"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time



class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self    ):






										"""simple docstring"""

										snake_case								=			{}

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase=1    ):






										"""simple docstring"""

										if self.graph.get(lowerCAmelCase    ):
																if self.graph[u].count([w, v]    ) == 0:
																						self.graph[u].append([w, v]    )
										else:
																snake_case								=			[[w, v]]
										if not self.graph.get(lowerCAmelCase    ):
																snake_case								=			[]

				def 		snake_case (				self    ):






										"""simple docstring"""

										return list(self.graph    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										if self.graph.get(lowerCAmelCase    ):
																for _ in self.graph[u]:
																						if _[1] == v:
																												self.graph[u].remove(lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase=-2		,    lowerCAmelCase=-1    ):






										"""simple docstring"""

										if s == d:
																return []
										snake_case								=			[]
										snake_case								=			[]
										if s == -2:
																snake_case								=			list(self.graph    )[0]
										stack.append(lowerCAmelCase    )
										visited.append(lowerCAmelCase    )
										snake_case								=			s

										while True:
																# check if there is any non isolated nodes
																if len(self.graph[s]    ) != 0:
																						snake_case								=			s
																						for node in self.graph[s]:
																												if visited.count(node[1]    ) < 1:
																																		if node[1] == d:
																																								visited.append(lowerCAmelCase    )
																																								return visited
																																		else:
																																								stack.append(node[1]    )
																																								visited.append(node[1]    )
																																								snake_case								=			node[1]
																																								break

            # check if all the children are visited
																if s == ss:
																						stack.pop()
																						if len(lowerCAmelCase    ) != 0:
																												snake_case								=			stack[len(lowerCAmelCase    ) - 1]
																else:
																						snake_case								=			ss

																# check if se have reached the starting point
																if len(lowerCAmelCase    ) == 0:
																						return visited

				def 		snake_case (				self		,    lowerCAmelCase=-1    ):






										"""simple docstring"""

										if c == -1:
																snake_case								=			floor(random() * 1_00_00    ) + 10
										for i in range(lowerCAmelCase    ):
																# every vertex has max 100 edges
																for _ in range(floor(random() * 1_02    ) + 1    ):
																						snake_case								=			floor(random() * c    ) + 1
																						if n != i:
																												self.add_pair(lowerCAmelCase		,    lowerCAmelCase		,    1    )

				def 		snake_case (				self		,    lowerCAmelCase=-2    ):






										"""simple docstring"""

										snake_case								=			deque()
										snake_case								=			[]
										if s == -2:
																snake_case								=			list(self.graph    )[0]
										d.append(lowerCAmelCase    )
										visited.append(lowerCAmelCase    )
										while d:
																snake_case								=			d.popleft()
																if len(self.graph[s]    ) != 0:
																						for node in self.graph[s]:
																												if visited.count(node[1]    ) < 1:
																																		d.append(node[1]    )
																																		visited.append(node[1]    )
										return visited

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			0
										for x in self.graph:
																for y in self.graph[x]:
																						if y[1] == u:
																												count += 1
										return count

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										return len(self.graph[u]    )

				def 		snake_case (				self		,    lowerCAmelCase=-2    ):






										"""simple docstring"""

										snake_case								=			[]
										snake_case								=			[]
										if s == -2:
																snake_case								=			list(self.graph    )[0]
										stack.append(lowerCAmelCase    )
										visited.append(lowerCAmelCase    )
										snake_case								=			s
										snake_case								=			[]

										while True:
																# check if there is any non isolated nodes
																if len(self.graph[s]    ) != 0:
																						snake_case								=			s
																						for node in self.graph[s]:
																												if visited.count(node[1]    ) < 1:
																																		stack.append(node[1]    )
																																		visited.append(node[1]    )
																																		snake_case								=			node[1]
																																		break

            # check if all the children are visited
																if s == ss:
																						sorted_nodes.append(stack.pop()    )
																						if len(lowerCAmelCase    ) != 0:
																												snake_case								=			stack[len(lowerCAmelCase    ) - 1]
																else:
																						snake_case								=			ss

