code: 
  
  
  '''simple docstring'''
  
  
  
  import os
  import shutil
  import unittest
  from unittest.mock import patch
  
  from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch
  
  
  
  
  
  class 							__UpperCamelCase       (	unittest.TestCase      ):
  
  
  
  
  	@patch('sys.argv'		,       ['fakeprogrampath', 'env']							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						import transformers.commands.transformers_cli
  
  						with CaptureStd() as cs:
  											transformers.commands.transformers_cli.main()
  						self.assertIn('Python version'		,       cs.out							)
  						self.assertIn('Platform'		,       cs.out							)
  						self.assertIn('Using distributed or parallel set-up in script?'		,       cs.out							)
  
  
  
  
  	@is_pt_tf_cross_test
  	@patch(
  	    'sys.argv'		,       ['fakeprogrampath', 'pt-to-tf', '--model-name', 'hf-internal-testing/tiny-random-gptj', '--no-pr']							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						import transformers.commands.transformers_cli
  
  						shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'		,       ignore_errors=__a							)  # cleans potential past runs
  						transformers.commands.transformers_cli.main()
  
  						# The original repo has no TF weights -- if they exist, they were created by the CLI
  						self.assertTrue(os.path.exists('/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5'							)							)
  
  
  
  
  	@require_torch
  	@patch('sys.argv'		,       ['fakeprogrampath', 'download', 'hf-internal-testing/tiny-random-gptj', '--cache-dir', '/tmp']							)
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						import transformers.commands.transformers_cli
  
  						# # remove any previously downloaded model to start clean
  						shutil.rmtree('/tmp/models--hf-internal-testing--tiny-random-gptj'		,       ignore_errors=__a							)
  
  						# run the command
  						transformers.commands.transformers_cli.main()
  
  						# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
  						self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/blobs'							)							)
  						self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/refs'							)							)
  						self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots'							)							)
  
  
  
  
  
  	@require_torch
  	@patch(
  	    'sys.argv'		,       [
  	        'fakeprogrampath',
  	        'download',
  	        'hf-internal-testing/test_dynamic_model_with_tokenizer',
  	        '--trust-remote-code',
  	        '--cache-dir',
  	        '/tmp',
  	    ]		,       )
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						import transformers.commands.transformers_cli
  
  						# # remove any previously downloaded model to start clean
  						shutil.rmtree('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer'		,       ignore_errors=__a							)
  
  						# run the command
  						transformers.commands.transformers_cli.main()
  
  						# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
  						self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs'							)							)
  						self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs'							)							)
  						self.assertTrue(
  						    os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots'							)							)
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  	A_			    =					(KDPMaAncestralDiscreteScheduler,)
  	A_			    =					10
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       **__a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Dict      					=						{
  						    'num_train_timesteps': 1100,
  						    'beta_start': 0.0001,
  						    'beta_end': 0.02,
  						    'beta_schedule': 'linear',
  						}
  
  						config.update(**__a							)
  						return config
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for timesteps in [10, 50, 100, 1000]:
  											self.check_over_configs(num_train_timesteps=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,       [0.0002, 0.002, 0.02]							):
  											self.check_over_configs(beta_start=__a		,       beta_end=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for schedule in ["linear", "scaled_linear"]:
  											self.check_over_configs(beta_schedule=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if torch_device == "mps":
  											return
  						__a    : Dict      					=						self.scheduler_classes[0]
  						__a    : Tuple      					=						self.get_scheduler_config()
  						__a    : Optional[int]      					=						scheduler_class(**__a							)
  
  						scheduler.set_timesteps(self.num_inference_steps							)
  
  						__a    : int      					=						torch.manual_seed(0							)
  
  						__a    : Dict      					=						self.dummy_model()
  						__a    : List[Any]      					=						self.dummy_sample_deter * scheduler.init_noise_sigma
  						__a    : int      					=						sample.to(__a							)
  
  						for i, t in enumerate(scheduler.timesteps							):
  											__a    : Any      					=						scheduler.scale_model_input(__a		,       __a							)
  
  											__a    : int      					=						model(__a		,       __a							)
  
  											__a    : Dict      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
  											__a    : int      					=						output.prev_sample
  
  						__a    : Tuple      					=						torch.sum(torch.abs(__a							)							)
  						__a    : List[str]      					=						torch.mean(torch.abs(__a							)							)
  
  						assert abs(result_sum.item() - 13849.3877							) < 1E-2
  						assert abs(result_mean.item() - 18.0331							) < 5E-3
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for prediction_type in ["epsilon", "v_prediction"]:
  											self.check_over_configs(prediction_type=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if torch_device == "mps":
  											return
  						__a    : Dict      					=						self.scheduler_classes[0]
  						__a    : Dict      					=						self.get_scheduler_config(prediction_type='v_prediction'							)
  						__a    : List[str]      					=						scheduler_class(**__a							)
  
  						scheduler.set_timesteps(self.num_inference_steps							)
  
  						__a    : Dict      					=						self.dummy_model()
  						__a    : List[str]      					=						self.dummy_sample_deter * scheduler.init_noise_sigma
  						__a    : List[str]      					=						sample.to(__a							)
  
  						__a    : Tuple      					=						torch.manual_seed(0							)
  
  						for i, t in enumerate(scheduler.timesteps							):
  											__a    : List[Any]      					=						scheduler.scale_model_input(__a		,       __a							)
  
  											__a    : Optional[int]      					=						model(__a		,       __a							)
  
  											__a    : Union[str, Any]      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
  											__a    : Union[str, Any]      					=						output.prev_sample
  
  						__a    : Any      					=						torch.sum(torch.abs(__a							)							)
  						__a    : Optional[int]      					=						torch.mean(torch.abs(__a							)							)
  
  						assert abs(result_sum.item() - 328.9970							) < 1E-2
  						assert abs(result_mean.item() - 0.4284							) < 1E-3
  
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if torch_device == "mps":
  											return
  						__a    : List[str]      					=						self.scheduler_classes[0]
  						__a    : Union[str, Any]      					=						self.get_scheduler_config()
  						__a    : List[Any]      					=						scheduler_class(**__a							)
  
  						scheduler.set_timesteps(self.num_inference_steps		,       device=__a							)
  						__a    : Union[str, Any]      					=						torch.manual_seed(0							)
  
  						__a    : List[str]      					=						self.dummy_model()
  						__a    : Any      					=						self.dummy_sample_deter.to(__a							) * scheduler.init_noise_sigma
  
  						for t in scheduler.timesteps:
  											__a    : List[Any]      					=						scheduler.scale_model_input(__a		,       __a							)
  
  											__a    : str      					=						model(__a		,       __a							)
  
  											__a    : str      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
  											__a    : Any      					=						output.prev_sample
  
  						__a    : Union[str, Any]      					=						torch.sum(torch.abs(__a							)							)
  						__a    : str      					=						torch.mean(torch.abs(__a							)							)
  
  						assert abs(result_sum.item() - 13849.3818							) < 1E-1
  						assert abs(result_mean.item() - 18.0331							) < 1E-3
  
  
  
  