from typing import Any

import numpy as np


def SCREAMING_SNAKE_CASE					(  lowercase_:						np.ndarray					):
       assert np.shape(lowercase_					)[0] == np.shape(lowercase_					)[1]

       # If matrix not symmetric, exit right away.
       if np.allclose(lowercase_		,    matrix.T					) is False:
              return False

       # Get eigenvalues and eignevectors for a symmetric matrix.
       _UpperCamelCase    ,   _UpperCamelCase  :							List[str]	    =	np.linalg.eigh(lowercase_					)

       # Check sign of all eigenvalues.
       # np.all returns a value of type np.bool_
       return bool(np.all(eigen_values > 0					)					)


def SCREAMING_SNAKE_CASE					(  lowercase_:						int					):
       _UpperCamelCase  :							Dict	    =	np.random.randn(lowercase_		,    lowercase_					)
       _UpperCamelCase  :							Any	    =	np.dot(lowercase_		,    random_matrix.T					)
       assert _is_matrix_spd(lowercase_					)
       return spd_matrix


def SCREAMING_SNAKE_CASE					(  lowercase_:						np.ndarray		,    lowercase_:						np.ndarray		,    lowercase_:						int = 1000		,    lowercase_:						float = 1e-8		,    ):
       assert np.shape(lowercase_					)[0] == np.shape(lowercase_					)[1]
       assert np.shape(lowercase_					)[0] == np.shape(lowercase_					)[0]
       assert _is_matrix_spd(lowercase_					)

       # Initialize solution guess, residual, search direction.
       _UpperCamelCase  :							Dict	    =	np.zeros((np.shape(lowercase_					)[0], 1)					)
       _UpperCamelCase  :							Optional[Any]	    =	np.copy(lowercase_					)
       _UpperCamelCase  :							Optional[Any]	    =	np.copy(lowercase_					)

       # Set initial errors in solution guess and residual.
       _UpperCamelCase  :							Tuple	    =	1e9
       _UpperCamelCase  :							Optional[int]	    =	1e9
       _UpperCamelCase  :							List[str]	    =	1e9

       # Set iteration counter to threshold number of iterations.
       _UpperCamelCase  :							List[str]	    =	0

       while error > tol:
              # Save this value so we only calculate the matrix-vector product once.
              _UpperCamelCase  :							str	    =	np.dot(lowercase_		,    lowercase_					)

              # The main algorithm.

              # Update search direction magnitude.
              _UpperCamelCase  :							str	    =	np.dot(ra.T		,    lowercase_					) / np.dot(pa.T		,    lowercase_					)
              # Update solution guess.
              _UpperCamelCase  :							List[str]	    =	xa + alpha * pa
              # Calculate new residual.
              _UpperCamelCase  :							Union[str, Any]	    =	ra - alpha * w
              # Calculate new Krylov subspace scale.
              _UpperCamelCase  :							Union[str, Any]	    =	np.dot(r.T		,    lowercase_					) / np.dot(ra.T		,    lowercase_					)
              # Calculate new A conjuage search direction.
              _UpperCamelCase  :							int	    =	r + beta * pa

              # Calculate errors.
              _UpperCamelCase  :							int	    =	np.linalg.norm(r - ra					)
              _UpperCamelCase  :							Union[str, Any]	    =	np.linalg.norm(x - xa					)
              _UpperCamelCase  :							Dict	    =	np.maximum(lowercase_		,    lowercase_					)

              # Update variables.
              _UpperCamelCase  :							Union[str, Any]	    =	np.copy(lowercase_					)
              _UpperCamelCase  :							Any	    =	np.copy(lowercase_					)
              _UpperCamelCase  :							Optional[Any]	    =	np.copy(lowercase_					)

