



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
							from PIL import Image

							from transformers import CLIPImageProcessor, CLIPProcessor


@require_vision
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    tempfile.mkdtemp()

								# fmt: off
								A__       =    ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
								# fmt: on
								A__       =    dict(zip(_snake_case				,					range(len(_snake_case					)					)					)					)
								A__       =    ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
								A__       =    {'unk_token': '<unk>'}

								A__       =    os.path.join(self.tmpdirname				,					VOCAB_FILES_NAMES['vocab_file']					)
								A__       =    os.path.join(self.tmpdirname				,					VOCAB_FILES_NAMES['merges_file']					)
								with open(self.vocab_file				,					'w'				,					encoding='utf-8'					) as fp:
											fp.write(json.dumps(_snake_case					) + '\n'					)
								with open(self.merges_file				,					'w'				,					encoding='utf-8'					) as fp:
											fp.write('\n'.join(_snake_case					)					)

								A__       =    {
								    'do_resize': True,
								    'size': 20,
								    'do_center_crop': True,
								    'crop_size': 18,
								    'do_normalize': True,
								    'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
								    'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
								}
								A__       =    os.path.join(self.tmpdirname				,					_snake_case					)
								with open(self.image_processor_file				,					'w'				,					encoding='utf-8'					) as fp:
											json.dump(_snake_case				,					_snake_case					)



					def     _a							(     self					:       Union[str, Any]				,					**_snake_case					:       Tuple					):






								"""simple docstring"""


								return CLIPTokenizer.from_pretrained(self.tmpdirname				,					**_snake_case					)



					def     _a							(     self					:       Dict				,					**_snake_case					:       Union[str, Any]					):






								"""simple docstring"""


								return CLIPTokenizerFast.from_pretrained(self.tmpdirname				,					**_snake_case					)



					def     _a							(     self					:       Optional[int]				,					**_snake_case					:       Union[str, Any]					):






								"""simple docstring"""


								return CLIPImageProcessor.from_pretrained(self.tmpdirname				,					**_snake_case					)



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								shutil.rmtree(self.tmpdirname					)



					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    [np.random.randint(2_55				,					size=(3, 30, 4_00)				,					dtype=np.uinta					)]

								A__       =    [Image.fromarray(np.moveaxis(_snake_case				,					0				,					-1					)					) for x in image_inputs]

								return image_inputs



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								A__       =    self.get_tokenizer()
								A__       =    self.get_rust_tokenizer()
								A__       =    self.get_image_processor()

								A__       =    CLIPProcessor(tokenizer=_snake_case				,					image_processor=_snake_case					)
								processor_slow.save_pretrained(self.tmpdirname					)
								A__       =    CLIPProcessor.from_pretrained(self.tmpdirname				,					use_fast=_snake_case					)

								A__       =    CLIPProcessor(tokenizer=_snake_case				,					image_processor=_snake_case					)
								processor_fast.save_pretrained(self.tmpdirname					)
								A__       =    CLIPProcessor.from_pretrained(self.tmpdirname					)

								self.assertEqual(processor_slow.tokenizer.get_vocab()				,					tokenizer_slow.get_vocab()					)
								self.assertEqual(processor_fast.tokenizer.get_vocab()				,					tokenizer_fast.get_vocab()					)
								self.assertEqual(tokenizer_slow.get_vocab()				,					tokenizer_fast.get_vocab()					)
								self.assertIsInstance(processor_slow.tokenizer				,					_snake_case					)
								self.assertIsInstance(processor_fast.tokenizer				,					_snake_case					)

								self.assertEqual(processor_slow.image_processor.to_json_string()				,					image_processor.to_json_string()					)
								self.assertEqual(processor_fast.image_processor.to_json_string()				,					image_processor.to_json_string()					)
								self.assertIsInstance(processor_slow.image_processor				,					_snake_case					)
								self.assertIsInstance(processor_fast.image_processor				,					_snake_case					)



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__       =    CLIPProcessor(tokenizer=self.get_tokenizer()				,					image_processor=self.get_image_processor()					)
								processor.save_pretrained(self.tmpdirname					)

