



import cva
import numpy as np


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

					try:
										return int(image[x_coordinate][y_coordinate] >= center    )
					except (IndexError, TypeError):
										return 0


def 	_UpperCAmelCase       (   snake_case			,		snake_case			,		snake_case    ):





					"""simple docstring"""

					_lowerCAmelCase      			=					image[x_coordinate][y_coordinate]
					_lowerCAmelCase      			=					[1, 2, 4, 8, 16, 32, 64, 1_28]

					# skip get_neighbors_pixel if center is null
					if center is None:
										return 0

					# Starting from the top right, assigning value to pixels clockwise
					_lowerCAmelCase      			=					[
					    get_neighbors_pixel(snake_case			,		x_coordinate - 1			,		y_coordinate + 1			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		snake_case			,		y_coordinate + 1			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		x_coordinate - 1			,		snake_case			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		x_coordinate + 1			,		y_coordinate + 1			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		x_coordinate + 1			,		snake_case			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		x_coordinate + 1			,		y_coordinate - 1			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		snake_case			,		y_coordinate - 1			,		snake_case    ),
					    get_neighbors_pixel(snake_case			,		x_coordinate - 1			,		y_coordinate - 1			,		snake_case    ),
					]

					# Converting the binary value to decimal.
					return sum(
					    binary_value * power for binary_value, power in zip(snake_case			,		snake_case    )    )


if __name__ == "__main__":
	# Reading the image and converting it to grayscale.
	A__			   = cva.imread(
	    """digital_image_processing/image_data/lena.jpg""", cva.IMREAD_GRAYSCALE
	)

	# Create a numpy array as the same height and width of read image
	A__			   = np.zeros((image.shape[0], image.shape[1]))

	# Iterating through the image and calculating the
	# local binary pattern value for each pixel.
	for i in range(0, image.shape[0]):
		for j in range(0, image.shape[1]):
			A__			   = local_binary_value(image, i, j)

	cva.imshow("""local binary pattern""", lbp_image)
	cva.waitKey(0)
	cva.destroyAllWindows()








import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
	    SpeechaTextaDecoder,
	    SpeechaTextaForCausalLM,
	)





@require_torch
class       __lowerCAmelCase				:




				def __init__(							self		,       _snake_case		,       _snake_case=99		,       _snake_case=13		,       _snake_case=16		,       _snake_case=7		,       _snake_case=True		,       _snake_case=True		,       _snake_case=True		,       _snake_case=False		,       _snake_case=True		,       _snake_case=2		,       _snake_case=32		,       _snake_case=4		,       _snake_case=4		,       _snake_case=30		,       _snake_case=0		,       _snake_case=1		,       _snake_case=2		,       _snake_case=None		,       ):



									"""simple docstring"""





									_lowerCAmelCase      			=					parent
									_lowerCAmelCase      			=					batch_size
									_lowerCAmelCase      			=					decoder_seq_length
									# For common tests
									_lowerCAmelCase      			=					self.decoder_seq_length
									_lowerCAmelCase      			=					is_training
									_lowerCAmelCase      			=					use_attention_mask
									_lowerCAmelCase      			=					use_labels

									_lowerCAmelCase      			=					vocab_size
									_lowerCAmelCase      			=					d_model
									_lowerCAmelCase      			=					d_model
									_lowerCAmelCase      			=					decoder_layers
									_lowerCAmelCase      			=					decoder_layers
									_lowerCAmelCase      			=					decoder_ffn_dim
									_lowerCAmelCase      			=					decoder_attention_heads
									_lowerCAmelCase      			=					decoder_attention_heads
									_lowerCAmelCase      			=					eos_token_id
									_lowerCAmelCase      			=					bos_token_id
									_lowerCAmelCase      			=					pad_token_id
									_lowerCAmelCase      			=					decoder_start_token_id
									_lowerCAmelCase      			=					use_cache
									_lowerCAmelCase      			=					max_position_embeddings

									_lowerCAmelCase      			=					None
									_lowerCAmelCase      			=					decoder_seq_length
									_lowerCAmelCase      			=					2
									_lowerCAmelCase      			=					1




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					ids_tensor([self.batch_size, self.decoder_seq_length]		,       self.vocab_size	)

