


"""simple docstring"""







import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

lowerCamelCase_       :       Optional[int]      						=  [
    # (stable-diffusion, HF Diffusers)
    ("""time_embed.0.weight""", """time_embedding.linear_1.weight"""),
    ("""time_embed.0.bias""", """time_embedding.linear_1.bias"""),
    ("""time_embed.2.weight""", """time_embedding.linear_2.weight"""),
    ("""time_embed.2.bias""", """time_embedding.linear_2.bias"""),
    ("""input_blocks.0.0.weight""", """conv_in.weight"""),
    ("""input_blocks.0.0.bias""", """conv_in.bias"""),
    ("""out.0.weight""", """conv_norm_out.weight"""),
    ("""out.0.bias""", """conv_norm_out.bias"""),
    ("""out.2.weight""", """conv_out.weight"""),
    ("""out.2.bias""", """conv_out.bias"""),
]

lowerCamelCase_       :       Dict      						=  [
    # (stable-diffusion, HF Diffusers)
    ("""in_layers.0""", """norm1"""),
    ("""in_layers.2""", """conv1"""),
    ("""out_layers.0""", """norm2"""),
    ("""out_layers.3""", """conv2"""),
    ("""emb_layers.1""", """time_emb_proj"""),
    ("""skip_connection""", """conv_shortcut"""),
]

lowerCamelCase_       :       Optional[Any]      						=  []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
       # loop over downblocks/upblocks

       for j in range(2):
              # loop over resnets/attentions for downblocks
              lowerCamelCase_       :       Optional[int]      						=  F'down_blocks.{i}.resnets.{j}.'
              lowerCamelCase_       :       int      						=  F'input_blocks.{3*i + j + 1}.0.'
              unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

              if i < 3:
                     # no attention layers in down_blocks.3
                     lowerCamelCase_       :       Dict      						=  F'down_blocks.{i}.attentions.{j}.'
                     lowerCamelCase_       :       Tuple      						=  F'input_blocks.{3*i + j + 1}.1.'
                     unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

       for j in range(3):
              # loop over resnets/attentions for upblocks
              lowerCamelCase_       :       Dict      						=  F'up_blocks.{i}.resnets.{j}.'
              lowerCamelCase_       :       List[str]      						=  F'output_blocks.{3*i + j}.0.'
              unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

              if i > 0:
                     # no attention layers in up_blocks.0
                     lowerCamelCase_       :       Union[str, Any]      						=  F'up_blocks.{i}.attentions.{j}.'
                     lowerCamelCase_       :       Optional[Any]      						=  F'output_blocks.{3*i + j}.1.'
                     unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

       if i < 3:
              # no downsample in down_blocks.3
              lowerCamelCase_       :       Optional[int]      						=  F'down_blocks.{i}.downsamplers.0.conv.'
              lowerCamelCase_       :       List[str]      						=  F'input_blocks.{3*(i+1)}.0.op.'
              unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

              # no upsample in up_blocks.3
              lowerCamelCase_       :       List[str]      						=  F'up_blocks.{i}.upsamplers.0.'
              lowerCamelCase_       :       Tuple      						=  F'output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'
              unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowerCamelCase_       :       Optional[int]      						=  """mid_block.attentions.0."""
lowerCamelCase_       :       List[str]      						=  """middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
       lowerCamelCase_       :       Optional[int]      						=  F'mid_block.resnets.{j}.'
       lowerCamelCase_       :       List[Any]      						=  F'middle_block.{2*j}.'
       unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def _A						(						lowercase		):







 """simple docstring"""
 # buyer beware: this is a *brittle* function,
 # and correct output requires that all of these pieces interact in
 # the exact order in which I have arranged them.
 a					={k: k for k in unet_state_dict.keys()}
 for sd_name, hf_name in unet_conversion_map:
  a					=sd_name
 for k, v in mapping.items():
  if "resnets" in k:
   for sd_part, hf_part in unet_conversion_map_resnet:
    a					=v.replace(lowercase	,   lowercase		)
   a					=v
 for k, v in mapping.items():
  for sd_part, hf_part in unet_conversion_map_layer:
   a					=v.replace(lowercase	,   lowercase		)
  a					=v
 a					={v: unet_state_dict[k] for k, v in mapping.items()}
 return new_state_dict