								A__       =    self.get_tokenizer(bos_token='(BOS)'				,					eos_token='(EOS)'					)
								A__       =    self.get_image_processor(do_normalize=_snake_case				,					padding_value=1.0					)

								A__       =    CLIPProcessor.from_pretrained(
								    self.tmpdirname				,					bos_token='(BOS)'				,					eos_token='(EOS)'				,					do_normalize=_snake_case				,					padding_value=1.0					)

								self.assertEqual(processor.tokenizer.get_vocab()				,					tokenizer_add_kwargs.get_vocab()					)
								self.assertIsInstance(processor.tokenizer				,					_snake_case					)

								self.assertEqual(processor.image_processor.to_json_string()				,					image_processor_add_kwargs.to_json_string()					)
								self.assertIsInstance(processor.image_processor				,					_snake_case					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__       =    self.get_image_processor()
								A__       =    self.get_tokenizer()

								A__       =    CLIPProcessor(tokenizer=_snake_case				,					image_processor=_snake_case					)

								A__       =    self.prepare_image_inputs()

								A__       =    image_processor(_snake_case				,					return_tensors='np'					)
								A__       =    processor(images=_snake_case				,					return_tensors='np'					)

								for key in input_image_proc.keys():
											self.assertAlmostEqual(input_image_proc[key].sum()				,					input_processor[key].sum()				,					delta=1E-2					)



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    self.get_image_processor()
								A__       =    self.get_tokenizer()

								A__       =    CLIPProcessor(tokenizer=_snake_case				,					image_processor=_snake_case					)

								A__       =    'lower newer'

								A__       =    processor(text=_snake_case					)

								A__       =    tokenizer(_snake_case					)

								for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]				,					encoded_processor[key]					)



					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    self.get_image_processor()
								A__       =    self.get_tokenizer()

								A__       =    CLIPProcessor(tokenizer=_snake_case				,					image_processor=_snake_case					)

								A__       =    'lower newer'
								A__       =    self.prepare_image_inputs()

								A__       =    processor(text=_snake_case				,					images=_snake_case					)

								self.assertListEqual(list(inputs.keys()					)				,					['input_ids', 'attention_mask', 'pixel_values']					)

								# test if it raises when no input is passed
								with pytest.raises(_snake_case					):
											processor()



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    self.get_image_processor()
								A__       =    self.get_tokenizer()

								A__       =    CLIPProcessor(tokenizer=_snake_case				,					image_processor=_snake_case					)

								A__       =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								A__       =    processor.batch_decode(_snake_case					)
								A__       =    tokenizer.batch_decode(_snake_case					)

								self.assertListEqual(_snake_case				,					_snake_case					)



					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    self.get_image_processor()
								A__       =    self.get_tokenizer()

								A__       =    CLIPProcessor(tokenizer=_snake_case				,					image_processor=_snake_case					)

								A__       =    'lower newer'
								A__       =    self.prepare_image_inputs()

								A__       =    processor(text=_snake_case				,					images=_snake_case					)

								self.assertListEqual(list(inputs.keys()					)				,					processor.model_input_names					)









import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


SCREAMING_SNAKE_CASE__       =      logging.getLogger(__name__)


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      List[Any]:
			return (preds == labels).mean()


@dataclass
class      __lowerCAmelCase     :
					"""simple docstring"""






					A__				:		str			       =    field(
					    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}      )
					A__				:		Optional[str]			       =    field(
					    default=UpperCAmelCase_							,     metadata={"help": "Pretrained config name or path if not the same as model_name"}      )
					A__				:		Optional[str]			       =    field(
					    default=UpperCAmelCase_							,     metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}      )
					A__				:		Optional[str]			       =    field(
					    default=UpperCAmelCase_							,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}							,     )







@dataclass
class      __lowerCAmelCase     :
					"""simple docstring"""






					A__				:		str			       =    field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()      )}      )
					A__				:		str			       =    field(metadata={"help": "Should contain the data files for the task."}      )
					A__				:		int			       =    field(
					    default=1_28							,     metadata={
					        "help": (
					            "The maximum total input sequence length after tokenization. Sequences longer "
					            "than this will be truncated, sequences shorter will be padded."
					        )
					    }							,     )
					A__				:		bool			       =    field(
					    default=UpperCAmelCase_							,     metadata={"help": "Overwrite the cached training and evaluation sets"}      )







def A						(       )						->      Any:
			# 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.

