
"""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_				)->							List[str]:







       """simple docstring"""
       with open(UpperCAmelCase_		,      "rb"				) as f:
              UpperCamelCase					 =		Image.open(UpperCAmelCase_				)
              return im.convert("RGB"				)



@dataclass
class   __a  :
       UpperCamelCase_		:					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).'''
           }    ,     )
       UpperCamelCase_		:					Optional[str]   =			field(
           default=_lowerCAmelCase    ,     metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}	)
       UpperCamelCase_		:					Optional[str]   =			field(default=_lowerCAmelCase    ,     metadata={'''help''': '''A folder containing the training data.'''}	)
       UpperCamelCase_		:					Optional[str]   =			field(default=_lowerCAmelCase    ,     metadata={'''help''': '''A folder containing the validation data.'''}	)
       UpperCamelCase_		:					Optional[float]   =			field(
           default=0.1_5    ,     metadata={'''help''': '''Percent to split off of train for validation.'''}	)
       UpperCamelCase_		:					Optional[int]   =			field(
           default=_lowerCAmelCase    ,     metadata={
               '''help''': (
                   '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                   '''value if set.'''
               )
           }    ,     )
       UpperCamelCase_		:					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 	_SCREAMING_SNAKE_CASE       (  self :							Any			)->  str:







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





def 	lowerCamelCase__			(							UpperCAmelCase_				)->							List[str]:







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



def 	lowerCamelCase__			(							)->							Dict:







       """simple docstring"""
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.

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

       UpperCamelCase					 =		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.
       UpperCamelCase					 =		None
       if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
              UpperCamelCase					 =		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:
              UpperCamelCase					 =		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:
              UpperCamelCase					 =		{}
              if data_args.train_dir is not None:
                     UpperCamelCase					 =		os.path.join(data_args.train_dir		,      "**"				)
              if data_args.validation_dir is not None:
                     UpperCamelCase					 =		os.path.join(data_args.validation_dir		,      "**"				)
              UpperCamelCase					 =		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.
       UpperCamelCase					 =		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:
              UpperCamelCase					 =		dataset["train"].train_test_split(data_args.train_val_split				)
              UpperCamelCase					 =		split["train"]
              UpperCamelCase					 =		split["test"]

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

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

       UpperCamelCase					 =		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		,      )
       UpperCamelCase					 =		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		,      )
       UpperCamelCase					 =		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:
              UpperCamelCase					 =		image_processor.size["shortest_edge"]
       else:
              UpperCamelCase					 =		(image_processor.size["height"], image_processor.size["width"])
       UpperCamelCase					 =		Normalize(mean=image_processor.image_mean		,      std=image_processor.image_std				)
       UpperCamelCase					 =		Compose(
           [
               RandomResizedCrop(UpperCAmelCase_				),
               RandomHorizontalFlip(),
               ToTensor(),
               normalize,
           ]				)
       UpperCamelCase					 =		Compose(
           [
               Resize(UpperCAmelCase_				),
               CenterCrop(UpperCAmelCase_				),
               ToTensor(),
               normalize,
           ]				)

       def train_transforms(UpperCAmelCase_				):
              UpperCamelCase					 =		[
                  _train_transforms(pil_img.convert("RGB"				)				) for pil_img in example_batch["image"]
              ]
              return example_batch

       def val_transforms(UpperCAmelCase_				):
              UpperCamelCase					 =		[_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:
                     UpperCamelCase					 =		(
                         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:
                     UpperCamelCase					 =		(
                         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
       UpperCamelCase					 =		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:
              UpperCamelCase					 =		None
              if training_args.resume_from_checkpoint is not None:
                     UpperCamelCase					 =		training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     UpperCamelCase					 =		last_checkpoint
              UpperCamelCase					 =		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:
              UpperCamelCase					 =		trainer.evaluate()
              trainer.log_metrics("eval"		,      UpperCAmelCase_				)
              trainer.save_metrics("eval"		,      UpperCAmelCase_				)

       # Write model card and (optionally) push to hub
       UpperCamelCase					 =		{
           "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()







"""simple docstring"""


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
      import torch

      from transformers import XLMRobertaModel



@require_sentencepiece
@require_tokenizers
@require_torch
class   __a  (      unittest.TestCase	):






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		XLMRobertaModel.from_pretrained("xlm-roberta-base"			)
              UpperCamelCase					 =		torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]			)
              # The dog is cute and lives in the garden house

              UpperCamelCase					 =		torch.Size((1, 12, 768)			)  # batch_size, sequence_length, embedding_vector_dim
              UpperCamelCase					 =		torch.tensor(
                  [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]			)
              #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
              #  xlmr.eval()
              #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
              with torch.no_grad():
                     UpperCamelCase					 =		model(UpperCAmelCase_			)["last_hidden_state"].detach()
              self.assertEqual(output.shape       ,      UpperCAmelCase_			)
              # compare the actual values for a slice of last dim
              self.assertTrue(torch.allclose(output[:, :, -1]       ,      UpperCAmelCase_       ,      atol=1e-3			)			)






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







              """simple docstring"""
              UpperCamelCase					 =		XLMRobertaModel.from_pretrained("xlm-roberta-large"			)
              UpperCamelCase					 =		torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]			)
              # The dog is cute and lives in the garden house

              UpperCamelCase					 =		torch.Size((1, 12, 1_024)			)  # batch_size, sequence_length, embedding_vector_dim
              UpperCamelCase					 =		torch.tensor(
                  [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]			)
              #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
              #  xlmr.eval()
              #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
              with torch.no_grad():
                     UpperCamelCase					 =		model(UpperCAmelCase_			)["last_hidden_state"].detach()
              self.assertEqual(output.shape       ,      UpperCAmelCase_			)
              # compare the actual values for a slice of last dim
              self.assertTrue(torch.allclose(output[:, :, -1]       ,      UpperCAmelCase_       ,      atol=1e-3			)			)





