



import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax
	import jax.numpy as jnp
	import numpy as np

	from transformers.modeling_flax_pytorch_utils import (
	    convert_pytorch_state_dict_to_flax,
	    load_flax_weights_in_pytorch_model,
	)
	from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
	import torch





@require_flax
class       __lowerCAmelCase				:




				def __init__(							self		,       _snake_case		,       _snake_case=14		,       _snake_case=7		,       _snake_case=True		,       _snake_case=True		,       _snake_case=True		,       _snake_case=99		,       _snake_case=32		,       _snake_case=5		,       _snake_case=4		,       _snake_case=37		,       _snake_case="gelu"		,       _snake_case=0.1		,       _snake_case=0.1		,       _snake_case=512		,       _snake_case=0.02		,       _snake_case=None		,       ):



									"""simple docstring"""





									_lowerCAmelCase      			=					parent
									_lowerCAmelCase      			=					batch_size
									_lowerCAmelCase      			=					seq_length
									_lowerCAmelCase      			=					is_training
									_lowerCAmelCase      			=					use_input_mask
									_lowerCAmelCase      			=					use_labels
									_lowerCAmelCase      			=					vocab_size
									_lowerCAmelCase      			=					d_model
									_lowerCAmelCase      			=					num_hidden_layers
									_lowerCAmelCase      			=					num_attention_heads
									_lowerCAmelCase      			=					ffn_dim
									_lowerCAmelCase      			=					activation_function
									_lowerCAmelCase      			=					activation_dropout
									_lowerCAmelCase      			=					attention_dropout
									_lowerCAmelCase      			=					max_position_embeddings
									_lowerCAmelCase      			=					initializer_range
									_lowerCAmelCase      			=					None
									_lowerCAmelCase      			=					0
									_lowerCAmelCase      			=					2
									_lowerCAmelCase      			=					1




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					np.clip(ids_tensor([self.batch_size, self.seq_length]		,       self.vocab_size	)		,       3		,       self.vocab_size	)

									_lowerCAmelCase      			=					None
									if self.use_input_mask:
														_lowerCAmelCase      			=					random_attention_mask([self.batch_size, self.seq_length]	)

									_lowerCAmelCase      			=					XGLMConfig(
									    vocab_size=self.vocab_size		,       d_model=self.hidden_size		,       num_layers=self.num_hidden_layers		,       attention_heads=self.num_attention_heads		,       ffn_dim=self.ffn_dim		,       activation_function=self.activation_function		,       activation_dropout=self.activation_dropout		,       attention_dropout=self.attention_dropout		,       max_position_embeddings=self.max_position_embeddings		,       initializer_range=self.initializer_range		,       use_cache=_snake_case		,       bos_token_id=self.bos_token_id		,       eos_token_id=self.eos_token_id		,       pad_token_id=self.pad_token_id		,       )

									return (config, input_ids, input_mask)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.prepare_config_and_inputs()
									_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase      			=					config_and_inputs
									_lowerCAmelCase      			=					{"""input_ids""": input_ids, """attention_mask""": attention_mask}
									return config, inputs_dict




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase      			=					self.prepare_config_and_inputs()

									_lowerCAmelCase      			=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
									_lowerCAmelCase      			=					ids_tensor([self.batch_size, self.seq_length]		,       vocab_size=2	)

									return (
									    config,
									    input_ids,
									    attention_mask,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)




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



									"""simple docstring"""





									_lowerCAmelCase      			=					20
									_lowerCAmelCase      			=					model_class_name(_snake_case	)

									_lowerCAmelCase      			=					model.init_cache(input_ids.shape[0]		,       _snake_case	)
									_lowerCAmelCase      			=					jnp.ones((input_ids.shape[0], max_decoder_length)		,       dtype="""i4"""	)

									_lowerCAmelCase      			=					jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1	)[None, :]		,       (input_ids.shape[0], input_ids.shape[-1] - 1)	)
									_lowerCAmelCase      			=					model(
									    input_ids[:, :-1]		,       attention_mask=_snake_case		,       past_key_values=_snake_case		,       position_ids=_snake_case		,       )

									_lowerCAmelCase      			=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,       dtype="""i4"""	)
									_lowerCAmelCase      			=					model(
									    input_ids[:, -1:]		,       attention_mask=_snake_case		,       past_key_values=outputs_cache.past_key_values		,       position_ids=_snake_case		,       )

									_lowerCAmelCase      			=					model(_snake_case	)

									_lowerCAmelCase      			=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
									self.parent.assertTrue(diff < 1e-3		,       msg=F'Max diff is {diff}'	)




