
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[Any]          =      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	:    List[str]          =      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






def 	a_		(	__lowercase       :     str			)			->  str:
			with open(__lowercase	, 'rb'			) as f:
						_snake_case          =      Image.open(__lowercase			)
						return im.convert('RGB'			)




@dataclass
class        SCREAMING_SNAKE_CASE__					:





	'''simple docstring'''



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



	def 			A							(  self     :   Optional[Any]     ):


				'''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__					:





	'''simple docstring'''



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






def 	a_		(	__lowercase       :     Optional[Any]			)			->  Optional[Any]:
			_snake_case          =      torch.stack([example['pixel_values'] for example in examples]			)
			_snake_case          =      torch.tensor([example['labels'] for example in examples]			)
			return {"pixel_values": pixel_values, "labels": labels}






def 	a_		(	)			->  int:
			# 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.

			_snake_case          =      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.
						_snake_case     , _snake_case     , _snake_case          =      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
			else:
						_snake_case     , _snake_case     , _snake_case          =      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()

			_snake_case          =      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.
			_snake_case          =      None
			if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
						_snake_case          =      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:
						_snake_case          =      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:
						_snake_case          =      {}
						if data_args.train_dir is not None:
									_snake_case          =      os.path.join(data_args.train_dir	, '**'			)
						if data_args.validation_dir is not None:
									_snake_case          =      os.path.join(data_args.validation_dir	, '**'			)
						_snake_case          =      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.
			_snake_case          =      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:
						_snake_case          =      dataset['train'].train_test_split(data_args.train_val_split			)
						_snake_case          =      split['train']
						_snake_case          =      split['test']

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

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

			_snake_case          =      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	, )
			_snake_case          =      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	, )
			_snake_case          =      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:
						_snake_case          =      image_processor.size['shortest_edge']
			else:
						_snake_case          =      (image_processor.size['height'], image_processor.size['width'])
			_snake_case          =      Normalize(mean=image_processor.image_mean	, std=image_processor.image_std			)
			_snake_case          =      Compose(
			    [
			        RandomResizedCrop(__lowercase			),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        normalize,
			    ]			)
			_snake_case          =      Compose(
			    [
			        Resize(__lowercase			),
			        CenterCrop(__lowercase			),
			        ToTensor(),
			        normalize,
			    ]			)

			def train_transforms(__lowercase       :     Union[str, Any]			):
						_snake_case          =      [
						    _train_transforms(pil_img.convert('RGB'			)			) for pil_img in example_batch['image']
						]
						return example_batch

			def val_transforms(__lowercase       :     int			):
						_snake_case          =      [_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:
									_snake_case          =      (
									    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:
									_snake_case          =      (
									    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
			_snake_case          =      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:
						_snake_case          =      None
						if training_args.resume_from_checkpoint is not None:
									_snake_case          =      training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									_snake_case          =      last_checkpoint
						_snake_case          =      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:
						_snake_case          =      trainer.evaluate()
						trainer.log_metrics('eval'	, __lowercase			)
						trainer.save_metrics('eval'	, __lowercase			)

			# Write model card and (optionally) push to hub
			_snake_case          =      {
			    '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 argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken






@torch.no_grad()
def 	a_		(	__lowercase       :     Optional[Any]	, __lowercase       :     Optional[int]	, __lowercase       :     str	, __lowercase       :     Optional[int]	, __lowercase       :     str			)			->  Union[str, Any]:
			# Load configuration defined in the metadata file
			with open(__lowercase			) as metadata_file:
						_snake_case          =      json.load(__lowercase			)
			_snake_case          =      LukeConfig(use_entity_aware_attention=__lowercase	, **metadata['model_config']			)

			# Load in the weights from the checkpoint_path
			_snake_case          =      torch.load(__lowercase	, map_location='cpu'			)['module']

			# Load the entity vocab file
			_snake_case          =      load_original_entity_vocab(__lowercase			)
			# add an entry for [MASK2]
			_snake_case          =      max(entity_vocab.values()			) + 1
			config.entity_vocab_size += 1

			_snake_case          =      XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']			)

