






import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


_SCREAMING_SNAKE_CASE   =  {
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 4_80_00,
        "sample_size": 6_55_36,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 4_80_00,
        "sample_size": 6_55_36,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 4_80_00,
        "sample_size": 13_10_72,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 1_60_00,
        "sample_size": 6_55_36,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 1_60_00,
        "sample_size": 6_55_36,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 1_60_00,
        "sample_size": 6_55_36,
    },
}





def        _snake_case   (_snake_case				:       Union[str, Any]      ,    _snake_case				:       List[str])					->	Any:
   return torch.atana(_snake_case      ,    _snake_case) / math.pi * 2





def        _snake_case   (_snake_case				:       Optional[int])					->	Union[str, Any]:
   _lowercase              =torch.sin(t * math.pi / 2) ** 2
   _lowercase              =(1 - sigma**2) ** 0.5
   return alpha_sigma_to_t(_snake_case      ,    _snake_case)






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       pass






class 					SCREAMING_SNAKE_CASE_  (       nn.Module ):
       """simple docstring"""


       def __init__(				self	:Optional[Any],      snake_case	:Optional[Any]):



          """simple docstring"""

          super().__init__()

          _lowercase              =DiffusionAttnUnetaD(snake_case,      n_attn_layers=4)
          _lowercase              =deepcopy(self.diffusion)
          _lowercase              =torch.quasirandom.SobolEngine(1,      scramble=snake_case)





def        _snake_case   (_snake_case				:       List[Any])					->	List[str]:
   _lowercase              =MODELS_MAP[model_name]['url']
   os.system(f'''wget {url} ./''')

   return f'''./{model_name}.ckpt'''


_SCREAMING_SNAKE_CASE   =  {
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
}
_SCREAMING_SNAKE_CASE   =  {
    "8": "resnets.0",
    "9": "attentions.0",
    "10": "resnets.1",
    "11": "attentions.1",
    "12": "resnets.2",
    "13": "attentions.2",
}
_SCREAMING_SNAKE_CASE   =  {
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
    "8": "resnets.3",
    "9": "attentions.3",
    "10": "resnets.4",
    "11": "attentions.4",
    "12": "resnets.5",
    "13": "attentions.5",
}
_SCREAMING_SNAKE_CASE   =  {
    "0": "resnets.0",
    "1": "resnets.1",
    "2": "resnets.2",
    "4": "resnets.0",
    "5": "resnets.1",
    "6": "resnets.2",
}

_SCREAMING_SNAKE_CASE   =  {
    "skip": "conv_skip",
    "main.0": "conv_1",
    "main.1": "group_norm_1",
    "main.3": "conv_2",
    "main.4": "group_norm_2",
}

_SCREAMING_SNAKE_CASE   =  {
    "norm": "group_norm",
    "qkv_proj": ["query", "key", "value"],
    "out_proj": ["proj_attn"],
}





def        _snake_case   (_snake_case				:       Optional[int])					->	int:
   if name.startswith('skip'):
      return name.replace('skip'      ,    RES_CONV_MAP['skip'])

   # name has to be of format main.{digit}
   if not name.startswith('main.'):
      raise ValueError(f'''ResConvBlock error with {name}''')

   return name.replace(name[:6]      ,    RES_CONV_MAP[name[:6]])





def        _snake_case   (_snake_case				:       Dict)					->	Any:
   for key, value in ATTN_MAP.items():
      if name.startswith(_snake_case) and not isinstance(_snake_case      ,    _snake_case):
         return name.replace(_snake_case      ,    _snake_case)
      elif name.startswith(_snake_case):
         return [name.replace(_snake_case      ,    _snake_case) for v in value]
   raise ValueError(f'''Attn error with {name}''')





def        _snake_case   (_snake_case				:       List[Any]      ,    _snake_case				:       Dict=13)					->	List[Any]:
   _lowercase              =input_string

   if string.split('.')[0] == "timestep_embed":
      return string.replace('timestep_embed'      ,    'time_proj')

   _lowercase              =0
   if string.startswith('net.3.'):
      depth += 1
      _lowercase              =string[6:]
   elif string.startswith('net.'):
      _lowercase              =string[4:]

   while string.startswith('main.7.'):
      depth += 1
      _lowercase              =string[7:]

   if string.startswith('main.'):
      _lowercase              =string[5:]

