


import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
   import torch

   from transformers import (
       MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
       MODEL_FOR_QUESTION_ANSWERING_MAPPING,
       MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
       LayoutLMvaConfig,
       LayoutLMvaForQuestionAnswering,
       LayoutLMvaForSequenceClassification,
       LayoutLMvaForTokenClassification,
       LayoutLMvaModel,
   )
   from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
   from PIL import Image

   from transformers import LayoutLMvaImageProcessor
class  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=2     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=3     ,							SCREAMING_SNAKE_CASE__				:       List[str]=4     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=2     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=7     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=True     ,							SCREAMING_SNAKE_CASE__				:       str=True     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=True     ,							SCREAMING_SNAKE_CASE__				:       int=True     ,							SCREAMING_SNAKE_CASE__				:       Tuple=9_9     ,							SCREAMING_SNAKE_CASE__				:       List[str]=3_6     ,							SCREAMING_SNAKE_CASE__				:       Any=3     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=4     ,							SCREAMING_SNAKE_CASE__				:       int=3_7     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]="gelu"     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=0.1     ,							SCREAMING_SNAKE_CASE__				:       List[str]=0.1     ,							SCREAMING_SNAKE_CASE__				:       Tuple=5_1_2     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]=1_6     ,							SCREAMING_SNAKE_CASE__				:       List[str]=2     ,							SCREAMING_SNAKE_CASE__				:       Dict=0.02     ,							SCREAMING_SNAKE_CASE__				:       Any=6     ,							SCREAMING_SNAKE_CASE__				:       Tuple=6     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=3     ,							SCREAMING_SNAKE_CASE__				:       List[str]=4     ,							SCREAMING_SNAKE_CASE__				:       Dict=None     ,							SCREAMING_SNAKE_CASE__				:       Tuple=1_0_0_0     ,							)		->			Tuple:
             a_					:		Any							=  parent
             a_					:		str							=  batch_size
             a_					:		Dict							=  num_channels
             a_					:		Tuple							=  image_size
             a_					:		List[Any]							=  patch_size
             a_					:		Dict							=  text_seq_length
             a_					:		Union[str, Any]							=  is_training
             a_					:		str							=  use_input_mask
             a_					:		Optional[int]							=  use_token_type_ids
             a_					:		int							=  use_labels
             a_					:		Optional[Any]							=  vocab_size
             a_					:		Optional[Any]							=  hidden_size
             a_					:		Dict							=  num_hidden_layers
             a_					:		str							=  num_attention_heads
             a_					:		Dict							=  intermediate_size
             a_					:		int							=  hidden_act
             a_					:		Dict							=  hidden_dropout_prob
             a_					:		List[str]							=  attention_probs_dropout_prob
             a_					:		Union[str, Any]							=  max_position_embeddings
             a_					:		Union[str, Any]							=  type_vocab_size
             a_					:		Optional[Any]							=  type_sequence_label_size
             a_					:		List[str]							=  initializer_range
             a_					:		int							=  coordinate_size
             a_					:		Optional[Any]							=  shape_size
             a_					:		Optional[int]							=  num_labels
             a_					:		Dict							=  num_choices
             a_					:		List[str]							=  scope
             a_					:		Tuple							=  range_bbox

             # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
             a_					:		List[str]							=  text_seq_length
             a_					:		Tuple							=  (image_size // patch_size) ** 2 + 1
             a_					:		Any							=  self.text_seq_length + self.image_seq_length
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			str:
             a_					:		List[Any]							=  ids_tensor([self.batch_size, self.text_seq_length]     ,							self.vocab_size     )

             a_					:		Union[str, Any]							=  ids_tensor([self.batch_size, self.text_seq_length, 4]     ,							self.range_bbox     )
             # Ensure that bbox is legal
             for i in range(bbox.shape[0]     ):
                    for j in range(bbox.shape[1]     ):
                           if bbox[i, j, 3] < bbox[i, j, 1]:
                                  a_					:		Dict							=  bbox[i, j, 3]
                                  a_					:		Tuple							=  bbox[i, j, 1]
                                  a_					:		Optional[int]							=  t
                           if bbox[i, j, 2] < bbox[i, j, 0]:
                                  a_					:		Dict							=  bbox[i, j, 2]
                                  a_					:		int							=  bbox[i, j, 0]
                                  a_					:		Union[str, Any]							=  t