			# Add special tokens to the token vocabulary for downstream tasks
			_snake_case          =      AddedToken('<ent>'	, lstrip=__lowercase	, rstrip=__lowercase			)
			_snake_case          =      AddedToken('<ent2>'	, lstrip=__lowercase	, rstrip=__lowercase			)
			tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]}			)
			config.vocab_size += 2

			print(f'''Saving tokenizer to {pytorch_dump_folder_path}'''			)
			tokenizer.save_pretrained(__lowercase			)
			with open(os.path.join(__lowercase	, 'tokenizer_config.json'			)	, 'r'			) as f:
						_snake_case          =      json.load(__lowercase			)
			_snake_case          =      'MLukeTokenizer'
			with open(os.path.join(__lowercase	, 'tokenizer_config.json'			)	, 'w'			) as f:
						json.dump(__lowercase	, __lowercase			)

			with open(os.path.join(__lowercase	, MLukeTokenizer.vocab_files_names['entity_vocab_file']			)	, 'w'			) as f:
						json.dump(__lowercase	, __lowercase			)

			_snake_case          =      MLukeTokenizer.from_pretrained(__lowercase			)

			# Initialize the embeddings of the special tokens
			_snake_case          =      tokenizer.convert_tokens_to_ids(['@']			)[0]
			_snake_case          =      tokenizer.convert_tokens_to_ids(['#']			)[0]

			_snake_case          =      state_dict['embeddings.word_embeddings.weight']
			_snake_case          =      word_emb[ent_init_index].unsqueeze(0			)
			_snake_case          =      word_emb[enta_init_index].unsqueeze(0			)
			_snake_case          =      torch.cat([word_emb, ent_emb, enta_emb]			)
			# add special tokens for 'entity_predictions.bias'
			for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
						_snake_case          =      state_dict[bias_name]
						_snake_case          =      decoder_bias[ent_init_index].unsqueeze(0			)
						_snake_case          =      decoder_bias[enta_init_index].unsqueeze(0			)
						_snake_case          =      torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]			)

			# Initialize the query layers of the entity-aware self-attention mechanism
			for layer_index in range(config.num_hidden_layers			):
						for matrix_name in ["query.weight", "query.bias"]:
									_snake_case          =      f'''encoder.layer.{layer_index}.attention.self.'''
									_snake_case          =      state_dict[prefix + matrix_name]
									_snake_case          =      state_dict[prefix + matrix_name]
									_snake_case          =      state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
			_snake_case          =      state_dict['entity_embeddings.entity_embeddings.weight']
			_snake_case          =      entity_emb[entity_vocab['[MASK]']].unsqueeze(0			)
			_snake_case          =      torch.cat([entity_emb, entity_mask_emb]			)
			# add [MASK2] for 'entity_predictions.bias'
			_snake_case          =      state_dict['entity_predictions.bias']
			_snake_case          =      entity_prediction_bias[entity_vocab['[MASK]']].unsqueeze(0			)
			_snake_case          =      torch.cat([entity_prediction_bias, entity_mask_bias]			)

			_snake_case          =      LukeForMaskedLM(config=__lowercase			).eval()

			state_dict.pop('entity_predictions.decoder.weight'			)
			state_dict.pop('lm_head.decoder.weight'			)
			state_dict.pop('lm_head.decoder.bias'			)
			_snake_case          =      OrderedDict()
			for key, value in state_dict.items():
						if not (key.startswith('lm_head'			) or key.startswith('entity_predictions'			)):
									_snake_case          =      state_dict[key]
						else:
									_snake_case          =      state_dict[key]

			_snake_case     , _snake_case          =      model.load_state_dict(__lowercase	, strict=__lowercase			)

			if set(__lowercase			) != {"luke.embeddings.position_ids"}:
						raise ValueError(f'''Unexpected unexpected_keys: {unexpected_keys}'''			)
			if set(__lowercase			) != {
			    "lm_head.decoder.weight",
			    "lm_head.decoder.bias",
			    "entity_predictions.decoder.weight",
			}:
						raise ValueError(f'''Unexpected missing_keys: {missing_keys}'''			)

			model.tie_weights()
			assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
			assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