# ================#
# VAE Conversion #
# ================#

lowerCamelCase_       :       Optional[Any]      						=  [
    # (stable-diffusion, HF Diffusers)
    ("""nin_shortcut""", """conv_shortcut"""),
    ("""norm_out""", """conv_norm_out"""),
    ("""mid.attn_1.""", """mid_block.attentions.0."""),
]

for i in range(4):
       # down_blocks have two resnets
       for j in range(2):
              lowerCamelCase_       :       Tuple      						=  F'encoder.down_blocks.{i}.resnets.{j}.'
              lowerCamelCase_       :       Optional[Any]      						=  F'encoder.down.{i}.block.{j}.'
              vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

       if i < 3:
              lowerCamelCase_       :       Optional[Any]      						=  F'down_blocks.{i}.downsamplers.0.'
              lowerCamelCase_       :       List[Any]      						=  F'down.{i}.downsample.'
              vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

              lowerCamelCase_       :       Optional[Any]      						=  F'up_blocks.{i}.upsamplers.0.'
              lowerCamelCase_       :       Union[str, Any]      						=  F'up.{3-i}.upsample.'
              vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

       # up_blocks have three resnets
       # also, up blocks in hf are numbered in reverse from sd
       for j in range(3):
              lowerCamelCase_       :       Any      						=  F'decoder.up_blocks.{i}.resnets.{j}.'
              lowerCamelCase_       :       Optional[Any]      						=  F'decoder.up.{3-i}.block.{j}.'
              vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
       lowerCamelCase_       :       Optional[int]      						=  F'mid_block.resnets.{i}.'
       lowerCamelCase_       :       Any      						=  F'mid.block_{i+1}.'
       vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


lowerCamelCase_       :       str      						=  [
    # (stable-diffusion, HF Diffusers)
    ("""norm.""", """group_norm."""),
    ("""q.""", """query."""),
    ("""k.""", """key."""),
    ("""v.""", """value."""),
    ("""proj_out.""", """proj_attn."""),
]
def _A						(						lowercase		):







 """simple docstring"""
 # convert HF linear weights to SD conv2d weights
 return w.reshape(*w.shape	,   1	,   1		)
def _A						(						lowercase		):







 """simple docstring"""
 a					={k: k for k in vae_state_dict.keys()}
 for k, v in mapping.items():
  for sd_part, hf_part in vae_conversion_map:
   a					=v.replace(lowercase	,   lowercase		)
  a					=v
 for k, v in mapping.items():
  if "attentions" in k:
   for sd_part, hf_part in vae_conversion_map_attn:
    a					=v.replace(lowercase	,   lowercase		)
   a					=v
 a					={v: vae_state_dict[k] for k, v in mapping.items()}
 a					=['''q''', '''k''', '''v''', '''proj_out''']
 for k, v in new_state_dict.items():
  for weight_name in weights_to_convert:
   if f'''mid.attn_1.{weight_name}.weight''' in k:
    print(f'''Reshaping {k} for SD format'''		)
    a					=reshape_weight_for_sd(lowercase		)
 return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


lowerCamelCase_       :       str      						=  [
    # (stable-diffusion, HF Diffusers)
    ("""resblocks.""", """text_model.encoder.layers."""),
    ("""ln_1""", """layer_norm1"""),
    ("""ln_2""", """layer_norm2"""),
    (""".c_fc.""", """.fc1."""),
    (""".c_proj.""", """.fc2."""),
    (""".attn""", """.self_attn"""),
    ("""ln_final.""", """transformer.text_model.final_layer_norm."""),
    ("""token_embedding.weight""", """transformer.text_model.embeddings.token_embedding.weight"""),
    ("""positional_embedding""", """transformer.text_model.embeddings.position_embedding.weight"""),
]
lowerCamelCase_       :       Any      						=  {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCamelCase_       :       List[str]      						=  re.compile("""|""".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowerCamelCase_       :       int      						=  {"""q""": 0, """k""": 1, """v""": 2}
def _A						(						lowercase		):







 """simple docstring"""
 a					={}
 a					={}
 a					={}
 for k, v in text_enc_dict.items():
  if (
      k.endswith('''.self_attn.q_proj.weight'''		)
      or k.endswith('''.self_attn.k_proj.weight'''		)
      or k.endswith('''.self_attn.v_proj.weight'''		)
  ):
   a					=k[: -len('''.q_proj.weight'''		)]
   a					=k[-len('''q_proj.weight'''		)]
   if k_pre not in capture_qkv_weight:
    a					=[None, None, None]
   a					=v
   continue

  if (
      k.endswith('''.self_attn.q_proj.bias'''		)
      or k.endswith('''.self_attn.k_proj.bias'''		)
      or k.endswith('''.self_attn.v_proj.bias'''		)
  ):
   a					=k[: -len('''.q_proj.bias'''		)]
   a					=k[-len('''q_proj.bias'''		)]
   if k_pre not in capture_qkv_bias:
    a					=[None, None, None]
   a					=v
   continue

