


"""simple docstring"""







import os
import tempfile
import unittest
from pathlib import Path

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


if is_torch_available():
 from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments






@require_torch
class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    def 	__snake_case							(					self				:    Tuple				, __UpperCAmelCase				:    Any):
      for model_result in results.values():
        for batch_size, sequence_length in zip(model_result["bs"]				, model_result["ss"]):
          a						:		Dict      							=			model_result["result"][batch_size][sequence_length]
          self.assertIsNotNone(__UpperCAmelCase)






    def 	__snake_case							(					self				:    Optional[Any]):
      a						:		Union[str, Any]      							=			"sshleifer/tiny-gpt2"
      a						:		Tuple      							=			PyTorchBenchmarkArguments(
          models=[MODEL_ID]				, training=__UpperCAmelCase				, inference=__UpperCAmelCase				, sequence_lengths=[8]				, batch_sizes=[1]				, multi_process=__UpperCAmelCase				, )
      a						:		Union[str, Any]      							=			PyTorchBenchmark(__UpperCAmelCase)
      a						:		Dict      							=			benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result)
      self.check_results_dict_not_empty(results.memory_inference_result)






    def 	__snake_case							(					self				:    Optional[int]):
      a						:		List[str]      							=			"sgugger/tiny-distilbert-classification"
      a						:		Dict      							=			PyTorchBenchmarkArguments(
          models=[MODEL_ID]				, training=__UpperCAmelCase				, inference=__UpperCAmelCase				, sequence_lengths=[8]				, batch_sizes=[1]				, multi_process=__UpperCAmelCase				, only_pretrain_model=__UpperCAmelCase				, )
      a						:		Optional[int]      							=			PyTorchBenchmark(__UpperCAmelCase)
      a						:		int      							=			benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result)
      self.check_results_dict_not_empty(results.memory_inference_result)






    def 	__snake_case							(					self				:    Any):
      a						:		List[str]      							=			"sshleifer/tiny-gpt2"
      a						:		Union[str, Any]      							=			PyTorchBenchmarkArguments(
          models=[MODEL_ID]				, training=__UpperCAmelCase				, inference=__UpperCAmelCase				, torchscript=__UpperCAmelCase				, sequence_lengths=[8]				, batch_sizes=[1]				, multi_process=__UpperCAmelCase				, )
      a						:		str      							=			PyTorchBenchmark(__UpperCAmelCase)
      a						:		List[Any]      							=			benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result)
      self.check_results_dict_not_empty(results.memory_inference_result)






    @unittest.skipIf(torch_device == "cpu"				, "Cant do half precision")
    def 	__snake_case							(					self				:    str):
      a						:		Any      							=			"sshleifer/tiny-gpt2"
      a						:		List[str]      							=			PyTorchBenchmarkArguments(
          models=[MODEL_ID]				, training=__UpperCAmelCase				, inference=__UpperCAmelCase				, fpaa=__UpperCAmelCase				, sequence_lengths=[8]				, batch_sizes=[1]				, multi_process=__UpperCAmelCase				, )
      a						:		Any      							=			PyTorchBenchmark(__UpperCAmelCase)
      a						:		Any      							=			benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result)
      self.check_results_dict_not_empty(results.memory_inference_result)






    def 	__snake_case							(					self				:    Tuple):
      a						:		Any      							=			"sshleifer/tiny-gpt2"
      a						:		Any      							=			AutoConfig.from_pretrained(__UpperCAmelCase)
      # set architectures equal to `None`
      a						:		List[Any]      							=			None
      a						:		Optional[Any]      							=			PyTorchBenchmarkArguments(
          models=[MODEL_ID]				, training=__UpperCAmelCase				, inference=__UpperCAmelCase				, sequence_lengths=[8]				, batch_sizes=[1]				, multi_process=__UpperCAmelCase				, )
      a						:		List[Any]      							=			PyTorchBenchmark(__UpperCAmelCase				, configs=[config])
      a						:		Tuple      							=			benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result)
      self.check_results_dict_not_empty(results.memory_inference_result)






    def 	__snake_case							(					self				:    List[str]):
      a						:		Tuple      							=			"sshleifer/tiny-gpt2"
      a						:		Optional[int]      							=			PyTorchBenchmarkArguments(
          models=[MODEL_ID]				, training=__UpperCAmelCase				, inference=__UpperCAmelCase				, sequence_lengths=[8]				, batch_sizes=[1]				, multi_process=__UpperCAmelCase				, )
      a						:		Dict      							=			PyTorchBenchmark(__UpperCAmelCase)
      a						:		Union[str, Any]      							=			benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result)
      self.check_results_dict_not_empty(results.memory_train_result)






