'''simple docstring'''





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase  :Dict  				=    logging.get_logger(__name__)




def     __lowerCAmelCase						(       a_       ,      a_=False		)      ->	List[str]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       []

     # fmt: off
     # stem:
     rename_keys.append(('cls_token', 'vit.embeddings.cls_token')		)
     rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings')		)

     rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight')		)
     rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias')		)

     # backbone
     rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight')		)
     rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight')		)
     rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias')		)

     for stage_idx in range(len(config.backbone_config.depths		)		):
          for layer_idx in range(config.backbone_config.depths[stage_idx]		):
               rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")		)
               rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")		)
               rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")		)
               rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")		)
               rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")		)
               rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")		)
               rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")		)
               rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")		)
               rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")		)

          rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")		)
          rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")		)
          rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")		)

     # transformer encoder
     for i in range(config.num_hidden_layers		):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")		)
          rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")		)
          rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")		)
          rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")		)
          rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")		)
          rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")		)
          rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")		)
          rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")		)
          rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")		)
          rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")		)

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ('norm.weight', 'layernorm.weight'),
                  ('norm.bias', 'layernorm.bias'),
                  ('pre_logits.fc.weight', 'pooler.dense.weight'),
                  ('pre_logits.fc.bias', 'pooler.dense.bias'),
              ]		)

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          SCREAMING_SNAKE_CASE					:			int    	=       [(pair[0], pair[1][4:]) if pair[1].startswith('vit'		) else pair for pair in rename_keys]
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('norm.weight', 'vit.layernorm.weight'),
                  ('norm.bias', 'vit.layernorm.bias'),
                  ('head.weight', 'classifier.weight'),
                  ('head.bias', 'classifier.bias'),
              ]		)
     # fmt: on

     return rename_keys




def     __lowerCAmelCase						(       a_       ,      a_       ,      a_=False		)      ->	str:





     '''simple docstring'''





     for i in range(config.num_hidden_layers		):
          if base_model:
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       ''
          else:
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       'vit.'
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          SCREAMING_SNAKE_CASE					:			Any    	=       state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""		)
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""		)
          # next, add query, keys and values (in that order) to the state dict
          SCREAMING_SNAKE_CASE					:			Tuple    	=       in_proj_weight[
              : config.hidden_size, :
          ]
          SCREAMING_SNAKE_CASE					:			Dict    	=       in_proj_bias[: config.hidden_size]
          SCREAMING_SNAKE_CASE					:			int    	=       in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          SCREAMING_SNAKE_CASE					:			Tuple    	=       in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       in_proj_weight[
              -config.hidden_size :, :
          ]
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       in_proj_bias[-config.hidden_size :]




def     __lowerCAmelCase						(       a_		)      ->	List[str]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			str    	=       ['head.weight', 'head.bias']
     for k in ignore_keys:
          state_dict.pop(a_       ,      a_		)




def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	Optional[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Any    	=       dct.pop(a_		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       val




def     __lowerCAmelCase						(       )      ->	str:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Any    	=       'http://images.cocodataset.org/val2017/000000039769.jpg'
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       Image.open(requests.get(a_       ,      stream=a_		).raw		)
     return im




@torch.no_grad()
def     __lowerCAmelCase						(       a_       ,      a_       ,      a_=False		)      ->	List[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Dict    	=       BitConfig(
         global_padding='same'       ,      layer_type='bottleneck'       ,      depths=(3, 4, 9)       ,      out_features=['stage3']       ,      embedding_dynamic_padding=a_       ,      )
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ViTHybridConfig(backbone_config=a_       ,      image_size=384       ,      num_labels=1000		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       False

     # load original model from timm
     SCREAMING_SNAKE_CASE					:			List[str]    	=       timm.create_model(a_       ,      pretrained=a_		)
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       timm_model.state_dict()
     if base_model:
          remove_classification_head_(a_		)
     SCREAMING_SNAKE_CASE					:			Tuple    	=       create_rename_keys(a_       ,      a_		)
     for src, dest in rename_keys:
          rename_key(a_       ,      a_       ,      a_		)
     read_in_q_k_v(a_       ,      a_       ,      a_		)

