self.max_length  ,       pad_to_multiple_of=self.pad_to_multiple_of  ,       return_tensors="""pt"""  ,       )
        __a					=  self.processor.pad(
            labels=__A  ,       padding=self.padding  ,       max_length=self.max_length_labels  ,       pad_to_multiple_of=self.pad_to_multiple_of_labels  ,       return_tensors="""pt"""  ,       )

        # replace padding with -100 to ignore loss correctly
        __a					=  labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1    )  ,       -100    )

        __a					=  labels

        return batch







class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def 	snake_case_  (	self  ,       __A  ,       __A    ):
        model.train()
        __a					=  self._prepare_inputs(__A    )

        if self.use_amp:
           with autocast():
              __a					=  self.compute_loss(__A  ,       __A    )
        else:
           __a					=  self.compute_loss(__A  ,       __A    )

        if self.args.n_gpu > 1:
           if model.module.config.ctc_loss_reduction == "mean":
              __a					=  loss.mean()
           elif model.module.config.ctc_loss_reduction == "sum":
              __a					=  loss.sum() / (inputs["""labels"""] >= 0).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:
           __a					=  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()

        return loss.detach()







def  a   ():
   # 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.

   __a					=  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
   if len(sys.argv   ) == 2 and sys.argv[1].endswith(""".json"""   ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      __a	,    __a	,    __a					=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
   else:
      __a	,    __a	,    __a					=  parser.parse_args_into_dataclasses()

   # Detecting last checkpoint.
   __a					=  None
   if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
      __a					=  get_last_checkpoint(training_args.output_dir   )
      if last_checkpoint is None and len(os.listdir(training_args.output_dir   )   ) > 0:
         raise ValueError(
             f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
             """Use --overwrite_output_dir to overcome."""   )
      elif last_checkpoint is not None:
         logger.info(
             f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
             """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""   )

    # Setup logging
   logging.basicConfig(
       format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""		,				datefmt="""%m/%d/%Y %H:%M:%S"""		,				handlers=[logging.StreamHandler(sys.stdout   )]		,				)
   logger.setLevel(logging.INFO if is_main_process(training_args.local_rank   ) else logging.WARN   )

   # Log on each process the small summary:
   logger.warning(
       f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
       + f'''distributed training: {bool(training_args.local_rank != -1   )}, 16-bits training: {training_args.fpaa}'''   )
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank   ):
      transformers.utils.logging.set_verbosity_info()
   logger.info("""Training/evaluation parameters %s"""		,				lowerCAmelCase__   )

   # Set seed before initializing model.
   set_seed(training_args.seed   )

   # Get the datasets:
   __a					=  datasets.load_dataset(
       """common_voice"""		,				data_args.dataset_config_name		,				split=data_args.train_split_name   )
   __a					=  datasets.load_dataset("""common_voice"""		,				data_args.dataset_config_name		,				split="""test"""   )

   # Create and save tokenizer
   __a					=  f'''[{''.join(data_args.chars_to_ignore   )}]'''

   def remove_special_characters(lowerCAmelCase__   ):
      __a					=  re.sub(lowerCAmelCase__		,				""""""		,				batch["""sentence"""]   ).lower() + """ """
      return batch

   __a					=  train_dataset.map(lowerCAmelCase__		,				remove_columns=["""sentence"""]   )
   __a					=  eval_dataset.map(lowerCAmelCase__		,				remove_columns=["""sentence"""]   )

   def extract_all_chars(lowerCAmelCase__   ):
      __a					=  """ """.join(batch["""text"""]   )
      __a					=  list(set(lowerCAmelCase__   )   )
      return {"vocab": [vocab], "all_text": [all_text]}

   __a					=  train_dataset.map(
       lowerCAmelCase__		,				batched=lowerCAmelCase__		,				batch_size=-1		,				keep_in_memory=lowerCAmelCase__		,				remove_columns=train_dataset.column_names		,				)
   __a					=  train_dataset.map(
       lowerCAmelCase__		,				batched=lowerCAmelCase__		,				batch_size=-1		,				keep_in_memory=lowerCAmelCase__		,				remove_columns=eval_dataset.column_names		,				)

