
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import (
					    AutoProcessor,
					    BertTokenizerFast,
					    BlipImageProcessor,
					    GPTaTokenizer,
					    InstructBlipProcessor,
					    PreTrainedTokenizerFast,
					)





@require_vision
class 				UpperCAmelCase (    unittest.TestCase    ):




	'''simple docstring'''





	def 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					tempfile.mkdtemp()

					__A       =					BlipImageProcessor()
					__A       =					GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"					)
					__A       =					BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert"					)

					__A       =					InstructBlipProcessor(A				,A				,A					)

					processor.save_pretrained(self.tmpdirname					)






	def 							UpperCamelCase_    (			self   :					Optional[Any]				,**A   :					Optional[Any]					):
					return AutoProcessor.from_pretrained(self.tmpdirname				,**A					).tokenizer






	def 							UpperCamelCase_    (			self   :					Optional[Any]				,**A   :					Tuple					):
					return AutoProcessor.from_pretrained(self.tmpdirname				,**A					).image_processor






	def 							UpperCamelCase_    (			self   :					Tuple				,**A   :					Tuple					):
					return AutoProcessor.from_pretrained(self.tmpdirname				,**A					).qformer_tokenizer






	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					shutil.rmtree(self.tmpdirname					)






	def 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					[np.random.randint(2_55				,size=(3, 30, 4_00)				,dtype=np.uinta					)]

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

					return image_inputs






	def 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					__A       =					InstructBlipProcessor(
					    tokenizer=self.get_tokenizer()				,image_processor=self.get_image_processor()				,qformer_tokenizer=self.get_qformer_tokenizer()				,)
					processor.save_pretrained(self.tmpdirname					)

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

					__A       =					InstructBlipProcessor.from_pretrained(
					    self.tmpdirname				,bos_token="(BOS)"				,eos_token="(EOS)"				,do_normalize=A				,padding_value=1.0					)

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

					self.assertEqual(processor.image_processor.to_json_string()				,image_processor_add_kwargs.to_json_string()					)
					self.assertIsInstance(processor.image_processor				,A					)
					self.assertIsInstance(processor.qformer_tokenizer				,A					)






	def 							UpperCamelCase_    (			self   :					Tuple					):
					__A       =					self.get_image_processor()
					__A       =					self.get_tokenizer()
					__A       =					self.get_qformer_tokenizer()

					__A       =					InstructBlipProcessor(
					    tokenizer=A				,image_processor=A				,qformer_tokenizer=A					)

					__A       =					self.prepare_image_inputs()

					__A       =					image_processor(A				,return_tensors="np"					)
					__A       =					processor(images=A				,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 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					__A       =					self.get_image_processor()
					__A       =					self.get_tokenizer()
					__A       =					self.get_qformer_tokenizer()

					__A       =					InstructBlipProcessor(
					    tokenizer=A				,image_processor=A				,qformer_tokenizer=A					)

					__A       =					"lower newer"

					__A       =					processor(text=A					)

					__A       =					tokenizer(A				,return_token_type_ids=A					)
					__A       =					qformer_tokenizer(A				,return_token_type_ids=A					)

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

					for key in encoded_tokens_qformer.keys():
									self.assertListEqual(encoded_tokens_qformer[key]				,encoded_processor["qformer_" + key]					)






	def 							UpperCamelCase_    (			self   :					int					):
					__A       =					self.get_image_processor()
					__A       =					self.get_tokenizer()
					__A       =					self.get_qformer_tokenizer()

					__A       =					InstructBlipProcessor(
					    tokenizer=A				,image_processor=A				,qformer_tokenizer=A					)

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

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

					self.assertListEqual(
					    list(inputs.keys()					)				,["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"]				,)

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






	def 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					self.get_image_processor()
					__A       =					self.get_tokenizer()
					__A       =					self.get_qformer_tokenizer()

					__A       =					InstructBlipProcessor(
					    tokenizer=A				,image_processor=A				,qformer_tokenizer=A					)

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

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

					self.assertListEqual(A				,A					)







	def 							UpperCamelCase_    (			self   :					List[Any]					):
					__A       =					self.get_image_processor()
					__A       =					self.get_tokenizer()
					__A       =					self.get_qformer_tokenizer()

					__A       =					InstructBlipProcessor(
					    tokenizer=A				,image_processor=A				,qformer_tokenizer=A					)

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

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

					self.assertListEqual(
					    list(inputs.keys()					)				,["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"]				,)