     SCREAMING_SNAKE_CASE					:			str    	=       'huggingface/label-files'
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       'imagenet-1k-id2label.json'
     SCREAMING_SNAKE_CASE					:			List[str]    	=       json.load(open(hf_hub_download(a_       ,      a_       ,      repo_type='dataset'		)       ,      'r'		)		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       {int(a_		): v for k, v in idalabel.items()}
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       idalabel
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       {v: k for k, v in idalabel.items()}

     # load HuggingFace model
     if vit_name[-5:] == "in21k":
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       ViTHybridModel(a_		).eval()
     else:
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       ViTHybridForImageClassification(a_		).eval()
     model.load_state_dict(a_		)

     # create image processor
     SCREAMING_SNAKE_CASE					:			Tuple    	=       create_transform(**resolve_data_config({}       ,      model=a_		)		)
     SCREAMING_SNAKE_CASE					:			str    	=       transform.transforms

     SCREAMING_SNAKE_CASE					:			Tuple    	=       {
         'bilinear': PILImageResampling.BILINEAR,
         'bicubic': PILImageResampling.BICUBIC,
         'nearest': PILImageResampling.NEAREST,
     }

     SCREAMING_SNAKE_CASE					:			Any    	=       ViTHybridImageProcessor(
         do_resize=a_       ,      size={'shortest_edge': timm_transforms[0].size}       ,      resample=pillow_resamplings[timm_transforms[0].interpolation.value]       ,      do_center_crop=a_       ,      crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}       ,      do_normalize=a_       ,      image_mean=timm_transforms[-1].mean.tolist()       ,      image_std=timm_transforms[-1].std.tolist()       ,      )

     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       prepare_img()
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       transform(a_		).unsqueeze(0		)
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       processor(a_       ,      return_tensors='pt'		).pixel_values

     # verify pixel values
     assert torch.allclose(a_       ,      a_		)

     # verify logits
     with torch.no_grad():
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model(a_		)
          SCREAMING_SNAKE_CASE					:			int    	=       outputs.logits

     print('Predicted class:'       ,      logits.argmax(-1		).item()		)
     if base_model:
          SCREAMING_SNAKE_CASE					:			Dict    	=       timm_model.forward_features(a_		)
          assert timm_pooled_output.shape == outputs.pooler_output.shape
          assert torch.allclose(a_       ,      outputs.pooler_output       ,      atol=1e-3		)
     else:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       timm_model(a_		)
          assert timm_logits.shape == outputs.logits.shape
          assert torch.allclose(a_       ,      outputs.logits       ,      atol=1e-3		)
     print('Looks ok!'		)

     if pytorch_dump_folder_path is not None:
          Path(a_		).mkdir(exist_ok=a_		)
          print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""		)
          model.save_pretrained(a_		)
          print(f"""Saving processor to {pytorch_dump_folder_path}"""		)
          processor.save_pretrained(a_		)

     if push_to_hub:
          print(f"""Pushing model and processor to the hub {vit_name}"""		)
          model.push_to_hub(f"""ybelkada/{vit_name}"""		)
          processor.push_to_hub(f"""ybelkada/{vit_name}"""		)


if __name__ == "__main__":
 _lowerCAmelCase  :Any  				=    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--vit_name""",
     default="""vit_base_r50_s16_384""",
     type=str,
     help="""Name of the hybrid ViT timm model you'd like to convert.""",
 )
 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 to upload the model to the HuggingFace hub."""
 )

 _lowerCAmelCase  :str  				=    parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''





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
_lowerCAmelCase  :Union[str, Any]  				=    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""")

_lowerCAmelCase  :List[str]  				=    list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
_lowerCAmelCase  :List[Any]  				=    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


def     __lowerCAmelCase						(       a_		)      ->	List[str]:





     '''simple docstring'''





     with open(a_       ,      'rb'		) as f:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       Image.open(a_		)
          return im.convert('RGB'		)






@dataclass
class 				UpperCAmelCase :




    '''simple docstring'''







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






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         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 				UpperCAmelCase :




    '''simple docstring'''







