



"""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
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""")

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

def  _lowerCAmelCase      (   lowercase_						):
						with open(lowercase_		,	'rb'						) as f:
												UpperCAmelCase             = Image.open(lowercase_						)
												return im.convert('RGB'						)







@dataclass
class       A_			:



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






						def 	UpperCAmelCase__     (					self :Tuple					)				->			str:
												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_			:



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


def  _lowerCAmelCase      (   lowercase_						):
						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      (   ):
						# 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'		,	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             = 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             = 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=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             = 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             = 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(lowercase_						):
												UpperCAmelCase             = str(lowercase_						)
												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(lowercase_						):
												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(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             = 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             = 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(lowercase_						),
						        RandomHorizontalFlip(),
						        ToTensor(),
						        normalize,
						    ]						)
						UpperCAmelCase             = Compose(
						    [
						        Resize(lowercase_						),
						        CenterCrop(lowercase_						),
						        ToTensor(),
						        normalize,
						    ]						)

						def train_transforms(lowercase_						):
												UpperCAmelCase             = [
												    _train_transforms(pil_img.convert('RGB'						)						) for pil_img in example_batch['image']
												]
												return example_batch

						def val_transforms(lowercase_						):
												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(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             = (
																		    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             = 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             = 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=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             = trainer.evaluate()
												trainer.log_metrics('eval'		,	lowercase_						)
												trainer.save_metrics('eval'		,	lowercase_						)

						# 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(**lowercase_						)
						else:
												trainer.create_model_card(**lowercase_						)


if __name__ == "__main__":
				main()







from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowercase  :					List[str]						    =							logging.get_logger(__name__)

lowercase  :					Optional[Any]						    =							{
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}

class __snake_case		(      lowerCAmelCase     ):
    _a		:						str=				"gpt_neo"
    _a		:						Optional[int]=				["past_key_values"]
    _a		:						Dict=				{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
    def __init__(  self		,snake_case=50257		,snake_case=2048		,snake_case=2048		,snake_case=24		,snake_case=[[["global", "local"], 12]]		,snake_case=16		,snake_case=None		,snake_case=256		,snake_case="gelu_new"		,snake_case=0.0		,snake_case=0.0		,snake_case=0.0		,snake_case=0.1		,snake_case=1e-5		,snake_case=0.02		,snake_case=True		,snake_case=50256		,snake_case=50256		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     int       	=      vocab_size
           lowercase							:     Union[str, Any]       	=      max_position_embeddings
           lowercase							:     Dict       	=      hidden_size
           lowercase							:     Union[str, Any]       	=      num_layers
           lowercase							:     Union[str, Any]       	=      num_heads
           lowercase							:     Optional[int]       	=      intermediate_size
           lowercase							:     List[str]       	=      window_size
           lowercase							:     Optional[int]       	=      activation_function
           lowercase							:     List[str]       	=      resid_dropout
           lowercase							:     int       	=      embed_dropout
           lowercase							:     Optional[int]       	=      attention_dropout
           lowercase							:     Tuple       	=      classifier_dropout
           lowercase							:     Optional[int]       	=      layer_norm_epsilon
           lowercase							:     Dict       	=      initializer_range
           lowercase							:     List[str]       	=      use_cache

           lowercase							:     Optional[int]       	=      bos_token_id
           lowercase							:     int       	=      eos_token_id

           lowercase							:     Union[str, Any]       	=      attention_types
           lowercase							:     Dict       	=      self.expand_attention_types_params(snake_case      )

           if len(self.attention_layers      ) != self.num_layers:
                  raise ValueError(
                      """Configuration for convolutional module is incorrect. """
                      """It is required that `len(config.attention_layers)` == `config.num_layers` """
                      f"but is `len(config.attention_layers) = {len(self.attention_layers      )}`, "
                      f"`config.num_layers = {self.num_layers}`. "
                      """`config.attention_layers` is prepared using `config.attention_types`. """
                      """Please verify the value of `config.attention_types` argument."""      )

           super().__init__(bos_token_id=snake_case		,eos_token_id=snake_case		,**snake_case      )
    @staticmethod
    def       _SCREAMING_SNAKE_CASE       (  snake_case      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      []
           for item in attention_types:
                  for _ in range(item[1]      ):
                         attentions.extend(item[0]      )
           return attentions







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Tuple:
       import torch

       lowercase							:     Tuple       	=      input.size()
       lowercase							:     int       	=      len(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Union[str, Any]       	=      shape[dimension]

       lowercase							:     int       	=      torch.arange(0					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[Any]       	=      torch.div(sizedim - size					,	SCREAMING_SNAKE_CASE__					,	rounding_mode="""floor"""				) + 1
       lowercase							:     Dict       	=      torch.arange(SCREAMING_SNAKE_CASE__				) + low_indices[:min_length][:, None]

       lowercase							:     Union[str, Any]       	=      [slice(SCREAMING_SNAKE_CASE__				)] * rank
       lowercase							:     Optional[Any]       	=      indices
       lowercase							:     List[str]       	=      input[s]

       lowercase							:     Optional[int]       	=      list(range(0					,	rank + 1				)				)
       perm.append(perm.pop(dimension + 1				)				)

       return sliced.permute(SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       import torch

       lowercase							:     Union[str, Any]       	=      torch.arange(1					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[Any]       	=      torch.remainder(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[Any]       	=      remainders == 0
       lowercase							:     Optional[int]       	=      candidates[divisor_indices]
       lowercase							:     List[Any]       	=      torch.max(SCREAMING_SNAKE_CASE__				)
       return largest_divisor, torch.div(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	rounding_mode="""floor"""				)




class __snake_case		(      lowerCAmelCase     ):
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
           if self.use_past:
                  self.fill_with_past_key_values_(snake_case		,direction="""inputs"""      )
                  lowercase							:     Dict       	=      {0: """batch""", 1: """past_sequence + sequence"""}
           else:
                  lowercase							:     List[str]       	=      {0: """batch""", 1: """sequence"""}

           return common_inputs
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return self._config.num_heads
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case = -1		,snake_case = -1		,snake_case = False		,snake_case = None		,):



           '''simple docstring'''







           lowercase							:     Any       	=      super(snake_case		,self      ).generate_dummy_inputs(
               snake_case		,batch_size=snake_case		,seq_length=snake_case		,is_pair=snake_case		,framework=snake_case      )

           # We need to order the input in the way they appears in the forward()
           lowercase							:     List[str]       	=      OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}      )

           # Need to add the past_keys
           if self.use_past:
                  if not is_torch_available():
                         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""      )
                  else:
                         import torch

                         lowercase	,			lowercase							:     List[Any]       	=      common_inputs["""input_ids"""].shape
                         # Not using the same length for past_key_values
                         lowercase							:     Optional[int]       	=      seqlen + 2
                         lowercase							:     int       	=      (
                             batch,
                             self.num_attention_heads,
                             past_key_values_length,
                             self._config.hidden_size // self.num_attention_heads,
                         )
                         lowercase							:     Optional[Any]       	=      [
                             (torch.zeros(snake_case      ), torch.zeros(snake_case      )) for _ in range(self.num_layers      )
                         ]

           lowercase							:     Optional[Any]       	=      common_inputs["""attention_mask"""]
           if self.use_past:
                  lowercase							:     Any       	=      ordered_inputs["""attention_mask"""].dtype
                  lowercase							:     Union[str, Any]       	=      torch.cat(
                      [ordered_inputs["""attention_mask"""], torch.ones(snake_case		,snake_case		,dtype=snake_case      )]		,dim=1      )

           return ordered_inputs
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return 13