																# check if se have reached the starting point
																if len(lowerCAmelCase    ) == 0:
																						return sorted_nodes

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			[]
										snake_case								=			[]
										snake_case								=			list(self.graph    )[0]
										stack.append(lowerCAmelCase    )
										visited.append(lowerCAmelCase    )
										snake_case								=			-2
										snake_case								=			[]
										snake_case								=			s
										snake_case								=			False
										snake_case								=			set()

										while True:
																# check if there is any non isolated nodes
																if len(self.graph[s]    ) != 0:
																						snake_case								=			s
																						for node in self.graph[s]:
																												if (
																												    visited.count(node[1]    ) > 0
																												    and node[1] != parent
																												    and indirect_parents.count(node[1]    ) > 0
																												    and not on_the_way_back
																												):
																																		snake_case								=			len(lowerCAmelCase    ) - 1
																																		while len_stack >= 0:
																																								if stack[len_stack] == node[1]:
																																														anticipating_nodes.add(node[1]    )
																																														break
																																								else:
																																														anticipating_nodes.add(stack[len_stack]    )
																																														len_stack -= 1
																												if visited.count(node[1]    ) < 1:
																																		stack.append(node[1]    )
																																		visited.append(node[1]    )
																																		snake_case								=			node[1]
																																		break

            # check if all the children are visited
																if s == ss:
																						stack.pop()
																						snake_case								=			True
																						if len(lowerCAmelCase    ) != 0:
																												snake_case								=			stack[len(lowerCAmelCase    ) - 1]
																else:
																						snake_case								=			False
																						indirect_parents.append(lowerCAmelCase    )
																						snake_case								=			s
																						snake_case								=			ss

																# check if se have reached the starting point
																if len(lowerCAmelCase    ) == 0:
																						return list(lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			[]
										snake_case								=			[]
										snake_case								=			list(self.graph    )[0]
										stack.append(lowerCAmelCase    )
										visited.append(lowerCAmelCase    )
										snake_case								=			-2
										snake_case								=			[]
										snake_case								=			s
										snake_case								=			False
										snake_case								=			set()

										while True:
																# check if there is any non isolated nodes
																if len(self.graph[s]    ) != 0:
																						snake_case								=			s
																						for node in self.graph[s]:
																												if (
																												    visited.count(node[1]    ) > 0
																												    and node[1] != parent
																												    and indirect_parents.count(node[1]    ) > 0
																												    and not on_the_way_back
																												):
																																		snake_case								=			len(lowerCAmelCase    ) - 1
																																		while len_stack_minus_one >= 0:
																																								if stack[len_stack_minus_one] == node[1]:
																																														anticipating_nodes.add(node[1]    )
																																														break
																																								else:
																																														return True
																												if visited.count(node[1]    ) < 1:
																																		stack.append(node[1]    )
																																		visited.append(node[1]    )
																																		snake_case								=			node[1]
																																		break

            # check if all the children are visited
																if s == ss:
																						stack.pop()
																						snake_case								=			True
																						if len(lowerCAmelCase    ) != 0:
																												snake_case								=			stack[len(lowerCAmelCase    ) - 1]
																else:
																						snake_case								=			False
																						indirect_parents.append(lowerCAmelCase    )
																						snake_case								=			s
																						snake_case								=			ss

																# check if se have reached the starting point
																if len(lowerCAmelCase    ) == 0:
																						return False

				def 		snake_case (				self		,    lowerCAmelCase=-2		,    lowerCAmelCase=-1    ):






										"""simple docstring"""

										snake_case								=			time()
										self.dfs(lowerCAmelCase		,    lowerCAmelCase    )
										snake_case								=			time()
										return end - begin

				def 		snake_case (				self		,    lowerCAmelCase=-2    ):






										"""simple docstring"""

										snake_case								=			time()
										self.bfs(lowerCAmelCase    )
										snake_case								=			time()
										return end - begin





class   lowerCAmelCase_    :



				"""simple docstring"""

				def __init__(				self    ):






										"""simple docstring"""

										snake_case								=			{}

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase=1    ):