   # mid block
   if string[:2].isdigit():
      _lowercase              =string[:2]
      _lowercase              =string[2:]
   else:
      _lowercase              =string[0]
      _lowercase              =string[1:]

   if depth == max_depth:
      _lowercase              =MID_NUM_TO_LAYER[layer_num]
      _lowercase              ='mid_block'
   elif depth > 0 and int(_snake_case) < 7:
      _lowercase              =DOWN_NUM_TO_LAYER[layer_num]
      _lowercase              =f'''down_blocks.{depth}'''
   elif depth > 0 and int(_snake_case) > 7:
      _lowercase              =UP_NUM_TO_LAYER[layer_num]
      _lowercase              =f'''up_blocks.{max_depth - depth - 1}'''
   elif depth == 0:
      _lowercase              =DEPTH_0_TO_LAYER[layer_num]
      _lowercase              =f'''up_blocks.{max_depth - 1}''' if int(_snake_case) > 3 else 'down_blocks.0'

   if not string_left.startswith('.'):
      raise ValueError(f'''Naming error with {input_string} and string_left: {string_left}.''')

   _lowercase              =string_left[1:]

   if "resnets" in new_layer:
      _lowercase              =convert_resconv_naming(_snake_case)
   elif "attentions" in new_layer:
      _lowercase              =convert_attn_naming(_snake_case)
      _lowercase              =new_string_left

   if not isinstance(_snake_case      ,    _snake_case):
      _lowercase              =prefix + '.' + new_layer + '.' + string_left
   else:
      _lowercase              =[prefix + '.' + new_layer + '.' + s for s in string_left]
   return new_string





def        _snake_case   (_snake_case				:       Union[str, Any])					->	Union[str, Any]:
   _lowercase              ={}
   for k, v in state_dict.items():
      if k.endswith('kernel'):
         # up- and downsample layers, don't have trainable weights
         continue

      _lowercase              =rename(_snake_case)

      # check if we need to transform from Conv => Linear for attention
      if isinstance(_snake_case      ,    _snake_case):
         _lowercase              =transform_conv_attns(_snake_case      ,    _snake_case      ,    _snake_case)
      else:
         _lowercase              =v

   return new_state_dict





def        _snake_case   (_snake_case				:       Union[str, Any]      ,    _snake_case				:       Dict      ,    _snake_case				:       Tuple)					->	Any:
   if len(_snake_case) == 1:
      if len(v.shape) == 3:
         # weight
         _lowercase              =v[:, :, 0]
      else:
         # bias
         _lowercase              =v
   else:
      # qkv matrices
      _lowercase              =v.shape[0]
      _lowercase              =trippled_shape // 3
      for i in range(3):
         if len(v.shape) == 3:
            _lowercase              =v[i * single_shape : (i + 1) * single_shape, :, 0]
         else:
            _lowercase              =v[i * single_shape : (i + 1) * single_shape]
   return new_state_dict





def        _snake_case   (_snake_case				:       Any)					->	str:
   _lowercase              =torch.device('cuda' if torch.cuda.is_available() else 'cpu')

   _lowercase              =args.model_path.split('/')[-1].split('.')[0]
   if not os.path.isfile(args.model_path):
      assert (
          model_name == args.model_path
      ), f'''Make sure to provide one of the official model names {MODELS_MAP.keys()}'''
      _lowercase              =download(_snake_case)

   _lowercase              =MODELS_MAP[model_name]['sample_rate']
   _lowercase              =MODELS_MAP[model_name]['sample_size']

   _lowercase              =Object()
   _lowercase              =sample_size
   _lowercase              =sample_rate
   _lowercase              =0

   _lowercase              =UNetaDModel(sample_size=_snake_case      ,    sample_rate=_snake_case)
   _lowercase              =diffusers_model.state_dict()

   _lowercase              =DiffusionUncond(_snake_case)
   orig_model.load_state_dict(torch.load(args.model_path      ,    map_location=_snake_case)['state_dict'])
   _lowercase              =orig_model.diffusion_ema.eval()
   _lowercase              =orig_model.state_dict()
   _lowercase              =rename_orig_weights(_snake_case)