   __a					=  list(set(vocab_train["""vocab"""][0]   ) | set(vocab_test["""vocab"""][0]   )   )
   __a					=  {v: k for k, v in enumerate(lowerCAmelCase__   )}
   __a					=  vocab_dict[""" """]
   del vocab_dict[" "]
   __a					=  len(lowerCAmelCase__   )
   __a					=  len(lowerCAmelCase__   )

   with open("""vocab.json"""		,				"""w"""   ) as vocab_file:
      json.dump(lowerCAmelCase__		,				lowerCAmelCase__   )

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   __a					=  WavaVecaCTCTokenizer(
       """vocab.json"""		,				unk_token="""[UNK]"""		,				pad_token="""[PAD]"""		,				word_delimiter_token="""|"""		,				)
   __a					=  WavaVecaFeatureExtractor(
       feature_size=1		,				sampling_rate=16_000		,				padding_value=0.0		,				do_normalize=lowerCAmelCase__		,				return_attention_mask=lowerCAmelCase__   )
   __a					=  WavaVecaProcessor(feature_extractor=lowerCAmelCase__		,				tokenizer=lowerCAmelCase__   )
   __a					=  WavaVecaForCTC.from_pretrained(
       model_args.model_name_or_path		,				cache_dir=model_args.cache_dir		,				activation_dropout=model_args.activation_dropout		,				attention_dropout=model_args.attention_dropout		,				hidden_dropout=model_args.hidden_dropout		,				feat_proj_dropout=model_args.feat_proj_dropout		,				mask_time_prob=model_args.mask_time_prob		,				gradient_checkpointing=training_args.gradient_checkpointing		,				layerdrop=model_args.layerdrop		,				ctc_loss_reduction="""mean"""		,				pad_token_id=processor.tokenizer.pad_token_id		,				vocab_size=len(processor.tokenizer   )		,				)

   if data_args.max_train_samples is not None:
      __a					=  min(len(lowerCAmelCase__   )		,				data_args.max_train_samples   )
      __a					=  train_dataset.select(range(lowerCAmelCase__   )   )

   if data_args.max_val_samples is not None:
      __a					=  eval_dataset.select(range(data_args.max_val_samples   )   )

   __a					=  torchaudio.transforms.Resample(48_000		,				16_000   )

   # Preprocessing the datasets.
   # We need to read the aduio files as arrays and tokenize the targets.
   def speech_file_to_array_fn(lowerCAmelCase__   ):
      __a	,    __a					=  torchaudio.load(batch["""path"""]   )
      __a					=  resampler(lowerCAmelCase__   ).squeeze().numpy()
      __a					=  16_000
      __a					=  batch["""text"""]
      return batch

   __a					=  train_dataset.map(
       lowerCAmelCase__		,				remove_columns=train_dataset.column_names		,				num_proc=data_args.preprocessing_num_workers		,				)
   __a					=  eval_dataset.map(
       lowerCAmelCase__		,				remove_columns=eval_dataset.column_names		,				num_proc=data_args.preprocessing_num_workers		,				)

   def prepare_dataset(lowerCAmelCase__   ):
      # check that all files have the correct sampling rate
      assert (
          len(set(batch["""sampling_rate"""]   )   ) == 1
      ), f'''Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'''

      __a					=  processor(
          audio=batch["""speech"""]		,				text=batch["""target_text"""]		,				sampling_rate=batch["""sampling_rate"""][0]   )
      batch.update(lowerCAmelCase__   )
      return batch

   __a					=  train_dataset.map(
       lowerCAmelCase__		,				remove_columns=train_dataset.column_names		,				batch_size=training_args.per_device_train_batch_size		,				batched=lowerCAmelCase__		,				num_proc=data_args.preprocessing_num_workers		,				)
   __a					=  eval_dataset.map(
       lowerCAmelCase__		,				remove_columns=eval_dataset.column_names		,				batch_size=training_args.per_device_train_batch_size		,				batched=lowerCAmelCase__		,				num_proc=data_args.preprocessing_num_workers		,				)