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



									"""simple docstring"""





									_lowerCAmelCase      			=					20
									_lowerCAmelCase      			=					model_class_name(_snake_case	)

									_lowerCAmelCase      			=					jnp.concatenate(
									    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])	)]		,       axis=-1		,       )

									_lowerCAmelCase      			=					model.init_cache(input_ids.shape[0]		,       _snake_case	)
									_lowerCAmelCase      			=					jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1	)[None, :]		,       (input_ids.shape[0], input_ids.shape[-1] - 1)	)

									_lowerCAmelCase      			=					model(
									    input_ids[:, :-1]		,       attention_mask=_snake_case		,       past_key_values=_snake_case		,       position_ids=_snake_case		,       )
									_lowerCAmelCase      			=					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,       dtype="""i4"""	)
									_lowerCAmelCase      			=					model(
									    input_ids[:, -1:]		,       past_key_values=outputs_cache.past_key_values		,       attention_mask=_snake_case		,       position_ids=_snake_case		,       )

									_lowerCAmelCase      			=					model(_snake_case		,       attention_mask=_snake_case	)
									_lowerCAmelCase      			=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
									self.parent.assertTrue(diff < 1e-3		,       msg=F'Max diff is {diff}'	)





@require_sentencepiece
@require_flax
class       __lowerCAmelCase				(  lowerCamelCase__ ,		lowerCamelCase__ ,		unittest.TestCase ):
				__lowerCamelCase				=       (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
				__lowerCamelCase				=       (FlaxXGLMForCausalLM,) if is_flax_available() else ()




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					FlaxXGLMModelTester(self	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									for model_class_name in self.all_model_classes:
														_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase      			=					self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward(_snake_case		,       _snake_case		,       _snake_case		,       _snake_case	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									for model_class_name in self.all_model_classes:
														_lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase      			=					self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward_with_attn_mask(
														    _snake_case		,       _snake_case		,       _snake_case		,       _snake_case	)




				@slow
				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					XGLMTokenizer.from_pretrained("""XGLM"""		,       padding_side="""left"""	)
									_lowerCAmelCase      			=					tokenizer(["""Hello this is a long string""", """Hey"""]		,       return_tensors="""np"""		,       padding=_snake_case		,       truncation=_snake_case	)

									_lowerCAmelCase      			=					FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""	)
									_lowerCAmelCase      			=					1
									_lowerCAmelCase      			=					False

									_lowerCAmelCase      			=					jax.jit(model.generate	)

									_lowerCAmelCase      			=					jit_generate(inputs["""input_ids"""]		,       attention_mask=inputs["""attention_mask"""]	).sequences

									_lowerCAmelCase      			=					tokenizer.batch_decode(_snake_case		,       skip_special_tokens=_snake_case	)

									_lowerCAmelCase      			=					[
									    """Hello this is a long string of questions, but I'm not sure if I'm""",
									    """Hey, I'm a newbie to the forum and I'""",
									]

									self.assertListEqual(_snake_case		,       _snake_case	)




				@is_pt_flax_cross_test
				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase			,			_lowerCAmelCase      			=					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__	):
																			# prepare inputs
																			_lowerCAmelCase      			=					self._prepare_for_class(_snake_case		,       _snake_case	)
																			_lowerCAmelCase      			=					{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

																			# load corresponding PyTorch class
																			_lowerCAmelCase      			=					model_class.__name__[4:]  # Skip the "Flax" at the beginning
																			_lowerCAmelCase      			=					getattr(_snake_case		,       _snake_case	)

																			_lowerCAmelCase			,			_lowerCAmelCase      			=					pt_inputs["""input_ids"""].shape
																			_lowerCAmelCase      			=					np.random.randint(0		,       seq_length - 1		,       size=(batch_size,)	)
																			for batch_idx, start_index in enumerate(_snake_case	):
																								_lowerCAmelCase      			=					0
																								_lowerCAmelCase      			=					1
																								_lowerCAmelCase      			=					0
																								_lowerCAmelCase      			=					1
																			_lowerCAmelCase      			=					pt_model_class(_snake_case	).eval()
																			# Flax models don't use the `use_cache` option and cache is not returned as a default.
																			# So we disable `use_cache` here for PyTorch model.
																			_lowerCAmelCase      			=					False
																			_lowerCAmelCase      			=					model_class(_snake_case		,       dtype=jnp.floataa	)
																			_lowerCAmelCase      			=					convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,       _snake_case	)
																			_lowerCAmelCase      			=					fx_state

																			with torch.no_grad():
																								_lowerCAmelCase      			=					pt_model(**_snake_case	).to_tuple()