   _lowercase              =set(renamed_state_dict.keys()) - set(diffusers_state_dict.keys())
   _lowercase              =set(diffusers_state_dict.keys()) - set(renamed_state_dict.keys())

   assert len(_snake_case) == 0, f'''Problem with {renamed_minus_diffusers}'''
   assert all(k.endswith('kernel') for k in list(_snake_case)), f'''Problem with {diffusers_minus_renamed}'''

   for key, value in renamed_state_dict.items():
      assert (
          diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
      ), f'''Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'''
      if key == "time_proj.weight":
         _lowercase              =value.squeeze()

      _lowercase              =value

   diffusers_model.load_state_dict(_snake_case)

   _lowercase              =100
   _lowercase              =33

   _lowercase              =IPNDMScheduler(num_train_timesteps=_snake_case)

   _lowercase              =torch.manual_seed(_snake_case)
   _lowercase              =torch.randn([1, 2, config.sample_size]      ,    generator=_snake_case).to(_snake_case)

   _lowercase              =torch.linspace(1      ,    0      ,    steps + 1      ,    device=_snake_case)[:-1]
   _lowercase              =get_crash_schedule(_snake_case)

   _lowercase              =DanceDiffusionPipeline(unet=_snake_case      ,    scheduler=_snake_case)

   _lowercase              =torch.manual_seed(33)
   _lowercase              =pipe(num_inference_steps=_snake_case      ,    generator=_snake_case).audios

   _lowercase              =sampling.iplms_sample(_snake_case      ,    _snake_case      ,    _snake_case      ,    {})
   _lowercase              =generated.clamp(-1      ,    1)

   _lowercase              =(generated - audio).abs().sum()
   _lowercase              =(generated - audio).abs().max()

   if args.save:
      pipe.save_pretrained(args.checkpoint_path)

   print('Diff sum'      ,    _snake_case)
   print('Diff max'      ,    _snake_case)

   assert diff_max < 1E-3, f'''Diff max: {diff_max} is too much :-/'''

   print(f'''Conversion for {model_name} successful!''')


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE   =  argparse.ArgumentParser()

    parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
    parser.add_argument(
        "--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
    )
    parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
    _SCREAMING_SNAKE_CASE   =  parser.parse_args()

    main(args)










import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_SCREAMING_SNAKE_CASE   =  logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")

_SCREAMING_SNAKE_CASE   =  list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_SCREAMING_SNAKE_CASE   =  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





def        _snake_case   (_snake_case				:       str)					->	List[str]:
   with open(_snake_case      ,    'rb') as f:
      _lowercase              =Image.open(_snake_case)
      return im.convert('RGB')






@dataclass
class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       __lowerCAmelCase    :		Optional[str]					=field(
           default=_a  ,						metadata={
               '''help''': '''Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'''
           }  ,						)
       __lowerCAmelCase    :		Optional[str]					=field(
           default=_a  ,						metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
       __lowerCAmelCase    :		Optional[str]					=field(default=_a  ,						metadata={'''help''': '''A folder containing the training data.'''} )
       __lowerCAmelCase    :		Optional[str]					=field(default=_a  ,						metadata={'''help''': '''A folder containing the validation data.'''} )
       __lowerCAmelCase    :		Optional[float]					=field(
           default=0.15  ,						metadata={'''help''': '''Percent to split off of train for validation.'''} )
       __lowerCAmelCase    :		Optional[int]					=field(
           default=_a  ,						metadata={
               '''help''': (
                   '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                   '''value if set.'''
               )
           }  ,						)
       __lowerCAmelCase    :		Optional[int]					=field(
           default=_a  ,						metadata={
               '''help''': (
                   '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                   '''value if set.'''
               )
           }  ,						)





       def 				UpperCamelCase__						(				self	:List[str]):



          """simple docstring"""

          if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
             raise ValueError(
                 'You must specify either a dataset name from the hub or a train and/or validation directory.')