   # Metric
   __a					=  datasets.load_metric("""wer"""   )

   def compute_metrics(lowerCAmelCase__   ):
      __a					=  pred.predictions
      __a					=  np.argmax(lowerCAmelCase__		,				axis=-1   )

      __a					=  processor.tokenizer.pad_token_id

      __a					=  processor.batch_decode(lowerCAmelCase__   )
      # we do not want to group tokens when computing the metrics
      __a					=  processor.batch_decode(pred.label_ids		,				group_tokens=lowerCAmelCase__   )

      __a					=  wer_metric.compute(predictions=lowerCAmelCase__		,				references=lowerCAmelCase__   )

      return {"wer": wer}

   if model_args.freeze_feature_extractor:
      model.freeze_feature_extractor()

   # Data collator
   __a					=  DataCollatorCTCWithPadding(processor=lowerCAmelCase__		,				padding=lowerCAmelCase__   )

   # Initialize our Trainer
   __a					=  CTCTrainer(
       model=lowerCAmelCase__		,				data_collator=lowerCAmelCase__		,				args=lowerCAmelCase__		,				compute_metrics=lowerCAmelCase__		,				train_dataset=train_dataset if training_args.do_train else None		,				eval_dataset=eval_dataset if training_args.do_eval else None		,				tokenizer=processor.feature_extractor		,				)

   # Training
   if training_args.do_train:
      if last_checkpoint is not None:
         __a					=  last_checkpoint
      elif os.path.isdir(model_args.model_name_or_path   ):
         __a					=  model_args.model_name_or_path
      else:
         __a					=  None

      # Save the feature_extractor and the tokenizer
      if is_main_process(training_args.local_rank   ):
         processor.save_pretrained(training_args.output_dir   )

      __a					=  trainer.train(resume_from_checkpoint=lowerCAmelCase__   )
      trainer.save_model()

      __a					=  train_result.metrics
      __a					=  (
          data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCAmelCase__   )
      )
      __a					=  min(lowerCAmelCase__		,				len(lowerCAmelCase__   )   )

      trainer.log_metrics("""train"""		,				lowerCAmelCase__   )
      trainer.save_metrics("""train"""		,				lowerCAmelCase__   )
      trainer.save_state()

   # Evaluation
   __a					=  {}
   if training_args.do_eval:
      logger.info("""*** Evaluate ***"""   )
      __a					=  trainer.evaluate()
      __a					=  data_args.max_val_samples if data_args.max_val_samples is not None else len(lowerCAmelCase__   )
      __a					=  min(lowerCAmelCase__		,				len(lowerCAmelCase__   )   )

      trainer.log_metrics("""eval"""		,				lowerCAmelCase__   )
      trainer.save_metrics("""eval"""		,				lowerCAmelCase__   )

   return results


if __name__ == "__main__":
  main()







import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)




def  a   (lowerCAmelCase__   ):
   __a					=  VideoMAEConfig()

   set_architecture_configs(lowerCAmelCase__		,				lowerCAmelCase__   )

   if "finetuned" not in model_name:
      __a					=  False

   if "finetuned" in model_name:
      __a					=  """huggingface/label-files"""
      if "kinetics" in model_name:
         __a					=  400
         __a					=  """kinetics400-id2label.json"""
      elif "ssv2" in model_name:
         __a					=  174
         __a					=  """something-something-v2-id2label.json"""
      else:
         raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."""   )
      __a					=  json.load(open(hf_hub_download(lowerCAmelCase__		,				lowerCAmelCase__		,				repo_type="""dataset"""   )		,				"""r"""   )   )
      __a					=  {int(lowerCAmelCase__   ): v for k, v in idalabel.items()}
      __a					=  idalabel
      __a					=  {v: k for k, v in idalabel.items()}