										"""simple docstring"""

										if self.graph.get(lowerCAmelCase    ):
																# if there already is a edge
																if self.graph[u].count([w, v]    ) == 0:
																						self.graph[u].append([w, v]    )
										else:
																# if u does not exist
																snake_case								=			[[w, v]]
										# add the other way
										if self.graph.get(lowerCAmelCase    ):
																# if there already is a edge
																if self.graph[v].count([w, u]    ) == 0:
																						self.graph[v].append([w, u]    )
										else:
																# if u does not exist
																snake_case								=			[[w, u]]

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										if self.graph.get(lowerCAmelCase    ):
																for _ in self.graph[u]:
																						if _[1] == v:
																												self.graph[u].remove(lowerCAmelCase    )
        # the other way round
										if self.graph.get(lowerCAmelCase    ):
																for _ in self.graph[v]:
																						if _[1] == u:
																												self.graph[v].remove(lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase=-2		,    lowerCAmelCase=-1    ):






										"""simple docstring"""

										if s == d:
																return []
										snake_case								=			[]
										snake_case								=			[]
										if s == -2:
																snake_case								=			list(self.graph    )[0]
										stack.append(lowerCAmelCase    )
										visited.append(lowerCAmelCase    )
										snake_case								=			s

										while True:
																# check if there is any non isolated nodes
																if len(self.graph[s]    ) != 0:
																						snake_case								=			s
																						for node in self.graph[s]:
																												if visited.count(node[1]    ) < 1:
																																		if node[1] == d:
																																								visited.append(lowerCAmelCase    )
																																								return visited
																																		else:
																																								stack.append(node[1]    )
																																								visited.append(node[1]    )
																																								snake_case								=			node[1]
																																								break

            # check if all the children are visited
																if s == ss:
																						stack.pop()
																						if len(lowerCAmelCase    ) != 0:
																												snake_case								=			stack[len(lowerCAmelCase    ) - 1]
																else:
																						snake_case								=			ss

																# check if se have reached the starting point
																if len(lowerCAmelCase    ) == 0:
																						return visited

				def 		snake_case (				self		,    lowerCAmelCase=-1    ):






										"""simple docstring"""

										if c == -1:
																snake_case								=			floor(random() * 1_00_00    ) + 10
										for i in range(lowerCAmelCase    ):
																# every vertex has max 100 edges
																for _ in range(floor(random() * 1_02    ) + 1    ):
																						snake_case								=			floor(random() * c    ) + 1
																						if n != i:
																												self.add_pair(lowerCAmelCase		,    lowerCAmelCase		,    1    )

				def 		snake_case (				self		,    lowerCAmelCase=-2    ):






										"""simple docstring"""

										snake_case								=			deque()
										snake_case								=			[]
										if s == -2:
																snake_case								=			list(self.graph    )[0]
										d.append(lowerCAmelCase    )
										visited.append(lowerCAmelCase    )
										while d:
																snake_case								=			d.popleft()
																if len(self.graph[s]    ) != 0:
																						for node in self.graph[s]:
																												if visited.count(node[1]    ) < 1:
																																		d.append(node[1]    )
																																		visited.append(node[1]    )
										return visited

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										return len(self.graph[u]    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			[]
										snake_case								=			[]
										snake_case								=			list(self.graph    )[0]
										stack.append(lowerCAmelCase    )
										visited.append(lowerCAmelCase    )
										snake_case								=			-2
										snake_case								=			[]
										snake_case								=			s
										snake_case								=			False
										snake_case								=			set()

										while True:
																# check if there is any non isolated nodes
																if len(self.graph[s]    ) != 0:
																						snake_case								=			s
																						for node in self.graph[s]:
																												if (
																												    visited.count(node[1]    ) > 0
																												    and node[1] != parent
																												    and indirect_parents.count(node[1]    ) > 0
																												    and not on_the_way_back
																												):
																																		snake_case								=			len(lowerCAmelCase    ) - 1
																																		while len_stack >= 0:
																																								if stack[len_stack] == node[1]:
																																														anticipating_nodes.add(node[1]    )
																																														break
																																								else:
																																														anticipating_nodes.add(stack[len_stack]    )
																																														len_stack -= 1
																												if visited.count(node[1]    ) < 1:
																																		stack.append(node[1]    )
																																		visited.append(node[1]    )
																																		snake_case								=			node[1]
																																		break

