



'''simple docstring'''






# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# 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.
import os
from typing import List




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						List[Any]:





       '''simple docstring'''


       if name != "training_function" and name != "main":
              raise ValueError(F'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''						)
       _UpperCAmelCase	,_UpperCAmelCase    :		int     	=					[], False
       for line in lines:
              if not found_start and F'''def {name}''' in line:
                     _UpperCAmelCase    :		int     	=					True
                     good_lines.append(lowerCAmelCase_						)
                     continue
              if found_start:
                     if name == "training_function" and "def main" in line:
                            return good_lines
                     if name == "main" and "if __name__" in line:
                            return good_lines
                     good_lines.append(lowerCAmelCase_						)




def        snake_case_   (	lowerCAmelCase_						)->						List[Any]:





       '''simple docstring'''


       return [line for line in lines if not line.lstrip().startswith("""#"""						) and line != "\n"]




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_ = None						)->						Optional[Any]:





       '''simple docstring'''


       with open(lowerCAmelCase_			,  """r"""						) as f:
              _UpperCAmelCase    :		List[str]     	=					f.readlines()
       with open(os.path.abspath(os.path.join("""examples"""			,  """nlp_example.py"""						)						)			,  """r"""						) as f:
              _UpperCAmelCase    :		Dict     	=					f.readlines()
       with open(lowerCAmelCase_			,  """r"""						) as f:
              _UpperCAmelCase    :		List[Any]     	=					f.readlines()
       if secondary_filename is not None:
              with open(lowerCAmelCase_			,  """r"""						) as f:
                     _UpperCAmelCase    :		List[Any]     	=					f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
       if parser_only:
              _UpperCAmelCase    :		List[str]     	=					clean_lines(get_function_contents_by_name(lowerCAmelCase_			,  """main"""						)						)
              _UpperCAmelCase    :		List[str]     	=					clean_lines(get_function_contents_by_name(lowerCAmelCase_			,  """main"""						)						)
              _UpperCAmelCase    :		List[str]     	=					clean_lines(get_function_contents_by_name(lowerCAmelCase_			,  """main"""						)						)
              if secondary_filename is not None:
                     _UpperCAmelCase    :		Optional[Any]     	=					clean_lines(get_function_contents_by_name(lowerCAmelCase_			,  """main"""						)						)
       else:
              _UpperCAmelCase    :		List[str]     	=					clean_lines(get_function_contents_by_name(lowerCAmelCase_			,  """training_function"""						)						)
              _UpperCAmelCase    :		Union[str, Any]     	=					clean_lines(get_function_contents_by_name(lowerCAmelCase_			,  """training_function"""						)						)
              _UpperCAmelCase    :		Any     	=					clean_lines(get_function_contents_by_name(lowerCAmelCase_			,  """training_function"""						)						)
              if secondary_filename is not None:
                     _UpperCAmelCase    :		Dict     	=					clean_lines(
                         get_function_contents_by_name(lowerCAmelCase_			,  """training_function"""						)						)

       _UpperCAmelCase    :		Union[str, Any]     	=					"""train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n"""

       # Specific code in our script that differs from the full version, aka what is new
       _UpperCAmelCase    :		str     	=					[]
       _UpperCAmelCase    :		Dict     	=					[]  # We keep track of the idxs just in case it's a repeated statement
       _UpperCAmelCase    :		Tuple     	=					iter(lowerCAmelCase_						)
       for i in range(len(lowerCAmelCase_						) - 1						):
              if i not in passed_idxs:
                     _UpperCAmelCase    :		str     	=					next(lowerCAmelCase_						)
                     if (line not in full_file_func) and (line.lstrip() != _dl_line):
                            if "TESTING_MOCKED_DATALOADERS" not in line:
                                   new_feature_code.append(lowerCAmelCase_						)
                                   passed_idxs.append(lowerCAmelCase_						)
                            else:
                                   # Skip over the `config['num_epochs'] = 2` statement
                                   _UpperCAmelCase    :		Optional[Any]     	=					next(lowerCAmelCase_						)

    # Extract out just the new parts from the full_file_training_func
       _UpperCAmelCase    :		Any     	=					[]
       _UpperCAmelCase    :		Optional[int]     	=					[]  # We keep track of the idxs just in case it's a repeated statement
       for i, line in enumerate(lowerCAmelCase_						):
              if i not in passed_idxs:
                     if (line not in full_file_func) and (line.lstrip() != _dl_line):
                            if "TESTING_MOCKED_DATALOADERS" not in line:
                                   new_full_example_parts.append(lowerCAmelCase_						)
                                   passed_idxs.append(lowerCAmelCase_						)

