





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
	from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
	_lowerCamelCase							=     False

if is_vision_available():
	from PIL import Image

	from transformers import (
	    AutoProcessor,
	    PixaStructImageProcessor,
	    PixaStructProcessor,
	    PreTrainedTokenizerFast,
	    TaTokenizer,
	)




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11			,					reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."			,					)
@require_vision
@require_torch
class 					UpperCamelCase_    (       unittest.TestCase      ):


							def        _snake_case					(							self      :Any						)				->				Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= tempfile.mkdtemp()

									SCREAMING_SNAKE_CASE__								= PixaStructImageProcessor()
									SCREAMING_SNAKE_CASE__								= TaTokenizer.from_pretrained("""t5-small"""						)

									SCREAMING_SNAKE_CASE__								= PixaStructProcessor(lowercase__							,			lowercase__						)

									processor.save_pretrained(self.tmpdirname						)


							def        _snake_case					(							self      :Dict							,			**__A      :List[str]						)				->				Dict:



									"""simple docstring"""



									return AutoProcessor.from_pretrained(self.tmpdirname							,			**lowercase__						).tokenizer


							def        _snake_case					(							self      :Optional[Any]							,			**__A      :str						)				->				Union[str, Any]:



									"""simple docstring"""



									return AutoProcessor.from_pretrained(self.tmpdirname							,			**lowercase__						).image_processor


							def        _snake_case					(							self      :Dict						)				->				Optional[int]:



									"""simple docstring"""



									shutil.rmtree(self.tmpdirname						)


							def        _snake_case					(							self      :Optional[int]						)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= [np.random.randint(255							,			size=(3, 30, 400)							,			dtype=np.uinta						)]

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

									return image_inputs


							def        _snake_case					(							self      :str						)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= PixaStructProcessor(tokenizer=self.get_tokenizer()							,			image_processor=self.get_image_processor()						)
									processor.save_pretrained(self.tmpdirname						)

									SCREAMING_SNAKE_CASE__								= self.get_tokenizer(bos_token="""(BOS)"""							,			eos_token="""(EOS)"""						)
									SCREAMING_SNAKE_CASE__								= self.get_image_processor(do_normalize=lowercase__							,			padding_value=1.0						)

									SCREAMING_SNAKE_CASE__								= PixaStructProcessor.from_pretrained(
									    self.tmpdirname							,			bos_token="""(BOS)"""							,			eos_token="""(EOS)"""							,			do_normalize=lowercase__							,			padding_value=1.0						)

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

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


							def        _snake_case					(							self      :Tuple						)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.get_image_processor()
									SCREAMING_SNAKE_CASE__								= self.get_tokenizer()

									SCREAMING_SNAKE_CASE__								= PixaStructProcessor(tokenizer=lowercase__							,			image_processor=lowercase__						)

									SCREAMING_SNAKE_CASE__								= self.prepare_image_inputs()

									SCREAMING_SNAKE_CASE__								= image_processor(lowercase__							,			return_tensors="""np"""						)
									SCREAMING_SNAKE_CASE__								= processor(images=lowercase__							,			return_tensors="""np"""						)

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


							def        _snake_case					(							self      :Any						)				->				List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.get_image_processor()
									SCREAMING_SNAKE_CASE__								= self.get_tokenizer()

									SCREAMING_SNAKE_CASE__								= PixaStructProcessor(tokenizer=lowercase__							,			image_processor=lowercase__						)

									SCREAMING_SNAKE_CASE__								= """lower newer"""

									SCREAMING_SNAKE_CASE__								= processor(text=lowercase__						)

									SCREAMING_SNAKE_CASE__								= tokenizer(lowercase__							,			return_token_type_ids=lowercase__							,			add_special_tokens=lowercase__						)

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


							def        _snake_case					(							self      :Any						)				->				List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.get_image_processor()
									SCREAMING_SNAKE_CASE__								= self.get_tokenizer()

									SCREAMING_SNAKE_CASE__								= PixaStructProcessor(tokenizer=lowercase__							,			image_processor=lowercase__						)

									SCREAMING_SNAKE_CASE__								= """lower newer"""
									SCREAMING_SNAKE_CASE__								= self.prepare_image_inputs()

									SCREAMING_SNAKE_CASE__								= processor(text=lowercase__							,			images=lowercase__						)

									self.assertListEqual(
									    list(inputs.keys()						)							,			["""flattened_patches""", """attention_mask""", """decoder_attention_mask""", """decoder_input_ids"""]						)

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


							def        _snake_case					(							self      :List[str]						)				->				Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.get_image_processor()
									SCREAMING_SNAKE_CASE__								= self.get_tokenizer()

