
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      __A(    unittest.TestCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=13	,      SCREAMING_SNAKE_CASE_=7	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=99	,      SCREAMING_SNAKE_CASE_=32	,      SCREAMING_SNAKE_CASE_=5	,      SCREAMING_SNAKE_CASE_=4	,      SCREAMING_SNAKE_CASE_=37	,      SCREAMING_SNAKE_CASE_="gelu"	,      SCREAMING_SNAKE_CASE_=0.1	,      SCREAMING_SNAKE_CASE_=0.1	,      SCREAMING_SNAKE_CASE_=5_12	,      SCREAMING_SNAKE_CASE_=16	,      SCREAMING_SNAKE_CASE_=2	,      SCREAMING_SNAKE_CASE_=0.02	,      SCREAMING_SNAKE_CASE_=4	,      ):
       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 	UpperCAmelCase_ (self						):
       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=SCREAMING_SNAKE_CASE_	,      initializer_range=self.initializer_range	,      )

       return config, input_ids, token_type_ids, attention_mask







  def 	UpperCAmelCase_ (self						):
       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 	UpperCAmelCase_ (self						):
       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      __A(    __lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      True

  SCREAMING_SNAKE_CASE__         =      (
      (
          FlaxRobertaPreLayerNormModel,
          FlaxRobertaPreLayerNormForCausalLM,
          FlaxRobertaPreLayerNormForMaskedLM,
          FlaxRobertaPreLayerNormForSequenceClassification,
          FlaxRobertaPreLayerNormForTokenClassification,
          FlaxRobertaPreLayerNormForMultipleChoice,
          FlaxRobertaPreLayerNormForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    FlaxRobertaPreLayerNormModelTester(self						)







  @slow
  def 	UpperCAmelCase_ (self						):
       for model_class_name in self.all_model_classes:
            UpperCamelCase__				=    model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""	,      from_pt=SCREAMING_SNAKE_CASE_						)
            UpperCamelCase__				=    model(np.ones((1, 1)						)						)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE_						)






@require_flax
class      __A(    unittest.TestCase			):

  """simple docstring"""







  @slow
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""	,      from_pt=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]	,      dtype=jnp.intaa						)
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_						)[0]
       UpperCamelCase__				=    [1, 11, 5_02_65]
       self.assertEqual(list(output.shape						)	,      SCREAMING_SNAKE_CASE_						)
       # compare the actual values for a slice.
       UpperCamelCase__				=    np.array(
           [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]	,      dtype=np.floataa						)
       self.assertTrue(np.allclose(output[:, :3, :3]	,      SCREAMING_SNAKE_CASE_	,      atol=1E-4						)						)







  @slow
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""	,      from_pt=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]	,      dtype=jnp.intaa						)
       UpperCamelCase__				=    model(SCREAMING_SNAKE_CASE_						)[0]
       # compare the actual values for a slice.
       UpperCamelCase__				=    np.array(
           [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]	,      dtype=np.floataa						)
       self.assertTrue(np.allclose(output[:, :3, :3]	,      SCREAMING_SNAKE_CASE_	,      atol=1E-4						)						)







from PIL import Image






def 						__magic_name__			(   __a      :	Image						):



     '''simple docstring'''




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

     for j in range(__a						):
          for i in range(__a						):
               UpperCamelCase__				=    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''')





