

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 	__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str )   ->       str:






				with open(_lowercase  ,      "rb" ) as f:
								_UpperCAmelCase       :		List[str] 		=    Image.open(_lowercase )
								return im.convert("RGB" )

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


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







def 	__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Tuple )   ->       Dict:
				_UpperCAmelCase       :		Tuple 		=    torch.stack([example["pixel_values"] for example in examples] )
				_UpperCAmelCase       :		Dict 		=    torch.tensor([example["labels"] for example in examples] )
				return {"pixel_values": pixel_values, "labels": labels}

def 	__SCREAMING_SNAKE_CASE							(				)   ->       Any:
				_UpperCAmelCase       :		str 		=    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       :		Union[str, Any] 		=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
				else:
								_UpperCAmelCase       :		int 		=    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"  ,      _lowercase  ,      _lowercase )

				# 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       :		Optional[int] 		=    training_args.get_process_log_level()
				logger.setLevel(_lowercase )
				transformers.utils.logging.set_verbosity(_lowercase )
				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       :		List[Any] 		=    None
				if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
								_UpperCAmelCase       :		Optional[int] 		=    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       :		Tuple 		=    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       :		Tuple 		=    {}
								if data_args.train_dir is not None:
												_UpperCAmelCase       :		int 		=    os.path.join(data_args.train_dir  ,      "**" )
								if data_args.validation_dir is not None:
												_UpperCAmelCase       :		Any 		=    os.path.join(data_args.validation_dir  ,      "**" )
								_UpperCAmelCase       :		Optional[Any] 		=    load_dataset(
								    "imagefolder"  ,      data_files=_lowercase  ,      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       :		int 		=    None if "validation" in dataset.keys() else data_args.train_val_split
				if isinstance(data_args.train_val_split  ,      _lowercase ) and data_args.train_val_split > 0.0:
								_UpperCAmelCase       :		int 		=    dataset["train"].train_test_split(data_args.train_val_split )
								_UpperCAmelCase       :		str 		=    split["train"]
								_UpperCAmelCase       :		Dict 		=    split["test"]

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

				# Load the accuracy metric from the datasets package
				_UpperCAmelCase       :		Union[str, Any] 		=    evaluate.load("accuracy" )

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

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

				def train_transforms(lowerCAmelCase:  List[str] ):
								_UpperCAmelCase       :		List[Any] 		=    [
								    _train_transforms(pil_img.convert("RGB" ) ) for pil_img in example_batch["image"]
								]
								return example_batch

				def val_transforms(lowerCAmelCase:  List[Any] ):
								_UpperCAmelCase       :		Union[str, Any] 		=    [_val_transforms(pil_img.convert("RGB" ) ) for pil_img in example_batch["image"]]
								return example_batch

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

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

				# Initalize our trainer
				_UpperCAmelCase       :		Union[str, Any] 		=    Trainer(
				    model=_lowercase  ,      args=_lowercase  ,      train_dataset=dataset["train"] if training_args.do_train else None  ,      eval_dataset=dataset["validation"] if training_args.do_eval else None  ,      compute_metrics=_lowercase  ,      tokenizer=_lowercase  ,      data_collator=_lowercase  ,      )

				# Training
				if training_args.do_train:
								_UpperCAmelCase       :		List[str] 		=    None
								if training_args.resume_from_checkpoint is not None:
												_UpperCAmelCase       :		List[str] 		=    training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												_UpperCAmelCase       :		Dict 		=    last_checkpoint
								_UpperCAmelCase       :		List[str] 		=    trainer.train(resume_from_checkpoint=_lowercase )
								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       :		Dict 		=    trainer.evaluate()
								trainer.log_metrics("eval"  ,      _lowercase )
								trainer.save_metrics("eval"  ,      _lowercase )

				# Write model card and (optionally) push to hub
				_UpperCAmelCase       :		List[str] 		=    {
				    "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(**_lowercase )
				else:
								trainer.create_model_card(**_lowercase )


if __name__ == "__main__":
					main()





import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


SCREAMING_SNAKE_CASE_        =      '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'

class 				a  (	unittest.TestCase							, UpperCAmelCase				):


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Tuple 		=    load_tool("text-question-answering"	)
										self.tool.setup()
										_UpperCAmelCase       :		str 		=    load_tool("text-question-answering"						,					remote=A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    self.tool(A_						,					"What did Hugging Face do in April 2021?"	)
										self.assertEqual(A_						,					"launched the BigScience Research Workshop"	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    self.remote_tool(A_						,					"What did Hugging Face do in April 2021?"	)
										self.assertEqual(A_						,					"launched the BigScience Research Workshop"	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.tool(text=A_						,					question="What did Hugging Face do in April 2021?"	)
										self.assertEqual(A_						,					"launched the BigScience Research Workshop"	)





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    self.remote_tool(text=A_						,					question="What did Hugging Face do in April 2021?"	)
										self.assertEqual(A_						,					"launched the BigScience Research Workshop"	)