    @unittest.skipIf(torch_device == "cpu"				, "Can't do half precision")
    def 	__snake_case							(					self				:    Optional[Any]):
      a						:		Any      							=			"sshleifer/tiny-gpt2"
      a						:		List[str]      							=			PyTorchBenchmarkArguments(
          models=[MODEL_ID]				, training=__UpperCAmelCase				, inference=__UpperCAmelCase				, sequence_lengths=[8]				, batch_sizes=[1]				, fpaa=__UpperCAmelCase				, multi_process=__UpperCAmelCase				, )
      a						:		str      							=			PyTorchBenchmark(__UpperCAmelCase)
      a						:		Union[str, Any]      							=			benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result)
      self.check_results_dict_not_empty(results.memory_train_result)






    def 	__snake_case							(					self				:    int):
      a						:		Optional[Any]      							=			"sshleifer/tiny-gpt2"
      a						:		str      							=			AutoConfig.from_pretrained(__UpperCAmelCase)
      a						:		Dict      							=			PyTorchBenchmarkArguments(
          models=[MODEL_ID]				, training=__UpperCAmelCase				, inference=__UpperCAmelCase				, sequence_lengths=[8]				, batch_sizes=[1]				, multi_process=__UpperCAmelCase				, )
      a						:		int      							=			PyTorchBenchmark(__UpperCAmelCase				, configs=[config])
      a						:		Tuple      							=			benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result)
      self.check_results_dict_not_empty(results.memory_inference_result)






    def 	__snake_case							(					self				:    Any):
      a						:		str      							=			"sshleifer/tinier_bart"
      a						:		Union[str, Any]      							=			AutoConfig.from_pretrained(__UpperCAmelCase)
      a						:		Any      							=			PyTorchBenchmarkArguments(
          models=[MODEL_ID]				, training=__UpperCAmelCase				, inference=__UpperCAmelCase				, sequence_lengths=[8]				, batch_sizes=[1]				, multi_process=__UpperCAmelCase				, )
      a						:		List[str]      							=			PyTorchBenchmark(__UpperCAmelCase				, configs=[config])
      a						:		Optional[Any]      							=			benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result)
      self.check_results_dict_not_empty(results.memory_inference_result)






    def 	__snake_case							(					self				:    Optional[Any]):
      a						:		Dict      							=			"sshleifer/tiny-gpt2"
      a						:		Dict      							=			AutoConfig.from_pretrained(__UpperCAmelCase)
      a						:		Union[str, Any]      							=			PyTorchBenchmarkArguments(
          models=[MODEL_ID]				, training=__UpperCAmelCase				, inference=__UpperCAmelCase				, sequence_lengths=[8]				, batch_sizes=[1]				, multi_process=__UpperCAmelCase				, )
      a						:		int      							=			PyTorchBenchmark(__UpperCAmelCase				, configs=[config])
      a						:		List[str]      							=			benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result)
      self.check_results_dict_not_empty(results.memory_train_result)






    def 	__snake_case							(					self				:    Optional[int]):
      a						:		Any      							=			"sshleifer/tinier_bart"
      a						:		Optional[Any]      							=			AutoConfig.from_pretrained(__UpperCAmelCase)
      a						:		Dict      							=			PyTorchBenchmarkArguments(
          models=[MODEL_ID]				, training=__UpperCAmelCase				, inference=__UpperCAmelCase				, sequence_lengths=[8]				, batch_sizes=[1]				, multi_process=__UpperCAmelCase				, )
      a						:		Dict      							=			PyTorchBenchmark(__UpperCAmelCase				, configs=[config])
      a						:		Optional[int]      							=			benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result)
      self.check_results_dict_not_empty(results.memory_train_result)






