"""simple docstring"""







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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast

@require_vision
class 		UpperCamelCase      (   unittest.TestCase						):



				def  _lowercase					(self    :				List[Any])   ->     int:
					__snake_case      :							Optional[Any]						=			tempfile.mkdtemp()

					__snake_case      :							int						=			BlipImageProcessor()
					__snake_case      :							List[str]						=			BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel')

					__snake_case      :							Tuple						=			BlipProcessor(_A     ,  _A)

					processor.save_pretrained(self.tmpdirname)



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



				def  _lowercase					(self    :				Any     ,  **_A    :				List[Any])   ->     List[Any]:
					return AutoProcessor.from_pretrained(self.tmpdirname     ,  **_A).image_processor



				def  _lowercase					(self    :				List[str])   ->     Optional[Any]:
					shutil.rmtree(self.tmpdirname)



				def  _lowercase					(self    :				Tuple)   ->     Union[str, Any]:
					__snake_case      :							List[Any]						=			[np.random.randint(2_55     ,  size=(3, 30, 4_00)     ,  dtype=np.uinta)]

					__snake_case      :							str						=			[Image.fromarray(np.moveaxis(_A     ,  0     ,  -1)) for x in image_inputs]

					return image_inputs



				def  _lowercase					(self    :				int)   ->     Dict:
					__snake_case      :							List[str]						=			BlipProcessor(tokenizer=self.get_tokenizer()     ,  image_processor=self.get_image_processor())
					processor.save_pretrained(self.tmpdirname)

					__snake_case      :							str						=			self.get_tokenizer(bos_token='(BOS)'     ,  eos_token='(EOS)')
					__snake_case      :							Optional[Any]						=			self.get_image_processor(do_normalize=_A     ,  padding_value=1.0)

					__snake_case      :							int						=			BlipProcessor.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)



				def  _lowercase					(self    :				List[Any])   ->     Dict:
					__snake_case      :							Optional[Any]						=			self.get_image_processor()
					__snake_case      :							Tuple						=			self.get_tokenizer()

					__snake_case      :							Optional[int]						=			BlipProcessor(tokenizer=_A     ,  image_processor=_A)

					__snake_case      :							Tuple						=			self.prepare_image_inputs()

					__snake_case      :							Dict						=			image_processor(_A     ,  return_tensors='np')
					__snake_case      :							str						=			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  _lowercase					(self    :				Any)   ->     Optional[int]:
					__snake_case      :							Union[str, Any]						=			self.get_image_processor()
					__snake_case      :							Union[str, Any]						=			self.get_tokenizer()

					__snake_case      :							int						=			BlipProcessor(tokenizer=_A     ,  image_processor=_A)

					__snake_case      :							int						=			'lower newer'

					__snake_case      :							List[Any]						=			processor(text=_A)

					__snake_case      :							Optional[int]						=			tokenizer(_A     ,  return_token_type_ids=_A)

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



				def  _lowercase					(self    :				Optional[Any])   ->     Optional[int]:
					__snake_case      :							Dict						=			self.get_image_processor()
					__snake_case      :							List[Any]						=			self.get_tokenizer()

					__snake_case      :							str						=			BlipProcessor(tokenizer=_A     ,  image_processor=_A)

					__snake_case      :							List[str]						=			'lower newer'
					__snake_case      :							Any						=			self.prepare_image_inputs()

					__snake_case      :							Any						=			processor(text=_A     ,  images=_A)

					self.assertListEqual(list(inputs.keys())     ,  ['pixel_values', 'input_ids', 'attention_mask'])

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



				def  _lowercase					(self    :				List[str])   ->     Union[str, Any]:
					__snake_case      :							Optional[Any]						=			self.get_image_processor()
					__snake_case      :							int						=			self.get_tokenizer()

					__snake_case      :							Union[str, Any]						=			BlipProcessor(tokenizer=_A     ,  image_processor=_A)

					__snake_case      :							Optional[Any]						=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__snake_case      :							int						=			processor.batch_decode(_A)
					__snake_case      :							List[Any]						=			tokenizer.batch_decode(_A)

					self.assertListEqual(_A     ,  _A)






				def  _lowercase					(self    :				List[Any])   ->     Tuple:
					__snake_case      :							str						=			self.get_image_processor()
					__snake_case      :							List[str]						=			self.get_tokenizer()