"""simple docstring"""



import numpy as np


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : np.ndarray			,					snake_case       : np.ndarray			,					snake_case       : float = 1E-1_2			,					snake_case       : int = 100			,					)->		tuple[float, np.ndarray]:






     '''simple docstring'''



     assert np.shape(snake_case				)[0] == np.shape(snake_case				)[1]
     # Ensure proper dimensionality.
     assert np.shape(snake_case				)[0] == np.shape(snake_case				)[0]
     # Ensure inputs are either both complex or both real
     assert np.iscomplexobj(snake_case				) == np.iscomplexobj(snake_case				)
     UpperCAmelCase__						:    str							=							np.iscomplexobj(snake_case				)
     if is_complex:
          # Ensure complex input_matrix is Hermitian
          assert np.array_equal(snake_case			,					input_matrix.conj().T				)

     # Set convergence to False. Will define convergence when we exceed max_iterations
     # or when we have small changes from one iteration to next.

     UpperCAmelCase__						:    Union[str, Any]							=							False
     UpperCAmelCase__						:    List[Any]							=							0
     UpperCAmelCase__						:    Any							=							0
     UpperCAmelCase__						:    List[str]							=							1E1_2

     while not convergence:
          # Multiple matrix by the vector.
          UpperCAmelCase__						:    Optional[Any]							=							np.dot(snake_case			,					snake_case				)
          # Normalize the resulting output vector.
          UpperCAmelCase__						:    Tuple							=							w / np.linalg.norm(snake_case				)
          # Find rayleigh quotient
          # (faster than usual b/c we know vector is normalized already)
          UpperCAmelCase__						:    Any							=							vector.conj().T if is_complex else vector.T
          UpperCAmelCase__						:    List[str]							=							np.dot(snake_case			,					np.dot(snake_case			,					snake_case				)				)

          # Check convergence.
          UpperCAmelCase__						:    Optional[Any]							=							np.abs(lambda_ - lambda_previous				) / lambda_
          iterations += 1

          if error <= error_tol or iterations >= max_iterations:
               UpperCAmelCase__						:    Tuple							=							True

          UpperCAmelCase__						:    Tuple							=							lambda_

     if is_complex:
          UpperCAmelCase__						:    str							=							np.real(lambda_				)

     return lambda_, vector


def 	SCREAMING_SNAKE_CASE__       (  )->		None:






     '''simple docstring'''



     UpperCAmelCase__						:    List[Any]							=							np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]				)
     UpperCAmelCase__						:    Dict							=							np.array([41, 4, 20]				)
     UpperCAmelCase__						:    Union[str, Any]							=							real_input_matrix.astype(np.complexaaa				)
     UpperCAmelCase__						:    Optional[int]							=							np.triu(1J * complex_input_matrix			,					1				)
     complex_input_matrix += imag_matrix
     complex_input_matrix += -1 * imag_matrix.T
     UpperCAmelCase__						:    int							=							np.array([41, 4, 20]				).astype(np.complexaaa				)

     for problem_type in ["real", "complex"]:
          if problem_type == "real":
               UpperCAmelCase__						:    List[str]							=							real_input_matrix
               UpperCAmelCase__						:    Any							=							real_vector
          elif problem_type == "complex":
               UpperCAmelCase__						:    str							=							complex_input_matrix
               UpperCAmelCase__						:    List[Any]							=							complex_vector

          # Our implementation.
          UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							power_iteration(snake_case			,					snake_case				)

          # Numpy implementation.

          # Get eigenvalues and eigenvectors using built-in numpy
          # eigh (eigh used for symmetric or hermetian matrices).
          UpperCAmelCase__							,				UpperCAmelCase__						:    List[Any]							=							np.linalg.eigh(snake_case				)
          # Last eigenvalue is the maximum one.
          UpperCAmelCase__						:    str							=							eigen_values[-1]
          # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
          UpperCAmelCase__						:    Optional[int]							=							eigen_vectors[:, -1]

          # Check our implementation and numpy gives close answers.
          assert np.abs(eigen_value - eigen_value_max				) <= 1E-6
          # Take absolute values element wise of each eigenvector.
          # as they are only unique to a minus sign.
          assert np.linalg.norm(np.abs(snake_case				) - np.abs(snake_case				)				) <= 1E-6


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 test_power_iteration()