									_lowerCAmelCase      			=					None
									if self.use_attention_mask:
														_lowerCAmelCase      			=					ids_tensor([self.batch_size, self.decoder_seq_length]		,       vocab_size=2	)

									_lowerCAmelCase      			=					None
									if self.use_labels:
														_lowerCAmelCase      			=					ids_tensor([self.batch_size, self.decoder_seq_length]		,       self.vocab_size	)

									_lowerCAmelCase      			=					SpeechaTextaConfig(
									    vocab_size=self.vocab_size		,       d_model=self.d_model		,       decoder_layers=self.decoder_layers		,       decoder_ffn_dim=self.decoder_ffn_dim		,       decoder_attention_heads=self.decoder_attention_heads		,       eos_token_id=self.eos_token_id		,       bos_token_id=self.bos_token_id		,       use_cache=self.use_cache		,       pad_token_id=self.pad_token_id		,       decoder_start_token_id=self.decoder_start_token_id		,       max_position_embeddings=self.max_position_embeddings		,       )

									return (
									    config,
									    input_ids,
									    attention_mask,
									    lm_labels,
									)




				def 							snake_case     (							self		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       ):



									"""simple docstring"""





									_lowerCAmelCase      			=					True
									_lowerCAmelCase      			=					SpeechaTextaDecoder(config=_snake_case	).to(_snake_case	).eval()
									_lowerCAmelCase      			=					input_ids[:2]

									input_ids[input_ids == 0] += 1
									# first forward pass
									_lowerCAmelCase      			=					model(_snake_case		,       use_cache=_snake_case	)
									_lowerCAmelCase      			=					model(_snake_case	)
									_lowerCAmelCase      			=					model(_snake_case		,       use_cache=_snake_case	)

									self.parent.assertTrue(len(_snake_case	) == len(_snake_case	)	)
									self.parent.assertTrue(len(_snake_case	) == len(_snake_case	) + 1	)

									_lowerCAmelCase      			=					outputs["""past_key_values"""]

									# create hypothetical next token and extent to next_input_ids
									_lowerCAmelCase      			=					ids_tensor((2, 1)		,       config.vocab_size - 1	) + 1

									# append to next input_ids and
									_lowerCAmelCase      			=					torch.cat([input_ids, next_tokens]		,       dim=-1	)

									print(_snake_case	)

									_lowerCAmelCase      			=					model(_snake_case	)["""last_hidden_state"""]
									_lowerCAmelCase      			=					model(_snake_case		,       past_key_values=_snake_case	)["""last_hidden_state"""]

									# select random slice
									_lowerCAmelCase      			=					ids_tensor((1,)		,       output_from_past.shape[-1]	).item()
									_lowerCAmelCase      			=					output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
									_lowerCAmelCase      			=					output_from_past[:, 0, random_slice_idx].detach()

									# test that outputs are equal for slice
									assert torch.allclose(_snake_case		,       _snake_case		,       atol=1e-3	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.prepare_config_and_inputs()
									(
									    (
									    _lowerCAmelCase
									)			,			(
									    _lowerCAmelCase
									)			,			(
									    _lowerCAmelCase
									)			,			(
									    _lowerCAmelCase
									)			,			
									)      			=					config_and_inputs

									_lowerCAmelCase      			=					{
									    """input_ids""": input_ids,
									    """attention_mask""": attention_mask,
									}
									return config, inputs_dict





@require_torch
class       __lowerCAmelCase				(  lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		unittest.TestCase ):
				__lowerCamelCase				=       (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
				__lowerCamelCase				=       (SpeechaTextaForCausalLM,) if is_torch_available() else ()
				__lowerCamelCase				=       {'''text-generation''': SpeechaTextaForCausalLM} if is_torch_available() else {}
				__lowerCamelCase				=       True
				__lowerCamelCase				=       False




				def 							snake_case     (							self		,       ):



									"""simple docstring"""





									_lowerCAmelCase      			=					SpeechaTextaStandaloneDecoderModelTester(self		,       is_training=_snake_case	)
									_lowerCAmelCase      			=					ConfigTester(self		,       config_class=_snake_case	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									pass




				def 							snake_case     (							self	):



									"""simple docstring"""





									pass




				def 							snake_case     (							self	):



									"""simple docstring"""





									pass




				def 							snake_case     (							self	):



									"""simple docstring"""





									self.config_tester.run_common_tests()




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past(*_snake_case	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									return



