'''simple docstring'''





import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
     MODEL_MAPPING,
     LevitForImageClassification,
     LevitForImageClassificationWithTeacher,
     LevitModel,
 )
 from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import LevitImageProcessor






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.config_class(**self.inputs_dict )
         self.parent.assertTrue(hasattr(lowercase__    ,							'hidden_sizes' ) )
         self.parent.assertTrue(hasattr(lowercase__    ,							'num_attention_heads' ) )






class 				UpperCAmelCase :




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__=13    ,							lowercase__=64    ,							lowercase__=3    ,							lowercase__=3    ,							lowercase__=2    ,							lowercase__=1    ,							lowercase__=16    ,							lowercase__=[128, 256, 384]    ,							lowercase__=[4, 6, 8]    ,							lowercase__=[2, 3, 4]    ,							lowercase__=[16, 16, 16]    ,							lowercase__=0    ,							lowercase__=[2, 2, 2]    ,							lowercase__=[2, 2, 2]    ,							lowercase__=0.0_2    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__=2    ,							)							-> int:
         SCREAMING_SNAKE_CASE					:			str    	=       parent
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       batch_size
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       image_size
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       num_channels
         SCREAMING_SNAKE_CASE					:			Dict    	=       kernel_size
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       stride
         SCREAMING_SNAKE_CASE					:			Dict    	=       padding
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       hidden_sizes
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			int    	=       depths
         SCREAMING_SNAKE_CASE					:			Dict    	=       key_dim
         SCREAMING_SNAKE_CASE					:			Any    	=       drop_path_rate
         SCREAMING_SNAKE_CASE					:			str    	=       patch_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       attention_ratio
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       mlp_ratio
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       initializer_range
         SCREAMING_SNAKE_CASE					:			int    	=       [
             ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
             ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
         ]
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       is_training
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       use_labels
         SCREAMING_SNAKE_CASE					:			Any    	=       num_labels
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       initializer_range






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Dict    	=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

         SCREAMING_SNAKE_CASE					:			Tuple    	=       None
         if self.use_labels:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       ids_tensor([self.batch_size]    ,							self.num_labels )

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_config()
         return config, pixel_values, labels






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         return LevitConfig(
             image_size=self.image_size    ,							num_channels=self.num_channels    ,							kernel_size=self.kernel_size    ,							stride=self.stride    ,							padding=self.padding    ,							patch_size=self.patch_size    ,							hidden_sizes=self.hidden_sizes    ,							num_attention_heads=self.num_attention_heads    ,							depths=self.depths    ,							key_dim=self.key_dim    ,							drop_path_rate=self.drop_path_rate    ,							mlp_ratio=self.mlp_ratio    ,							attention_ratio=self.attention_ratio    ,							initializer_range=self.initializer_range    ,							down_ops=self.down_ops    ,							)






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			str    	=       LevitModel(config=lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       model(lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       (self.image_size, self.image_size)
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			int    	=       image_size[0], image_size[1]
         for _ in range(4 ):
              SCREAMING_SNAKE_CASE					:			str    	=       floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
              SCREAMING_SNAKE_CASE					:			int    	=       floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
         self.parent.assertEqual(
             result.last_hidden_state.shape    ,							(self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1])    ,							)






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.num_labels
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       LevitForImageClassification(lowercase__ )
         model.to(lowercase__ )
         model.eval()
         SCREAMING_SNAKE_CASE					:			Dict    	=       model(lowercase__    ,							labels=lowercase__ )
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_labels) )






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.prepare_config_and_inputs()
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Any    	=       config_and_inputs
         SCREAMING_SNAKE_CASE					:			Any    	=       {'pixel_values': pixel_values}
         return config, inputs_dict






@require_torch
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE   ,    _SCREAMING_SNAKE_CASE   ,    unittest.TestCase ):