			# Check outputs
			_snake_case          =      MLukeTokenizer.from_pretrained(__lowercase	, task='entity_classification'			)

			_snake_case          =      'ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'
			_snake_case          =      (0, 9)
			_snake_case          =      tokenizer(__lowercase	, entity_spans=[span]	, return_tensors='pt'			)

			_snake_case          =      model(**__lowercase			)

			# Verify word hidden states
			if model_size == "large":
						raise NotImplementedError
			else:  # base
						_snake_case          =      torch.Size((1, 33, 768)			)
						_snake_case          =      torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]]			)

			if not (outputs.last_hidden_state.shape == expected_shape):
						raise ValueError(
						    f'''Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'''			)
			if not torch.allclose(outputs.last_hidden_state[0, :3, :3]	, __lowercase	, atol=1E-4			):
						raise ValueError

			# Verify entity hidden states
			if model_size == "large":
						raise NotImplementedError
			else:  # base
						_snake_case          =      torch.Size((1, 1, 768)			)
						_snake_case          =      torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]]			)

			if not (outputs.entity_last_hidden_state.shape == expected_shape):
						raise ValueError(
						    f'''Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'''
						    f''' {expected_shape}'''			)
			if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]	, __lowercase	, atol=1E-4			):
						raise ValueError

			# Verify masked word/entity prediction
			_snake_case          =      MLukeTokenizer.from_pretrained(__lowercase			)
			_snake_case          =      'Tokyo is the capital of <mask>.'
			_snake_case          =      (24, 30)
			_snake_case          =      tokenizer(__lowercase	, entity_spans=[span]	, return_tensors='pt'			)

			_snake_case          =      model(**__lowercase			)

			_snake_case          =      encoding['input_ids'][0].tolist()
			_snake_case          =      input_ids.index(tokenizer.convert_tokens_to_ids('<mask>'			)			)
			_snake_case          =      outputs.logits[0][mask_position_id].argmax(dim=-1			)
			assert "Japan" == tokenizer.decode(__lowercase			)

			_snake_case          =      outputs.entity_logits[0][0].argmax().item()
			_snake_case          =      [
			    entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
			]
			assert [e for e in multilingual_predicted_entities if e.startswith('en:'			)][0] == "en:Japan"

			# Finally, save our PyTorch model and tokenizer
			print('Saving PyTorch model to {}'.format(__lowercase			)			)
			model.save_pretrained(__lowercase			)






def 	a_		(	__lowercase       :     int			)			->  int:
			_snake_case          =      ['[MASK]', '[PAD]', '[UNK]']

			_snake_case          =      [json.loads(__lowercase			) for line in open(__lowercase			)]

			_snake_case          =      {}
			for entry in data:
						_snake_case          =      entry['id']
						for entity_name, language in entry["entities"]:
									if entity_name in SPECIAL_TOKENS:
												_snake_case          =      entity_id
												break
									_snake_case          =      f'''{language}:{entity_name}'''
									_snake_case          =      entity_id
			return new_mapping


if __name__ == "__main__":
			_lowerCamelCase	:    Union[str, Any]          =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument('''--checkpoint_path''', type=str, help='''Path to a pytorch_model.bin file.''')
			parser.add_argument(
			    '''--metadata_path''', default=None, type=str, help='''Path to a metadata.json file, defining the configuration.'''
			)
			parser.add_argument(
			    '''--entity_vocab_path''',
			    default=None,
			    type=str,
			    help='''Path to an entity_vocab.tsv file, containing the entity vocabulary.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to where to dump the output PyTorch model.'''
			)
			parser.add_argument(
			    '''--model_size''', default='''base''', type=str, choices=['''base''', '''large'''], help='''Size of the model to be converted.'''
			)
			_lowerCamelCase	:    List[str]          =      parser.parse_args()
			convert_luke_checkpoint(
			    args.checkpoint_path,
			    args.metadata_path,
			    args.entity_vocab_path,
			    args.pytorch_dump_folder_path,
			    args.model_size,
			)