   return config




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   if "small" in model_name:
      __a					=  384
      __a					=  1_536
      __a					=  12
      __a					=  16
      __a					=  12
      __a					=  3
      __a					=  192
      __a					=  768
   elif "large" in model_name:
      __a					=  1_024
      __a					=  4_096
      __a					=  24
      __a					=  16
      __a					=  12
      __a					=  8
      __a					=  512
      __a					=  2_048
   elif "huge" in model_name:
      __a					=  1_280
      __a					=  5_120
      __a					=  32
      __a					=  16
      __a					=  12
      __a					=  8
      __a					=  640
      __a					=  2_560
   elif "base" not in model_name:
      raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\""""   )




def  a   (lowerCAmelCase__   ):
   if "encoder." in name:
      __a					=  name.replace("""encoder."""		,				""""""   )
   if "cls_token" in name:
      __a					=  name.replace("""cls_token"""		,				"""videomae.embeddings.cls_token"""   )
   if "decoder_pos_embed" in name:
      __a					=  name.replace("""decoder_pos_embed"""		,				"""decoder.decoder_pos_embed"""   )
   if "pos_embed" in name and "decoder" not in name:
      __a					=  name.replace("""pos_embed"""		,				"""videomae.embeddings.position_embeddings"""   )
   if "patch_embed.proj" in name:
      __a					=  name.replace("""patch_embed.proj"""		,				"""videomae.embeddings.patch_embeddings.projection"""   )
   if "patch_embed.norm" in name:
      __a					=  name.replace("""patch_embed.norm"""		,				"""videomae.embeddings.norm"""   )
   if "decoder.blocks" in name:
      __a					=  name.replace("""decoder.blocks"""		,				"""decoder.decoder_layers"""   )
   if "blocks" in name:
      __a					=  name.replace("""blocks"""		,				"""videomae.encoder.layer"""   )
   if "attn.proj" in name:
      __a					=  name.replace("""attn.proj"""		,				"""attention.output.dense"""   )
   if "attn" in name and "bias" not in name:
      __a					=  name.replace("""attn"""		,				"""attention.self"""   )
   if "attn" in name:
      __a					=  name.replace("""attn"""		,				"""attention.attention"""   )
   if "norm1" in name:
      __a					=  name.replace("""norm1"""		,				"""layernorm_before"""   )
   if "norm2" in name:
      __a					=  name.replace("""norm2"""		,				"""layernorm_after"""   )
   if "mlp.fc1" in name:
      __a					=  name.replace("""mlp.fc1"""		,				"""intermediate.dense"""   )
   if "mlp.fc2" in name:
      __a					=  name.replace("""mlp.fc2"""		,				"""output.dense"""   )
   if "decoder_embed" in name:
      __a					=  name.replace("""decoder_embed"""		,				"""decoder.decoder_embed"""   )
   if "decoder_norm" in name:
      __a					=  name.replace("""decoder_norm"""		,				"""decoder.decoder_norm"""   )
   if "decoder_pred" in name:
      __a					=  name.replace("""decoder_pred"""		,				"""decoder.decoder_pred"""   )
   if "norm.weight" in name and "decoder" not in name and "fc" not in name:
      __a					=  name.replace("""norm.weight"""		,				"""videomae.layernorm.weight"""   )
   if "norm.bias" in name and "decoder" not in name and "fc" not in name:
      __a					=  name.replace("""norm.bias"""		,				"""videomae.layernorm.bias"""   )
   if "head" in name and "decoder" not in name:
      __a					=  name.replace("""head"""		,				"""classifier"""   )

   return name




def  a   (lowerCAmelCase__		,				lowerCAmelCase__   ):
   for key in orig_state_dict.copy().keys():
      __a					=  orig_state_dict.pop(lowerCAmelCase__   )

      if key.startswith("""encoder."""   ):
         __a					=  key.replace("""encoder."""		,				""""""   )

      if "qkv" in key:
         __a					=  key.split("""."""   )
         if key.startswith("""decoder.blocks"""   ):
            __a					=  config.decoder_hidden_size
            __a					=  int(key_split[2]   )
            __a					=  """decoder.decoder_layers."""
            if "weight" in key:
               __a					=  val[:dim, :]
               __a					=  val[dim : dim * 2, :]
               __a					=  val[-dim:, :]
         else:
            __a					=  config.hidden_size
            __a					=  int(key_split[1]   )
            __a					=  """videomae.encoder.layer."""
            if "weight" in key:
               __a					=  val[:dim, :]
               __a					=  val[dim : dim * 2, :]
               __a					=  val[-dim:, :]
      else:
         __a					=  val

