





'''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			):




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    List[Any]    ):
      for model_result in results.values():
           for batch_size, sequence_length in zip(model_result["bs"]	,			model_result["ss"]    ):
                _UpperCAmelCase     							=   model_result["result"][batch_size][sequence_length]
                self.assertIsNotNone(snake_case_    )




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   "sshleifer/tiny-gpt2"
      _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
          models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=snake_case_	,			)
      _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_    )
      _UpperCAmelCase     							=   benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result    )
      self.check_results_dict_not_empty(results.memory_inference_result    )




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




 def      lowercase	(					self       :    str    ):
      _UpperCAmelCase     							=   "sshleifer/tiny-gpt2"
      _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
          models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			torchscript=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=snake_case_	,			)
      _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_    )
      _UpperCAmelCase     							=   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      lowercase	(					self       :    str    ):
      _UpperCAmelCase     							=   "sshleifer/tiny-gpt2"
      _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
          models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			fpaa=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=snake_case_	,			)
      _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_    )
      _UpperCAmelCase     							=   benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result    )
      self.check_results_dict_not_empty(results.memory_inference_result    )




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




 def      lowercase	(					self       :    List[str]    ):
      _UpperCAmelCase     							=   "sshleifer/tiny-gpt2"
      _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
          models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=snake_case_	,			)
      _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_    )
      _UpperCAmelCase     							=   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      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   "sshleifer/tiny-gpt2"
      _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
          models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			fpaa=snake_case_	,			multi_process=snake_case_	,			)
      _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_    )
      _UpperCAmelCase     							=   benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result    )
      self.check_results_dict_not_empty(results.memory_train_result    )




 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   "sshleifer/tiny-gpt2"
      _UpperCAmelCase     							=   AutoConfig.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
          models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=snake_case_	,			)
      _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_	,			configs=[config]    )
      _UpperCAmelCase     							=   benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result    )
      self.check_results_dict_not_empty(results.memory_inference_result    )




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   "sshleifer/tinier_bart"
      _UpperCAmelCase     							=   AutoConfig.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
          models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=snake_case_	,			)
      _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_	,			configs=[config]    )
      _UpperCAmelCase     							=   benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result    )
      self.check_results_dict_not_empty(results.memory_inference_result    )




 def      lowercase	(					self       :    Any    ):
      _UpperCAmelCase     							=   "sshleifer/tiny-gpt2"
      _UpperCAmelCase     							=   AutoConfig.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
          models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=snake_case_	,			)
      _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_	,			configs=[config]    )
      _UpperCAmelCase     							=   benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result    )
      self.check_results_dict_not_empty(results.memory_train_result    )




 def      lowercase	(					self       :    List[str]    ):
      _UpperCAmelCase     							=   "sshleifer/tinier_bart"
      _UpperCAmelCase     							=   AutoConfig.from_pretrained(snake_case_    )
      _UpperCAmelCase     							=   PyTorchBenchmarkArguments(
          models=[MODEL_ID]	,			training=snake_case_	,			inference=snake_case_	,			sequence_lengths=[8]	,			batch_sizes=[1]	,			multi_process=snake_case_	,			)
      _UpperCAmelCase     							=   PyTorchBenchmark(snake_case_	,			configs=[config]    )
      _UpperCAmelCase     							=   benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result    )
      self.check_results_dict_not_empty(results.memory_train_result    )




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




 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   "sshleifer/tiny-gpt2"

      def _check_summary_is_not_empty(snake_case_       :    Any    ):
           self.assertTrue(hasattr(snake_case_	,			"sequential"    )    )
           self.assertTrue(hasattr(snake_case_	,			"cumulative"    )    )
           self.assertTrue(hasattr(snake_case_	,			"current"    )    )
           self.assertTrue(hasattr(snake_case_	,			"total"    )    )

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











'''simple docstring'''

import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

lowercase_					= """Usage of script: script_name <size_of_canvas:int>"""

lowercase_					= [0] * 100 + [1] * 10
random.shuffle(choice)





def lowerCamelCase						(     __lowerCamelCase :    int    )						->list[list[bool]]:
    _SCREAMING_SNAKE_CASE									=			[[False for i in range(__lowerCamelCase    )] for j in range(__lowerCamelCase    )]
    return canvas





def lowerCamelCase						(     __lowerCamelCase :    list[list[bool]]    )						->None:
    for i, row in enumerate(__lowerCamelCase    ):
        for j, _ in enumerate(__lowerCamelCase    ):
            _SCREAMING_SNAKE_CASE									=			bool(random.getrandbits(1    )    )





def lowerCamelCase						(     __lowerCamelCase :    list[list[bool]]    )						->list[list[bool]]:
    _SCREAMING_SNAKE_CASE									=			np.array(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			np.array(create_canvas(current_canvas.shape[0]    )    )
    for r, row in enumerate(__lowerCamelCase    ):
        for c, pt in enumerate(__lowerCamelCase    ):
            _SCREAMING_SNAKE_CASE									=			__judge_point(
                __lowerCamelCase					,       current_canvas[r - 1 : r + 2, c - 1 : c + 2]    )

    _SCREAMING_SNAKE_CASE									=			next_gen_canvas
    del next_gen_canvas  # cleaning memory as we move on.
    _SCREAMING_SNAKE_CASE									=			current_canvas.tolist()
    return return_canvas





def lowerCamelCase						(     __lowerCamelCase :    bool					,       __lowerCamelCase :    list[list[bool]]    )						->bool:
    _SCREAMING_SNAKE_CASE									=			0
    _SCREAMING_SNAKE_CASE									=			0
    # finding dead or alive neighbours count.
    for i in neighbours:
        for status in i:
            if status:
                alive += 1
            else:
                dead += 1

    # handling duplicate entry for focus pt.
    if pt:
        alive -= 1
    else:
        dead -= 1

    # running the rules of game here.
    _SCREAMING_SNAKE_CASE									=			pt
    if pt:
        if alive < 2:
            _SCREAMING_SNAKE_CASE									=			False
        elif alive == 2 or alive == 3:
            _SCREAMING_SNAKE_CASE									=			True
        elif alive > 3:
            _SCREAMING_SNAKE_CASE									=			False
    else:
        if alive == 3:
            _SCREAMING_SNAKE_CASE									=			True

    return state


if __name__ == "__main__":
      if len(sys.argv) != 2:
            raise Exception(usage_doc)

      lowercase_					= int(sys.argv[1])
      # main working structure of this module.
      lowercase_					= create_canvas(canvas_size)
      seed(c)
      lowercase_				,			lowercase_					= plt.subplots()
      fig.show()
      lowercase_					= ListedColormap(["""w""", """k"""])
      try:
            while True:
                  lowercase_					= run(c)
                  ax.matshow(c, cmap=cmap)
                  fig.canvas.draw()
                  ax.cla()
      except KeyboardInterrupt:
            # do nothing.
            pass