			A__       =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
			A__     , A__     , A__       =    parser.parse_args_into_dataclasses()

			if (
			    os.path.exists(training_args.output_dir					)
			    and os.listdir(training_args.output_dir					)
			    and training_args.do_train
			    and not training_args.overwrite_output_dir
			):
						raise ValueError(
						    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
						    ' --overwrite_output_dir to overcome.'					)

			# Setup logging
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' ,	datefmt='%m/%d/%Y %H:%M:%S' ,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,	)
			logger.warning(
			    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s' ,	training_args.local_rank ,	training_args.device ,	training_args.n_gpu ,	bool(training_args.local_rank != -1					) ,	training_args.fpaa ,	)
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank					):
						transformers.utils.logging.set_verbosity_info()
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
			logger.info('Training/evaluation parameters %s' ,	__UpperCamelCase					)

			# Set seed
			set_seed(training_args.seed					)

			try:
						A__       =    processors[data_args.task_name]()
						A__       =    processor.get_labels()
						A__       =    len(__UpperCamelCase					)
			except KeyError:
						raise ValueError('Task not found: %s' % (data_args.task_name)					)

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.

			A__       =    AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,	num_labels=__UpperCamelCase ,	finetuning_task=data_args.task_name ,	cache_dir=model_args.cache_dir ,	)
			A__       =    AutoTokenizer.from_pretrained(
			    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,	cache_dir=model_args.cache_dir ,	)
			A__       =    AutoModelForMultipleChoice.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 ,	)

			# Get datasets
			A__       =    (
			    MultipleChoiceDataset(
			        data_dir=data_args.data_dir ,	tokenizer=__UpperCamelCase ,	task=data_args.task_name ,	max_seq_length=data_args.max_seq_length ,	overwrite_cache=data_args.overwrite_cache ,	mode=Split.train ,	)
			    if training_args.do_train
			    else None
			)
			A__       =    (
			    MultipleChoiceDataset(
			        data_dir=data_args.data_dir ,	tokenizer=__UpperCamelCase ,	task=data_args.task_name ,	max_seq_length=data_args.max_seq_length ,	overwrite_cache=data_args.overwrite_cache ,	mode=Split.dev ,	)
			    if training_args.do_eval
			    else None
			)

			def compute_metrics(__UpperCamelCase					) -> Dict:
						A__       =    np.argmax(p.predictions ,	axis=1					)
						return {"acc": simple_accuracy(__UpperCamelCase ,	p.label_ids					)}

			# Data collator
			A__       =    DataCollatorWithPadding(__UpperCamelCase ,	pad_to_multiple_of=8					) if training_args.fpaa else None

			# Initialize our Trainer
			A__       =    Trainer(
			    model=__UpperCamelCase ,	args=__UpperCamelCase ,	train_dataset=__UpperCamelCase ,	eval_dataset=__UpperCamelCase ,	compute_metrics=__UpperCamelCase ,	data_collator=__UpperCamelCase ,	)

			# Training
			if training_args.do_train:
						trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
						trainer.save_model()
						# For convenience, we also re-save the tokenizer to the same directory,
						# so that you can share your model easily on huggingface.co/models =)
						if trainer.is_world_master():
									tokenizer.save_pretrained(training_args.output_dir					)

    # Evaluation
			A__       =    {}
			if training_args.do_eval:
						logger.info('*** Evaluate ***'					)

						A__       =    trainer.evaluate()

						A__       =    os.path.join(training_args.output_dir ,	'eval_results.txt'					)
						if trainer.is_world_master():
									with open(__UpperCamelCase ,	'w'					) as writer:
												logger.info('***** Eval results *****'					)
												for key, value in result.items():
															logger.info('  %s = %s' ,	__UpperCamelCase ,	__UpperCamelCase					)
															writer.write('%s = %s\n' % (key, value)					)

												results.update(__UpperCamelCase					)

			return results


def A						(       __UpperCamelCase					)						->      List[Any]:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
							main()




