




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					(PNDMScheduler,)
						UpperCamelCase     						=					(("""num_inference_steps""", 50),)

						def     A__						(		self   :Union[str, Any]    , **__snake_case   :Any ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								={
										    """num_train_timesteps""": 10_00,
										    """beta_start""": 0.0001,
										    """beta_end""": 0.02,
										    """beta_schedule""": """linear""",
										}

										config.update(**__snake_case )
										return config

						def     A__						(		self   :List[str]    , __snake_case   :int=0    , **__snake_case   :str ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=dict(self.forward_default_kwargs )
										__magic_name__	:	str								=kwargs.pop("""num_inference_steps"""    , __snake_case )
										__magic_name__	:	Dict								=self.dummy_sample
										__magic_name__	:	int								=0.1 * sample
										__magic_name__	:	List[Any]								=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														__magic_name__	:	Tuple								=self.get_scheduler_config(**__snake_case )
														__magic_name__	:	List[Any]								=scheduler_class(**__snake_case )
														scheduler.set_timesteps(__snake_case )
														# copy over dummy past residuals
														__magic_name__	:	Optional[int]								=dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(__snake_case )
																		__magic_name__	:	List[str]								=scheduler_class.from_pretrained(__snake_case )
																		new_scheduler.set_timesteps(__snake_case )
																		# copy over dummy past residuals
																		__magic_name__	:	Tuple								=dummy_past_residuals[:]

														__magic_name__	:	Union[str, Any]								=scheduler.step_prk(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample
														__magic_name__	:	Union[str, Any]								=new_scheduler.step_prk(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample

														assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"

														__magic_name__	:	str								=scheduler.step_plms(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample
														__magic_name__	:	List[Any]								=new_scheduler.step_plms(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample

														assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										pass

						def     A__						(		self   :List[Any]    , __snake_case   :Optional[Any]=0    , **__snake_case   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Any								=dict(self.forward_default_kwargs )
										__magic_name__	:	List[str]								=kwargs.pop("""num_inference_steps"""    , __snake_case )
										__magic_name__	:	List[Any]								=self.dummy_sample
										__magic_name__	:	Optional[Any]								=0.1 * sample
										__magic_name__	:	Optional[Any]								=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														__magic_name__	:	Tuple								=self.get_scheduler_config()
														__magic_name__	:	Optional[Any]								=scheduler_class(**__snake_case )
														scheduler.set_timesteps(__snake_case )

														# copy over dummy past residuals (must be after setting timesteps)
														__magic_name__	:	str								=dummy_past_residuals[:]

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(__snake_case )
																		__magic_name__	:	List[Any]								=scheduler_class.from_pretrained(__snake_case )
																		# copy over dummy past residuals
																		new_scheduler.set_timesteps(__snake_case )

																		# copy over dummy past residual (must be after setting timesteps)
																		__magic_name__	:	Optional[Any]								=dummy_past_residuals[:]

														__magic_name__	:	str								=scheduler.step_prk(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample
														__magic_name__	:	Tuple								=new_scheduler.step_prk(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample

														assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"

														__magic_name__	:	Union[str, Any]								=scheduler.step_plms(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample
														__magic_name__	:	Union[str, Any]								=new_scheduler.step_plms(__snake_case    , __snake_case    , __snake_case    , **__snake_case ).prev_sample

														assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"

						def     A__						(		self   :List[Any]    , **__snake_case   :Union[str, Any] ):
										'''simple docstring'''

										__magic_name__	:	str								=self.scheduler_classes[0]
										__magic_name__	:	Union[str, Any]								=self.get_scheduler_config(**__snake_case )
										__magic_name__	:	Dict								=scheduler_class(**__snake_case )

										__magic_name__	:	Tuple								=10
										__magic_name__	:	List[Any]								=self.dummy_model()
										__magic_name__	:	Dict								=self.dummy_sample_deter
										scheduler.set_timesteps(__snake_case )

										for i, t in enumerate(scheduler.prk_timesteps ):
														__magic_name__	:	List[str]								=model(__snake_case    , __snake_case )
														__magic_name__	:	List[Any]								=scheduler.step_prk(__snake_case    , __snake_case    , __snake_case ).prev_sample

										for i, t in enumerate(scheduler.plms_timesteps ):
														__magic_name__	:	Tuple								=model(__snake_case    , __snake_case )
														__magic_name__	:	List[Any]								=scheduler.step_plms(__snake_case    , __snake_case    , __snake_case ).prev_sample

										return sample

						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=dict(self.forward_default_kwargs )

										__magic_name__	:	Dict								=kwargs.pop("""num_inference_steps"""    , __snake_case )

										for scheduler_class in self.scheduler_classes:
														__magic_name__	:	Optional[int]								=self.get_scheduler_config()
														__magic_name__	:	int								=scheduler_class(**__snake_case )

														__magic_name__	:	Optional[Any]								=self.dummy_sample
														__magic_name__	:	List[str]								=0.1 * sample

														if num_inference_steps is not None and hasattr(__snake_case    , """set_timesteps""" ):
																		scheduler.set_timesteps(__snake_case )
														elif num_inference_steps is not None and not hasattr(__snake_case    , """set_timesteps""" ):
																		__magic_name__	:	str								=num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														__magic_name__	:	Any								=[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
														__magic_name__	:	Tuple								=dummy_past_residuals[:]

														__magic_name__	:	Optional[int]								=scheduler.step_prk(__snake_case    , 0    , __snake_case    , **__snake_case ).prev_sample
														__magic_name__	:	Optional[Any]								=scheduler.step_prk(__snake_case    , 1    , __snake_case    , **__snake_case ).prev_sample

														self.assertEqual(output_a.shape    , sample.shape )
														self.assertEqual(output_a.shape    , output_a.shape )