    '''simple docstring'''







    snake_case__							:   Union[str, Any]							=						(
        (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
        if is_torch_available()
        else ()
    )
    snake_case__							:   Optional[Any]							=						(
        {
            "feature-extraction": LevitModel,
            "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
        }
        if is_torch_available()
        else {}
    )

    snake_case__							:   int							=						False
    snake_case__							:   Tuple							=						False
    snake_case__							:   str							=						False
    snake_case__							:   Any							=						False
    snake_case__							:   List[Any]							=						False






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       LevitModelTester(self )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       ConfigTester(self    ,							config_class=lowercase__    ,							has_text_modality=lowercase__    ,							hidden_size=37 )






    def       _UpperCamelCase    (       self )							-> List[str]:
         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         return






    @unittest.skip(reason='Levit does not use inputs_embeds' )
    def       _UpperCamelCase    (       self )							-> Optional[int]:
         pass






    @unittest.skip(reason='Levit does not support input and output embeddings' )
    def       _UpperCamelCase    (       self )							-> List[Any]:
         pass






    @unittest.skip(reason='Levit does not output attentions' )
    def       _UpperCamelCase    (       self )							-> Dict:
         pass






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model_class(lowercase__ )
              SCREAMING_SNAKE_CASE					:			Tuple    	=       inspect.signature(model.forward )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              SCREAMING_SNAKE_CASE					:			str    	=       [*signature.parameters.keys()]

              SCREAMING_SNAKE_CASE					:			Dict    	=       ['pixel_values']
              self.assertListEqual(arg_names[:1]    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         def check_hidden_states_output(lowercase__    ,							lowercase__    ,							lowercase__ ):
              SCREAMING_SNAKE_CASE					:			int    	=       model_class(lowercase__ )
              model.to(lowercase__ )
              model.eval()

              with torch.no_grad():
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       model(**self._prepare_for_class(lowercase__    ,							lowercase__ ) )

              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       outputs.hidden_states

              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       len(self.model_tester.depths ) + 1
              self.assertEqual(len(lowercase__ )    ,							lowercase__ )

              SCREAMING_SNAKE_CASE					:			List[Any]    	=       (self.model_tester.image_size, self.model_tester.image_size)
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       image_size[0], image_size[1]
              for _ in range(4 ):
                   SCREAMING_SNAKE_CASE					:			Dict    	=       floor(
                       (
                           (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                           / self.model_tester.stride
                       )
                       + 1 )
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       floor(
                       (
                           (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                           / self.model_tester.stride
                       )
                       + 1 )
              # verify the first hidden states (first block)
              self.assertListEqual(
                  list(hidden_states[0].shape[-2:] )    ,							[
                      height * width,
                      self.model_tester.hidden_sizes[0],
                  ]    ,							)

         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       True
              check_hidden_states_output(lowercase__    ,							lowercase__    ,							lowercase__ )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              SCREAMING_SNAKE_CASE					:			Tuple    	=       True

              check_hidden_states_output(lowercase__    ,							lowercase__    ,							lowercase__ )






    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
    def       _UpperCamelCase    (       self )							-> Dict:
         pass






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__=False )							-> int:
         SCREAMING_SNAKE_CASE					:			str    	=       super()._prepare_for_class(lowercase__    ,							lowercase__    ,							return_labels=lowercase__ )

         if return_labels:
              if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                   del inputs_dict["labels"]

         return inputs_dict






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			int    	=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowercase__ )






    def       _UpperCamelCase    (       self )							-> Dict:
         if not self.model_tester.is_training:
              return

         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Tuple    	=       self.model_tester.prepare_config_and_inputs_for_common()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       True

         for model_class in self.all_model_classes:
              # LevitForImageClassificationWithTeacher supports inference-only
              if (
                  model_class in get_values(lowercase__ )
                  or model_class.__name__ == "LevitForImageClassificationWithTeacher"
              ):
                   continue
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model_class(lowercase__ )
              model.to(lowercase__ )
              model.train()
              SCREAMING_SNAKE_CASE					:			Dict    	=       self._prepare_for_class(lowercase__    ,							lowercase__    ,							return_labels=lowercase__ )
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       model(**lowercase__ ).loss
              loss.backward()






