



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 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	tempfile.mkdtemp()

         _lowerCAmelCase			      =	PixaStructImageProcessor()
         _lowerCAmelCase			      =	TaTokenizer.from_pretrained('''t5-small'''      )

         _lowerCAmelCase			      =	PixaStructProcessor(_lowerCAmelCase							,  _lowerCAmelCase      )

         processor.save_pretrained(self.tmpdirname      )





   def 						__lowerCAmelCase				(						self							,  **_lowerCAmelCase      ):
         return AutoProcessor.from_pretrained(self.tmpdirname							,  **_lowerCAmelCase      ).tokenizer





   def 						__lowerCAmelCase				(						self							,  **_lowerCAmelCase      ):
         return AutoProcessor.from_pretrained(self.tmpdirname							,  **_lowerCAmelCase      ).image_processor





   def 						__lowerCAmelCase				(						self      ):
         shutil.rmtree(self.tmpdirname      )





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	[np.random.randint(255							,  size=(3, 30, 400)							,  dtype=np.uinta      )]

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

         return image_inputs





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	PixaStructProcessor(tokenizer=self.get_tokenizer()							,  image_processor=self.get_image_processor()      )
         processor.save_pretrained(self.tmpdirname      )

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

         _lowerCAmelCase			      =	PixaStructProcessor.from_pretrained(
             self.tmpdirname							,  bos_token='''(BOS)'''							,  eos_token='''(EOS)'''							,  do_normalize=_lowerCAmelCase							,  padding_value=1.0      )

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

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





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	self.get_image_processor()
         _lowerCAmelCase			      =	self.get_tokenizer()

         _lowerCAmelCase			      =	PixaStructProcessor(tokenizer=_lowerCAmelCase							,  image_processor=_lowerCAmelCase      )

         _lowerCAmelCase			      =	self.prepare_image_inputs()

         _lowerCAmelCase			      =	image_processor(_lowerCAmelCase							,  return_tensors='''np'''      )
         _lowerCAmelCase			      =	processor(images=_lowerCAmelCase							,  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      ):
         _lowerCAmelCase			      =	self.get_image_processor()
         _lowerCAmelCase			      =	self.get_tokenizer()

         _lowerCAmelCase			      =	PixaStructProcessor(tokenizer=_lowerCAmelCase							,  image_processor=_lowerCAmelCase      )

         _lowerCAmelCase			      =	'''lower newer'''

         _lowerCAmelCase			      =	processor(text=_lowerCAmelCase      )

         _lowerCAmelCase			      =	tokenizer(_lowerCAmelCase							,  return_token_type_ids=_lowerCAmelCase							,  add_special_tokens=_lowerCAmelCase      )

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





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	self.get_image_processor()
         _lowerCAmelCase			      =	self.get_tokenizer()

         _lowerCAmelCase			      =	PixaStructProcessor(tokenizer=_lowerCAmelCase							,  image_processor=_lowerCAmelCase      )

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

         _lowerCAmelCase			      =	processor(text=_lowerCAmelCase							,  images=_lowerCAmelCase      )

         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(_lowerCAmelCase      ):
               processor()





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	self.get_image_processor()
         _lowerCAmelCase			      =	self.get_tokenizer()

         _lowerCAmelCase			      =	PixaStructProcessor(tokenizer=_lowerCAmelCase							,  image_processor=_lowerCAmelCase      )

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

         _lowerCAmelCase			      =	processor(text=_lowerCAmelCase							,  images=_lowerCAmelCase      )

         _lowerCAmelCase			      =	[512, 1_024, 2_048, 4_096]
         _lowerCAmelCase			      =	[770, 770, 770, 770]
         # with text
         for i, max_patch in enumerate(_lowerCAmelCase      ):
               _lowerCAmelCase			      =	processor(text=_lowerCAmelCase							,  images=_lowerCAmelCase							,  max_patches=_lowerCAmelCase      )
               self.assertEqual(inputs['''flattened_patches'''][0].shape[0]							,  _lowerCAmelCase      )
               self.assertEqual(inputs['''flattened_patches'''][0].shape[1]							,  expected_hidden_size[i]      )

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





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	self.get_image_processor()
         _lowerCAmelCase			      =	self.get_tokenizer()

         _lowerCAmelCase			      =	PixaStructProcessor(tokenizer=_lowerCAmelCase							,  image_processor=_lowerCAmelCase      )

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

         _lowerCAmelCase			      =	processor.batch_decode(_lowerCAmelCase      )
         _lowerCAmelCase			      =	tokenizer.batch_decode(_lowerCAmelCase      )

         self.assertListEqual(_lowerCAmelCase							,  _lowerCAmelCase      )





   def 						__lowerCAmelCase				(						self      ):
         _lowerCAmelCase			      =	self.get_image_processor()
         _lowerCAmelCase			      =	self.get_tokenizer()

         _lowerCAmelCase			      =	PixaStructProcessor(tokenizer=_lowerCAmelCase							,  image_processor=_lowerCAmelCase      )

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

         _lowerCAmelCase			      =	processor(text=_lowerCAmelCase							,  images=_lowerCAmelCase      )

         # 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''']      )

         _lowerCAmelCase			      =	processor(text=_lowerCAmelCase      )

         # 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 numpy as np
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						Tuple					)->Union[str, Any]:
      _lowerCAmelCase      , _lowerCAmelCase			      =	a.shape
      _lowerCAmelCase			      =	min(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE					)
      _lowerCAmelCase			      =	np.eye(_SCREAMING_SNAKE_CASE					)
      _lowerCAmelCase			      =	a.copy()

      for k in range(t - 1					):
            # select a column of modified matrix A':
            _lowerCAmelCase			      =	r[k:, [k]]
            # construct first basis vector
            _lowerCAmelCase			      =	np.zeros_like(_SCREAMING_SNAKE_CASE					)
            _lowerCAmelCase			      =	1.0
            # determine scaling factor
            _lowerCAmelCase			      =	np.linalg.norm(_SCREAMING_SNAKE_CASE					)
            # construct vector v for Householder reflection
            _lowerCAmelCase			      =	x + np.sign(x[0]					) * alpha * ea
            v /= np.linalg.norm(_SCREAMING_SNAKE_CASE					)

            # construct the Householder matrix
            _lowerCAmelCase			      =	np.eye(m - k					) - 2.0 * v @ v.T
            # pad with ones and zeros as necessary
            _lowerCAmelCase			      =	np.block([[np.eye(_SCREAMING_SNAKE_CASE					), np.zeros((k, m - k)					)], [np.zeros((m - k, k)					), q_k]]					)

            _lowerCAmelCase			      =	q @ q_k.T
            _lowerCAmelCase			      =	q_k @ r

      return q, r


if __name__ == "__main__":
    import doctest

    doctest.testmod()