




'''simple docstring'''






import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
				import torch_xla.core.xla_model as xm
				import torch_xla.debug.metrics as met



class 					A		( SCREAMING_SNAKE_CASE__		):




				'''simple docstring'''





				def __init__(self ,  *_UpperCAmelCase ,  _UpperCAmelCase=None ,  _UpperCAmelCase=None ,  **_UpperCAmelCase			)	->       Union[str, Any]:
							super().__init__(*__lowerCAmelCase ,  **__lowerCAmelCase			)
							__UpperCamelCase      :			str									=   eval_examples
							__UpperCamelCase      :			List[Any]									=   post_process_function
				def        a_       (self ,  _UpperCAmelCase = None ,  _UpperCAmelCase=None ,  _UpperCAmelCase = None ,  _UpperCAmelCase = "eval" ,  **_UpperCAmelCase ,  )	->       Dict[str, float]:
							__UpperCamelCase      :			Any									=   gen_kwargs.copy()
							__UpperCamelCase      :			List[str]									=   (
							    gen_kwargs["max_length"] if gen_kwargs.get("max_length"			) is not None else self.args.generation_max_length
							)
							__UpperCamelCase      :			Union[str, Any]									=   (
							    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"			) is not None else self.args.generation_num_beams
							)
							__UpperCamelCase      :			str									=   gen_kwargs

							__UpperCamelCase      :			str									=   self.eval_dataset if eval_dataset is None else eval_dataset
							__UpperCamelCase      :			Dict									=   self.get_eval_dataloader(__lowerCAmelCase			)
							__UpperCamelCase      :			int									=   self.eval_examples if eval_examples is None else eval_examples

							# Temporarily disable metric computation, we will do it in the loop here.
							__UpperCamelCase      :			Optional[Any]									=   self.compute_metrics
							__UpperCamelCase      :			Optional[Any]									=   None
							__UpperCamelCase      :			List[Any]									=   time.time()
							__UpperCamelCase      :			int									=   self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							try:
										__UpperCamelCase      :			str									=   eval_loop(
										    __lowerCAmelCase ,  description="Evaluation" ,  prediction_loss_only=True if compute_metrics is None else None ,  ignore_keys=__lowerCAmelCase ,  metric_key_prefix=__lowerCAmelCase ,  )
							finally:
										__UpperCamelCase      :			List[Any]									=   compute_metrics
							__UpperCamelCase      :			List[Any]									=   self.args.eval_batch_size * self.args.world_size
							if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
										start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
							output.metrics.update(
							    speed_metrics(
							        __lowerCAmelCase ,  __lowerCAmelCase ,  num_samples=output.num_samples ,  num_steps=math.ceil(output.num_samples / total_batch_size			) ,  )			)

							if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
										# Only the main node write the results by default
										__UpperCamelCase      :			Dict									=   self.post_process_function(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase			)
										__UpperCamelCase      :			Tuple									=   self.compute_metrics(__lowerCAmelCase			)

										# Prefix all keys with metric_key_prefix + '_'
										for key in list(metrics.keys()			):
													if not key.startswith(f"{metric_key_prefix}_"			):
																__UpperCamelCase      :			Any									=   metrics.pop(__lowerCAmelCase			)

										metrics.update(output.metrics			)
							else:
										__UpperCamelCase      :			List[str]									=   output.metrics

							if self.args.should_log:
										# Only the main node log the results by default
										self.log(__lowerCAmelCase			)

							if self.args.tpu_metrics_debug or self.args.debug:
										# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
										xm.master_print(met.metrics_report()			)

							__UpperCamelCase      :			Dict									=   self.callback_handler.on_evaluate(self.args ,  self.state ,  self.control ,  __lowerCAmelCase			)
							return metrics





				def        a_       (self ,  _UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase=None ,  _UpperCAmelCase = "test" ,  **_UpperCAmelCase			)	->       Any:
							__UpperCamelCase      :			Optional[Any]									=   gen_kwargs.copy()

							__UpperCamelCase      :			Union[str, Any]									=   self.get_test_dataloader(__lowerCAmelCase			)

							# Temporarily disable metric computation, we will do it in the loop here.
							__UpperCamelCase      :			Tuple									=   self.compute_metrics
							__UpperCamelCase      :			Optional[Any]									=   None
							__UpperCamelCase      :			List[str]									=   time.time()
							__UpperCamelCase      :			int									=   self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
							try:
										__UpperCamelCase      :			Dict									=   eval_loop(
										    __lowerCAmelCase ,  description="Prediction" ,  prediction_loss_only=True if compute_metrics is None else None ,  ignore_keys=__lowerCAmelCase ,  metric_key_prefix=__lowerCAmelCase ,  )
							finally:
										__UpperCamelCase      :			Any									=   compute_metrics

							__UpperCamelCase      :			int									=   self.args.eval_batch_size * self.args.world_size
							if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
										start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
							output.metrics.update(
							    speed_metrics(
							        __lowerCAmelCase ,  __lowerCAmelCase ,  num_samples=output.num_samples ,  num_steps=math.ceil(output.num_samples / total_batch_size			) ,  )			)
							if self.post_process_function is None or self.compute_metrics is None:
										return output

							__UpperCamelCase      :			int									=   self.post_process_function(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  "predict"			)
							__UpperCamelCase      :			Union[str, Any]									=   self.compute_metrics(__lowerCAmelCase			)

							# Prefix all keys with metric_key_prefix + '_'
							for key in list(metrics.keys()			):
										if not key.startswith(f"{metric_key_prefix}_"			):
													__UpperCamelCase      :			Optional[Any]									=   metrics.pop(__lowerCAmelCase			)
							metrics.update(output.metrics			)
							return PredictionOutput(predictions=predictions.predictions ,  label_ids=predictions.label_ids ,  metrics=__lowerCAmelCase			)










from __future__ import annotations

from PIL import Image

# Define glider example
A    :					Any =      [
    [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    :					Optional[Any] =      [[0, 1, 0], [0, 1, 0], [0, 1, 0]]







def    __lowerCamelCase					(							__a   :list[list[int]]    )		->				list[list[int]]:






		"""simple docstring"""

		A__        =   []
		for i in range(len(__a    )    ):
				A__        =   []
				for j in range(len(cells[i]    )    ):
						# Get the number of live neighbours
						A__        =   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.
						A__        =   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    )		->				list[Image.Image]:






		"""simple docstring"""

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

				# Save cells to image
				for x in range(len(__a    )    ):
						for y in range(len(cells[0]    )    ):
								A__        =   2_5_5 - cells[y][x] * 2_5_5
								A__        =   (colour, colour, colour)

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


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