             a_					:		str							=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

             a_					:		Any							=  None
             if self.use_input_mask:
                    a_					:		Union[str, Any]							=  random_attention_mask([self.batch_size, self.text_seq_length]     )

             a_					:		Union[str, Any]							=  None
             if self.use_token_type_ids:
                    a_					:		Optional[Any]							=  ids_tensor([self.batch_size, self.text_seq_length]     ,							self.type_vocab_size     )

             a_					:		List[Any]							=  None
             a_					:		Dict							=  None
             if self.use_labels:
                    a_					:		int							=  ids_tensor([self.batch_size]     ,							self.type_sequence_label_size     )
                    a_					:		List[str]							=  ids_tensor([self.batch_size, self.text_seq_length]     ,							self.num_labels     )

             a_					:		Union[str, Any]							=  LayoutLMvaConfig(
                 vocab_size=self.vocab_size     ,							hidden_size=self.hidden_size     ,							num_hidden_layers=self.num_hidden_layers     ,							num_attention_heads=self.num_attention_heads     ,							intermediate_size=self.intermediate_size     ,							hidden_act=self.hidden_act     ,							hidden_dropout_prob=self.hidden_dropout_prob     ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,							max_position_embeddings=self.max_position_embeddings     ,							type_vocab_size=self.type_vocab_size     ,							initializer_range=self.initializer_range     ,							coordinate_size=self.coordinate_size     ,							shape_size=self.shape_size     ,							input_size=self.image_size     ,							patch_size=self.patch_size     ,							)

             return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Dict     )		->			str:
             a_					:		Union[str, Any]							=  LayoutLMvaModel(config=UpperCamelCase_     )
             model.to(UpperCamelCase_     )
             model.eval()

             # text + image
             a_					:		List[Any]							=  model(UpperCamelCase_     ,							pixel_values=UpperCamelCase_     )
             a_					:		Dict							=  model(
                 UpperCamelCase_     ,							bbox=UpperCamelCase_     ,							pixel_values=UpperCamelCase_     ,							attention_mask=UpperCamelCase_     ,							token_type_ids=UpperCamelCase_     )
             a_					:		int							=  model(UpperCamelCase_     ,							bbox=UpperCamelCase_     ,							pixel_values=UpperCamelCase_     ,							token_type_ids=UpperCamelCase_     )
             a_					:		int							=  model(UpperCamelCase_     ,							bbox=UpperCamelCase_     ,							pixel_values=UpperCamelCase_     )

             self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)     )

             # text only
             a_					:		Union[str, Any]							=  model(UpperCamelCase_     )
             self.parent.assertEqual(
                 result.last_hidden_state.shape     ,							(self.batch_size, self.text_seq_length, self.hidden_size)     )

             # image only
             a_					:		Optional[int]							=  model(pixel_values=UpperCamelCase_     )
             self.parent.assertEqual(
                 result.last_hidden_state.shape     ,							(self.batch_size, self.image_seq_length, self.hidden_size)     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			Tuple:
             a_					:		str							=  self.num_labels
             a_					:		List[Any]							=  LayoutLMvaForSequenceClassification(UpperCamelCase_     )
             model.to(UpperCamelCase_     )
             model.eval()
             a_					:		Tuple							=  model(
                 UpperCamelCase_     ,							bbox=UpperCamelCase_     ,							pixel_values=UpperCamelCase_     ,							attention_mask=UpperCamelCase_     ,							token_type_ids=UpperCamelCase_     ,							labels=UpperCamelCase_     ,							)
             self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.num_labels)     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			str:
             a_					:		Dict							=  self.num_labels
             a_					:		Tuple							=  LayoutLMvaForTokenClassification(config=UpperCamelCase_     )
             model.to(UpperCamelCase_     )
             model.eval()
             a_					:		Optional[int]							=  model(
                 UpperCamelCase_     ,							bbox=UpperCamelCase_     ,							pixel_values=UpperCamelCase_     ,							attention_mask=UpperCamelCase_     ,							token_type_ids=UpperCamelCase_     ,							labels=UpperCamelCase_     ,							)
             self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.text_seq_length, self.num_labels)     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Tuple     )		->			Optional[Any]:
             a_					:		str							=  LayoutLMvaForQuestionAnswering(config=UpperCamelCase_     )
             model.to(UpperCamelCase_     )
             model.eval()
             a_					:		Any							=  model(
                 UpperCamelCase_     ,							bbox=UpperCamelCase_     ,							pixel_values=UpperCamelCase_     ,							attention_mask=UpperCamelCase_     ,							token_type_ids=UpperCamelCase_     ,							start_positions=UpperCamelCase_     ,							end_positions=UpperCamelCase_     ,							)
             self.parent.assertEqual(result.start_logits.shape     ,							(self.batch_size, self.seq_length)     )
             self.parent.assertEqual(result.end_logits.shape     ,							(self.batch_size, self.seq_length)     )

