






"""simple docstring"""



from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers.models.layoutlm.modeling_tf_layoutlm import (
						    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFLayoutLMForMaskedLM,
						    TFLayoutLMForQuestionAnswering,
						    TFLayoutLMForSequenceClassification,
						    TFLayoutLMForTokenClassification,
						    TFLayoutLMModel,
						)





class 					_UpperCAmelCase       :




					def __init__(				self     :						Any    ,						A     :						Optional[int]    ,						A     :						Dict=13    ,						A     :						List[Any]=7    ,						A     :						List[str]=True    ,						A     :						Any=True    ,						A     :						List[Any]=True    ,						A     :						List[Any]=True    ,						A     :						Optional[int]=99    ,						A     :						Tuple=32    ,						A     :						Union[str, Any]=2    ,						A     :						Dict=4    ,						A     :						Any=37    ,						A     :						List[str]="gelu"    ,						A     :						Optional[int]=0.1    ,						A     :						Union[str, Any]=0.1    ,						A     :						str=5_12    ,						A     :						str=16    ,						A     :						Optional[int]=2    ,						A     :						Optional[Any]=0.02    ,						A     :						Dict=3    ,						A     :						Union[str, Any]=4    ,						A     :						Dict=None    ,						A     :						List[Any]=10_00    ,						)  ->  List[str]:
							lowercase_ : str							       =   parent
							lowercase_ : Optional[int]							       =   batch_size
							lowercase_ : str							       =   seq_length
							lowercase_ : int							       =   is_training
							lowercase_ : Union[str, Any]							       =   use_input_mask
							lowercase_ : int							       =   use_token_type_ids
							lowercase_ : List[str]							       =   use_labels
							lowercase_ : str							       =   vocab_size
							lowercase_ : Union[str, Any]							       =   hidden_size
							lowercase_ : str							       =   num_hidden_layers
							lowercase_ : Tuple							       =   num_attention_heads
							lowercase_ : List[str]							       =   intermediate_size
							lowercase_ : Dict							       =   hidden_act
							lowercase_ : Tuple							       =   hidden_dropout_prob
							lowercase_ : Any							       =   attention_probs_dropout_prob
							lowercase_ : Union[str, Any]							       =   max_position_embeddings
							lowercase_ : Tuple							       =   type_vocab_size
							lowercase_ : Tuple							       =   type_sequence_label_size
							lowercase_ : Dict							       =   initializer_range
							lowercase_ : List[Any]							       =   num_labels
							lowercase_ : Dict							       =   num_choices
							lowercase_ : int							       =   scope
							lowercase_ : str							       =   range_bbox




					def   A  (				self     :						str   )  ->  Tuple:
							lowercase_ : Any							       =   ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size   )

							# convert bbox to numpy since TF does not support item assignment
							lowercase_ : Any							       =   ids_tensor([self.batch_size, self.seq_length, 4]    ,						self.range_bbox   ).numpy()
							# 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]:
													lowercase_ : Any							       =   bbox[i, j, 3]
													lowercase_ : str							       =   bbox[i, j, 1]
													lowercase_ : List[str]							       =   t
											if bbox[i, j, 2] < bbox[i, j, 0]:
													lowercase_ : str							       =   bbox[i, j, 2]
													lowercase_ : Tuple							       =   bbox[i, j, 0]
													lowercase_ : Optional[int]							       =   t
							lowercase_ : Optional[int]							       =   tf.convert_to_tensor(A   )

							lowercase_ : Optional[int]							       =   None
							if self.use_input_mask:
									lowercase_ : List[str]							       =   random_attention_mask([self.batch_size, self.seq_length]   )

							lowercase_ : List[Any]							       =   None
							if self.use_token_type_ids:
									lowercase_ : Optional[Any]							       =   ids_tensor([self.batch_size, self.seq_length]    ,						self.type_vocab_size   )

							lowercase_ : List[Any]							       =   None
							lowercase_ : Any							       =   None
							lowercase_ : Union[str, Any]							       =   None
							if self.use_labels:
									lowercase_ : Union[str, Any]							       =   ids_tensor([self.batch_size]    ,						self.type_sequence_label_size   )
									lowercase_ : Optional[Any]							       =   ids_tensor([self.batch_size, self.seq_length]    ,						self.num_labels   )
									lowercase_ : List[Any]							       =   ids_tensor([self.batch_size]    ,						self.num_choices   )

							lowercase_ : Tuple							       =   LayoutLMConfig(
							    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    ,						)

							return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




					def   A  (				self     :						Tuple    ,						A     :						Dict    ,						A     :						List[str]    ,						A     :						str    ,						A     :						Optional[Any]    ,						A     :						List[str]    ,						A     :						Tuple    ,						A     :						Tuple    ,						A     :						Union[str, Any]   )  ->  Any:
							lowercase_ : Optional[Any]							       =   TFLayoutLMModel(config=A   )

