'''simple docstring'''





import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


_lowerCAmelCase  :Tuple  				=    logging.get_logger(__name__)

_lowerCAmelCase  :Optional[int]  				=    {
    'artists_file': 'artists.json',
    'lyrics_file': 'lyrics.json',
    'genres_file': 'genres.json',
}

_lowerCAmelCase  :int  				=    {
    'artists_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json',
    },
    'genres_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json',
    },
    'lyrics_file': {
        'jukebox': 'https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json',
    },
}

_lowerCAmelCase  :List[str]  				=    {
    'jukebox': 512,
}






class 				UpperCAmelCase (       __lowercase ):




    '''simple docstring'''







    snake_case__							:   List[str]							=						VOCAB_FILES_NAMES
    snake_case__							:   List[str]							=						PRETRAINED_VOCAB_FILES_MAP
    snake_case__							:   int							=						PRETRAINED_LYRIC_TOKENS_SIZES
    snake_case__							:   Union[str, Any]							=						["input_ids", "attention_mask"]






    def __init__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__=["v3", "v2", "v2"]    ,							lowercase__=512    ,							lowercase__=5    ,							lowercase__="<|endoftext|>"    ,							**lowercase__    ,							)							-> Any:
         SCREAMING_SNAKE_CASE					:			Any    	=       AddedToken(lowercase__    ,							lstrip=lowercase__    ,							rstrip=lowercase__ ) if isinstance(lowercase__    ,							lowercase__ ) else unk_token
         super().__init__(
             unk_token=lowercase__    ,							n_genres=lowercase__    ,							version=lowercase__    ,							max_n_lyric_tokens=lowercase__    ,							**lowercase__    ,							)
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       version
         SCREAMING_SNAKE_CASE					:			int    	=       max_n_lyric_tokens
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       n_genres

         with open(lowercase__    ,							encoding='utf-8' ) as vocab_handle:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       json.load(lowercase__ )

         with open(lowercase__    ,							encoding='utf-8' ) as vocab_handle:
              SCREAMING_SNAKE_CASE					:			Dict    	=       json.load(lowercase__ )

         with open(lowercase__    ,							encoding='utf-8' ) as vocab_handle:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       json.load(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       r"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"
         # In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
         if len(self.lyrics_encoder ) == 79:
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       oov.replace(r'\-\''    ,							r'\-+\'' )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       regex.compile(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       {v: k for k, v in self.artists_encoder.items()}
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       {v: k for k, v in self.genres_encoder.items()}
         SCREAMING_SNAKE_CASE					:			Any    	=       {v: k for k, v in self.lyrics_encoder.items()}






    @property
    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         return len(self.artists_encoder ) + len(self.genres_encoder ) + len(self.lyrics_encoder )






    def       _UpperCamelCase    (       self )							-> str:
         return dict(self.artists_encoder    ,							self.genres_encoder    ,							self.lyrics_encoder )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [self.artists_encoder.get(lowercase__    ,							0 ) for artist in list_artists]
         for genres in range(len(lowercase__ ) ):
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       [self.genres_encoder.get(lowercase__    ,							0 ) for genre in list_genres[genres]]
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres] ))

         SCREAMING_SNAKE_CASE					:			Any    	=       [[self.lyrics_encoder.get(lowercase__    ,							0 ) for character in list_lyrics[0]], [], []]
         return artists_id, list_genres, lyric_ids






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Tuple:
         return list(lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							**lowercase__ )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.prepare_for_tokenization(lowercase__    ,							lowercase__    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self._tokenize(lowercase__ )
         return artist, genre, lyrics






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ = False )							-> Tuple[str, str, str, Dict[str, Any]]:
         for idx in range(len(self.version ) ):
              if self.version[idx] == "v3":
                   SCREAMING_SNAKE_CASE					:			int    	=       artists[idx].lower()
                   SCREAMING_SNAKE_CASE					:			str    	=       [genres[idx].lower()]
              else:
                   SCREAMING_SNAKE_CASE					:			List[Any]    	=       self._normalize(artists[idx] ) + ".v2"
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       [
                       self._normalize(lowercase__ ) + ".v2" for genre in genres[idx].split('_' )
                   ]  # split is for the full dictionary with combined genres

         if self.version[0] == "v2":
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       regex.compile(r'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+' )
              SCREAMING_SNAKE_CASE					:			int    	=       "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+'\"()[] \t\n"
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       {vocab[index]: index + 1 for index in range(len(lowercase__ ) )}
              SCREAMING_SNAKE_CASE					:			List[str]    	=       0
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       len(lowercase__ ) + 1
              SCREAMING_SNAKE_CASE					:			int    	=       self.vocab
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       {v: k for k, v in self.vocab.items()}
              SCREAMING_SNAKE_CASE					:			int    	=       ""
         else:
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       regex.compile(r'[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+' )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self._run_strip_accents(lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       lyrics.replace('\\'    ,							'\n' )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.out_of_vocab.sub(''    ,							lowercase__ ), [], []
         return artists, genres, lyrics






