
"""simple docstring"""
import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class  _snake_case	(    _lowercase							):
							SCREAMING_SNAKE_CASE		  =    (EulerAncestralDiscreteScheduler,)
							SCREAMING_SNAKE_CASE		  =    10






							def 				__lowercase	(       self    :      int    ,			**snake_case__    :      Optional[int]     ):






												"""simple docstring"""



												__a:    List[Any]		     =       {
												    """num_train_timesteps""": 1_1_0_0,
												    """beta_start""": 0.0_001,
												    """beta_end""": 0.02,
												    """beta_schedule""": """linear""",
												}

												config.update(**snake_case__     )
												return config






							def 				__lowercase	(       self    :      Union[str, Any]     ):






												"""simple docstring"""



												for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=snake_case__     )






							def 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]    ,			[0.0_002, 0.002, 0.02]     ):
																	self.check_over_configs(beta_start=snake_case__    ,			beta_end=snake_case__     )






							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												for schedule in ["linear", "scaled_linear"]:
																	self.check_over_configs(beta_schedule=snake_case__     )






							def 				__lowercase	(       self    :      Any     ):






												"""simple docstring"""



												for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=snake_case__     )






							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												__a:    int		     =       self.scheduler_classes[0]
												__a:    List[str]		     =       self.get_scheduler_config()
												__a:    Dict		     =       scheduler_class(**snake_case__     )

												scheduler.set_timesteps(self.num_inference_steps     )

												__a:    Optional[int]		     =       torch.manual_seed(0     )

												__a:    Dict		     =       self.dummy_model()
												__a:    Tuple		     =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
												__a:    List[str]		     =       sample.to(snake_case__     )

												for i, t in enumerate(scheduler.timesteps     ):
																	__a:    Dict		     =       scheduler.scale_model_input(snake_case__    ,			snake_case__     )

																	__a:    int		     =       model(snake_case__    ,			snake_case__     )

																	__a:    List[str]		     =       scheduler.step(snake_case__    ,			snake_case__    ,			snake_case__    ,			generator=snake_case__     )
																	__a:    Optional[Any]		     =       output.prev_sample

												__a:    List[str]		     =       torch.sum(torch.abs(snake_case__     )     )
												__a:    Tuple		     =       torch.mean(torch.abs(snake_case__     )     )

												assert abs(result_sum.item() - 152.3_192     ) < 1e-2
												assert abs(result_mean.item() - 0.1_983     ) < 1e-3






							def 				__lowercase	(       self    :      int     ):






												"""simple docstring"""



												__a:    Union[str, Any]		     =       self.scheduler_classes[0]
												__a:    Dict		     =       self.get_scheduler_config(prediction_type="""v_prediction"""     )
												__a:    str		     =       scheduler_class(**snake_case__     )

												scheduler.set_timesteps(self.num_inference_steps     )

												__a:    List[Any]		     =       torch.manual_seed(0     )

												__a:    Optional[int]		     =       self.dummy_model()
												__a:    List[Any]		     =       self.dummy_sample_deter * scheduler.init_noise_sigma
												__a:    Dict		     =       sample.to(snake_case__     )

												for i, t in enumerate(scheduler.timesteps     ):
																	__a:    Tuple		     =       scheduler.scale_model_input(snake_case__    ,			snake_case__     )

																	__a:    Tuple		     =       model(snake_case__    ,			snake_case__     )

																	__a:    Optional[int]		     =       scheduler.step(snake_case__    ,			snake_case__    ,			snake_case__    ,			generator=snake_case__     )
																	__a:    Optional[Any]		     =       output.prev_sample

												__a:    Dict		     =       torch.sum(torch.abs(snake_case__     )     )
												__a:    str		     =       torch.mean(torch.abs(snake_case__     )     )

												assert abs(result_sum.item() - 108.4_439     ) < 1e-2
												assert abs(result_mean.item() - 0.1_412     ) < 1e-3






							def 				__lowercase	(       self    :      Any     ):






												"""simple docstring"""



												__a:    str		     =       self.scheduler_classes[0]
												__a:    List[str]		     =       self.get_scheduler_config()
												__a:    Dict		     =       scheduler_class(**snake_case__     )

												scheduler.set_timesteps(self.num_inference_steps    ,			device=snake_case__     )
												__a:    List[Any]		     =       torch.manual_seed(0     )

												__a:    List[str]		     =       self.dummy_model()
												__a:    Tuple		     =       self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
												__a:    Union[str, Any]		     =       sample.to(snake_case__     )

												for t in scheduler.timesteps:
																	__a:    Tuple		     =       scheduler.scale_model_input(snake_case__    ,			snake_case__     )

																	__a:    Tuple		     =       model(snake_case__    ,			snake_case__     )

																	__a:    Tuple		     =       scheduler.step(snake_case__    ,			snake_case__    ,			snake_case__    ,			generator=snake_case__     )
																	__a:    List[Any]		     =       output.prev_sample

												__a:    Optional[Any]		     =       torch.sum(torch.abs(snake_case__     )     )
												__a:    List[Any]		     =       torch.mean(torch.abs(snake_case__     )     )

												assert abs(result_sum.item() - 152.3_192     ) < 1e-2
												assert abs(result_mean.item() - 0.1_983     ) < 1e-3


"""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  _snake_case	(    unittest.TestCase							):






							@patch("""sys.argv"""    ,			["""fakeprogrampath""", """env"""]     )
							def 				__lowercase	(       self    :      Tuple     ):






												"""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 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												import transformers.commands.transformers_cli

												shutil.rmtree("""/tmp/hf-internal-testing/tiny-random-gptj"""    ,			ignore_errors=snake_case__     )  # 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 				__lowercase	(       self    :      Optional[Any]     ):






												"""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=snake_case__     )

												# 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 				__lowercase	(       self    :      Union[str, Any]     ):






												"""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=snake_case__     )

												# 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"""     )     )
