





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:
     _UpperCAmelCase										=					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 lowercase			(		self:			List[str]		)							->			Optional[int]:




              """simple docstring"""


              UpperCamelCase_ 							=	tempfile.mkdtemp()

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

              UpperCamelCase_ 							=	PixaStructProcessor(_UpperCAmelCase       , _UpperCAmelCase		)

              processor.save_pretrained(self.tmpdirname		)

       def lowercase			(		self:			Union[str, Any]       , **_SCREAMING_SNAKE_CASE:			List[str]		)							->			Dict:




              """simple docstring"""


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

       def lowercase			(		self:			Optional[int]       , **_SCREAMING_SNAKE_CASE:			List[Any]		)							->			List[Any]:




              """simple docstring"""


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

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




              """simple docstring"""


              shutil.rmtree(self.tmpdirname		)

       def lowercase			(		self:			str		)							->			List[Any]:




              """simple docstring"""


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

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

              return image_inputs

       def lowercase			(		self:			List[Any]		)							->			Optional[Any]:




              """simple docstring"""


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

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

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

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

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

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




              """simple docstring"""


              UpperCamelCase_ 							=	self.get_image_processor()
              UpperCamelCase_ 							=	self.get_tokenizer()

              UpperCamelCase_ 							=	PixaStructProcessor(tokenizer=_UpperCAmelCase       , image_processor=_UpperCAmelCase		)

              UpperCamelCase_ 							=	self.prepare_image_inputs()

              UpperCamelCase_ 							=	image_processor(_UpperCAmelCase       , return_tensors="np"		)
              UpperCamelCase_ 							=	processor(images=_UpperCAmelCase       , 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 lowercase			(		self:			Optional[Any]		)							->			Dict:




              """simple docstring"""


              UpperCamelCase_ 							=	self.get_image_processor()
              UpperCamelCase_ 							=	self.get_tokenizer()

              UpperCamelCase_ 							=	PixaStructProcessor(tokenizer=_UpperCAmelCase       , image_processor=_UpperCAmelCase		)

              UpperCamelCase_ 							=	"lower newer"

              UpperCamelCase_ 							=	processor(text=_UpperCAmelCase		)

              UpperCamelCase_ 							=	tokenizer(_UpperCAmelCase       , return_token_type_ids=_UpperCAmelCase       , add_special_tokens=_UpperCAmelCase		)

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

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




              """simple docstring"""


              UpperCamelCase_ 							=	self.get_image_processor()
              UpperCamelCase_ 							=	self.get_tokenizer()

              UpperCamelCase_ 							=	PixaStructProcessor(tokenizer=_UpperCAmelCase       , image_processor=_UpperCAmelCase		)

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

              UpperCamelCase_ 							=	processor(text=_UpperCAmelCase       , images=_UpperCAmelCase		)

              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(_UpperCAmelCase		):
                     processor()

       def lowercase			(		self:			Dict		)							->			List[str]:




              """simple docstring"""


              UpperCamelCase_ 							=	self.get_image_processor()
              UpperCamelCase_ 							=	self.get_tokenizer()

              UpperCamelCase_ 							=	PixaStructProcessor(tokenizer=_UpperCAmelCase       , image_processor=_UpperCAmelCase		)

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

              UpperCamelCase_ 							=	processor(text=_UpperCAmelCase       , images=_UpperCAmelCase		)

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

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

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




              """simple docstring"""


              UpperCamelCase_ 							=	self.get_image_processor()
              UpperCamelCase_ 							=	self.get_tokenizer()

              UpperCamelCase_ 							=	PixaStructProcessor(tokenizer=_UpperCAmelCase       , image_processor=_UpperCAmelCase		)

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

              UpperCamelCase_ 							=	processor.batch_decode(_UpperCAmelCase		)
              UpperCamelCase_ 							=	tokenizer.batch_decode(_UpperCAmelCase		)

              self.assertListEqual(_UpperCAmelCase       , _UpperCAmelCase		)



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




              """simple docstring"""


              UpperCamelCase_ 							=	self.get_image_processor()
              UpperCamelCase_ 							=	self.get_tokenizer()

              UpperCamelCase_ 							=	PixaStructProcessor(tokenizer=_UpperCAmelCase       , image_processor=_UpperCAmelCase		)

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

              UpperCamelCase_ 							=	processor(text=_UpperCAmelCase       , images=_UpperCAmelCase		)

              # 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"]		)

              UpperCamelCase_ 							=	processor(text=_UpperCAmelCase		)

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


"""simple docstring"""


def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				sorted(A     ,					key=lambda A   : value[2]     ,					reverse=A   )

     # Create a list of size equal to the maximum deadline
     # and initialize it with -1
     lowercase__								=				max(A     ,					key=lambda A   : value[1]   )[1]
     lowercase__								=				[-1] * max_deadline

     # Finding the maximum profit and the count of jobs
     lowercase__								=				0
     lowercase__								=				0
     for job in jobs:
          # Find a free time slot for this job
          # (Note that we start from the last possible slot)
          for i in range(job[1] - 1     ,					-1     ,					-1   ):
               if time_slots[i] == -1:
                    lowercase__								=				job[0]
                    count += 1
                    max_profit += job[2]
                    break
     return [count, max_profit]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