      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			List[Any]:
             a_					:		int							=  self.prepare_config_and_inputs()
             (
                 (
                 a_		
             )							,		(
                 a_		
             )							,		(
                 a_		
             )							,		(
                 a_		
             )							,		(
                 a_		
             )							,		(
                 a_		
             )							,		(
                 a_		
             )							,		(
                 a_		
             )							,				
             )					:		Tuple							=  config_and_inputs
             a_					:		Any							=  {
                 'input_ids': input_ids,
                 'bbox': bbox,
                 'pixel_values': pixel_values,
                 'token_type_ids': token_type_ids,
                 'attention_mask': input_mask,
             }
             return config, inputs_dict
@require_torch
class  SCREAMING_SNAKE_CASE__  (				UpperCamelCase__						,    UpperCamelCase__						,    unittest.TestCase			):
      snake_case__				:     List[str]        =						False
      snake_case__				:     List[Any]        =						False
      snake_case__				:     int        =						False

      snake_case__				:     Optional[int]        =						(
          (
              LayoutLMvaModel,
              LayoutLMvaForSequenceClassification,
              LayoutLMvaForTokenClassification,
              LayoutLMvaForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      snake_case__				:     Optional[Any]        =						(
          {'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
          if is_torch_available()
          else {}
      )
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Tuple     )		->			Optional[int]:
             return True
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Optional[int]:
             a_					:		Union[str, Any]							=  LayoutLMvaModelTester(self     )
             a_					:		Union[str, Any]							=  ConfigTester(self     ,							config_class=UpperCamelCase_     ,							hidden_size=3_7     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=False     )		->			Tuple:
             a_					:		List[Any]							=  copy.deepcopy(UpperCamelCase_     )
             if model_class in get_values(UpperCamelCase_     ):
                    a_					:		Optional[Any]							=  {
                        k: v.unsqueeze(1     ).expand(-1     ,							self.model_tester.num_choices     ,							-1     ).contiguous()
                        if isinstance(UpperCamelCase_     ,							torch.Tensor     ) and v.ndim > 1
                        else v
                        for k, v in inputs_dict.items()
                    }
             if return_labels:
                    if model_class in get_values(UpperCamelCase_     ):
                           a_					:		Optional[Any]							=  torch.ones(self.model_tester.batch_size     ,							dtype=torch.long     ,							device=UpperCamelCase_     )
                    elif model_class in get_values(UpperCamelCase_     ):
                           a_					:		int							=  torch.zeros(
                               self.model_tester.batch_size     ,							dtype=torch.long     ,							device=UpperCamelCase_     )
                           a_					:		List[str]							=  torch.zeros(
                               self.model_tester.batch_size     ,							dtype=torch.long     ,							device=UpperCamelCase_     )
                    elif model_class in [
                        *get_values(UpperCamelCase_     ),
                    ]:
                           a_					:		Any							=  torch.zeros(
                               self.model_tester.batch_size     ,							dtype=torch.long     ,							device=UpperCamelCase_     )
                    elif model_class in [
                        *get_values(UpperCamelCase_     ),
                    ]:
                           a_					:		List[str]							=  torch.zeros(
                               (self.model_tester.batch_size, self.model_tester.text_seq_length)     ,							dtype=torch.long     ,							device=UpperCamelCase_     ,							)

             return inputs_dict
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Any:
             self.config_tester.run_common_tests()
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Optional[Any]:
             a_					:		Dict							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*UpperCamelCase_     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Dict:
             a_					:		Optional[int]							=  self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    a_					:		List[Any]							=  type
                    self.model_tester.create_and_check_model(*UpperCamelCase_     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			List[str]:
             a_					:		Any							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase_     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			Dict:
             a_					:		Union[str, Any]							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*UpperCamelCase_     )
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			str:
             a_					:		Optional[Any]							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*UpperCamelCase_     )