  a					=textenc_pattern.sub(lambda lowercase		: protected[re.escape(m.group(0		)		)]	,   lowercase		)
  a					=v

 for k_pre, tensors in capture_qkv_weight.items():
  if None in tensors:
   raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''		)
  a					=textenc_pattern.sub(lambda lowercase		: protected[re.escape(m.group(0		)		)]	,   lowercase		)
  a					=torch.cat(lowercase		)

 for k_pre, tensors in capture_qkv_bias.items():
  if None in tensors:
   raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''		)
  a					=textenc_pattern.sub(lambda lowercase		: protected[re.escape(m.group(0		)		)]	,   lowercase		)
  a					=torch.cat(lowercase		)

 return new_state_dict
def _A						(						lowercase		):







 """simple docstring"""
 return text_enc_dict


if __name__ == "__main__":
       lowerCamelCase_       :       List[str]      						=  argparse.ArgumentParser()

       parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
       parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
       parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
       parser.add_argument(
           """--use_safetensors""", action="""store_true""", help="""Save weights use safetensors, default is ckpt."""
       )

       lowerCamelCase_       :       int      						=  parser.parse_args()

       assert args.model_path is not None, "Must provide a model path!"

       assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

       # Path for safetensors
       lowerCamelCase_       :       Union[str, Any]      						=  osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
       lowerCamelCase_       :       Optional[Any]      						=  osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
       lowerCamelCase_       :       int      						=  osp.join(args.model_path, """text_encoder""", """model.safetensors""")

       # Load models from safetensors if it exists, if it doesn't pytorch
       if osp.exists(unet_path):
              lowerCamelCase_       :       Union[str, Any]      						=  load_file(unet_path, device="""cpu""")
       else:
              lowerCamelCase_       :       List[Any]      						=  osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
              lowerCamelCase_       :       List[str]      						=  torch.load(unet_path, map_location="""cpu""")

       if osp.exists(vae_path):
              lowerCamelCase_       :       Dict      						=  load_file(vae_path, device="""cpu""")
       else:
              lowerCamelCase_       :       Optional[Any]      						=  osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
              lowerCamelCase_       :       Optional[int]      						=  torch.load(vae_path, map_location="""cpu""")

       if osp.exists(text_enc_path):
              lowerCamelCase_       :       int      						=  load_file(text_enc_path, device="""cpu""")
       else:
              lowerCamelCase_       :       List[Any]      						=  osp.join(args.model_path, """text_encoder""", """pytorch_model.bin""")
              lowerCamelCase_       :       int      						=  torch.load(text_enc_path, map_location="""cpu""")

       # Convert the UNet model
       lowerCamelCase_       :       str      						=  convert_unet_state_dict(unet_state_dict)
       lowerCamelCase_       :       List[Any]      						=  {"""model.diffusion_model.""" + k: v for k, v in unet_state_dict.items()}

       # Convert the VAE model
       lowerCamelCase_       :       List[Any]      						=  convert_vae_state_dict(vae_state_dict)
       lowerCamelCase_       :       List[str]      						=  {"""first_stage_model.""" + k: v for k, v in vae_state_dict.items()}

       # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
       lowerCamelCase_       :       Union[str, Any]      						=  """text_model.encoder.layers.22.layer_norm2.bias""" in text_enc_dict

       if is_vaa_model:
              # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
              lowerCamelCase_       :       str      						=  {"""transformer.""" + k: v for k, v in text_enc_dict.items()}
              lowerCamelCase_       :       str      						=  convert_text_enc_state_dict_vaa(text_enc_dict)
              lowerCamelCase_       :       Dict      						=  {"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
       else:
              lowerCamelCase_       :       Tuple      						=  convert_text_enc_state_dict(text_enc_dict)
              lowerCamelCase_       :       List[str]      						=  {"""cond_stage_model.transformer.""" + k: v for k, v in text_enc_dict.items()}

       # Put together new checkpoint
       lowerCamelCase_       :       List[Any]      						=  {**unet_state_dict, **vae_state_dict, **text_enc_dict}
       if args.half:
              lowerCamelCase_       :       Optional[int]      						=  {k: v.half() for k, v in state_dict.items()}

       if args.use_safetensors:
              save_file(state_dict, args.checkpoint_path)
       else:
              lowerCamelCase_       :       List[Any]      						=  {"""state_dict""": state_dict}
              torch.save(state_dict, args.checkpoint_path)



"""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_       :       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_       :       Any      						=  list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowerCamelCase_       :       Dict      						=  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def _A						(						lowercase		):