    def 	__snake_case							(					self				:    int):
      a						:		List[str]      							=			"sshleifer/tiny-gpt2"
      with tempfile.TemporaryDirectory() as tmp_dir:
        a						:		Tuple      							=			PyTorchBenchmarkArguments(
            models=[MODEL_ID]				, training=__UpperCAmelCase				, inference=__UpperCAmelCase				, save_to_csv=__UpperCAmelCase				, sequence_lengths=[8]				, batch_sizes=[1]				, inference_time_csv_file=os.path.join(__UpperCAmelCase				, "inf_time.csv")				, train_memory_csv_file=os.path.join(__UpperCAmelCase				, "train_mem.csv")				, inference_memory_csv_file=os.path.join(__UpperCAmelCase				, "inf_mem.csv")				, train_time_csv_file=os.path.join(__UpperCAmelCase				, "train_time.csv")				, env_info_csv_file=os.path.join(__UpperCAmelCase				, "env.csv")				, multi_process=__UpperCAmelCase				, )
        a						:		List[str]      							=			PyTorchBenchmark(__UpperCAmelCase)
        benchmark.run()
        self.assertTrue(Path(os.path.join(__UpperCAmelCase				, "inf_time.csv")).exists())
        self.assertTrue(Path(os.path.join(__UpperCAmelCase				, "train_time.csv")).exists())
        self.assertTrue(Path(os.path.join(__UpperCAmelCase				, "inf_mem.csv")).exists())
        self.assertTrue(Path(os.path.join(__UpperCAmelCase				, "train_mem.csv")).exists())
        self.assertTrue(Path(os.path.join(__UpperCAmelCase				, "env.csv")).exists())






    def 	__snake_case							(					self				:    int):
      a						:		Union[str, Any]      							=			"sshleifer/tiny-gpt2"

      def _check_summary_is_not_empty(__UpperCAmelCase				:    str):
        self.assertTrue(hasattr(__UpperCAmelCase				, "sequential"))
        self.assertTrue(hasattr(__UpperCAmelCase				, "cumulative"))
        self.assertTrue(hasattr(__UpperCAmelCase				, "current"))
        self.assertTrue(hasattr(__UpperCAmelCase				, "total"))

      with tempfile.TemporaryDirectory() as tmp_dir:
        a						:		Tuple      							=			PyTorchBenchmarkArguments(
            models=[MODEL_ID]				, training=__UpperCAmelCase				, inference=__UpperCAmelCase				, sequence_lengths=[8]				, batch_sizes=[1]				, log_filename=os.path.join(__UpperCAmelCase				, "log.txt")				, log_print=__UpperCAmelCase				, trace_memory_line_by_line=__UpperCAmelCase				, multi_process=__UpperCAmelCase				, )
        a						:		Optional[Any]      							=			PyTorchBenchmark(__UpperCAmelCase)
        a						:		int      							=			benchmark.run()
        _check_summary_is_not_empty(result.inference_summary)
        _check_summary_is_not_empty(result.train_summary)
        self.assertTrue(Path(os.path.join(__UpperCAmelCase				, "log.txt")).exists())










"""simple docstring"""







from __future__ import annotations

from PIL import Image

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


def      lowercase			(				A_       )->      list[list[int]]:
  '''simple docstring'''







  a						:		str      							=			[]
  for i in range(len(A_       )       ):
    a						:		str      							=			[]
    for j in range(len(cells[i]       )       ):
      # Get the number of live neighbours
      a						:		Union[str, Any]      							=			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						:		Tuple      							=			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      lowercase			(				A_  ,   A_       )->      list[Image.Image]:
  '''simple docstring'''







  a						:		List[str]      							=			[]
  for _ in range(A_       ):
    # Create output image
    a						:		str      							=			Image.new("RGB"  ,   (len(cells[0]       ), len(A_       ))       )
    a						:		Union[str, Any]      							=			img.load()

    # Save cells to image
    for x in range(len(A_       )       ):
      for y in range(len(cells[0]       )       ):
        a						:		Optional[Any]      							=			255 - cells[y][x] * 255
        a						:		str      							=			(colour, colour, colour)

        # Save image
    images.append(A_       )
    a						:		Tuple      							=			new_generation(A_       )
  return images


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