														__magic_name__	:	Optional[int]								=scheduler.step_plms(__snake_case    , 0    , __snake_case    , **__snake_case ).prev_sample
														__magic_name__	:	Tuple								=scheduler.step_plms(__snake_case    , 1    , __snake_case    , **__snake_case ).prev_sample

														self.assertEqual(output_a.shape    , sample.shape )
														self.assertEqual(output_a.shape    , output_a.shape )

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										for timesteps in [1_00, 10_00]:
														self.check_over_configs(num_train_timesteps=__snake_case )

						def     A__						(		self   :Any ):
										'''simple docstring'''

										for steps_offset in [0, 1]:
														self.check_over_configs(steps_offset=__snake_case )

										__magic_name__	:	Any								=self.scheduler_classes[0]
										__magic_name__	:	str								=self.get_scheduler_config(steps_offset=1 )
										__magic_name__	:	Optional[int]								=scheduler_class(**__snake_case )
										scheduler.set_timesteps(10 )
										assert torch.equal(
										    scheduler.timesteps    , torch.LongTensor(
										        [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] )    , )

						def     A__						(		self   :Any ):
										'''simple docstring'''

										for beta_start, beta_end in zip([0.0001, 0.001]    , [0.002, 0.02] ):
														self.check_over_configs(beta_start=__snake_case    , beta_end=__snake_case )

						def     A__						(		self   :Dict ):
										'''simple docstring'''

										for schedule in ["linear", "squaredcos_cap_v2"]:
														self.check_over_configs(beta_schedule=__snake_case )

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

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

						def     A__						(		self   :List[Any] ):
										'''simple docstring'''

										for t in [1, 5, 10]:
														self.check_over_forward(time_step=__snake_case )

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										for t, num_inference_steps in zip([1, 5, 10]    , [10, 50, 1_00] ):
														self.check_over_forward(num_inference_steps=__snake_case )

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=27

										for scheduler_class in self.scheduler_classes:
														__magic_name__	:	Optional[Any]								=self.dummy_sample
														__magic_name__	:	Optional[int]								=0.1 * sample

														__magic_name__	:	List[str]								=self.get_scheduler_config()
														__magic_name__	:	Any								=scheduler_class(**__snake_case )

														scheduler.set_timesteps(__snake_case )

														# before power of 3 fix, would error on first step, so we only need to do two
														for i, t in enumerate(scheduler.prk_timesteps[:2] ):
																		__magic_name__	:	Union[str, Any]								=scheduler.step_prk(__snake_case    , __snake_case    , __snake_case ).prev_sample

						def     A__						(		self   :Tuple ):
										'''simple docstring'''

										with self.assertRaises(__snake_case ):
														__magic_name__	:	Any								=self.scheduler_classes[0]
														__magic_name__	:	Optional[Any]								=self.get_scheduler_config()
														__magic_name__	:	Any								=scheduler_class(**__snake_case )

														scheduler.step_plms(self.dummy_sample    , 1    , self.dummy_sample ).prev_sample

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=self.full_loop()
										__magic_name__	:	List[str]								=torch.sum(torch.abs(__snake_case ) )
										__magic_name__	:	Union[str, Any]								=torch.mean(torch.abs(__snake_case ) )

										assert abs(result_sum.item() - 198.1318 ) < 1E-2
										assert abs(result_mean.item() - 0.2580 ) < 1E-3

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	Optional[int]								=self.full_loop(prediction_type="""v_prediction""" )
										__magic_name__	:	int								=torch.sum(torch.abs(__snake_case ) )
										__magic_name__	:	Tuple								=torch.mean(torch.abs(__snake_case ) )

										assert abs(result_sum.item() - 67.3986 ) < 1E-2
										assert abs(result_mean.item() - 0.0878 ) < 1E-3

						def     A__						(		self   :str ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=self.full_loop(set_alpha_to_one=__snake_case    , beta_start=0.01 )
										__magic_name__	:	Dict								=torch.sum(torch.abs(__snake_case ) )
										__magic_name__	:	Dict								=torch.mean(torch.abs(__snake_case ) )

										assert abs(result_sum.item() - 230.0399 ) < 1E-2
										assert abs(result_mean.item() - 0.2995 ) < 1E-3


						def     A__						(		self   :int ):
										'''simple docstring'''

										__magic_name__	:	Optional[Any]								=self.full_loop(set_alpha_to_one=__snake_case    , beta_start=0.01 )
										__magic_name__	:	Union[str, Any]								=torch.sum(torch.abs(__snake_case ) )
										__magic_name__	:	Any								=torch.mean(torch.abs(__snake_case ) )

										assert abs(result_sum.item() - 186.9482 ) < 1E-2
										assert abs(result_mean.item() - 0.2434 ) < 1E-3










# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


UpperCAmelCase_		:				str									=     "3"

print("Python version:", sys.version)
print("transformers version:", transformers.__version__)

try:
							import torch

							print("Torch version:", torch.__version__)
							print("Cuda available:", torch.cuda.is_available())
							print("Cuda version:", torch.version.cuda)
							print("CuDNN version:", torch.backends.cudnn.version())
							print("Number of GPUs available:", torch.cuda.device_count())
							print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
							print("Torch version:", None)

try:
							import deepspeed

							print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
							print("DeepSpeed version:", None)

try:
							import tensorflow as tf

							print("TensorFlow version:", tf.__version__)
							print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
							print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
							print("TensorFlow version:", None)




