
"""simple docstring"""

from PIL import Image




def     lowercase__						(					lowerCAmelCase				:					List[Any] ) ->      List[Any]:






    """simple docstring"""




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

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


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





"""simple docstring"""

import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, 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():
   import jax.numpy as jnp

   from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
       FlaxRobertaPreLayerNormForCausalLM,
       FlaxRobertaPreLayerNormForMaskedLM,
       FlaxRobertaPreLayerNormForMultipleChoice,
       FlaxRobertaPreLayerNormForQuestionAnswering,
       FlaxRobertaPreLayerNormForSequenceClassification,
       FlaxRobertaPreLayerNormForTokenClassification,
       FlaxRobertaPreLayerNormModel,
   )
class      _UpperCAmelCase							(		unittest.TestCase							):




      def __init__(							self   ,       lowercase_   ,       lowercase_=1_3   ,       lowercase_=7   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_=9_9   ,       lowercase_=3_2   ,       lowercase_=5   ,       lowercase_=4   ,       lowercase_=3_7   ,       lowercase_="gelu"   ,       lowercase_=0.1   ,       lowercase_=0.1   ,       lowercase_=5_1_2   ,       lowercase_=1_6   ,       lowercase_=2   ,       lowercase_=0.0_2   ,       lowercase_=4   ,       )						->					Dict:
          UpperCAmelCase									=					parent
          UpperCAmelCase									=					batch_size
          UpperCAmelCase									=					seq_length
          UpperCAmelCase									=					is_training
          UpperCAmelCase									=					use_attention_mask
          UpperCAmelCase									=					use_token_type_ids
          UpperCAmelCase									=					use_labels
          UpperCAmelCase									=					vocab_size
          UpperCAmelCase									=					hidden_size
          UpperCAmelCase									=					num_hidden_layers
          UpperCAmelCase									=					num_attention_heads
          UpperCAmelCase									=					intermediate_size
          UpperCAmelCase									=					hidden_act
          UpperCAmelCase									=					hidden_dropout_prob
          UpperCAmelCase									=					attention_probs_dropout_prob
          UpperCAmelCase									=					max_position_embeddings
          UpperCAmelCase									=					type_vocab_size
          UpperCAmelCase									=					type_sequence_label_size
          UpperCAmelCase									=					initializer_range
          UpperCAmelCase									=					num_choices




      def     a_      (							self							)						->					int:
          UpperCAmelCase									=					ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size							)

          UpperCAmelCase									=					None
          if self.use_attention_mask:
              UpperCAmelCase									=					random_attention_mask([self.batch_size, self.seq_length]							)

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

          UpperCAmelCase									=					RobertaPreLayerNormConfig(
              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=lowercase_   ,       initializer_range=self.initializer_range   ,       )

          return config, input_ids, token_type_ids, attention_mask




      def     a_      (							self							)						->					List[str]:
          UpperCAmelCase									=					self.prepare_config_and_inputs()
          UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase									=					config_and_inputs
          UpperCAmelCase									=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					self.prepare_config_and_inputs()
          UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase									=					config_and_inputs

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

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

@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	True

      __SCREAMING_SNAKE_CASE   :							int							      =	(
          (
              FlaxRobertaPreLayerNormModel,
              FlaxRobertaPreLayerNormForCausalLM,
              FlaxRobertaPreLayerNormForMaskedLM,
              FlaxRobertaPreLayerNormForSequenceClassification,
              FlaxRobertaPreLayerNormForTokenClassification,
              FlaxRobertaPreLayerNormForMultipleChoice,
              FlaxRobertaPreLayerNormForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					FlaxRobertaPreLayerNormModelTester(self							)




      @slow
      def     a_      (							self							)						->					Dict:
          for model_class_name in self.all_model_classes:
              UpperCAmelCase									=					model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'   ,       from_pt=lowercase_							)
              UpperCAmelCase									=					model(np.ones((1, 1)							)							)
              self.assertIsNotNone(lowercase_							)

@require_flax
class      _UpperCAmelCase							(		unittest.TestCase							):




      @slow
      def     a_      (							self							)						->					Tuple:
          UpperCAmelCase									=					FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'   ,       from_pt=lowercase_							)

          UpperCAmelCase									=					np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]   ,       dtype=jnp.intaa							)
          UpperCAmelCase									=					model(lowercase_							)[0]
          UpperCAmelCase									=					[1, 1_1, 5_0_2_6_5]
          self.assertEqual(list(output.shape							)   ,       lowercase_							)
          # compare the actual values for a slice.
          UpperCAmelCase									=					np.array(
              [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]   ,       dtype=np.floataa							)
          self.assertTrue(np.allclose(output[:, :3, :3]   ,       lowercase_   ,       atol=1E-4							)							)




      @slow
      def     a_      (							self							)						->					int:
          UpperCAmelCase									=					FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'   ,       from_pt=lowercase_							)

          UpperCAmelCase									=					np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]   ,       dtype=jnp.intaa							)
          UpperCAmelCase									=					model(lowercase_							)[0]
          # compare the actual values for a slice.
          UpperCAmelCase									=					np.array(
              [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]   ,       dtype=np.floataa							)
          self.assertTrue(np.allclose(output[:, :3, :3]   ,       lowercase_   ,       atol=1E-4							)							)