							lowercase_ : Any							       =   model(A    ,						A    ,						attention_mask=A    ,						token_type_ids=A   )
							lowercase_ : Optional[Any]							       =   model(A    ,						A    ,						token_type_ids=A   )
							lowercase_ : str							       =   model(A    ,						A   )
							self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)   )
							self.parent.assertEqual(result.pooler_output.shape    ,						(self.batch_size, self.hidden_size)   )




					def   A  (				self     :						str    ,						A     :						Optional[int]    ,						A     :						int    ,						A     :						Any    ,						A     :						Optional[int]    ,						A     :						int    ,						A     :						Any    ,						A     :						int    ,						A     :						Dict   )  ->  List[str]:
							lowercase_ : Optional[int]							       =   TFLayoutLMForMaskedLM(config=A   )

							lowercase_ : Tuple							       =   model(A    ,						A    ,						attention_mask=A    ,						token_type_ids=A    ,						labels=A   )
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.vocab_size)   )




					def   A  (				self     :						Tuple    ,						A     :						List[str]    ,						A     :						Any    ,						A     :						List[str]    ,						A     :						Union[str, Any]    ,						A     :						List[str]    ,						A     :						Dict    ,						A     :						Optional[Any]    ,						A     :						List[Any]   )  ->  Union[str, Any]:
							lowercase_ : List[Any]							       =   self.num_labels
							lowercase_ : Optional[Any]							       =   TFLayoutLMForSequenceClassification(config=A   )

							lowercase_ : Tuple							       =   model(A    ,						A    ,						attention_mask=A    ,						token_type_ids=A   )
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels)   )




					def   A  (				self     :						List[Any]    ,						A     :						List[str]    ,						A     :						Optional[int]    ,						A     :						Union[str, Any]    ,						A     :						Union[str, Any]    ,						A     :						int    ,						A     :						str    ,						A     :						Tuple    ,						A     :						Union[str, Any]   )  ->  Dict:
							lowercase_ : Tuple							       =   self.num_labels
							lowercase_ : List[Any]							       =   TFLayoutLMForTokenClassification(config=A   )

							lowercase_ : List[Any]							       =   model(A    ,						A    ,						attention_mask=A    ,						token_type_ids=A    ,						labels=A   )
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.num_labels)   )




					def   A  (				self     :						str    ,						A     :						Tuple    ,						A     :						Any    ,						A     :						Dict    ,						A     :						Tuple    ,						A     :						Optional[int]    ,						A     :						Optional[int]    ,						A     :						List[str]    ,						A     :						Optional[int]   )  ->  int:
							lowercase_ : Any							       =   TFLayoutLMForQuestionAnswering(config=A   )

							lowercase_ : Any							       =   model(A    ,						A    ,						attention_mask=A    ,						token_type_ids=A   )
							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   A  (				self     :						List[Any]   )  ->  Dict:
							lowercase_ : Any							       =   self.prepare_config_and_inputs()
							(
							    (
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					
							) : Union[str, Any]							       =   config_and_inputs
							lowercase_ : List[Any]							       =   {
							    '''input_ids''': input_ids,
							    '''bbox''': bbox,
							    '''token_type_ids''': token_type_ids,
							    '''attention_mask''': input_mask,
							}
							return config, inputs_dict





@require_tf
class 					_UpperCAmelCase       ( _A   ,						_A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Dict							    =   (
					    (
					        TFLayoutLMModel,
					        TFLayoutLMForMaskedLM,
					        TFLayoutLMForTokenClassification,
					        TFLayoutLMForSequenceClassification,
					        TFLayoutLMForQuestionAnswering,
					    )
					    if is_tf_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   (
					    {
					        "feature-extraction": TFLayoutLMModel,
					        "fill-mask": TFLayoutLMForMaskedLM,
					        "text-classification": TFLayoutLMForSequenceClassification,
					        "token-classification": TFLayoutLMForTokenClassification,
					        "zero-shot": TFLayoutLMForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					SCREAMING_SNAKE_CASE_		:				int							    =   False
					SCREAMING_SNAKE_CASE_		:				List[Any]							    =   True
					SCREAMING_SNAKE_CASE_		:				Any							    =   10




					def   A  (				self     :						List[Any]   )  ->  Union[str, Any]:
							lowercase_ : Tuple							       =   TFLayoutLMModelTester(self   )
							lowercase_ : Optional[int]							       =   ConfigTester(self    ,						config_class=A    ,						hidden_size=37   )




					def   A  (				self     :						Dict   )  ->  Tuple:
							self.config_tester.run_common_tests()




					def   A  (				self     :						List[Any]   )  ->  Union[str, Any]:
							lowercase_ : List[str]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A   )




					def   A  (				self     :						int   )  ->  List[str]:
							lowercase_ : Union[str, Any]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*A   )




					def   A  (				self     :						Dict   )  ->  str:
							lowercase_ : List[str]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*A   )




					def   A  (				self     :						int   )  ->  Optional[int]:
							lowercase_ : Dict							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*A   )




					def   A  (				self     :						Union[str, Any]   )  ->  List[Any]:
							lowercase_ : Dict							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*A   )




