


'''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
lowerCAmelCase   : List[str]             =				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   : Optional[Any]             =				list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowerCAmelCase   : Optional[int]             =				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




def 	_A  (       A							)  ->				Dict:
	with open(A							,"rb"							) as f:
		lowercase						:		List[str]   					=		Image.open(A							)
		return im.convert("RGB"							)




@dataclass
class        _UpperCamelCase	:


				'''simple docstring'''


				_snake_case						       =				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).'''
				    }						,			)
				_snake_case						       =				field(
				    default=SCREAMING_SNAKE_CASE						,			metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''})
				_snake_case						       =				field(default=SCREAMING_SNAKE_CASE						,			metadata={'''help''': '''A folder containing the training data.'''})
				_snake_case						       =				field(default=SCREAMING_SNAKE_CASE						,			metadata={'''help''': '''A folder containing the validation data.'''})
				_snake_case						       =				field(
				    default=0.1_5						,			metadata={'''help''': '''Percent to split off of train for validation.'''})
				_snake_case						       =				field(
				    default=SCREAMING_SNAKE_CASE						,			metadata={
				        '''help''': (
				            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
				            '''value if set.'''
				        )
				    }						,			)
				_snake_case						       =				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 	a__		(     self							)    ->  List[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        _UpperCamelCase	:


				'''simple docstring'''


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




def 	_A  (       A							)  ->				Optional[Any]:
	lowercase						:		Any   					=		torch.stack([example["pixel_values"] for example in examples]							)
	lowercase						:		Union[str, Any]   					=		torch.tensor([example["labels"] for example in examples]							)
	return {"pixel_values": pixel_values, "labels": labels}




def 	_A  (       )  ->				Tuple:
	# 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.

	lowercase						:		Dict   					=		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.
		lowercase					,		lowercase					,		lowercase						:		int   					=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
	else:
		lowercase					,		lowercase					,		lowercase						:		Tuple   					=		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"							,A							,A							)

	# 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()

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

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

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

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

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

	def val_transforms(A							):
		lowercase						:		Tuple   					=		[_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:
			lowercase						:		List[Any]   					=		(
			    dataset["train"].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
			)
		# Set the training transforms
		dataset["train"].set_transform(A							)

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

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

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

	# Write model card and (optionally) push to hub
	lowercase						:		Optional[Any]   					=		{
	    "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(**A							)
	else:
		trainer.create_model_card(**A							)


if __name__ == "__main__":
					main()






'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


lowerCAmelCase   : Any             =				[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ["""memory_attention""", """encoder_attn"""],
    ["""attention""", """attn"""],
    ["""/""", """."""],
    [""".LayerNorm.gamma""", """_layer_norm.weight"""],
    [""".LayerNorm.beta""", """_layer_norm.bias"""],
    ["""r.layer_""", """r.layers."""],
    ["""output_proj""", """out_proj"""],
    ["""ffn.dense_1.""", """fc2."""],
    ["""ffn.dense.""", """fc1."""],
    ["""ffn_layer_norm""", """final_layer_norm"""],
    ["""kernel""", """weight"""],
    ["""encoder_layer_norm.""", """encoder.layer_norm."""],
    ["""decoder_layer_norm.""", """decoder.layer_norm."""],
    ["""embeddings.weights""", """shared.weight"""],
]




def 	_A  (       A							)  ->				List[str]:
	for pegasus_name, hf_name in PATTERNS:
		lowercase						:		int   					=		k.replace(A							,A							)
	return k




def 	_A  (       A							,A							)  ->				PegasusForConditionalGeneration:
	lowercase						:		int   					=		DEFAULTS.copy()
	cfg_kwargs.update(A							)
	lowercase						:		Any   					=		PegasusConfig(**A							)
	lowercase						:		Optional[Any]   					=		PegasusForConditionalGeneration(A							)
	lowercase						:		Dict   					=		torch_model.model.state_dict()
	lowercase						:		List[str]   					=		{}
	for k, v in tf_weights.items():
		lowercase						:		Optional[Any]   					=		rename_state_dict_key(A							)
		if new_k not in sd:
			raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''							)

		if "dense" in k or "proj" in new_k:
			lowercase						:		Tuple   					=		v.T
		lowercase						:		str   					=		torch.tensor(A							,dtype=sd[new_k].dtype							)
		assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
	# make sure embedding.padding_idx is respected
	lowercase						:		Union[str, Any]   					=		torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1]							)
	lowercase						:		Optional[int]   					=		mapping["shared.weight"]
	lowercase						:		int   					=		mapping["shared.weight"]
	lowercase						:		Optional[Any]   					=		{k: torch.zeros_like(A							) for k, v in sd.items() if k.endswith("bias"							) and k not in mapping}
	mapping.update(**A							)
	lowercase					,		lowercase						:		Union[str, Any]   					=		torch_model.model.load_state_dict(A							,strict=A							)
	lowercase						:		List[str]   					=		[
	    k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
	]
	assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
	assert extra == [], F'''no matches found for the following tf keys {extra}'''
	return torch_model




def 	_A  (       A="./ckpt/aeslc/model.ckpt-32000"							)  ->				Dict:
	lowercase						:		Optional[int]   					=		tf.train.list_variables(A							)
	lowercase						:		List[str]   					=		{}
	lowercase						:		List[str]   					=		["Adafactor", "global_step"]
	for name, shape in tqdm(A							,desc="converting tf checkpoint to dict"							):
		lowercase						:		Any   					=		any(pat in name for pat in ignore_name							)
		if skip_key:
			continue
		lowercase						:		Optional[Any]   					=		tf.train.load_variable(A							,A							)
		lowercase						:		Dict   					=		array
	return tf_weights




def 	_A  (       A							,A							)  ->				Union[str, Any]:
	# save tokenizer first
	lowercase						:		int   					=		Path(A							).parent.name
	lowercase						:		str   					=		task_specific_params[F'''summarization_{dataset}''']["max_position_embeddings"]
	lowercase						:		int   					=		PegasusTokenizer.from_pretrained("sshleifer/pegasus"							,model_max_length=A							)
	assert tok.model_max_length == desired_max_model_length
	tok.save_pretrained(A							)

	# convert model
	lowercase						:		Dict   					=		get_tf_weights_as_numpy(A							)
	lowercase						:		str   					=		task_specific_params[F'''summarization_{dataset}''']
	if dataset == "large":
		lowercase						:		str   					=		task_specific_params
	lowercase						:		List[str]   					=		convert_pegasus(A							,A							)
	torch_model.save_pretrained(A							)
	lowercase						:		Union[str, Any]   					=		torch_model.state_dict()
	sd.pop("model.decoder.embed_positions.weight"							)
	sd.pop("model.encoder.embed_positions.weight"							)
	torch.save(A							,Path(A							) / "pytorch_model.bin"							)


if __name__ == "__main__":
					lowerCAmelCase   : Optional[int]             =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("""tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
					parser.add_argument("""save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
					lowerCAmelCase   : Dict             =				parser.parse_args()
					if args.save_dir is None:
										lowerCAmelCase   : Dict             =				Path(args.tf_ckpt_path).parent.name
										lowerCAmelCase   : Optional[int]             =				os.path.join("""pegasus""", dataset)
					convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)


