






import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							import jax.numpy as jnp

							from transformers.models.albert.modeling_flax_albert import (
							    FlaxAlbertForMaskedLM,
							    FlaxAlbertForMultipleChoice,
							    FlaxAlbertForPreTraining,
							    FlaxAlbertForQuestionAnswering,
							    FlaxAlbertForSequenceClassification,
							    FlaxAlbertForTokenClassification,
							    FlaxAlbertModel,
							)

class 				lowerCAmelCase_	(		unittest.TestCase			):


				def __init__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase=1_3 ,						_lowerCAmelCase=7 ,						_lowerCAmelCase=True ,						_lowerCAmelCase=True ,						_lowerCAmelCase=True ,						_lowerCAmelCase=True ,						_lowerCAmelCase=9_9 ,						_lowerCAmelCase=3_2 ,						_lowerCAmelCase=5 ,						_lowerCAmelCase=4 ,						_lowerCAmelCase=3_7 ,						_lowerCAmelCase="gelu" ,						_lowerCAmelCase=0.1 ,						_lowerCAmelCase=0.1 ,						_lowerCAmelCase=5_1_2 ,						_lowerCAmelCase=1_6 ,						_lowerCAmelCase=2 ,						_lowerCAmelCase=0.02 ,						_lowerCAmelCase=4 ,						):
											_lowercase							:   List[Any] 							=   parent
											_lowercase							:   Optional[int] 							=   batch_size
											_lowercase							:   Tuple 							=   seq_length
											_lowercase							:   Optional[Any] 							=   is_training
											_lowercase							:   List[Any] 							=   use_attention_mask
											_lowercase							:   Tuple 							=   use_token_type_ids
											_lowercase							:   Optional[int] 							=   use_labels
											_lowercase							:   Union[str, Any] 							=   vocab_size
											_lowercase							:   Optional[Any] 							=   hidden_size
											_lowercase							:   str 							=   num_hidden_layers
											_lowercase							:   List[Any] 							=   num_attention_heads
											_lowercase							:   str 							=   intermediate_size
											_lowercase							:   Optional[Any] 							=   hidden_act
											_lowercase							:   int 							=   hidden_dropout_prob
											_lowercase							:   Any 							=   attention_probs_dropout_prob
											_lowercase							:   Any 							=   max_position_embeddings
											_lowercase							:   Optional[int] 							=   type_vocab_size
											_lowercase							:   List[Any] 							=   type_sequence_label_size
											_lowercase							:   str 							=   initializer_range
											_lowercase							:   Union[str, Any] 							=   num_choices


				def 						__a					(					self      ):
											_lowercase							:   int 							=   ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size      )

											_lowercase							:   List[Any] 							=   None
											if self.use_attention_mask:
																		_lowercase							:   Dict 							=   random_attention_mask([self.batch_size, self.seq_length]      )

											_lowercase							:   Dict 							=   None
											if self.use_token_type_ids:
																		_lowercase							:   Dict 							=   ids_tensor([self.batch_size, self.seq_length] ,						self.type_vocab_size      )

											_lowercase							:   List[str] 							=   AlbertConfig(
											    vocab_size=self.vocab_size ,						hidden_size=self.hidden_size ,						num_hidden_layers=self.num_hidden_layers ,						num_attention_heads=self.num_attention_heads ,						intermediate_size=self.intermediate_size ,						hidden_act=self.hidden_act ,						hidden_dropout_prob=self.hidden_dropout_prob ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob ,						max_position_embeddings=self.max_position_embeddings ,						type_vocab_size=self.type_vocab_size ,						is_decoder=_lowerCAmelCase ,						initializer_range=self.initializer_range ,						)

											return config, input_ids, token_type_ids, attention_mask


				def 						__a					(					self      ):
											_lowercase							:   Union[str, Any] 							=   self.prepare_config_and_inputs()
											_lowercase							,							_lowercase							,							_lowercase							,							_lowercase							:   List[str] 							=   config_and_inputs
											_lowercase							:   Any 							=   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
											return config, inputs_dict




@require_flax
class 				lowerCAmelCase_	(		__lowerCamelCase   ,		unittest.TestCase			):
				_UpperCamelCase					:       Tuple							       = (
				    (
				        FlaxAlbertModel,
				        FlaxAlbertForPreTraining,
				        FlaxAlbertForMaskedLM,
				        FlaxAlbertForMultipleChoice,
				        FlaxAlbertForQuestionAnswering,
				        FlaxAlbertForSequenceClassification,
				        FlaxAlbertForTokenClassification,
				        FlaxAlbertForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)


				def 						__a					(					self      ):
											_lowercase							:   Union[str, Any] 							=   FlaxAlbertModelTester(self      )


				@slow
				def 						__a					(					self      ):






											for model_class_name in self.all_model_classes:
																		_lowercase							:   List[Any] 							=   model_class_name.from_pretrained('albert-base-v2'      )
																		_lowercase							:   Optional[int] 							=   model(np.ones((1, 1)      )      )
																		self.assertIsNotNone(_lowerCAmelCase      )




@require_flax
class 				lowerCAmelCase_	(		unittest.TestCase			):


				@slow
				def 						__a					(					self      ):
											_lowercase							:   str 							=   FlaxAlbertModel.from_pretrained('albert-base-v2'      )
											_lowercase							:   str 							=   np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]      )
											_lowercase							:   List[Any] 							=   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
											_lowercase							:   Optional[int] 							=   model(_lowerCAmelCase ,						attention_mask=_lowerCAmelCase      )[0]
											_lowercase							:   Union[str, Any] 							=   (1, 1_1, 7_6_8)
											self.assertEqual(output.shape ,						_lowerCAmelCase      )
											_lowercase							:   Dict 							=   np.array(
											    [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]      )

											self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] ,						_lowerCAmelCase ,						atol=1E-4      )      )






from PIL import Image


def    a      ( a	)     ->Image:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= image.size
			SCREAMING_SNAKE_CASE     						= 0
			SCREAMING_SNAKE_CASE     						= image.load()
			for i in range(a	):
						for j in range(a	):
									SCREAMING_SNAKE_CASE     						= pixels[j, i]
									mean += pixel
			mean //= width * height

			for j in range(a	):
						for i in range(a	):
									SCREAMING_SNAKE_CASE     						= 255 if pixels[i, j] > mean else 0
			return image


if __name__ == "__main__":
							__lowerCAmelCase					   =       mean_threshold(Image.open('path_to_image').convert('L'))
							image.save('output_image_path')