





"""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        __a  (  self )      ->					Any:
								a :						Optional[Any]      =							tempfile.mkdtemp()

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

								a :						Dict      =							BlipProcessor(lowerCAmelCase__ ,   lowerCAmelCase__ )

								processor.save_pretrained(self.tmpdirname )






							def        __a  (  self ,   **lowerCAmelCase__ )      ->					Tuple:
								return AutoProcessor.from_pretrained(self.tmpdirname ,   **lowerCAmelCase__ ).tokenizer






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






							def        __a  (  self )      ->					Optional[Any]:
								shutil.rmtree(self.tmpdirname )






							def        __a  (  self )      ->					Any:
								a :						Dict      =							[np.random.randint(255 ,   size=(3, 30, 400) ,   dtype=np.uinta )]

								a :						Tuple      =							[Image.fromarray(np.moveaxis(lowerCAmelCase__ ,   0 ,   -1 ) ) for x in image_inputs]

								return image_inputs






							def        __a  (  self )      ->					Dict:
								a :						str      =							BlipProcessor(tokenizer=self.get_tokenizer() ,   image_processor=self.get_image_processor() )
								processor.save_pretrained(self.tmpdirname )

								a :						Tuple      =							self.get_tokenizer(bos_token="(BOS)" ,   eos_token="(EOS)" )
								a :						List[Any]      =							self.get_image_processor(do_normalize=lowerCAmelCase__ ,   padding_value=1.0 )

								a :						List[str]      =							BlipProcessor.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        __a  (  self )      ->					Dict:
								a :						List[Any]      =							self.get_image_processor()
								a :						List[Any]      =							self.get_tokenizer()

								a :						Tuple      =							BlipProcessor(tokenizer=lowerCAmelCase__ ,   image_processor=lowerCAmelCase__ )

								a :						Union[str, Any]      =							self.prepare_image_inputs()

								a :						int      =							image_processor(lowerCAmelCase__ ,   return_tensors="np" )
								a :						List[str]      =							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        __a  (  self )      ->					Tuple:
								a :						Any      =							self.get_image_processor()
								a :						List[Any]      =							self.get_tokenizer()

								a :						Union[str, Any]      =							BlipProcessor(tokenizer=lowerCAmelCase__ ,   image_processor=lowerCAmelCase__ )

								a :						Optional[Any]      =							"lower newer"

								a :						int      =							processor(text=lowerCAmelCase__ )

								a :						Dict      =							tokenizer(lowerCAmelCase__ ,   return_token_type_ids=lowerCAmelCase__ )

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






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

								a :						Optional[Any]      =							BlipProcessor(tokenizer=lowerCAmelCase__ ,   image_processor=lowerCAmelCase__ )

								a :						Optional[Any]      =							"lower newer"
								a :						Any      =							self.prepare_image_inputs()

								a :						Dict      =							processor(text=lowerCAmelCase__ ,   images=lowerCAmelCase__ )

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

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






							def        __a  (  self )      ->					List[str]:
								a :						List[Any]      =							self.get_image_processor()
								a :						Any      =							self.get_tokenizer()

								a :						List[str]      =							BlipProcessor(tokenizer=lowerCAmelCase__ ,   image_processor=lowerCAmelCase__ )

								a :						Tuple      =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								a :						List[Any]      =							processor.batch_decode(lowerCAmelCase__ )
								a :						List[Any]      =							tokenizer.batch_decode(lowerCAmelCase__ )

								self.assertListEqual(lowerCAmelCase__ ,   lowerCAmelCase__ )






							def        __a  (  self )      ->					Tuple:
								a :						int      =							self.get_image_processor()
								a :						Tuple      =							self.get_tokenizer()

								a :						List[str]      =							BlipProcessor(tokenizer=lowerCAmelCase__ ,   image_processor=lowerCAmelCase__ )

								a :						List[Any]      =							"lower newer"
								a :						int      =							self.prepare_image_inputs()

								a :						int      =							processor(text=lowerCAmelCase__ ,   images=lowerCAmelCase__ )

								# 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"""




from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
a		:					int	      =     numpy.array([0, 0])
a		:					Optional[Any]	      =     numpy.array([0.5, 0.866_0254])
a		:					Tuple	      =     numpy.array([1, 0])
a		:					List[str]	      =     [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[numpy.ndarray]       ,			_lowercase		:       int   )					->list[numpy.ndarray]:






	'''simple docstring'''






	a :						List[str]      =							initial_vectors
	for _ in range(_lowercase   ):
		a :						Optional[Any]      =							iteration_step(_lowercase   )
	return vectors






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[numpy.ndarray]   )					->list[numpy.ndarray]:






	'''simple docstring'''






	a :						Union[str, Any]      =							[]
	for i, start_vector in enumerate(vectors[:-1]   ):
		a :						str      =							vectors[i + 1]
		new_vectors.append(_lowercase   )
		a :						Optional[int]      =							end_vector - start_vector
		new_vectors.append(start_vector + difference_vector / 3   )
		new_vectors.append(
		    start_vector + difference_vector / 3 + rotate(difference_vector / 3       ,			60   )   )
		new_vectors.append(start_vector + difference_vector * 2 / 3   )
	new_vectors.append(vectors[-1]   )
	return new_vectors






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       numpy.ndarray       ,			_lowercase		:       float   )					->numpy.ndarray:






	'''simple docstring'''






	a :						int      =							numpy.radians(_lowercase   )
	a,			a :						Optional[int]      =							numpy.cos(_lowercase   ), numpy.sin(_lowercase   )
	a :						Dict      =							numpy.array(((c, -s), (s, c))   )
	return numpy.dot(_lowercase       ,			_lowercase   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[numpy.ndarray]   )					->None:






	'''simple docstring'''






	a :						Dict      =							plt.gca()
	axes.set_aspect("equal"   )

	# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
	# y-coordinates as inputs, which are constructed from the vector-list using
	# zip()
	a,			a :						Any      =							zip(*_lowercase   )
	plt.plot(_lowercase       ,			_lowercase   )
	plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		a		:					Optional[int]	      =     iterate(INITIAL_VECTORS, 5)
		plot(processed_vectors)