              # Update number of iterations.
              iterations += 1
              if iterations > max_iterations:
                     break

       return x


def SCREAMING_SNAKE_CASE					(  ):
       _UpperCamelCase  :							Optional[Any]	    =	3
       _UpperCamelCase  :							List[str]	    =	_create_spd_matrix(lowercase_					)
       _UpperCamelCase  :							Tuple	    =	np.random.randn(lowercase_		,    1					)
       _UpperCamelCase  :							List[Any]	    =	np.dot(lowercase_		,    lowercase_					)

       # Numpy solution.
       _UpperCamelCase  :							int	    =	np.linalg.solve(lowercase_		,    lowercase_					)

       # Our implementation.
       _UpperCamelCase  :							List[Any]	    =	conjugate_gradient(lowercase_		,    lowercase_					)

       # Ensure both solutions are close to x_true (and therefore one another).
       assert np.linalg.norm(x_numpy - x_true					) <= 1e-6
       assert np.linalg.norm(x_conjugate_gradient - x_true					) <= 1e-6


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 test_conjugate_gradient()





'''simple docstring'''





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							     = 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 			lowerCAmelCase__ (							unittest.TestCase							):



  '''simple docstring'''






  def 		_lowerCamelCase						(self	:			Tuple     ) ->							Tuple:
        snake_case_    							=				tempfile.mkdtemp()

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

        snake_case_    							=				PixaStructProcessor(lowerCAmelCase_						,			lowerCAmelCase_     )

        processor.save_pretrained(self.tmpdirname     )

  def 		_lowerCamelCase						(self	:			List[str]						,			**lowerCAmelCase_	:			int     ) ->							Optional[Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname						,			**lowerCAmelCase_     ).tokenizer

  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			**lowerCAmelCase_	:			List[str]     ) ->							List[Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname						,			**lowerCAmelCase_     ).image_processor

  def 		_lowerCamelCase						(self	:			int     ) ->							List[str]:
        shutil.rmtree(self.tmpdirname     )

  def 		_lowerCamelCase						(self	:			Any     ) ->							Optional[Any]:
        snake_case_    							=				[np.random.randint(2_55						,			size=(3, 30, 4_00)						,			dtype=np.uinta     )]

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

        return image_inputs

  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							Dict:
        snake_case_    							=				PixaStructProcessor(tokenizer=self.get_tokenizer()						,			image_processor=self.get_image_processor()     )
        processor.save_pretrained(self.tmpdirname     )

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

        snake_case_    							=				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	:			Dict     ) ->							Optional[int]:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				self.prepare_image_inputs()

        snake_case_    							=				image_processor(lowerCAmelCase_						,			return_tensors="np"     )
        snake_case_    							=				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	:			Tuple     ) ->							List[str]:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

        snake_case_    							=				"lower newer"

        snake_case_    							=				processor(text=lowerCAmelCase_     )

        snake_case_    							=				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	:			str     ) ->							List[str]:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

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

        snake_case_    							=				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	:			Optional[Any]     ) ->							Any:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

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

        snake_case_    							=				processor(text=lowerCAmelCase_						,			images=lowerCAmelCase_     )

        snake_case_    							=				[5_12, 10_24, 20_48, 40_96]
        snake_case_    							=				[7_70, 7_70, 7_70, 7_70]
        # with text
        for i, max_patch in enumerate(lowerCAmelCase_     ):
              snake_case_    							=				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_     ):
              snake_case_    							=				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	:			int     ) ->							Any:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

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

        snake_case_    							=				processor.batch_decode(lowerCAmelCase_     )
        snake_case_    							=				tokenizer.batch_decode(lowerCAmelCase_     )

        self.assertListEqual(lowerCAmelCase_						,			lowerCAmelCase_     )


  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							str:
        snake_case_    							=				self.get_image_processor()
        snake_case_    							=				self.get_tokenizer()

        snake_case_    							=				PixaStructProcessor(tokenizer=lowerCAmelCase_						,			image_processor=lowerCAmelCase_     )

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

        snake_case_    							=				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"]     )

        snake_case_    							=				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"]     )