   return orig_state_dict




def  a   ():
   __a					=  hf_hub_download(
       repo_id="""hf-internal-testing/spaghetti-video"""		,				filename="""eating_spaghetti.npy"""		,				repo_type="""dataset"""   )
   __a					=  np.load(lowerCAmelCase__   )
   return list(lowerCAmelCase__   )




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__   ):
   __a					=  get_videomae_config(lowerCAmelCase__   )

   if "finetuned" in model_name:
      __a					=  VideoMAEForVideoClassification(lowerCAmelCase__   )
   else:
      __a					=  VideoMAEForPreTraining(lowerCAmelCase__   )

   # download original checkpoint, hosted on Google Drive
   __a					=  """pytorch_model.bin"""
   gdown.cached_download(lowerCAmelCase__		,				lowerCAmelCase__		,				quiet=lowerCAmelCase__   )
   __a					=  torch.load(lowerCAmelCase__		,				map_location="""cpu"""   )
   if "model" in files:
      __a					=  files["""model"""]
   else:
      __a					=  files["""module"""]
   __a					=  convert_state_dict(lowerCAmelCase__		,				lowerCAmelCase__   )

   model.load_state_dict(lowerCAmelCase__   )
   model.eval()

   # verify model on basic input
   __a					=  VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]		,				image_std=[0.5, 0.5, 0.5]   )
   __a					=  prepare_video()
   __a					=  image_processor(lowerCAmelCase__		,				return_tensors="""pt"""   )

   if "finetuned" not in model_name:
      __a					=  hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos"""		,				filename="""bool_masked_pos.pt"""   )
      __a					=  torch.load(lowerCAmelCase__   )

   __a					=  model(**lowerCAmelCase__   )
   __a					=  outputs.logits

   __a					=  [
       """videomae-small-finetuned-kinetics""",
       """videomae-small-finetuned-ssv2""",
       # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
       """videomae-base-short""",
       """videomae-base-short-finetuned-kinetics""",
       """videomae-base""",
       """videomae-base-finetuned-kinetics""",
       """videomae-large""",
       """videomae-large-finetuned-kinetics""",
       """videomae-huge-finetuned-kinetics""",
       # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
       """videomae-base-short-ssv2""",
       """videomae-base-short-finetuned-ssv2""",
       """videomae-base-ssv2""",
       """videomae-base-finetuned-ssv2""",
   ]

   # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
   if model_name == "videomae-small-finetuned-kinetics":
      __a					=  torch.Size([1, 400]   )
      __a					=  torch.tensor([-0.9_2_9_1, -0.4_0_6_1, -0.9_3_0_7]   )
   elif model_name == "videomae-small-finetuned-ssv2":
      __a					=  torch.Size([1, 174]   )
      __a					=  torch.tensor([0.2_6_7_1, -0.4_6_8_9, -0.8_2_3_5]   )
   elif model_name == "videomae-base":
      __a					=  torch.Size([1, 1_408, 1_536]   )
      __a					=  torch.tensor([[0.7_7_3_9, 0.7_9_6_8, 0.7_0_8_9], [0.6_7_0_1, 0.7_4_8_7, 0.6_2_0_9], [0.4_2_8_7, 0.5_1_5_8, 0.4_7_7_3]]   )
   elif model_name == "videomae-base-short":
      __a					=  torch.Size([1, 1_408, 1_536]   )
      __a					=  torch.tensor([[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]   )
      # we verified the loss both for normalized and unnormalized targets for this one
      __a					=  torch.tensor([0.5_1_4_2]   ) if config.norm_pix_loss else torch.tensor([0.6_4_6_9]   )
   elif model_name == "videomae-large":
      __a					=  torch.Size([1, 1_408, 1_536]   )
      __a					=  torch.tensor([[0.7_1_4_9, 0.7_9_9_7, 0.6_9_6_6], [0.6_7_6_8, 0.7_8_6_9, 0.6_9_4_8], [0.5_1_3_9, 0.6_2_2_1, 0.5_6_0_5]]   )
   elif model_name == "videomae-large-finetuned-kinetics":
      __a					=  torch.Size([1, 400]   )
      __a					=  torch.tensor([0.0_7_7_1, 0.0_0_1_1, -0.3_6_2_5]   )
   elif model_name == "videomae-huge-finetuned-kinetics":
      __a					=  torch.Size([1, 400]   )
      __a					=  torch.tensor([0.2_4_3_3, 0.1_6_3_2, -0.4_8_9_4]   )
   elif model_name == "videomae-base-short-finetuned-kinetics":
      __a					=  torch.Size([1, 400]   )
      __a					=  torch.tensor([0.6_5_8_8, 0.0_9_9_0, -0.2_4_9_3]   )
   elif model_name == "videomae-base-finetuned-kinetics":
      __a					=  torch.Size([1, 400]   )
      __a					=  torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]   )
   elif model_name == "videomae-base-short-ssv2":
      __a					=  torch.Size([1, 1_408, 1_536]   )
      __a					=  torch.tensor([[0.4_7_1_2, 0.5_2_9_6, 0.5_7_8_6], [0.2_2_7_8, 0.2_7_2_9, 0.4_0_2_6], [0.0_3_5_2, 0.0_7_3_0, 0.2_5_0_6]]   )
   elif model_name == "videomae-base-short-finetuned-ssv2":
      __a					=  torch.Size([1, 174]   )
      __a					=  torch.tensor([-0.0_5_3_7, -0.1_5_3_9, -0.3_2_6_6]   )
   elif model_name == "videomae-base-ssv2":
      __a					=  torch.Size([1, 1_408, 1_536]   )
      __a					=  torch.tensor([[0.8_1_3_1, 0.8_7_2_7, 0.8_5_4_6], [0.7_3_6_6, 0.9_3_7_7, 0.8_8_7_0], [0.5_9_3_5, 0.8_8_7_4, 0.8_5_6_4]]   )
   elif model_name == "videomae-base-finetuned-ssv2":
      __a					=  torch.Size([1, 174]   )
      __a					=  torch.tensor([0.1_9_6_1, -0.8_3_3_7, -0.6_3_8_9]   )
   else:
      raise ValueError(f'''Model name not supported. Should be one of {model_names}'''   )

   # verify logits
   assert logits.shape == expected_shape
   if "finetuned" in model_name:
      assert torch.allclose(logits[0, :3]		,				lowerCAmelCase__		,				atol=1E-4   )
   else:
      print("""Logits:"""		,				logits[0, :3, :3]   )
      assert torch.allclose(logits[0, :3, :3]		,				lowerCAmelCase__		,				atol=1E-4   )
   print("""Logits ok!"""   )

   # verify loss, if applicable
   if model_name == "videomae-base-short":
      __a					=  outputs.loss
      assert torch.allclose(lowerCAmelCase__		,				lowerCAmelCase__		,				atol=1E-4   )
      print("""Loss ok!"""   )

   if pytorch_dump_folder_path is not None:
      print(f'''Saving model and image processor to {pytorch_dump_folder_path}'''   )
      image_processor.save_pretrained(lowerCAmelCase__   )
      model.save_pretrained(lowerCAmelCase__   )

   if push_to_hub:
      print("""Pushing to the hub..."""   )
      model.push_to_hub(lowerCAmelCase__		,				organization="""nielsr"""   )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE							    =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--checkpoint_url',
      default='https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4',
      type=str,
      help=(
          'URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'
          ' download link.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default='/Users/nielsrogge/Documents/VideoMAE/Test',
      type=str,
      help='Path to the output PyTorch model directory.',
  )
  parser.add_argument('--model_name', default='videomae-base', type=str, help='Name of the model.')
  parser.add_argument(
      '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
  )

  SCREAMING_SNAKE_CASE							    =							parser.parse_args()
  convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)



