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 						lowercase    (     unittest.TestCase     ):




							def 	__snake_case(						self   :					int      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       tempfile.mkdtemp()

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

												SCREAMING_SNAKE_CASE    			=       InstructBlipProcessor(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )

												processor.save_pretrained(self.tmpdirname      )




							def 	__snake_case(						self   :					List[str]	,							**_UpperCamelCase   :					int      )					->				str:



												'''simple docstring'''

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




							def 	__snake_case(						self   :					List[str]	,							**_UpperCamelCase   :					List[str]      )					->				Optional[int]:



												'''simple docstring'''

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




							def 	__snake_case(						self   :					str	,							**_UpperCamelCase   :					int      )					->				int:



												'''simple docstring'''

												return AutoProcessor.from_pretrained(self.tmpdirname	,							**_UpperCamelCase      ).qformer_tokenizer




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



												'''simple docstring'''

												shutil.rmtree(self.tmpdirname      )




							def 	__snake_case(						self   :					Tuple      )					->				Optional[Any]:



												'''simple docstring'''

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

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

												return image_inputs




							def 	__snake_case(						self   :					Optional[int]      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       InstructBlipProcessor(
												    tokenizer=self.get_tokenizer()	,							image_processor=self.get_image_processor()	,							qformer_tokenizer=self.get_qformer_tokenizer()	,							)
												processor.save_pretrained(self.tmpdirname      )

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

												SCREAMING_SNAKE_CASE    			=       InstructBlipProcessor.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      )
												self.assertIsInstance(processor.qformer_tokenizer	,							_UpperCamelCase      )




							def 	__snake_case(						self   :					str      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_image_processor()
												SCREAMING_SNAKE_CASE    			=       self.get_tokenizer()
												SCREAMING_SNAKE_CASE    			=       self.get_qformer_tokenizer()

												SCREAMING_SNAKE_CASE    			=       InstructBlipProcessor(
												    tokenizer=_UpperCamelCase	,							image_processor=_UpperCamelCase	,							qformer_tokenizer=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       self.prepare_image_inputs()

												SCREAMING_SNAKE_CASE    			=       image_processor(_UpperCamelCase	,							return_tensors="np"      )
												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					Dict      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_image_processor()
												SCREAMING_SNAKE_CASE    			=       self.get_tokenizer()
												SCREAMING_SNAKE_CASE    			=       self.get_qformer_tokenizer()

												SCREAMING_SNAKE_CASE    			=       InstructBlipProcessor(
												    tokenizer=_UpperCamelCase	,							image_processor=_UpperCamelCase	,							qformer_tokenizer=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       "lower newer"

												SCREAMING_SNAKE_CASE    			=       processor(text=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       tokenizer(_UpperCamelCase	,							return_token_type_ids=_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       qformer_tokenizer(_UpperCamelCase	,							return_token_type_ids=_UpperCamelCase      )

												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 	__snake_case(						self   :					str      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_image_processor()
												SCREAMING_SNAKE_CASE    			=       self.get_tokenizer()
												SCREAMING_SNAKE_CASE    			=       self.get_qformer_tokenizer()

												SCREAMING_SNAKE_CASE    			=       InstructBlipProcessor(
												    tokenizer=_UpperCamelCase	,							image_processor=_UpperCamelCase	,							qformer_tokenizer=_UpperCamelCase      )

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

												SCREAMING_SNAKE_CASE    			=       processor(text=_UpperCamelCase	,							images=_UpperCamelCase      )

												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(_UpperCamelCase      ):
																	processor()




							def 	__snake_case(						self   :					Optional[int]      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_image_processor()
												SCREAMING_SNAKE_CASE    			=       self.get_tokenizer()
												SCREAMING_SNAKE_CASE    			=       self.get_qformer_tokenizer()