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Any    	=       unicodedata.normalize('NFD'    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       []
         for char in text:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       unicodedata.category(lowercase__ )
              if cat == "Mn":
                   continue
              output.append(lowercase__ )
         return "".join(lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> str:
         SCREAMING_SNAKE_CASE					:			Any    	=       (
             [chr(lowercase__ ) for i in range(ord('a' )    ,							ord('z' ) + 1 )]
             + [chr(lowercase__ ) for i in range(ord('A' )    ,							ord('Z' ) + 1 )]
             + [chr(lowercase__ ) for i in range(ord('0' )    ,							ord('9' ) + 1 )]
             + ["."]
         )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       frozenset(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Dict    	=       re.compile(r'_+' )
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       "".join([c if c in accepted else '_' for c in text.lower()] )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       pattern.sub('_'    ,							lowercase__ ).strip('_' )
         return text






    def       _UpperCamelCase    (       self    ,							lowercase__ )							-> str:
         return " ".join(lowercase__ )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None    ,							lowercase__ = False )							-> Dict:
         # Convert to TensorType
         if not isinstance(lowercase__    ,							lowercase__ ):
              SCREAMING_SNAKE_CASE					:			Dict    	=       TensorType(lowercase__ )

         # Get a function reference for the correct framework
         if tensor_type == TensorType.TENSORFLOW:
              if not is_tf_available():
                   raise ImportError(
                       'Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.' )
              import tensorflow as tf

              SCREAMING_SNAKE_CASE					:			int    	=       tf.constant
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       tf.is_tensor
         elif tensor_type == TensorType.PYTORCH:
              if not is_torch_available():
                   raise ImportError('Unable to convert output to PyTorch tensors format, PyTorch is not installed.' )
              import torch

              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.tensor
              SCREAMING_SNAKE_CASE					:			int    	=       torch.is_tensor
         elif tensor_type == TensorType.JAX:
              if not is_flax_available():
                   raise ImportError('Unable to convert output to JAX tensors format, JAX is not installed.' )
              import jax.numpy as jnp  # noqa: F811

              SCREAMING_SNAKE_CASE					:			Dict    	=       jnp.array
              SCREAMING_SNAKE_CASE					:			Dict    	=       _is_jax
         else:
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       np.asarray
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       _is_numpy

         # Do the tensor conversion in batch

         try:
              if prepend_batch_axis:
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       [inputs]

              if not is_tensor(lowercase__ ):
                   SCREAMING_SNAKE_CASE					:			int    	=       as_tensor(lowercase__ )
         except:  # noqa E722
              raise ValueError(
                  'Unable to create tensor, you should probably activate truncation and/or padding '
                  'with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.' )

         return inputs






    def __call__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__=""    ,							lowercase__="pt" )							-> BatchEncoding:
         SCREAMING_SNAKE_CASE					:			Any    	=       [0, 0, 0]
         SCREAMING_SNAKE_CASE					:			Dict    	=       [artist] * len(self.version )
         SCREAMING_SNAKE_CASE					:			Any    	=       [genres] * len(self.version )

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.tokenize(lowercase__    ,							lowercase__    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self._convert_token_to_id(lowercase__    ,							lowercase__    ,							lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [-INFINITY] * len(full_tokens[-1] )
         SCREAMING_SNAKE_CASE					:			Dict    	=       [
             self.convert_to_tensors(
                 [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]    ,							tensor_type=lowercase__ )
             for i in range(len(self.version ) )
         ]
         return BatchEncoding({'input_ids': input_ids, 'attention_masks': attention_masks} )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ = None )							-> Tuple[str]:
         if not os.path.isdir(lowercase__ ):
              logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
              return

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       os.path.join(
             lowercase__    ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['artists_file'] )
         with open(lowercase__    ,							'w'    ,							encoding='utf-8' ) as f:
              f.write(json.dumps(self.artists_encoder    ,							ensure_ascii=lowercase__ ) )

         SCREAMING_SNAKE_CASE					:			Any    	=       os.path.join(
             lowercase__    ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['genres_file'] )
         with open(lowercase__    ,							'w'    ,							encoding='utf-8' ) as f:
              f.write(json.dumps(self.genres_encoder    ,							ensure_ascii=lowercase__ ) )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       os.path.join(
             lowercase__    ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['lyrics_file'] )
         with open(lowercase__    ,							'w'    ,							encoding='utf-8' ) as f:
              f.write(json.dumps(self.lyrics_encoder    ,							ensure_ascii=lowercase__ ) )

         return (artists_file, genres_file, lyrics_file)






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.artists_decoder.get(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Dict    	=       [self.genres_decoder.get(lowercase__ ) for genre in genres_index]
         SCREAMING_SNAKE_CASE					:			str    	=       [self.lyrics_decoder.get(lowercase__ ) for character in lyric_index]
         return artist, genres, lyrics




'''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()



