"""simple docstring"""






from __future__ import annotations

from PIL import Image

# Define glider example
_A   					=    [
    [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
_A   					=    [[0, 1, 0], [0, 1, 0], [0, 1, 0]]




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   list[list[int]]:
				SCREAMING_SNAKE_CASE__		  =				[]
				for i in range(len(__UpperCAmelCase       )       ):
								SCREAMING_SNAKE_CASE__		  =				[]
								for j in range(len(cells[i]       )       ):
												# Get the number of live neighbours
												SCREAMING_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(__UpperCAmelCase       ) - 1 and j > 0:
																neighbour_count += cells[i + 1][j - 1]
												if i < len(__UpperCAmelCase       ) - 1:
																neighbour_count += cells[i + 1][j]
												if i < len(__UpperCAmelCase       ) - 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.
												SCREAMING_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(__UpperCAmelCase       )
				return next_generation




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   list[Image.Image]:
				SCREAMING_SNAKE_CASE__		  =				[]
				for _ in range(__UpperCAmelCase       ):
								# Create output image
								SCREAMING_SNAKE_CASE__		  =				Image.new("RGB"  ,					(len(cells[0]       ), len(__UpperCAmelCase       ))       )
								SCREAMING_SNAKE_CASE__		  =				img.load()

								# Save cells to image
								for x in range(len(__UpperCAmelCase       )       ):
												for y in range(len(cells[0]       )       ):
																SCREAMING_SNAKE_CASE__		  =				255 - cells[y][x] * 255
																SCREAMING_SNAKE_CASE__		  =				(colour, colour, colour)

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


if __name__ == "__main__":
						_A   					=    generate_images(GLIDER, 1_6)
						images[0].save('out.gif', save_all=True, append_images=images[1:])

"""simple docstring"""






import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    MODEL_FOR_PRETRAINING_MAPPING,
						    AlbertForMaskedLM,
						    AlbertForMultipleChoice,
						    AlbertForPreTraining,
						    AlbertForQuestionAnswering,
						    AlbertForSequenceClassification,
						    AlbertForTokenClassification,
						    AlbertModel,
						)
						from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST

class lowerCamelCase		:






					'''simple docstring'''


					def __init__(						self			:				str   , _snake_case			:				List[str]   , _snake_case			:				List[Any]=13   , _snake_case			:				List[str]=7   , _snake_case			:				Dict=True   , _snake_case			:				str=True   , _snake_case			:				Optional[Any]=True   , _snake_case			:				Tuple=True   , _snake_case			:				List[Any]=99   , _snake_case			:				Dict=16   , _snake_case			:				Tuple=36   , _snake_case			:				Optional[int]=6   , _snake_case			:				Optional[int]=6   , _snake_case			:				Tuple=6   , _snake_case			:				Optional[int]=37   , _snake_case			:				Dict="gelu"   , _snake_case			:				str=0.1   , _snake_case			:				Tuple=0.1   , _snake_case			:				List[str]=512   , _snake_case			:				Any=16   , _snake_case			:				Optional[int]=2   , _snake_case			:				Optional[int]=0.02   , _snake_case			:				Union[str, Any]=3   , _snake_case			:				int=4   , _snake_case			:				Optional[int]=None   , )							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				parent
									SCREAMING_SNAKE_CASE__		  =				batch_size
									SCREAMING_SNAKE_CASE__		  =				seq_length
									SCREAMING_SNAKE_CASE__		  =				is_training
									SCREAMING_SNAKE_CASE__		  =				use_input_mask
									SCREAMING_SNAKE_CASE__		  =				use_token_type_ids
									SCREAMING_SNAKE_CASE__		  =				use_labels
									SCREAMING_SNAKE_CASE__		  =				vocab_size
									SCREAMING_SNAKE_CASE__		  =				embedding_size
									SCREAMING_SNAKE_CASE__		  =				hidden_size
									SCREAMING_SNAKE_CASE__		  =				num_hidden_layers
									SCREAMING_SNAKE_CASE__		  =				num_hidden_groups
									SCREAMING_SNAKE_CASE__		  =				num_attention_heads
									SCREAMING_SNAKE_CASE__		  =				intermediate_size
									SCREAMING_SNAKE_CASE__		  =				hidden_act
									SCREAMING_SNAKE_CASE__		  =				hidden_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				max_position_embeddings
									SCREAMING_SNAKE_CASE__		  =				type_vocab_size
									SCREAMING_SNAKE_CASE__		  =				type_sequence_label_size
									SCREAMING_SNAKE_CASE__		  =				initializer_range
									SCREAMING_SNAKE_CASE__		  =				num_labels
									SCREAMING_SNAKE_CASE__		  =				num_choices
									SCREAMING_SNAKE_CASE__		  =				scope



