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 			lowercase(	UpperCamelCase_       )		->	Dict:







 '''simple docstring'''
 with open(UpperCamelCase_							,					"""rb"""       ) as f:
  UpperCamelCase			  =      Image.open(UpperCamelCase_       )
  return im.convert("""RGB"""       )
@dataclass
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				field(
       default=__lowerCAmelCase  ,     metadata={
           """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
       }  ,     )
   __lowerCAmelCase           =				field(
       default=__lowerCAmelCase  ,     metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}		)
   __lowerCAmelCase           =				field(default=__lowerCAmelCase  ,     metadata={"""help""": """A folder containing the training data."""}		)
   __lowerCAmelCase           =				field(default=__lowerCAmelCase  ,     metadata={"""help""": """A folder containing the validation data."""}		)
   __lowerCAmelCase           =				field(
       default=0.15  ,     metadata={"""help""": """Percent to split off of train for validation."""}		)
   __lowerCAmelCase           =				field(
       default=__lowerCAmelCase  ,     metadata={
           """help""": (
               """For debugging purposes or quicker training, truncate the number of training examples to this """
               """value if set."""
           )
       }  ,     )
   __lowerCAmelCase           =				field(
       default=__lowerCAmelCase  ,     metadata={
           """help""": (
               """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
               """value if set."""
           )
       }  ,     )







   def 						lowerCamelCase_	(     self						:						Dict				):





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





def 			lowercase(	UpperCamelCase_       )		->	Optional[Any]:







 '''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 			lowercase(	)		->	Tuple:







 '''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			  =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
 else:
  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			  =      {}, {}
 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'''
from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image


lowerCAmelCase   : List[Any]             =				["""text""", """image""", """audio"""]




def 	_A  (       A							)  ->				Dict:
	lowercase						:		str   					=		[]

	for input_type in input_types:
		if input_type == "text":
			inputs.append("Text input"							)
		elif input_type == "image":
			inputs.append(
			    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"							)							) / "000000039769.png"							).resize((5_1_2, 5_1_2)							)							)
		elif input_type == "audio":
			inputs.append(torch.ones(3_0_0_0							)							)
		elif isinstance(A							,A							):
			inputs.append(create_inputs(A							)							)
		else:
			raise ValueError(F'''Invalid type requested: {input_type}'''							)

	return inputs




def 	_A  (       A							)  ->				str:
	lowercase						:		Tuple   					=		[]

	for output in outputs:
		if isinstance(A							,(str, AgentText)							):
			output_types.append("text"							)
		elif isinstance(A							,(Image.Image, AgentImage)							):
			output_types.append("image"							)
		elif isinstance(A							,(torch.Tensor, AgentAudio)							):
			output_types.append("audio"							)
		else:
			raise ValueError(F'''Invalid output: {output}'''							)

	return output_types






@is_tool_test
class        _UpperCamelCase	:


				'''simple docstring'''


				def 	a__		(     self							)    ->  Optional[Any]:
					self.assertTrue(hasattr(self.tool							,      "inputs"							)							)
					self.assertTrue(hasattr(self.tool							,      "outputs"							)							)

					lowercase						:		Optional[Any]   					=		self.tool.inputs
					for _input in inputs:
						if isinstance(_input							,      a_							):
							for __input in _input:
								self.assertTrue(__input in authorized_types							)
						else:
							self.assertTrue(_input in authorized_types							)

					lowercase						:		Any   					=		self.tool.outputs
					for _output in outputs:
						self.assertTrue(_output in authorized_types							)

				def 	a__		(     self							)    ->  Any:
					lowercase						:		Any   					=		create_inputs(self.tool.inputs							)
					lowercase						:		Tuple   					=		self.tool(*a_							)

					# There is a single output
					if len(self.tool.outputs							) == 1:
						lowercase						:		Any   					=		[outputs]

					self.assertListEqual(output_types(a_							)							,      self.tool.outputs							)

				def 	a__		(     self							)    ->  List[str]:
					self.assertTrue(hasattr(self.tool							,      "description"							)							)
					self.assertTrue(hasattr(self.tool							,      "default_checkpoint"							)							)
					self.assertTrue(self.tool.description.startswith("This is a tool that"							)							)

				def 	a__		(     self							)    ->  int:
					lowercase						:		str   					=		create_inputs(self.tool.inputs							)
					lowercase						:		str   					=		self.tool(*a_							)

					if not isinstance(a_							,      a_							):
						lowercase						:		Union[str, Any]   					=		[outputs]

					self.assertEqual(len(a_							)							,      len(self.tool.outputs							)							)

					for output, output_type in zip(a_							,      self.tool.outputs							):
						lowercase						:		List[str]   					=		AGENT_TYPE_MAPPING[output_type]
						self.assertTrue(isinstance(a_							,      a_							)							)


				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		int   					=		create_inputs(self.tool.inputs							)

					lowercase						:		str   					=		[]

					for _input, input_type in zip(a_							,      self.tool.inputs							):
						if isinstance(a_							,      a_							):
							_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input							) for _input_type in input_type]							)
						else:
							_inputs.append(AGENT_TYPE_MAPPING[input_type](_input							)							)

        # Should not raise an error
					lowercase						:		Optional[int]   					=		self.tool(*a_							)

					if not isinstance(a_							,      a_							):
						lowercase						:		str   					=		[outputs]

					self.assertEqual(len(a_							)							,      len(self.tool.outputs							)							)