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






def     __lowerCAmelCase						(       a_		)      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			int    	=       torch.stack([example['pixel_values'] for example in examples]		)
     SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.tensor([example['labels'] for example in examples]		)
     return {"pixel_values": pixel_values, "labels": labels}


def     __lowerCAmelCase						(       )      ->	Optional[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       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.
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[Any]    	=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
     else:
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[str]    	=       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'       ,      a_       ,      a_		)

     # 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()

     SCREAMING_SNAKE_CASE					:			List[Any]    	=       training_args.get_process_log_level()
     logger.setLevel(a_		)
     transformers.utils.logging.set_verbosity(a_		)
     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.
     SCREAMING_SNAKE_CASE					:			List[str]    	=       None
     if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       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:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       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:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       {}
          if data_args.train_dir is not None:
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       os.path.join(data_args.train_dir       ,      '**'		)
          if data_args.validation_dir is not None:
               SCREAMING_SNAKE_CASE					:			int    	=       os.path.join(data_args.validation_dir       ,      '**'		)
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       load_dataset(
              'imagefolder'       ,      data_files=a_       ,      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.
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       None if 'validation' in dataset.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split       ,      a_		) and data_args.train_val_split > 0.0:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       dataset['train'].train_test_split(data_args.train_val_split		)
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       split['train']
          SCREAMING_SNAKE_CASE					:			Tuple    	=       split['test']

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

     # Load the accuracy metric from the datasets package
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       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(a_		):
          return metric.compute(predictions=np.argmax(p.predictions       ,      axis=1		)       ,      references=p.label_ids		)

     SCREAMING_SNAKE_CASE					:			Tuple    	=       AutoConfig.from_pretrained(
         model_args.config_name or model_args.model_name_or_path       ,      num_labels=len(a_		)       ,      labelaid=a_       ,      idalabel=a_       ,      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       ,      )
     SCREAMING_SNAKE_CASE					:			str    	=       AutoModelForImageClassification.from_pretrained(
         model_args.model_name_or_path       ,      from_tf=bool('.ckpt' in model_args.model_name_or_path		)       ,      config=a_       ,      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       ,      )
     SCREAMING_SNAKE_CASE					:			List[str]    	=       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:
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       image_processor.size['shortest_edge']
     else:
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       (image_processor.size['height'], image_processor.size['width'])
     SCREAMING_SNAKE_CASE					:			Any    	=       Normalize(mean=image_processor.image_mean       ,      std=image_processor.image_std		)
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       Compose(
         [
             RandomResizedCrop(a_		),
             RandomHorizontalFlip(),
             ToTensor(),
             normalize,
         ]		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       Compose(
         [
             Resize(a_		),
             CenterCrop(a_		),
             ToTensor(),
             normalize,
         ]		)

     def train_transforms(a_		):
          SCREAMING_SNAKE_CASE					:			int    	=       [
              _train_transforms(pil_img.convert('RGB'		)		) for pil_img in example_batch['image']
          ]
          return example_batch

     def val_transforms(a_		):
          SCREAMING_SNAKE_CASE					:			Any    	=       [_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:
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       (
                   dataset['train'].shuffle(seed=training_args.seed		).select(range(data_args.max_train_samples		)		)
               )
          # Set the training transforms
          dataset["train"].set_transform(a_		)

     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:
               SCREAMING_SNAKE_CASE					:			int    	=       (
                   dataset['validation'].shuffle(seed=training_args.seed		).select(range(data_args.max_eval_samples		)		)
               )
          # Set the validation transforms
          dataset["validation"].set_transform(a_		)

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

     # Training
     if training_args.do_train:
          SCREAMING_SNAKE_CASE					:			int    	=       None
          if training_args.resume_from_checkpoint is not None:
               SCREAMING_SNAKE_CASE					:			Dict    	=       training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               SCREAMING_SNAKE_CASE					:			Tuple    	=       last_checkpoint
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       trainer.train(resume_from_checkpoint=a_		)
          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:
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       trainer.evaluate()
          trainer.log_metrics('eval'       ,      a_		)
          trainer.save_metrics('eval'       ,      a_		)

     # Write model card and (optionally) push to hub
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       {
         '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(**a_		)
     else:
          trainer.create_model_card(**a_		)


if __name__ == "__main__":
 main()