    """simple docstring"""
    with open(lowercase	,   '''rb'''		) as f:
        a					=Image.open(lowercase		)
        return im.convert('''RGB'''		)






@dataclass
class 				__A    :






 """simple docstring"""




 __lowerCAmelCase			 =  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)."
     },   )
 __lowerCAmelCase			 =  field(
     default=_SCREAMING_SNAKE_CASE,   metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}      )
 __lowerCAmelCase			 =  field(default=_SCREAMING_SNAKE_CASE,   metadata={"help": "A folder containing the training data."}      )
 __lowerCAmelCase			 =  field(default=_SCREAMING_SNAKE_CASE,   metadata={"help": "A folder containing the validation data."}      )
 __lowerCAmelCase			 =  field(
     default=0.1_5,   metadata={"help": "Percent to split off of train for validation."}      )
 __lowerCAmelCase			 =  field(
     default=_SCREAMING_SNAKE_CASE,   metadata={
         "help": (
             "For debugging purposes or quicker training, truncate the number of training examples to this "
             "value if set."
         )
     },   )
 __lowerCAmelCase			 =  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 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, 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 				__A    :






 """simple docstring"""




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

def _A						(						lowercase		):







    """simple docstring"""
    a					=torch.stack([example['''pixel_values'''] for example in examples]		)
    a					=torch.tensor([example['''labels'''] for example in examples]		)
    return {"pixel_values": pixel_values, "labels": labels}
def _A						(						):







    """simple docstring"""
    # 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()

    # 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'''	,   lowercase	,   lowercase		)

    # 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()

    a					=training_args.get_process_log_level()
    logger.setLevel(lowercase		)
    transformers.utils.logging.set_verbosity(lowercase		)
    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.
    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 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:
        a					=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:
        a					={}
        if data_args.train_dir is not None:
            a					=os.path.join(data_args.train_dir	,   '''**'''		)
        if data_args.validation_dir is not None:
            a					=os.path.join(data_args.validation_dir	,   '''**'''		)
        a					=load_dataset(
            '''imagefolder'''	,   data_files=lowercase	,   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.
    a					=None if '''validation''' in dataset.keys() else data_args.train_val_split
    if isinstance(data_args.train_val_split	,   lowercase		) and data_args.train_val_split > 0.0:
        a					=dataset['''train'''].train_test_split(data_args.train_val_split		)
        a					=split['''train''']
        a					=split['''test''']

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

    # Load the accuracy metric from the datasets package
    a					=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(lowercase		):
        return metric.compute(predictions=np.argmax(p.predictions	,   axis=1		)	,   references=p.label_ids		)

    a					=AutoConfig.from_pretrained(
        model_args.config_name or model_args.model_name_or_path	,   num_labels=len(lowercase		)	,   labelaid=lowercase	,   idalabel=lowercase	,   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	,   )
    a					=AutoModelForImageClassification.from_pretrained(
        model_args.model_name_or_path	,   from_tf=bool('''.ckpt''' in model_args.model_name_or_path		)	,   config=lowercase	,   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	,   )
    a					=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:
        a					=image_processor.size['''shortest_edge''']
    else:
        a					=(image_processor.size['''height'''], image_processor.size['''width'''])
    a					=Normalize(mean=image_processor.image_mean	,   std=image_processor.image_std		)
    a					=Compose(
        [
            RandomResizedCrop(lowercase		),
            RandomHorizontalFlip(),
            ToTensor(),
            normalize,
        ]		)
    a					=Compose(
        [
            Resize(lowercase		),
            CenterCrop(lowercase		),
            ToTensor(),
            normalize,
        ]		)

    def train_transforms(lowercase		):
        a					=[
            _train_transforms(pil_img.convert('''RGB'''		)		) for pil_img in example_batch['''image''']
        ]
        return example_batch

    def val_transforms(lowercase		):
        a					=[_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:
            a					=(
                dataset['''train'''].shuffle(seed=training_args.seed		).select(range(data_args.max_train_samples		)		)
            )
        # Set the training transforms
        dataset["train"].set_transform(lowercase		)

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

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

    # Training
    if training_args.do_train:
        a					=None
        if training_args.resume_from_checkpoint is not None:
            a					=training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            a					=last_checkpoint
        a					=trainer.train(resume_from_checkpoint=lowercase		)
        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:
        a					=trainer.evaluate()
        trainer.log_metrics('''eval'''	,   lowercase		)
        trainer.save_metrics('''eval'''	,   lowercase		)

    # Write model card and (optionally) push to hub
    a					={
        '''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(**lowercase		)
    else:
        trainer.create_model_card(**lowercase		)


if __name__ == "__main__":
       main()