																			_lowerCAmelCase      			=					fx_model(**_snake_case	).to_tuple()
																			self.assertEqual(len(_snake_case	)		,       len(_snake_case	)		,       """Output lengths differ between Flax and PyTorch"""	)
																			for fx_output, pt_output in zip(_snake_case		,       _snake_case	):
																								self.assert_almost_equals(fx_output[:, -1]		,       pt_output[:, -1].numpy()		,       4e-2	)

																			with tempfile.TemporaryDirectory() as tmpdirname:
																								pt_model.save_pretrained(_snake_case	)
																								_lowerCAmelCase      			=					model_class.from_pretrained(_snake_case		,       from_pt=_snake_case	)

																			_lowerCAmelCase      			=					fx_model_loaded(**_snake_case	).to_tuple()
																			self.assertEqual(
																			    len(_snake_case	)		,       len(_snake_case	)		,       """Output lengths differ between Flax and PyTorch"""	)
																			for fx_output_loaded, pt_output in zip(_snake_case		,       _snake_case	):
																								self.assert_almost_equals(fx_output_loaded[:, -1]		,       pt_output[:, -1].numpy()		,       4e-2	)




				@is_pt_flax_cross_test
				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase			,			_lowerCAmelCase      			=					self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__	):
																			# prepare inputs
																			_lowerCAmelCase      			=					self._prepare_for_class(_snake_case		,       _snake_case	)
																			_lowerCAmelCase      			=					{k: torch.tensor(v.tolist()	) for k, v in prepared_inputs_dict.items()}

																			# load corresponding PyTorch class
																			_lowerCAmelCase      			=					model_class.__name__[4:]  # Skip the "Flax" at the beginning
																			_lowerCAmelCase      			=					getattr(_snake_case		,       _snake_case	)

																			_lowerCAmelCase      			=					pt_model_class(_snake_case	).eval()
																			_lowerCAmelCase      			=					False
																			_lowerCAmelCase      			=					model_class(_snake_case		,       dtype=jnp.floataa	)

																			_lowerCAmelCase      			=					load_flax_weights_in_pytorch_model(_snake_case		,       fx_model.params	)
																			_lowerCAmelCase			,			_lowerCAmelCase      			=					pt_inputs["""input_ids"""].shape
																			_lowerCAmelCase      			=					np.random.randint(0		,       seq_length - 1		,       size=(batch_size,)	)
																			for batch_idx, start_index in enumerate(_snake_case	):
																								_lowerCAmelCase      			=					0
																								_lowerCAmelCase      			=					1
																								_lowerCAmelCase      			=					0
																								_lowerCAmelCase      			=					1

																			# make sure weights are tied in PyTorch
																			pt_model.tie_weights()

																			with torch.no_grad():
																								_lowerCAmelCase      			=					pt_model(**_snake_case	).to_tuple()

																			_lowerCAmelCase      			=					fx_model(**_snake_case	).to_tuple()
																			self.assertEqual(len(_snake_case	)		,       len(_snake_case	)		,       """Output lengths differ between Flax and PyTorch"""	)
																			for fx_output, pt_output in zip(_snake_case		,       _snake_case	):
																								self.assert_almost_equals(fx_output[:, -1]		,       pt_output[:, -1].numpy()		,       4e-2	)

																			with tempfile.TemporaryDirectory() as tmpdirname:
																								fx_model.save_pretrained(_snake_case	)
																								_lowerCAmelCase      			=					pt_model_class.from_pretrained(_snake_case		,       from_flax=_snake_case	)

																			with torch.no_grad():
																								_lowerCAmelCase      			=					pt_model_loaded(**_snake_case	).to_tuple()

																			self.assertEqual(
																			    len(_snake_case	)		,       len(_snake_case	)		,       """Output lengths differ between Flax and PyTorch"""	)
																			for fx_output, pt_output in zip(_snake_case		,       _snake_case	):
																								self.assert_almost_equals(fx_output[:, -1]		,       pt_output[:, -1].numpy()		,       4e-2	)




				@slow
				def 							snake_case     (							self	):



									"""simple docstring"""





									for model_class_name in self.all_model_classes:
														_lowerCAmelCase      			=					model_class_name.from_pretrained("""facebook/xglm-564M"""	)
														_lowerCAmelCase      			=					model(np.ones((1, 1)	)	)
														self.assertIsNotNone(_snake_case	)








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()



