




import colorsys

from PIL import Image  # type: ignore







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_						)   ->       float:



       """simple docstring"""

       A__       =    x
       A__       =    y
       for step in range(lowercase_						):  # noqa: B007
              A__       =    a * a - b * b + x
              A__       =    2 * a * b + y
              A__       =    a_new

              # divergence happens for all complex number with an absolute value
              # greater than 4
              if a * a + b * b > 4:
                     break
       return step / (max_step - 1)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       tuple:



       """simple docstring"""

       if distance == 1:
              return (0, 0, 0)
       else:
              return (255, 255, 255)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       tuple:



       """simple docstring"""

       if distance == 1:
              return (0, 0, 0)
       else:
              return tuple(round(i * 255						) for i in colorsys.hsv_to_rgb(lowercase_ ,				1 ,				1						)						)







def SCREAMING_SNAKE_CASE				(       lowercase_ = 800 ,				lowercase_ = 600 ,				lowercase_ = -0.6 ,				lowercase_ = 0 ,				lowercase_ = 3.2 ,				lowercase_ = 50 ,				lowercase_ = True ,				)   ->       Image.Image:



       """simple docstring"""

       A__       =    Image.new('''RGB''' ,				(image_width, image_height)						)
       A__       =    img.load()

       # loop through the image-coordinates
       for image_x in range(lowercase_						):
              for image_y in range(lowercase_						):
                     # determine the figure-coordinates based on the image-coordinates
                     A__       =    figure_width / image_width * image_height
                     A__       =    figure_center_x + (image_x / image_width - 0.5) * figure_width
                     A__       =    figure_center_y + (image_y / image_height - 0.5) * figure_height

                     A__       =    get_distance(lowercase_ ,				lowercase_ ,				lowercase_						)

                     # color the corresponding pixel based on the selected coloring-function
                     if use_distance_color_coding:
                            A__       =    get_color_coded_rgb(lowercase_						)
                     else:
                            A__       =    get_black_and_white_rgb(lowercase_						)

       return img


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # colored version, full figure
  _lowerCamelCase	:		List[str]							       =   get_image()

  # uncomment for colored version, different section, zoomed in
  # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
  # figure_width = 0.8)

  # uncomment for black and white version, full figure
  # img = get_image(use_distance_color_coding = False)

  # uncomment to save the image
  # img.save("mandelbrot.png")

  img.show()




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 				lowercase					(		unittest.TestCase   ):



		def __UpperCamelCase ( self				,      A_       )  ->					List[str]:
				"""simple docstring"""
				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(A_       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     'sgugger/tiny-distilbert-classification'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      only_pretrain_model=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      torchscript=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				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 __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      fpaa=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result       )
				self.check_results_dict_not_empty(results.memory_inference_result       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     AutoConfig.from_pretrained(A_       )
				# set architectures equal to `None`
				UpperCamelCase		=     None
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_				,      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 __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				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 __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      fpaa=A_				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_       )
				UpperCamelCase		=     benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result       )
				self.check_results_dict_not_empty(results.memory_train_result       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     AutoConfig.from_pretrained(A_       )
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_				,      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 __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tinier_bart'
				UpperCamelCase		=     AutoConfig.from_pretrained(A_       )
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_				,      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 __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				UpperCamelCase		=     AutoConfig.from_pretrained(A_       )
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_				,      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 __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tinier_bart'
				UpperCamelCase		=     AutoConfig.from_pretrained(A_       )
				UpperCamelCase		=     PyTorchBenchmarkArguments(
				    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      multi_process=A_				,      )
				UpperCamelCase		=     PyTorchBenchmark(A_				,      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 __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'
				with tempfile.TemporaryDirectory() as tmp_dir:
						UpperCamelCase		=     PyTorchBenchmarkArguments(
						    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      save_to_csv=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      inference_time_csv_file=os.path.join(A_				,      'inf_time.csv'       )				,      train_memory_csv_file=os.path.join(A_				,      'train_mem.csv'       )				,      inference_memory_csv_file=os.path.join(A_				,      'inf_mem.csv'       )				,      train_time_csv_file=os.path.join(A_				,      'train_time.csv'       )				,      env_info_csv_file=os.path.join(A_				,      'env.csv'       )				,      multi_process=A_				,      )
						UpperCamelCase		=     PyTorchBenchmark(A_       )
						benchmark.run()
						self.assertTrue(Path(os.path.join(A_				,      'inf_time.csv'       )       ).exists()       )
						self.assertTrue(Path(os.path.join(A_				,      'train_time.csv'       )       ).exists()       )
						self.assertTrue(Path(os.path.join(A_				,      'inf_mem.csv'       )       ).exists()       )
						self.assertTrue(Path(os.path.join(A_				,      'train_mem.csv'       )       ).exists()       )
						self.assertTrue(Path(os.path.join(A_				,      'env.csv'       )       ).exists()       )



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     'sshleifer/tiny-gpt2'

				def _check_summary_is_not_empty(A_       ):
						self.assertTrue(hasattr(A_				,      'sequential'       )       )
						self.assertTrue(hasattr(A_				,      'cumulative'       )       )
						self.assertTrue(hasattr(A_				,      'current'       )       )
						self.assertTrue(hasattr(A_				,      'total'       )       )

				with tempfile.TemporaryDirectory() as tmp_dir:
						UpperCamelCase		=     PyTorchBenchmarkArguments(
						    models=[MODEL_ID]				,      training=A_				,      inference=A_				,      sequence_lengths=[8]				,      batch_sizes=[1]				,      log_filename=os.path.join(A_				,      'log.txt'       )				,      log_print=A_				,      trace_memory_line_by_line=A_				,      multi_process=A_				,      )
						UpperCamelCase		=     PyTorchBenchmark(A_       )
						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(A_				,      'log.txt'       )       ).exists()       )