      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Optional[int]:







             for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    a_					:		Optional[int]							=  LayoutLMvaModel.from_pretrained(UpperCamelCase_     )
                    self.assertIsNotNone(UpperCamelCase_     )







def        SCREAMING_SNAKE_CASE_						(			)	->		int:

       """simple docstring"""

       a_					:		str							=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       return image
@require_torch
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      @cached_property
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			str:
             return LayoutLMvaImageProcessor(apply_ocr=UpperCamelCase_     ) if is_vision_available() else None

      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			str:
             a_					:		Optional[Any]							=  LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'     ).to(UpperCamelCase_     )

             a_					:		Any							=  self.default_image_processor
             a_					:		int							=  prepare_img()
             a_					:		Optional[int]							=  image_processor(images=UpperCamelCase_     ,							return_tensors='pt'     ).pixel_values.to(UpperCamelCase_     )

             a_					:		Dict							=  torch.tensor([[1, 2]]     )
             a_					:		int							=  torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]     ).unsqueeze(0     )

             # forward pass
             a_					:		List[Any]							=  model(
                 input_ids=input_ids.to(UpperCamelCase_     )     ,							bbox=bbox.to(UpperCamelCase_     )     ,							pixel_values=pixel_values.to(UpperCamelCase_     )     ,							)

             # verify the logits
             a_					:		Union[str, Any]							=  torch.Size((1, 1_9_9, 7_6_8)     )
             self.assertEqual(outputs.last_hidden_state.shape     ,							UpperCamelCase_     )

             a_					:		List[str]							=  torch.tensor(
                 [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]     ).to(UpperCamelCase_     )

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






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       =							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(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowerCAmelCase       =							tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def 			_a (      SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		with open(SCREAMING_SNAKE_CASE						,				'''rb'''     ) as f:
				lowercase__            =      Image.open(SCREAMING_SNAKE_CASE     )
				return im.convert('''RGB'''     )


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

							def       lowerCamelCase_ (							self:	Any							)			->   int:





									"""simple docstring"""







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


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


		"""simple docstring"""




		lowercase__            =      torch.stack([example['''pixel_values'''] for example in examples]     )
		lowercase__            =      torch.tensor([example['''labels'''] for example in examples]     )
		return {"pixel_values": pixel_values, "labels": labels}
def 			_a (      ):


		"""simple docstring"""




		lowercase__            =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
		if len(sys.argv     ) == 2 and sys.argv[1].endswith('''.json'''     ):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				lowercase__     ,		lowercase__     ,		lowercase__            =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
		else:
				lowercase__     ,		lowercase__     ,		lowercase__            =      parser.parse_args_into_dataclasses()

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

		# Setup logging
		logging.basicConfig(
		    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''						,				datefmt='''%m/%d/%Y %H:%M:%S'''						,				handlers=[logging.StreamHandler(sys.stdout     )]						,				)

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

		lowercase__            =      training_args.get_process_log_level()
		logger.setLevel(SCREAMING_SNAKE_CASE     )
		transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE     )
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()

		# Log on each process the small summary:
		logger.warning(
		    f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
		    + f'distributed training: {bool(training_args.local_rank != -1     )}, 16-bits training: {training_args.fpaa}'     )
		logger.info(f'Training/evaluation parameters {training_args}'     )

		# Detecting last checkpoint.
		lowercase__            =      None
		if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
				lowercase__            =      get_last_checkpoint(training_args.output_dir     )
				if last_checkpoint is None and len(os.listdir(training_args.output_dir     )     ) > 0:
						raise ValueError(
						    f'Output directory ({training_args.output_dir}) already exists and is not empty. '
						    '''Use --overwrite_output_dir to overcome.'''     )
				elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
						logger.info(
						    f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
						    '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''     )

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

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

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

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

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

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

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

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

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

		def val_transforms(SCREAMING_SNAKE_CASE     ):
				lowercase__            =      [_val_transforms(pil_img.convert('''RGB'''     )     ) for pil_img in example_batch['''image''']]
				return example_batch

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

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

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

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

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

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


if __name__ == "__main__":
			main()