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size		)

									SCREAMING_SNAKE_CASE__		  =				None
									if self.use_input_mask:
													SCREAMING_SNAKE_CASE__		  =				random_attention_mask([self.batch_size, self.seq_length]		)

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

									SCREAMING_SNAKE_CASE__		  =				None
									SCREAMING_SNAKE_CASE__		  =				None
									SCREAMING_SNAKE_CASE__		  =				None
									if self.use_labels:
													SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size]   , self.type_sequence_label_size		)
													SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size, self.seq_length]   , self.num_labels		)
													SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size]   , self.num_choices		)

									SCREAMING_SNAKE_CASE__		  =				self.get_config()

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Union[str, Any]:
									return 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   , initializer_range=self.initializer_range   , num_hidden_groups=self.num_hidden_groups   , )



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				Tuple   , _snake_case			:				Optional[Any]   , _snake_case			:				Union[str, Any]   , _snake_case			:				int   , _snake_case			:				Union[str, Any]   , _snake_case			:				str   , _snake_case			:				int		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				AlbertModel(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , token_type_ids=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)
									self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, self.seq_length, self.hidden_size)		)
									self.parent.assertEqual(result.pooler_output.shape   , (self.batch_size, self.hidden_size)		)



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				Optional[Any]   , _snake_case			:				int   , _snake_case			:				str   , _snake_case			:				List[Any]   , _snake_case			:				Dict   , _snake_case			:				Union[str, Any]   , _snake_case			:				str		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				AlbertForPreTraining(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(
									    _snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case   , sentence_order_label=_snake_case   , )
									self.parent.assertEqual(result.prediction_logits.shape   , (self.batch_size, self.seq_length, self.vocab_size)		)
									self.parent.assertEqual(result.sop_logits.shape   , (self.batch_size, config.num_labels)		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				int   , _snake_case			:				Any   , _snake_case			:				List[Any]   , _snake_case			:				Any   , _snake_case			:				str   , _snake_case			:				Tuple   , _snake_case			:				List[Any]		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				AlbertForMaskedLM(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.vocab_size)		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Any   , _snake_case			:				Optional[int]   , _snake_case			:				Optional[Any]   , _snake_case			:				Tuple		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				AlbertForQuestionAnswering(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(
									    _snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , start_positions=_snake_case   , end_positions=_snake_case   , )
									self.parent.assertEqual(result.start_logits.shape   , (self.batch_size, self.seq_length)		)
									self.parent.assertEqual(result.end_logits.shape   , (self.batch_size, self.seq_length)		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				str   , _snake_case			:				Optional[int]   , _snake_case			:				int   , _snake_case			:				List[Any]   , _snake_case			:				List[Any]   , _snake_case			:				str		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.num_labels
									SCREAMING_SNAKE_CASE__		  =				AlbertForSequenceClassification(_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_labels)		)