@dataclass
class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       __lowerCAmelCase    :		str					=field(
           default='''google/vit-base-patch16-224-in21k'''  ,						metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}  ,						)
       __lowerCAmelCase    :		Optional[str]					=field(
           default=_a  ,						metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(_a )}  ,						)
       __lowerCAmelCase    :		Optional[str]					=field(
           default=_a  ,						metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
       __lowerCAmelCase    :		Optional[str]					=field(
           default=_a  ,						metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''} )
       __lowerCAmelCase    :		str					=field(
           default='''main'''  ,						metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}  ,						)
       __lowerCAmelCase    :		str					=field(default=_a  ,						metadata={'''help''': '''Name or path of preprocessor config.'''} )
       __lowerCAmelCase    :		bool					=field(
           default=_a  ,						metadata={
               '''help''': (
                   '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
                   '''with private models).'''
               )
           }  ,						)
       __lowerCAmelCase    :		bool					=field(
           default=_a  ,						metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}  ,						)





def        _snake_case   (_snake_case				:       Any)					->	Dict:
   _lowercase              =torch.stack([example['pixel_values'] for example in examples])
   _lowercase              =torch.tensor([example['labels'] for example in examples])
   return {"pixel_values": pixel_values, "labels": labels}





def        _snake_case   ()					->	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              =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.
      _lowercase       ,       _lowercase       ,       _lowercase              =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
   else:
      _lowercase       ,       _lowercase       ,       _lowercase              =parser.parse_args_into_dataclasses()

   # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
   # information sent is the one passed as arguments along with your Python/PyTorch versions.
   send_example_telemetry('run_image_classification'      ,    _snake_case      ,    _snake_case)

   # 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)]      ,    )

   if training_args.should_log:
      # The default of training_args.log_level is passive, so we set log level at info here to have that default.
      transformers.utils.logging.set_verbosity_info()

   _lowercase              =training_args.get_process_log_level()
   logger.setLevel(_snake_case)
   transformers.utils.logging.set_verbosity(_snake_case)
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()

   # 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}''')
   logger.info(f'''Training/evaluation parameters {training_args}''')

   # Detecting last checkpoint.
   _lowercase              =None
   if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
      _lowercase              =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 and training_args.resume_from_checkpoint is 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.')

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

   # Initialize our dataset and prepare it for the 'image-classification' task.
   if data_args.dataset_name is not None:
      _lowercase              =load_dataset(
          data_args.dataset_name      ,    data_args.dataset_config_name      ,    cache_dir=model_args.cache_dir      ,    task='image-classification'      ,    use_auth_token=True if model_args.use_auth_token else None      ,    )
   else:
      _lowercase              ={}
      if data_args.train_dir is not None:
         _lowercase              =os.path.join(data_args.train_dir      ,    '**')
      if data_args.validation_dir is not None:
         _lowercase              =os.path.join(data_args.validation_dir      ,    '**')
      _lowercase              =load_dataset(
          'imagefolder'      ,    data_files=_snake_case      ,    cache_dir=model_args.cache_dir      ,    task='image-classification'      ,    )

   # If we don't have a validation split, split off a percentage of train as validation.
   _lowercase              =None if 'validation' in dataset.keys() else data_args.train_val_split
   if isinstance(data_args.train_val_split      ,    _snake_case) and data_args.train_val_split > 0.0:
      _lowercase              =dataset['train'].train_test_split(data_args.train_val_split)
      _lowercase              =split['train']
      _lowercase              =split['test']

   # Prepare label mappings.
   # We'll include these in the model's config to get human readable labels in the Inference API.
   _lowercase              =dataset['train'].features['labels'].names
   _lowercase       ,       _lowercase              ={}, {}
   for i, label in enumerate(_snake_case):
      _lowercase              =str(_snake_case)
      _lowercase              =label

   # Load the accuracy metric from the datasets package
   _lowercase              =evaluate.load('accuracy')

   # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
   # predictions and label_ids field) and has to return a dictionary string to float.
   def compute_metrics(_snake_case				:       Any):
      return metric.compute(predictions=np.argmax(p.predictions      ,    axis=1)      ,    references=p.label_ids)

