





"""simple docstring"""





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       List[str]         =       (KDPMaDiscreteScheduler,)
      _A     :       Optional[Any]         =       10

      def       A_					(							self       :      Dict						,				**__lowercase       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Union[str, Any]       =						{
            '''num_train_timesteps''': 1_100,
            '''beta_start''': 0.0_0_0_1,
            '''beta_end''': 0.0_2,
            '''beta_schedule''': '''linear''',
        }

        config.update(**__lowercase )
        return config

      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        for timesteps in [10, 50, 100, 1_000]:
          self.check_over_configs(num_train_timesteps=__lowercase )

      def       A_					(							self       :      Any ):


        '''simple docstring'''




        for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]						,				[0.0_0_0_2, 0.0_0_2, 0.0_2] ):
          self.check_over_configs(beta_start=__lowercase						,				beta_end=__lowercase )

      def       A_					(							self       :      Dict ):


        '''simple docstring'''




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

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


        '''simple docstring'''




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

      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						self.scheduler_classes[0]
        __UpperCAmelCase    :						List[str]       =						self.get_scheduler_config(prediction_type='''v_prediction''' )
        __UpperCAmelCase    :						Tuple       =						scheduler_class(**__lowercase )

        scheduler.set_timesteps(self.num_inference_steps )

        __UpperCAmelCase    :						Optional[int]       =						self.dummy_model()
        __UpperCAmelCase    :						List[str]       =						self.dummy_sample_deter * scheduler.init_noise_sigma
        __UpperCAmelCase    :						Any       =						sample.to(__lowercase )

        for i, t in enumerate(scheduler.timesteps ):
          __UpperCAmelCase    :						Tuple       =						scheduler.scale_model_input(__lowercase						,				__lowercase )

          __UpperCAmelCase    :						Optional[Any]       =						model(__lowercase						,				__lowercase )

          __UpperCAmelCase    :						Tuple       =						scheduler.step(__lowercase						,				__lowercase						,				__lowercase )
          __UpperCAmelCase    :						Dict       =						output.prev_sample

        __UpperCAmelCase    :						List[str]       =						torch.sum(torch.abs(__lowercase ) )
        __UpperCAmelCase    :						Optional[Any]       =						torch.mean(torch.abs(__lowercase ) )

        if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 4.6934e-07 ) < 1e-2
          assert abs(result_mean.item() - 6.1112e-10 ) < 1e-3
        else:
          # CUDA
          assert abs(result_sum.item() - 4.693_4286_5017_0972e-07 ) < 1e-2
          assert abs(result_mean.item() - 0.0_0_0_2 ) < 1e-3

      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        if torch_device == "mps":
          return
        __UpperCAmelCase    :						Dict       =						self.scheduler_classes[0]
        __UpperCAmelCase    :						Optional[Any]       =						self.get_scheduler_config()
        __UpperCAmelCase    :						Optional[Any]       =						scheduler_class(**__lowercase )

        scheduler.set_timesteps(self.num_inference_steps )

        __UpperCAmelCase    :						Optional[int]       =						self.dummy_model()
        __UpperCAmelCase    :						List[Any]       =						self.dummy_sample_deter * scheduler.init_noise_sigma
        __UpperCAmelCase    :						Union[str, Any]       =						sample.to(__lowercase )

        for i, t in enumerate(scheduler.timesteps ):
          __UpperCAmelCase    :						List[str]       =						scheduler.scale_model_input(__lowercase						,				__lowercase )

          __UpperCAmelCase    :						List[Any]       =						model(__lowercase						,				__lowercase )

          __UpperCAmelCase    :						str       =						scheduler.step(__lowercase						,				__lowercase						,				__lowercase )
          __UpperCAmelCase    :						List[str]       =						output.prev_sample

        __UpperCAmelCase    :						Dict       =						torch.sum(torch.abs(__lowercase ) )
        __UpperCAmelCase    :						Union[str, Any]       =						torch.mean(torch.abs(__lowercase ) )

        if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
          assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
        else:
          # CUDA
          assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
          assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3







      def       A_					(							self       :      str ):


        '''simple docstring'''




        if torch_device == "mps":
          return
        __UpperCAmelCase    :						Optional[Any]       =						self.scheduler_classes[0]
        __UpperCAmelCase    :						Tuple       =						self.get_scheduler_config()
        __UpperCAmelCase    :						Any       =						scheduler_class(**__lowercase )

        scheduler.set_timesteps(self.num_inference_steps						,				device=__lowercase )

        __UpperCAmelCase    :						Optional[Any]       =						self.dummy_model()
        __UpperCAmelCase    :						Optional[Any]       =						self.dummy_sample_deter.to(__lowercase ) * scheduler.init_noise_sigma

        for t in scheduler.timesteps:
          __UpperCAmelCase    :						Any       =						scheduler.scale_model_input(__lowercase						,				__lowercase )

          __UpperCAmelCase    :						Optional[Any]       =						model(__lowercase						,				__lowercase )

          __UpperCAmelCase    :						Optional[int]       =						scheduler.step(__lowercase						,				__lowercase						,				__lowercase )
          __UpperCAmelCase    :						Any       =						output.prev_sample

        __UpperCAmelCase    :						Optional[int]       =						torch.sum(torch.abs(__lowercase ) )
        __UpperCAmelCase    :						Optional[Any]       =						torch.mean(torch.abs(__lowercase ) )

        if str(__lowercase ).startswith('''cpu''' ):
          # The following sum varies between 148 and 156 on mps. Why?
          assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
          assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
        else:
          # CUDA
          assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
          assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3






"""simple docstring"""





# 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


lowercase__				:int				   =       '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)