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				Any   , _snake_case			:				str   , _snake_case			:				List[Any]   , _snake_case			:				Any   , _snake_case			:				Union[str, Any]   , _snake_case			:				Dict   , _snake_case			:				Tuple		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.num_labels
									SCREAMING_SNAKE_CASE__		  =				AlbertForTokenClassification(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.num_labels)		)



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				str   , _snake_case			:				Any   , _snake_case			:				Any   , _snake_case			:				int   , _snake_case			:				List[Any]   , _snake_case			:				List[str]   , _snake_case			:				int		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				self.num_choices
									SCREAMING_SNAKE_CASE__		  =				AlbertForMultipleChoice(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				input_ids.unsqueeze(1		).expand(-1   , self.num_choices   , -1		).contiguous()
									SCREAMING_SNAKE_CASE__		  =				token_type_ids.unsqueeze(1		).expand(-1   , self.num_choices   , -1		).contiguous()
									SCREAMING_SNAKE_CASE__		  =				input_mask.unsqueeze(1		).expand(-1   , self.num_choices   , -1		).contiguous()
									SCREAMING_SNAKE_CASE__		  =				model(
									    _snake_case   , attention_mask=_snake_case   , token_type_ids=_snake_case   , labels=_snake_case   , )
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_choices)		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.prepare_config_and_inputs()
									(
									    (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       (
									    SCREAMING_SNAKE_CASE__
									)  ,       
									)		  =				config_and_inputs
									SCREAMING_SNAKE_CASE__		  =				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
									return config, inputs_dict



@require_torch
class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		(
					    (
					        AlbertModel,
					        AlbertForPreTraining,
					        AlbertForMaskedLM,
					        AlbertForMultipleChoice,
					        AlbertForSequenceClassification,
					        AlbertForTokenClassification,
					        AlbertForQuestionAnswering,
					    )
					    if is_torch_available()
					    else ()
					)
					a   	=		(
					    {
					        "feature-extraction": AlbertModel,
					        "fill-mask": AlbertForMaskedLM,
					        "question-answering": AlbertForQuestionAnswering,
					        "text-classification": AlbertForSequenceClassification,
					        "token-classification": AlbertForTokenClassification,
					        "zero-shot": AlbertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					a   	=		True



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				Dict   , _snake_case			:				str   , _snake_case			:				str=False		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				super()._prepare_for_class(_snake_case   , _snake_case   , return_labels=_snake_case		)

									if return_labels:
													if model_class in get_values(_snake_case		):
																	SCREAMING_SNAKE_CASE__		  =				torch.zeros(
																	    (self.model_tester.batch_size, self.model_tester.seq_length)   , dtype=torch.long   , device=_snake_case		)
																	SCREAMING_SNAKE_CASE__		  =				torch.zeros(
																	    self.model_tester.batch_size   , dtype=torch.long   , device=_snake_case		)
									return inputs_dict



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				AlbertModelTester(self		)
									SCREAMING_SNAKE_CASE__		  =				ConfigTester(self   , config_class=_snake_case   , hidden_size=37		)



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       List[str]:
									self.config_tester.run_common_tests()



					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Any		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_multiple_choice(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
													SCREAMING_SNAKE_CASE__		  =				type
													self.model_tester.create_and_check_model(*_snake_case		)



					@slow
					def 			lowerCAmelCase_					(						self			:				Any		)							->       str:
									for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE__		  =				AlbertModel.from_pretrained(_snake_case		)
													self.assertIsNotNone(_snake_case		)



@require_torch
class lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					@slow
					def 			lowerCAmelCase_					(						self			:				Any		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				AlbertModel.from_pretrained("albert-base-v2"		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
									with torch.no_grad():
													SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case		)[0]
									SCREAMING_SNAKE_CASE__		  =				torch.Size((1, 11, 768)		)
									self.assertEqual(output.shape   , _snake_case		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor(
									    [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]		)

									self.assertTrue(torch.allclose(output[:, 1:4, 1:4]   , _snake_case   , atol=1e-4		)		)