   _lowercase              =AutoConfig.from_pretrained(
       model_args.config_name or model_args.model_name_or_path      ,    num_labels=len(_snake_case)      ,    labelaid=_snake_case      ,    idalabel=_snake_case      ,    finetuning_task='image-classification'      ,    cache_dir=model_args.cache_dir      ,    revision=model_args.model_revision      ,    use_auth_token=True if model_args.use_auth_token else None      ,    )
   _lowercase              =AutoModelForImageClassification.from_pretrained(
       model_args.model_name_or_path      ,    from_tf=bool('.ckpt' in model_args.model_name_or_path)      ,    config=_snake_case      ,    cache_dir=model_args.cache_dir      ,    revision=model_args.model_revision      ,    use_auth_token=True if model_args.use_auth_token else None      ,    ignore_mismatched_sizes=model_args.ignore_mismatched_sizes      ,    )
   _lowercase              =AutoImageProcessor.from_pretrained(
       model_args.image_processor_name or model_args.model_name_or_path      ,    cache_dir=model_args.cache_dir      ,    revision=model_args.model_revision      ,    use_auth_token=True if model_args.use_auth_token else None      ,    )

   # Define torchvision transforms to be applied to each image.
   if "shortest_edge" in image_processor.size:
      _lowercase              =image_processor.size['shortest_edge']
   else:
      _lowercase              =(image_processor.size['height'], image_processor.size['width'])
   _lowercase              =Normalize(mean=image_processor.image_mean      ,    std=image_processor.image_std)
   _lowercase              =Compose(
       [
           RandomResizedCrop(_snake_case),
           RandomHorizontalFlip(),
           ToTensor(),
           normalize,
       ])
   _lowercase              =Compose(
       [
           Resize(_snake_case),
           CenterCrop(_snake_case),
           ToTensor(),
           normalize,
       ])

   def train_transforms(_snake_case				:       str):
      _lowercase              =[
          _train_transforms(pil_img.convert('RGB')) for pil_img in example_batch['image']
      ]
      return example_batch

   def val_transforms(_snake_case				:       Dict):
      _lowercase              =[_val_transforms(pil_img.convert('RGB')) for pil_img in example_batch['image']]
      return example_batch

   if training_args.do_train:
      if "train" not in dataset:
         raise ValueError('--do_train requires a train dataset')
      if data_args.max_train_samples is not None:
         _lowercase              =(
             dataset['train'].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples))
         )
      # Set the training transforms
      dataset["train"].set_transform(_snake_case)

   if training_args.do_eval:
      if "validation" not in dataset:
         raise ValueError('--do_eval requires a validation dataset')
      if data_args.max_eval_samples is not None:
         _lowercase              =(
             dataset['validation'].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples))
         )
      # Set the validation transforms
      dataset["validation"].set_transform(_snake_case)

   # Initalize our trainer
   _lowercase              =Trainer(
       model=_snake_case      ,    args=_snake_case      ,    train_dataset=dataset['train'] if training_args.do_train else None      ,    eval_dataset=dataset['validation'] if training_args.do_eval else None      ,    compute_metrics=_snake_case      ,    tokenizer=_snake_case      ,    data_collator=_snake_case      ,    )

   # Training
   if training_args.do_train:
      _lowercase              =None
      if training_args.resume_from_checkpoint is not None:
         _lowercase              =training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         _lowercase              =last_checkpoint
      _lowercase              =trainer.train(resume_from_checkpoint=_snake_case)
      trainer.save_model()
      trainer.log_metrics('train'      ,    train_result.metrics)
      trainer.save_metrics('train'      ,    train_result.metrics)
      trainer.save_state()

   # Evaluation
   if training_args.do_eval:
      _lowercase              =trainer.evaluate()
      trainer.log_metrics('eval'      ,    _snake_case)
      trainer.save_metrics('eval'      ,    _snake_case)

   # Write model card and (optionally) push to hub
   _lowercase              ={
       'finetuned_from': model_args.model_name_or_path,
       'tasks': 'image-classification',
       'dataset': data_args.dataset_name,
       'tags': ['image-classification', 'vision'],
   }
   if training_args.push_to_hub:
      trainer.push_to_hub(**_snake_case)
   else:
      trainer.create_model_card(**_snake_case)


if __name__ == "__main__":
    main()