									SCREAMING_SNAKE_CASE__								= PixaStructProcessor(tokenizer=lowercase__							,			image_processor=lowercase__						)

									SCREAMING_SNAKE_CASE__								= """lower newer"""
									SCREAMING_SNAKE_CASE__								= self.prepare_image_inputs()

									SCREAMING_SNAKE_CASE__								= processor(text=lowercase__							,			images=lowercase__						)

									SCREAMING_SNAKE_CASE__								= [512, 1024, 2048, 4096]
									SCREAMING_SNAKE_CASE__								= [770, 770, 770, 770]
									# with text
									for i, max_patch in enumerate(lowercase__						):
											SCREAMING_SNAKE_CASE__								= processor(text=lowercase__							,			images=lowercase__							,			max_patches=lowercase__						)
											self.assertEqual(inputs["""flattened_patches"""][0].shape[0]							,			lowercase__						)
											self.assertEqual(inputs["""flattened_patches"""][0].shape[1]							,			expected_hidden_size[i]						)

									# without text input
									for i, max_patch in enumerate(lowercase__						):
											SCREAMING_SNAKE_CASE__								= processor(images=lowercase__							,			max_patches=lowercase__						)
											self.assertEqual(inputs["""flattened_patches"""][0].shape[0]							,			lowercase__						)
											self.assertEqual(inputs["""flattened_patches"""][0].shape[1]							,			expected_hidden_size[i]						)


							def        _snake_case					(							self      :Any						)				->				Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.get_image_processor()
									SCREAMING_SNAKE_CASE__								= self.get_tokenizer()

									SCREAMING_SNAKE_CASE__								= PixaStructProcessor(tokenizer=lowercase__							,			image_processor=lowercase__						)

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

									SCREAMING_SNAKE_CASE__								= processor.batch_decode(lowercase__						)
									SCREAMING_SNAKE_CASE__								= tokenizer.batch_decode(lowercase__						)

									self.assertListEqual(lowercase__							,			lowercase__						)


							def        _snake_case					(							self      :Optional[Any]						)				->				Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= self.get_image_processor()
									SCREAMING_SNAKE_CASE__								= self.get_tokenizer()

									SCREAMING_SNAKE_CASE__								= PixaStructProcessor(tokenizer=lowercase__							,			image_processor=lowercase__						)

									SCREAMING_SNAKE_CASE__								= """lower newer"""
									SCREAMING_SNAKE_CASE__								= self.prepare_image_inputs()

									SCREAMING_SNAKE_CASE__								= processor(text=lowercase__							,			images=lowercase__						)

									# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
									self.assertListEqual(
									    list(inputs.keys()						)							,			["""flattened_patches""", """attention_mask""", """decoder_attention_mask""", """decoder_input_ids"""]						)

									SCREAMING_SNAKE_CASE__								= processor(text=lowercase__						)

									# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
									self.assertListEqual(list(inputs.keys()						)							,			["""input_ids""", """attention_mask"""]						)






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase							=     500000

_lowerCamelCase			,    _lowerCamelCase							=     os.path.split(__file__)
_lowerCamelCase							=     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.select(range(0   ,				len(UpperCamelCase__      )   ,				2      )      )






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.sort("""numbers"""      )






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.shuffle()






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.train_test_split(0.1      )






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset   ,				UpperCamelCase__:							Tuple=10      ):
		for shard_id in range(UpperCamelCase__      ):
				SCREAMING_SNAKE_CASE__								= dataset.shard(UpperCamelCase__   ,				UpperCamelCase__      )






def 			SCREAMING_SNAKE_CASE__							(    ):
		SCREAMING_SNAKE_CASE__								= {"""num examples""": SPEED_TEST_N_EXAMPLES}
		SCREAMING_SNAKE_CASE__								= (select, sort, shuffle, train_test_split, shard)
		with tempfile.TemporaryDirectory() as tmp_dir:
				print("""generating dataset"""      )
				SCREAMING_SNAKE_CASE__								= datasets.Features({"""text""": datasets.Value("""string"""      ), """numbers""": datasets.Value("""float32"""      )}      )
				SCREAMING_SNAKE_CASE__								= generate_example_dataset(
				    os.path.join(UpperCamelCase__   ,				"""dataset.arrow"""      )   ,				UpperCamelCase__   ,				num_examples=UpperCamelCase__      )
				print("""Functions"""      )
				for func in functions:
						print(func.__name__      )
						SCREAMING_SNAKE_CASE__								= func(UpperCamelCase__      )

		with open(UpperCamelCase__   ,				"""wb"""      ) as f:
				f.write(json.dumps(UpperCamelCase__      ).encode("""utf-8"""      )      )


if __name__ == "__main__":  # useful to run the profiler
	benchmark_indices_mapping()