												SCREAMING_SNAKE_CASE    			=       InstructBlipProcessor(
												    tokenizer=_UpperCamelCase	,							image_processor=_UpperCamelCase	,							qformer_tokenizer=_UpperCamelCase      )

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

												SCREAMING_SNAKE_CASE    			=       processor.batch_decode(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       tokenizer.batch_decode(_UpperCamelCase      )

												self.assertListEqual(_UpperCamelCase	,							_UpperCamelCase      )





							def 	__snake_case(						self   :					Optional[int]      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_image_processor()
												SCREAMING_SNAKE_CASE    			=       self.get_tokenizer()
												SCREAMING_SNAKE_CASE    			=       self.get_qformer_tokenizer()

												SCREAMING_SNAKE_CASE    			=       InstructBlipProcessor(
												    tokenizer=_UpperCamelCase	,							image_processor=_UpperCamelCase	,							qformer_tokenizer=_UpperCamelCase      )

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

												SCREAMING_SNAKE_CASE    			=       processor(text=_UpperCamelCase	,							images=_UpperCamelCase      )

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





import numpy as np





def 	__lowerCamelCase							(UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							float = 1e-12      , UpperCAmelCase__     :							int = 1_0_0      , ):
					assert np.shape(UpperCAmelCase__  )[0] == np.shape(UpperCAmelCase__  )[1]
					# Ensure proper dimensionality.
					assert np.shape(UpperCAmelCase__  )[0] == np.shape(UpperCAmelCase__  )[0]
					# Ensure inputs are either both complex or both real
					assert np.iscomplexobj(UpperCAmelCase__  ) == np.iscomplexobj(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       np.iscomplexobj(UpperCAmelCase__  )
					if is_complex:
										# Ensure complex input_matrix is Hermitian
										assert np.array_equal(UpperCAmelCase__      , 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.

					SCREAMING_SNAKE_CASE    			=       False
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       1e12

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

										# Check convergence.
										SCREAMING_SNAKE_CASE    			=       np.abs(lambda_ - lambda_previous  ) / lambda_
										iterations += 1

										if error <= error_tol or iterations >= max_iterations:
															SCREAMING_SNAKE_CASE    			=       True

										SCREAMING_SNAKE_CASE    			=       lambda_

					if is_complex:
										SCREAMING_SNAKE_CASE    			=       np.real(lambda_  )

					return lambda_, vector





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]  )
					SCREAMING_SNAKE_CASE    			=       np.array([4_1, 4, 2_0]  )
					SCREAMING_SNAKE_CASE    			=       real_input_matrix.astype(np.complexaaa  )
					SCREAMING_SNAKE_CASE    			=       np.triu(1j * complex_input_matrix      , 1  )
					complex_input_matrix += imag_matrix
					complex_input_matrix += -1 * imag_matrix.T
					SCREAMING_SNAKE_CASE    			=       np.array([4_1, 4, 2_0]  ).astype(np.complexaaa  )

					for problem_type in ["real", "complex"]:
										if problem_type == "real":
															SCREAMING_SNAKE_CASE    			=       real_input_matrix
															SCREAMING_SNAKE_CASE    			=       real_vector
										elif problem_type == "complex":
															SCREAMING_SNAKE_CASE    			=       complex_input_matrix
															SCREAMING_SNAKE_CASE    			=       complex_vector

										# Our implementation.
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       power_iteration(UpperCAmelCase__      , UpperCAmelCase__  )

										# Numpy implementation.

										# Get eigenvalues and eigenvectors using built-in numpy
										# eigh (eigh used for symmetric or hermetian matrices).
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       np.linalg.eigh(UpperCAmelCase__  )
										# Last eigenvalue is the maximum one.
										SCREAMING_SNAKE_CASE    			=       eigen_values[-1]
										# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
										SCREAMING_SNAKE_CASE    			=       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(UpperCAmelCase__  ) - np.abs(UpperCAmelCase__  )  ) <= 1e-6


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_power_iteration()




