"""simple docstring"""






import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM
@require_torch
class 				snake_case       :




					"""simple docstring"""

					def __init__(				self   :	str    ,lowerCamelCase__   :	Optional[Any]    ,lowerCamelCase__   :	Optional[Any]=99    ,lowerCamelCase__   :	List[str]=13    ,lowerCamelCase__   :	int=16    ,lowerCamelCase__   :	Tuple=7    ,lowerCamelCase__   :	List[str]=True    ,lowerCamelCase__   :	List[str]=True    ,lowerCamelCase__   :	Optional[int]=True    ,lowerCamelCase__   :	Dict=False    ,lowerCamelCase__   :	str=True    ,lowerCamelCase__   :	Union[str, Any]=2    ,lowerCamelCase__   :	Union[str, Any]=32    ,lowerCamelCase__   :	Any=4    ,lowerCamelCase__   :	Dict=4    ,lowerCamelCase__   :	str=30    ,lowerCamelCase__   :	List[str]=0    ,lowerCamelCase__   :	Dict=1    ,lowerCamelCase__   :	Optional[Any]=2    ,lowerCamelCase__   :	Dict=None    ,):
									UpperCAmelCase__             =							parent
									UpperCAmelCase__             =							batch_size
									UpperCAmelCase__             =							decoder_seq_length
									# For common tests
									UpperCAmelCase__             =							self.decoder_seq_length
									UpperCAmelCase__             =							is_training
									UpperCAmelCase__             =							use_attention_mask
									UpperCAmelCase__             =							use_labels

									UpperCAmelCase__             =							vocab_size
									UpperCAmelCase__             =							d_model
									UpperCAmelCase__             =							d_model
									UpperCAmelCase__             =							decoder_layers
									UpperCAmelCase__             =							decoder_layers
									UpperCAmelCase__             =							decoder_ffn_dim
									UpperCAmelCase__             =							decoder_attention_heads
									UpperCAmelCase__             =							decoder_attention_heads
									UpperCAmelCase__             =							eos_token_id
									UpperCAmelCase__             =							bos_token_id
									UpperCAmelCase__             =							pad_token_id
									UpperCAmelCase__             =							decoder_start_token_id
									UpperCAmelCase__             =							use_cache
									UpperCAmelCase__             =							max_position_embeddings

									UpperCAmelCase__             =							None
									UpperCAmelCase__             =							decoder_seq_length
									UpperCAmelCase__             =							2
									UpperCAmelCase__             =							1







					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							ids_tensor([self.batch_size, self.decoder_seq_length]    ,self.vocab_size			)

									UpperCAmelCase__             =							None
									if self.use_attention_mask:
													UpperCAmelCase__             =							ids_tensor([self.batch_size, self.decoder_seq_length]    ,vocab_size=2			)

									UpperCAmelCase__             =							None
									if self.use_labels:
													UpperCAmelCase__             =							ids_tensor([self.batch_size, self.decoder_seq_length]    ,self.vocab_size			)

									UpperCAmelCase__             =							TrOCRConfig(
									    vocab_size=self.vocab_size    ,d_model=self.d_model    ,decoder_layers=self.decoder_layers    ,decoder_ffn_dim=self.decoder_ffn_dim    ,decoder_attention_heads=self.decoder_attention_heads    ,eos_token_id=self.eos_token_id    ,bos_token_id=self.bos_token_id    ,use_cache=self.use_cache    ,pad_token_id=self.pad_token_id    ,decoder_start_token_id=self.decoder_start_token_id    ,max_position_embeddings=self.max_position_embeddings    ,)

									return (config, input_ids, attention_mask, lm_labels)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	Optional[Any]    ,lowerCamelCase__   :	Dict    ,):
									UpperCAmelCase__             =							True
									UpperCAmelCase__             =							TrOCRDecoder(config=UpperCamelCase_			).to(UpperCamelCase_			).eval()
									UpperCAmelCase__             =							input_ids[:2]

									input_ids[input_ids == 0] += 1
									# first forward pass
									UpperCAmelCase__             =							model(UpperCamelCase_    ,use_cache=UpperCamelCase_			)
									UpperCAmelCase__             =							model(UpperCamelCase_			)
									UpperCAmelCase__             =							model(UpperCamelCase_    ,use_cache=UpperCamelCase_			)

									self.parent.assertTrue(len(UpperCamelCase_			) == len(UpperCamelCase_			)			)
									self.parent.assertTrue(len(UpperCamelCase_			) == len(UpperCamelCase_			) + 1			)

									UpperCAmelCase__             =							outputs['past_key_values']

									# create hypothetical next token and extent to next_input_ids
									UpperCAmelCase__             =							ids_tensor((2, 1)    ,config.vocab_size - 1			) + 1

									# append to next input_ids and
									UpperCAmelCase__             =							torch.cat([input_ids, next_tokens]    ,dim=-1			)

									UpperCAmelCase__             =							model(UpperCamelCase_			)['last_hidden_state']
									UpperCAmelCase__             =							model(UpperCamelCase_    ,past_key_values=UpperCamelCase_			)['last_hidden_state']

									# select random slice
									UpperCAmelCase__             =							ids_tensor((1,)    ,output_from_past.shape[-1]			).item()
									UpperCAmelCase__             =							output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
									UpperCAmelCase__             =							output_from_past[:, 0, random_slice_idx].detach()

									# test that outputs are equal for slice
									assert torch.allclose(UpperCamelCase_    ,UpperCamelCase_    ,atol=1e-3			)







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									UpperCAmelCase__             =							self.prepare_config_and_inputs()
									UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__      ,	UpperCAmelCase__             =							config_and_inputs

									UpperCAmelCase__             =							{'input_ids': input_ids, 'attention_mask': attention_mask}
									return config, inputs_dict






@require_torch
class 				snake_case       ( __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
					snake_case__				    = (TrOCRForCausalLM,) if is_torch_available() else ()
					snake_case__				    = {'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
					snake_case__				    = True
					snake_case__				    = False







					def 		__lowerCAmelCase						(				self   :	str			):
									UpperCAmelCase__             =							TrOCRStandaloneDecoderModelTester(self    ,is_training=UpperCamelCase_			)
									UpperCAmelCase__             =							ConfigTester(self    ,config_class=UpperCamelCase_			)







					def 		__lowerCAmelCase						(				self   :	str			):
									pass







					def 		__lowerCAmelCase						(				self   :	List[str]			):
									pass







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									pass







					def 		__lowerCAmelCase						(				self   :	List[str]			):
									self.config_tester.run_common_tests()







					def 		__lowerCAmelCase						(				self   :	Dict			):
									UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past(*UpperCamelCase_			)







					def 		__lowerCAmelCase						(				self   :	str			):
									return







					@unittest.skip('The model doesn\'t support left padding'			)  # and it's not used enough to be worth fixing :)
					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									pass









from __future__ import annotations

from PIL import Image

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




def 					lowerCamelCase__       (						A__       :					list[list[int]]	):





       '''simple docstring'''



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




def 					lowerCamelCase__       (						A__       :					list[list[int]]  ,			A__       :					int	):





       '''simple docstring'''



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

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

        # Save image
              images.append(A__	)
              __lowerCamelCase   =						new_generation(A__	)
       return images


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