					__snake_case      :							Optional[Any]						=			BlipProcessor(tokenizer=_A     ,  image_processor=_A)

					__snake_case      :							Optional[int]						=			'lower newer'
					__snake_case      :							Union[str, Any]						=			self.prepare_image_inputs()

					__snake_case      :							int						=			processor(text=_A     ,  images=_A)

					# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
					self.assertListEqual(list(inputs.keys())     ,  ['pixel_values', 'input_ids', 'attention_mask'])






"""simple docstring"""







import numpy

# List of input, output pairs
_a					:					Optional[int]=			(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_a					:					int=			(((515, 22, 13), 555), ((61, 35, 49), 150))
_a					:					List[Any]=			[2, 4, 1, 5]
_a					:					Optional[int]=			len(train_data)
_a					:					str=			0.0_0_9
def   __UpperCAmelCase    (   UpperCAmelCase_   :    List[Any]      ,							UpperCAmelCase_   :    List[str]="train"  )							->      int:

	'''simple docstring'''





	return calculate_hypothesis_value(UpperCAmelCase_      ,							UpperCAmelCase_  ) - output(
	    UpperCAmelCase_      ,							UpperCAmelCase_  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Tuple  )							->      Dict:

	'''simple docstring'''





	__snake_case      :							int						=			0
	for i in range(len(UpperCAmelCase_  ) - 1  ):
		hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
	hyp_val += parameter_vector[0]
	return hyp_val
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    List[Any]  )							->      List[Any]:

	'''simple docstring'''





	if data_set == "train":
		return train_data[example_no][1]
	elif data_set == "test":
		return test_data[example_no][1]
	return None
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[Any]      ,							UpperCAmelCase_   :    Optional[int]  )							->      Any:

	'''simple docstring'''





	if data_set == "train":
		return _hypothesis_value(train_data[example_no][0]  )
	elif data_set == "test":
		return _hypothesis_value(test_data[example_no][0]  )
	return None
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Union[str, Any]      ,							UpperCAmelCase_   :    Any=m  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							Union[str, Any]						=			0
	for i in range(UpperCAmelCase_  ):
		if index == -1:
			summation_value += _error(UpperCAmelCase_  )
		else:
			summation_value += _error(UpperCAmelCase_  ) * train_data[i][0][index]
	return summation_value
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int  )							->      str:

	'''simple docstring'''





	__snake_case      :							Dict						=			summation_of_cost_derivative(UpperCAmelCase_      ,							UpperCAmelCase_  ) / m
	return cost_derivative_value
def   __UpperCAmelCase    (   )							->      List[str]:

	'''simple docstring'''





	global parameter_vector
	# Tune these values to set a tolerance value for predicted output
	__snake_case      :							Dict						=			0.000_002
	__snake_case      :							Optional[int]						=			0
	__snake_case      :							str						=			0
	while True:
		j += 1
		__snake_case      :							Optional[int]						=			[0, 0, 0, 0]
		for i in range(0      ,							len(UpperCAmelCase_  )  ):
			__snake_case      :							Union[str, Any]						=			get_cost_derivative(i - 1  )
			__snake_case      :							Any						=			(
			    parameter_vector[i] - LEARNING_RATE * cost_derivative
			)
		if numpy.allclose(
		    UpperCAmelCase_      ,							UpperCAmelCase_      ,							atol=UpperCAmelCase_      ,							rtol=UpperCAmelCase_      ,							):
			break
		__snake_case      :							Optional[Any]						=			temp_parameter_vector
	print(('Number of iterations:', j)  )
def   __UpperCAmelCase    (   )							->      int:

	'''simple docstring'''





	for i in range(len(UpperCAmelCase_  )  ):
		print(('Actual output value:', output(UpperCAmelCase_      ,							'test'  ))  )
		print(('Hypothesis output:', calculate_hypothesis_value(UpperCAmelCase_      ,							'test'  ))  )


if __name__ == "__main__":
	run_gradient_descent()
	print("\nTesting gradient descent for a linear hypothesis function.\n")
	test_gradient_descent()





