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
__lowerCamelCase					:	Dict       =  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''')

__lowerCamelCase					:	Dict       =  list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__lowerCamelCase					:	int       =  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




def       _snake_case	(   lowerCAmelCase							:   str	):
		"""simple docstring"""







		with open(lowerCAmelCase   , "rb"	) as f:
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    Image.open(lowerCAmelCase	)
				return im.convert("RGB"	)






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







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







def       _snake_case	(   lowerCAmelCase							:   List[Any]	):
		"""simple docstring"""







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




def       _snake_case	(   ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Tuple				      =    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.
				SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		str				      =    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
		else:
				SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Optional[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"   , lowerCAmelCase   , lowerCAmelCase	)

		# 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()

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

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

		# Load the accuracy metric from the datasets package
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    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[Any]	):
				return metric.compute(predictions=np.argmax(p.predictions   , axis=1	)   , references=p.label_ids	)

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

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

		def val_transforms(lowerCAmelCase							:   Union[str, Any]	):
				SCREAMING_SNAKE_CASE_  :		int				      =    [_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:
						SCREAMING_SNAKE_CASE_  :		List[str]				      =    (
						    dataset["train"].shuffle(seed=training_args.seed	).select(range(data_args.max_train_samples	)	)
						)
				# Set the training transforms
				dataset["train"].set_transform(lowerCAmelCase	)

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

		# Initalize our trainer
		SCREAMING_SNAKE_CASE_  :		Dict				      =    Trainer(
		    model=lowerCAmelCase   , args=lowerCAmelCase   , train_dataset=dataset["train"] if training_args.do_train else None   , eval_dataset=dataset["validation"] if training_args.do_eval else None   , compute_metrics=lowerCAmelCase   , tokenizer=lowerCAmelCase   , data_collator=lowerCAmelCase   , )

		# Training
		if training_args.do_train:
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    None
				if training_args.resume_from_checkpoint is not None:
						SCREAMING_SNAKE_CASE_  :		Any				      =    training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						SCREAMING_SNAKE_CASE_  :		List[Any]				      =    last_checkpoint
				SCREAMING_SNAKE_CASE_  :		Any				      =    trainer.train(resume_from_checkpoint=lowerCAmelCase	)
				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:
				SCREAMING_SNAKE_CASE_  :		int				      =    trainer.evaluate()
				trainer.log_metrics("eval"   , lowerCAmelCase	)
				trainer.save_metrics("eval"   , lowerCAmelCase	)

		# Write model card and (optionally) push to hub
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    {
		    "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(**lowerCAmelCase	)
		else:
				trainer.create_model_card(**lowerCAmelCase	)


if __name__ == "__main__":
		main()






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_A     =			'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
_A     =			[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
_A     =			dict(zip(vocab, range(len(vocab))))
_A     =			['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
						_A     =			Path(tmpdirname)
						_A     =			build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
						_A     =			build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
						_A     =			build_dir / VOCAB_FILES_NAMES['''merges_file''']
						with open(src_vocab_file, '''w''') as fp:
												fp.write(json.dumps(vocab_tokens))
						with open(tgt_vocab_file, '''w''') as fp:
												fp.write(json.dumps(vocab_tokens))
						with open(merges_file, '''w''') as fp:
												fp.write('''\n'''.join(merges))

						_A     =			FSMTTokenizer(
						    langs=['''en''', '''ru'''],
						    src_vocab_size=len(vocab),
						    tgt_vocab_size=len(vocab),
						    src_vocab_file=src_vocab_file,
						    tgt_vocab_file=tgt_vocab_file,
						    merges_file=merges_file,
						)

_A     =			FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_A     =			FSMTForConditionalGeneration(config)
print(f"num of params {tiny_model.num_parameters()}")

# Test
_A     =			tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_A     =			tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-ru