            # check if all the children are visited
																if s == ss:
																						stack.pop()
																						snake_case								=			True
																						if len(lowerCAmelCase    ) != 0:
																												snake_case								=			stack[len(lowerCAmelCase    ) - 1]
																else:
																						snake_case								=			False
																						indirect_parents.append(lowerCAmelCase    )
																						snake_case								=			s
																						snake_case								=			ss

																# check if se have reached the starting point
																if len(lowerCAmelCase    ) == 0:
																						return list(lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			[]
										snake_case								=			[]
										snake_case								=			list(self.graph    )[0]
										stack.append(lowerCAmelCase    )
										visited.append(lowerCAmelCase    )
										snake_case								=			-2
										snake_case								=			[]
										snake_case								=			s
										snake_case								=			False
										snake_case								=			set()

										while True:
																# check if there is any non isolated nodes
																if len(self.graph[s]    ) != 0:
																						snake_case								=			s
																						for node in self.graph[s]:
																												if (
																												    visited.count(node[1]    ) > 0
																												    and node[1] != parent
																												    and indirect_parents.count(node[1]    ) > 0
																												    and not on_the_way_back
																												):
																																		snake_case								=			len(lowerCAmelCase    ) - 1
																																		while len_stack_minus_one >= 0:
																																								if stack[len_stack_minus_one] == node[1]:
																																														anticipating_nodes.add(node[1]    )
																																														break
																																								else:
																																														return True
																												if visited.count(node[1]    ) < 1:
																																		stack.append(node[1]    )
																																		visited.append(node[1]    )
																																		snake_case								=			node[1]
																																		break

            # check if all the children are visited
																if s == ss:
																						stack.pop()
																						snake_case								=			True
																						if len(lowerCAmelCase    ) != 0:
																												snake_case								=			stack[len(lowerCAmelCase    ) - 1]
																else:
																						snake_case								=			False
																						indirect_parents.append(lowerCAmelCase    )
																						snake_case								=			s
																						snake_case								=			ss

																# check if se have reached the starting point
																if len(lowerCAmelCase    ) == 0:
																						return False

				def 		snake_case (				self    ):






										"""simple docstring"""

										return list(self.graph    )

				def 		snake_case (				self		,    lowerCAmelCase=-2		,    lowerCAmelCase=-1    ):






										"""simple docstring"""

										snake_case								=			time()
										self.dfs(lowerCAmelCase		,    lowerCAmelCase    )
										snake_case								=			time()
										return end - begin

				def 		snake_case (				self		,    lowerCAmelCase=-2    ):






										"""simple docstring"""

										snake_case								=			time()
										self.bfs(lowerCAmelCase    )
										snake_case								=			time()
										return end - begin



"""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
SCREAMING_SNAKE_CASE__      						=  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")

SCREAMING_SNAKE_CASE__      						=  list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
SCREAMING_SNAKE_CASE__      						=  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





def    lowerCAmelCase__    (			_UpperCamelCase       :				str					)					->				Dict:





						"""simple docstring"""






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



@dataclass
class   lowerCAmelCase_    :



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

				def 		snake_case (				self    ):






										"""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   lowerCAmelCase_    :



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





def    lowerCAmelCase__    (			_UpperCamelCase       :				str					)					->				Union[str, Any]:





						"""simple docstring"""






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





def    lowerCAmelCase__    (			)					->				List[Any]:





						"""simple docstring"""






						snake_case								=			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.
												snake_case					,snake_case					,snake_case								=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
						else:
												snake_case					,snake_case					,snake_case								=			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' ,			_UpperCamelCase ,			_UpperCamelCase					)

						# 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()

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

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

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

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

						def train_transforms(_UpperCamelCase       :				Union[str, Any]					):
												snake_case								=			[
												    _train_transforms(pil_img.convert('RGB'					)					) for pil_img in example_batch['image']
												]
												return example_batch

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

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

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

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

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


if __name__ == "__main__":
					main()