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       self.model_tester.prepare_config_and_inputs_for_common()
         if not self.model_tester.is_training:
              return

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       False
         SCREAMING_SNAKE_CASE					:			Any    	=       True

         for model_class in self.all_model_classes:
              if model_class in get_values(lowercase__ ) or not model_class.supports_gradient_checkpointing:
                   continue
              # LevitForImageClassificationWithTeacher supports inference-only
              if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                   continue
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       model_class(lowercase__ )
              model.gradient_checkpointing_enable()
              model.to(lowercase__ )
              model.train()
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self._prepare_for_class(lowercase__    ,							lowercase__    ,							return_labels=lowercase__ )
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model(**lowercase__ ).loss
              loss.backward()






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.model_tester.prepare_config_and_inputs_for_common()

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [
             {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
             {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
             {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
         ]

         for model_class in self.all_model_classes:
              if (
                  model_class
                  not in [
                      *get_values(lowercase__ ),
                  ]
                  or model_class.__name__ == "LevitForImageClassificationWithTeacher"
              ):
                   continue

              for problem_type in problem_types:
                   with self.subTest(msg=F"""Testing {model_class} with {problem_type["title"]}""" ):
                        SCREAMING_SNAKE_CASE					:			Tuple    	=       problem_type['title']
                        SCREAMING_SNAKE_CASE					:			str    	=       problem_type['num_labels']

                        SCREAMING_SNAKE_CASE					:			Any    	=       model_class(lowercase__ )
                        model.to(lowercase__ )
                        model.train()

                        SCREAMING_SNAKE_CASE					:			Tuple    	=       self._prepare_for_class(lowercase__    ,							lowercase__    ,							return_labels=lowercase__ )

                        if problem_type["num_labels"] > 1:
                             SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       inputs['labels'].unsqueeze(1 ).repeat(1    ,							problem_type['num_labels'] )

                        SCREAMING_SNAKE_CASE					:			Any    	=       inputs['labels'].to(problem_type['dtype'] )

                        # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                        # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                        # they have the same size." which is a symptom something in wrong for the regression problem.
                        # See https://github.com/huggingface/transformers/issues/11780
                        with warnings.catch_warnings(record=lowercase__ ) as warning_list:
                             SCREAMING_SNAKE_CASE					:			List[Any]    	=       model(**lowercase__ ).loss
                        for w in warning_list:
                             if "Using a target size that is different to the input size" in str(w.message ):
                                  raise ValueError(
                                      F"""Something is going wrong in the regression problem: intercepted {w.message}""" )

                        loss.backward()






    @slow
    def       _UpperCamelCase    (       self )							-> Dict:
         for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       LevitModel.from_pretrained(lowercase__ )
              self.assertIsNotNone(lowercase__ )






def     __lowerCAmelCase						(       )      ->	str:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Tuple    	=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
     return image






@require_torch
@require_vision
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    @cached_property
    def       _UpperCamelCase    (       self )							-> int:
         return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )






    @slow
    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			Any    	=       LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
             lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.default_image_processor
         SCREAMING_SNAKE_CASE					:			Dict    	=       prepare_img()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       image_processor(images=lowercase__    ,							return_tensors='pt' ).to(lowercase__ )

         # forward pass
         with torch.no_grad():
              SCREAMING_SNAKE_CASE					:			Dict    	=       model(**lowercase__ )

         # verify the logits
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.Size((1, 1_000) )
         self.assertEqual(outputs.logits.shape    ,							lowercase__ )

         SCREAMING_SNAKE_CASE					:			int    	=       torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7] ).to(lowercase__ )

         self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,							lowercase__    ,							atol=1E-4 ) )




'''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()