					@slow
					def   A  (				self     :						Any   )  ->  List[Any]:
							for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase_ : Optional[int]							       =   TFLayoutLMModel.from_pretrained(A   )
									self.assertIsNotNone(A   )






					@unittest.skip('''Onnx compliancy broke with TF 2.10'''   )
					def   A  (				self     :						int   )  ->  Tuple:
							pass



def 						lowercase    (				):
		# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
		# fmt: off
		lowercase_ : Union[str, Any]							       =   tf.convert_to_tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]	)  # noqa: E231
		lowercase_ : Any							       =   tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]	)  # noqa: E231
		lowercase_ : Tuple							       =   tf.convert_to_tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]	)  # noqa: E231
		lowercase_ : List[Any]							       =   tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]	)  # noqa: E231
		# these are sequence labels (i.e. at the token level)
		lowercase_ : str							       =   tf.convert_to_tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]	)  # noqa: E231
		# fmt: on

		return input_ids, attention_mask, bbox, token_type_ids, labels






@require_tf
class 					_UpperCAmelCase       ( unittest.TestCase							):




					@slow
					def   A  (				self     :						List[str]   )  ->  Optional[Any]:
							lowercase_ : str							       =   TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''   )

							lowercase_       ,					lowercase_       ,					lowercase_       ,					lowercase_       ,					lowercase_ : Optional[int]							       =   prepare_layoutlm_batch_inputs()

							# forward pass
							lowercase_ : Tuple							       =   model(input_ids=A    ,						bbox=A    ,						attention_mask=A    ,						token_type_ids=A   )

							# test the sequence output on [0, :3, :3]
							lowercase_ : Dict							       =   tf.convert_to_tensor(
							    [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]    ,						)

							self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]    ,						A    ,						atol=1e-3   )   )

							# test the pooled output on [1, :3]
							lowercase_ : List[str]							       =   tf.convert_to_tensor([-0.6580, -0.0214, 0.8552]   )

							self.assertTrue(np.allclose(outputs.pooler_output[1, :3]    ,						A    ,						atol=1e-3   )   )




					@slow
					def   A  (				self     :						List[str]   )  ->  List[Any]:
							# initialize model with randomly initialized sequence classification head
							lowercase_ : Optional[int]							       =   TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''    ,						num_labels=2   )

							lowercase_       ,					lowercase_       ,					lowercase_       ,					lowercase_       ,					lowercase_ : Union[str, Any]							       =   prepare_layoutlm_batch_inputs()

							# forward pass
							lowercase_ : Tuple							       =   model(
							    input_ids=A    ,						bbox=A    ,						attention_mask=A    ,						token_type_ids=A    ,						labels=tf.convert_to_tensor([1, 1]   )    ,						)

							# test whether we get a loss as a scalar
							lowercase_ : Dict							       =   outputs.loss
							lowercase_ : List[str]							       =   (2,)
							self.assertEqual(loss.shape    ,						A   )

							# test the shape of the logits
							lowercase_ : Optional[Any]							       =   outputs.logits
							lowercase_ : Union[str, Any]							       =   (2, 2)
							self.assertEqual(logits.shape    ,						A   )




					@slow
					def   A  (				self     :						List[Any]   )  ->  List[str]:
							# initialize model with randomly initialized token classification head
							lowercase_ : str							       =   TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''    ,						num_labels=13   )

							lowercase_       ,					lowercase_       ,					lowercase_       ,					lowercase_       ,					lowercase_ : Optional[Any]							       =   prepare_layoutlm_batch_inputs()

							# forward pass
							lowercase_ : Union[str, Any]							       =   model(
							    input_ids=A    ,						bbox=A    ,						attention_mask=A    ,						token_type_ids=A    ,						labels=A   )

							# test the shape of the logits
							lowercase_ : List[str]							       =   outputs.logits
							lowercase_ : Dict							       =   tf.convert_to_tensor((2, 25, 13)   )
							self.assertEqual(logits.shape    ,						A   )






					@slow
					def   A  (				self     :						int   )  ->  Any:
							# initialize model with randomly initialized token classification head
							lowercase_ : Tuple							       =   TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''   )

							lowercase_       ,					lowercase_       ,					lowercase_       ,					lowercase_       ,					lowercase_ : str							       =   prepare_layoutlm_batch_inputs()

							# forward pass
							lowercase_ : List[Any]							       =   model(input_ids=A    ,						bbox=A    ,						attention_mask=A    ,						token_type_ids=A   )

							# test the shape of the logits
							lowercase_ : Optional[Any]							       =   tf.convert_to_tensor((2, 25)   )
							self.assertEqual(outputs.start_logits.shape    ,						A   )
							self.assertEqual(outputs.end_logits.shape    ,						A   )







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()


