






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:
  __a :    List[Any]								=				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  __lowercase (		unittest.TestCase					):







     '''simple docstring'''




     def 						lowerCAmelCase_       (			self    :     Optional[int]			):


          """simple docstring"""



          __A             =		tempfile.mkdtemp()

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

          __A             =		PixaStructProcessor(UpperCamelCase_	,		UpperCamelCase_			)

          processor.save_pretrained(self.tmpdirname			)




     def 						lowerCAmelCase_       (			self    :     Optional[Any]	,		**UpperCamelCase_    :     Union[str, Any]			):


          """simple docstring"""



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




     def 						lowerCAmelCase_       (			self    :     str	,		**UpperCamelCase_    :     Optional[int]			):


          """simple docstring"""



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




     def 						lowerCAmelCase_       (			self    :     Optional[Any]			):


          """simple docstring"""



          shutil.rmtree(self.tmpdirname			)




     def 						lowerCAmelCase_       (			self    :     int			):


          """simple docstring"""



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

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

          return image_inputs




     def 						lowerCAmelCase_       (			self    :     Tuple			):


          """simple docstring"""



          __A             =		PixaStructProcessor(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=UpperCamelCase_	,		padding_value=1.0			)

          __A             =		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 						lowerCAmelCase_       (			self    :     Tuple			):


          """simple docstring"""



          __A             =		self.get_image_processor()
          __A             =		self.get_tokenizer()

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

          __A             =		self.prepare_image_inputs()

          __A             =		image_processor(UpperCamelCase_	,		return_tensors="""np"""			)
          __A             =		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 						lowerCAmelCase_       (			self    :     Any			):


          """simple docstring"""



          __A             =		self.get_image_processor()
          __A             =		self.get_tokenizer()

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

          __A             =		"""lower newer"""

          __A             =		processor(text=UpperCamelCase_			)

          __A             =		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 						lowerCAmelCase_       (			self    :     Tuple			):


          """simple docstring"""



          __A             =		self.get_image_processor()
          __A             =		self.get_tokenizer()

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

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

          __A             =		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 						lowerCAmelCase_       (			self    :     int			):


          """simple docstring"""



          __A             =		self.get_image_processor()
          __A             =		self.get_tokenizer()

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

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

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

          __A             =		[512, 1_024, 2_048, 4_096]
          __A             =		[770, 770, 770, 770]
          # with text
          for i, max_patch in enumerate(UpperCamelCase_			):
               __A             =		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_			):
               __A             =		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 						lowerCAmelCase_       (			self    :     List[str]			):


          """simple docstring"""



          __A             =		self.get_image_processor()
          __A             =		self.get_tokenizer()

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

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

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

          self.assertListEqual(UpperCamelCase_	,		UpperCamelCase_			)





     def 						lowerCAmelCase_       (			self    :     Optional[Any]			):


          """simple docstring"""



          __A             =		self.get_image_processor()
          __A             =		self.get_tokenizer()

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

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

          __A             =		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"""]			)

          __A             =		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"""]			)












def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     list[int]      )  -> list[int]:





    """simple docstring"""

    if len(__lowercase      ) > 1:
        __A             =		len(__lowercase      ) // 2  # Finds the middle of the array
        __A             =		arr[
            :middle_length
        ]  # Creates an array of the elements in the first half.
        __A             =		arr[
            middle_length:
        ]  # Creates an array of the elements in the second half.
        __A             =		len(__lowercase      )
        __A             =		len(__lowercase      )
        merge(__lowercase      )  # Starts sorting the left.
        merge(__lowercase      )  # Starts sorting the right
        __A             =		0  # Left Counter
        __A             =		0  # Right Counter
        __A             =		0  # Position Counter
        while (
            left_index < left_size and right_index < right_size
        ):  # Runs until the lowers size of the left and right are sorted.
            if left_array[left_index] < right_array[right_index]:
                __A             =		left_array[left_index]
                left_index += 1
            else:
                __A             =		right_array[right_index]
                right_index += 1
            index += 1
        while (
            left_index < left_size
        ):  # Adds the left over elements in the left half of the array
            __A             =		left_array[left_index]
            left_index += 1
            index += 1
        while (
            right_index < right_size
        ):  # Adds the left over elements in the right half of the array
            __A             =		right_array[right_index]
            right_index += 1
            index += 1
    return arr


if __name__ == "__main__":
  import doctest

  doctest.testmod()




