





'''simple docstring'''



import unittest

import numpy as np

from transformers import RobertaConfig, 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.roberta.modeling_flax_roberta import (
     FlaxRobertaForCausalLM,
     FlaxRobertaForMaskedLM,
     FlaxRobertaForMultipleChoice,
     FlaxRobertaForQuestionAnswering,
     FlaxRobertaForSequenceClassification,
     FlaxRobertaForTokenClassification,
     FlaxRobertaModel,
 )


class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



       '''simple docstring'''






       def __init__(					self						,   lowerCamelCase						,   lowerCamelCase=13						,   lowerCamelCase=7						,   lowerCamelCase=True						,   lowerCamelCase=True						,   lowerCamelCase=True						,   lowerCamelCase=True						,   lowerCamelCase=99						,   lowerCamelCase=32						,   lowerCamelCase=5						,   lowerCamelCase=4						,   lowerCamelCase=37						,   lowerCamelCase="gelu"						,   lowerCamelCase=0.1						,   lowerCamelCase=0.1						,   lowerCamelCase=512						,   lowerCamelCase=16						,   lowerCamelCase=2						,   lowerCamelCase=0.02						,   lowerCamelCase=4						,   ):
         _snake_case     		=				parent
         _snake_case     		=				batch_size
         _snake_case     		=				seq_length
         _snake_case     		=				is_training
         _snake_case     		=				use_attention_mask
         _snake_case     		=				use_token_type_ids
         _snake_case     		=				use_labels
         _snake_case     		=				vocab_size
         _snake_case     		=				hidden_size
         _snake_case     		=				num_hidden_layers
         _snake_case     		=				num_attention_heads
         _snake_case     		=				intermediate_size
         _snake_case     		=				hidden_act
         _snake_case     		=				hidden_dropout_prob
         _snake_case     		=				attention_probs_dropout_prob
         _snake_case     		=				max_position_embeddings
         _snake_case     		=				type_vocab_size
         _snake_case     		=				type_sequence_label_size
         _snake_case     		=				initializer_range
         _snake_case     		=				num_choices

       def 	UpperCamelCase(					self	):
         _snake_case     		=				ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size	)

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

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

         _snake_case     		=				RobertaConfig(
             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 	UpperCamelCase(					self	):
         _snake_case     		=				self.prepare_config_and_inputs()
         _snake_case		,		_snake_case		,		_snake_case		,		_snake_case     		=				config_and_inputs
         _snake_case     		=				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
         return config, inputs_dict






       def 	UpperCamelCase(					self	):
         _snake_case     		=				self.prepare_config_and_inputs()
         _snake_case		,		_snake_case		,		_snake_case		,		_snake_case     		=				config_and_inputs

         _snake_case     		=				True
         _snake_case     		=				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
         _snake_case     		=				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
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    unittest.TestCase			):



       '''simple docstring'''






       UpperCAmelCase__       :						Dict      =   True

       UpperCAmelCase__       :						Optional[Any]      =   (
           (
               FlaxRobertaModel,
               FlaxRobertaForCausalLM,
               FlaxRobertaForMaskedLM,
               FlaxRobertaForSequenceClassification,
               FlaxRobertaForTokenClassification,
               FlaxRobertaForMultipleChoice,
               FlaxRobertaForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )

       def 	UpperCamelCase(					self	):
         _snake_case     		=				FlaxRobertaModelTester(self	)






       @slow
       def 	UpperCamelCase(					self	):
         for model_class_name in self.all_model_classes:
           _snake_case     		=				model_class_name.from_pretrained("roberta-base"						,   from_pt=lowerCamelCase	)
           _snake_case     		=				model(np.ones((1, 1)	)	)
           self.assertIsNotNone(lowerCamelCase	)









'''simple docstring'''



from __future__ import annotations

from PIL import Image

# Define glider example
__magic_name__   :						str    =			[
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
__magic_name__   :						List[Any]    =			[[0, 1, 0], [0, 1, 0], [0, 1, 0]]




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				[]
  for i in range(len(SCREAMING_SNAKE_CASE__       )       ):
    _snake_case     		=				[]
    for j in range(len(cells[i]       )       ):
      # Get the number of live neighbours
      _snake_case     		=				0
      if i > 0 and j > 0:
        neighbour_count += cells[i - 1][j - 1]
      if i > 0:
        neighbour_count += cells[i - 1][j]
      if i > 0 and j < len(cells[i]       ) - 1:
        neighbour_count += cells[i - 1][j + 1]
      if j > 0:
        neighbour_count += cells[i][j - 1]
      if j < len(cells[i]       ) - 1:
        neighbour_count += cells[i][j + 1]
      if i < len(SCREAMING_SNAKE_CASE__       ) - 1 and j > 0:
        neighbour_count += cells[i + 1][j - 1]
      if i < len(SCREAMING_SNAKE_CASE__       ) - 1:
        neighbour_count += cells[i + 1][j]
      if i < len(SCREAMING_SNAKE_CASE__       ) - 1 and j < len(cells[i]       ) - 1:
        neighbour_count += cells[i + 1][j + 1]

      # Rules of the game of life (excerpt from Wikipedia):
      # 1. Any live cell with two or three live neighbours survives.
      # 2. Any dead cell with three live neighbours becomes a live cell.
      # 3. All other live cells die in the next generation.
      #    Similarly, all other dead cells stay dead.
      _snake_case     		=				cells[i][j] == 1
      if (
          (alive and 2 <= neighbour_count <= 3)
          or not alive
          and neighbour_count == 3
      ):
        next_generation_row.append(1       )
      else:
        next_generation_row.append(0       )

    next_generation.append(SCREAMING_SNAKE_CASE__       )
  return next_generation




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				[]
  for _ in range(SCREAMING_SNAKE_CASE__       ):
    # Create output image
    _snake_case     		=				Image.new("RGB"					,	(len(cells[0]       ), len(SCREAMING_SNAKE_CASE__       ))       )
    _snake_case     		=				img.load()

    # Save cells to image
    for x in range(len(SCREAMING_SNAKE_CASE__       )       ):
      for y in range(len(cells[0]       )       ):
        _snake_case     		=				2_55 - cells[y][x] * 2_55
        _snake_case     		=				(colour, colour, colour)

        # Save image
    images.append(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				new_generation(SCREAMING_SNAKE_CASE__       )
  return images


if __name__ == "__main__":
 __magic_name__   :						Optional[Any]    =			generate_images(GLIDER, 16)
 images[0].save("""out.gif""", save_all=True, append_images=images[1:])


