



import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
						from transformers.models.bert.modeling_flax_bert import (
						    FlaxBertForMaskedLM,
						    FlaxBertForMultipleChoice,
						    FlaxBertForNextSentencePrediction,
						    FlaxBertForPreTraining,
						    FlaxBertForQuestionAnswering,
						    FlaxBertForSequenceClassification,
						    FlaxBertForTokenClassification,
						    FlaxBertModel,
						)



class   UpperCAmelCase_    (       unittest.TestCase):







							def __init__(   self,			__a,			__a=13,			__a=7,			__a=True,			__a=True,			__a=True,			__a=True,			__a=99,			__a=32,			__a=5,			__a=4,			__a=37,			__a="gelu",			__a=0.1,			__a=0.1,			__a=512,			__a=16,			__a=2,			__a=0.02,			__a=4,			):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    parent
														_lowerCAmelCase     :							Dict    =    batch_size
														_lowerCAmelCase     :							Optional[Any]    =    seq_length
														_lowerCAmelCase     :							Optional[int]    =    is_training
														_lowerCAmelCase     :							Dict    =    use_attention_mask
														_lowerCAmelCase     :							List[str]    =    use_token_type_ids
														_lowerCAmelCase     :							Union[str, Any]    =    use_labels
														_lowerCAmelCase     :							List[Any]    =    vocab_size
														_lowerCAmelCase     :							Union[str, Any]    =    hidden_size
														_lowerCAmelCase     :							str    =    num_hidden_layers
														_lowerCAmelCase     :							Optional[int]    =    num_attention_heads
														_lowerCAmelCase     :							Any    =    intermediate_size
														_lowerCAmelCase     :							Union[str, Any]    =    hidden_act
														_lowerCAmelCase     :							Any    =    hidden_dropout_prob
														_lowerCAmelCase     :							Any    =    attention_probs_dropout_prob
														_lowerCAmelCase     :							str    =    max_position_embeddings
														_lowerCAmelCase     :							Dict    =    type_vocab_size
														_lowerCAmelCase     :							Union[str, Any]    =    type_sequence_label_size
														_lowerCAmelCase     :							str    =    initializer_range
														_lowerCAmelCase     :							List[Any]    =    num_choices







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    ids_tensor([self.batch_size, self.seq_length],			self.vocab_size)

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

														_lowerCAmelCase     :							Any    =    None
														if self.use_token_type_ids:
																					_lowerCAmelCase     :							List[str]    =    ids_tensor([self.batch_size, self.seq_length],			self.type_vocab_size)

														_lowerCAmelCase     :							Dict    =    BertConfig(
														    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=__a,			initializer_range=self.initializer_range,			)

														return config, input_ids, token_type_ids, attention_mask







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    self.prepare_config_and_inputs()
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							Any    =    config_and_inputs
														_lowerCAmelCase     :							int    =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
														return config, inputs_dict







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    self.prepare_config_and_inputs()
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							str    =    config_and_inputs

														_lowerCAmelCase     :							Tuple    =    True
														_lowerCAmelCase     :							Union[str, Any]    =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
														_lowerCAmelCase     :							Optional[Any]    =    ids_tensor([self.batch_size, self.seq_length],			vocab_size=2)

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



@require_flax
class   UpperCAmelCase_    (       a   ,					unittest.TestCase):
							lowerCamelCase__   			=		True

							lowerCamelCase__   			=		(
							    (
							        FlaxBertModel,
							        FlaxBertForPreTraining,
							        FlaxBertForMaskedLM,
							        FlaxBertForMultipleChoice,
							        FlaxBertForQuestionAnswering,
							        FlaxBertForNextSentencePrediction,
							        FlaxBertForSequenceClassification,
							        FlaxBertForTokenClassification,
							        FlaxBertForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							str    =    FlaxBertModelTester(self)







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    FlaxBertModel.from_pretrained("bert-base-cased")
														_lowerCAmelCase     :							Dict    =    model(np.ones((1, 1)))
														self.assertIsNotNone(__a)





from PIL import Image






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    image.size
							_lowerCAmelCase     :							Any    =    0
							_lowerCAmelCase     :							Tuple    =    image.load()
							for i in range(_lowerCamelCase  ):
														for j in range(_lowerCamelCase  ):
																					_lowerCAmelCase     :							Union[str, Any]    =    pixels[j, i]
																					mean += pixel
							mean //= width * height

							for j in range(_lowerCamelCase  ):
														for i in range(_lowerCamelCase  ):
																					_lowerCAmelCase     :							Optional[Any]    =    255 if pixels[i, j] > mean else 0
							return image


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