    # Finally, get the overall diff
       _UpperCAmelCase    :		Optional[Any]     	=					[line for line in new_feature_code if line not in new_full_example_parts]
       if secondary_filename is not None:
              _UpperCAmelCase    :		Dict     	=					[line for line in full_file_contents if line not in secondary_file_func]
              _UpperCAmelCase    :		str     	=					[line for line in diff_from_example if line not in diff_from_two]

       return diff_from_example











'''simple docstring'''







import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest




class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    List[Any]				       =					(DDIMScheduler,)
      SCREAMING_SNAKE_CASE				:    str				       =					(("eta", 0.0), ("num_inference_steps", 5_0))




      def 							lowerCamelCase     (					self  ,		**_UpperCAmelCase						):
             UpperCAmelCase__	:   List[str]												=	{
                 '''num_train_timesteps''': 1000,
                 '''beta_start''': 0.00_01,
                 '''beta_end''': 0.02,
                 '''beta_schedule''': '''linear''',
                 '''clip_sample''': True,
             }

             config.update(**_UpperCAmelCase						)
             return config




      def 							lowerCamelCase     (					self  ,		**_UpperCAmelCase						):
             UpperCAmelCase__	:   Dict												=	self.scheduler_classes[0]
             UpperCAmelCase__	:   Union[str, Any]												=	self.get_scheduler_config(**_UpperCAmelCase						)
             UpperCAmelCase__	:   str												=	scheduler_class(**_UpperCAmelCase						)

             UpperCAmelCase__							,							UpperCAmelCase__	:   int												=	10, 0.0

             UpperCAmelCase__	:   Optional[Any]												=	self.dummy_model()
             UpperCAmelCase__	:   List[Any]												=	self.dummy_sample_deter

             scheduler.set_timesteps(_UpperCAmelCase						)

             for t in scheduler.timesteps:
                    UpperCAmelCase__	:   Dict												=	model(_UpperCAmelCase  ,		_UpperCAmelCase						)
                    UpperCAmelCase__	:   int												=	scheduler.step(_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase						).prev_sample

             return sample




      def 							lowerCamelCase     (					self						):
             for timesteps in [100, 500, 1000]:
                    self.check_over_configs(num_train_timesteps=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for steps_offset in [0, 1]:
                    self.check_over_configs(steps_offset=_UpperCAmelCase						)

             UpperCAmelCase__	:   Optional[int]												=	self.scheduler_classes[0]
             UpperCAmelCase__	:   Any												=	self.get_scheduler_config(steps_offset=1						)
             UpperCAmelCase__	:   Optional[Any]												=	scheduler_class(**_UpperCAmelCase						)
             scheduler.set_timesteps(5						)
             assert torch.equal(scheduler.timesteps  ,		torch.LongTensor([801, 601, 401, 201, 1]						)						)




      def 							lowerCamelCase     (					self						):
             for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]  ,		[0.0_02, 0.02, 0.2, 2]						):
                    self.check_over_configs(beta_start=_UpperCAmelCase  ,		beta_end=_UpperCAmelCase						)




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




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




      def 							lowerCamelCase     (					self						):
             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for timestep_spacing in ["trailing", "leading"]:
                    self.check_over_configs(timestep_spacing=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for rescale_betas_zero_snr in [True, False]:
                    self.check_over_configs(rescale_betas_zero_snr=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             self.check_over_configs(thresholding=_UpperCAmelCase						)
             for threshold in [0.5, 1.0, 2.0]:
                    for prediction_type in ["epsilon", "v_prediction"]:
                           self.check_over_configs(
                               thresholding=_UpperCAmelCase  ,		prediction_type=_UpperCAmelCase  ,		sample_max_value=_UpperCAmelCase  ,		)




      def 							lowerCamelCase     (					self						):
             for t in [1, 10, 49]:
                    self.check_over_forward(time_step=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for t, num_inference_steps in zip([1, 10, 50]  ,		[10, 50, 500]						):
                    self.check_over_forward(time_step=_UpperCAmelCase  ,		num_inference_steps=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             for t, eta in zip([1, 10, 49]  ,		[0.0, 0.5, 1.0]						):
                    self.check_over_forward(time_step=_UpperCAmelCase  ,		eta=_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   str												=	self.scheduler_classes[0]
             UpperCAmelCase__	:   int												=	self.get_scheduler_config()
             UpperCAmelCase__	:   Optional[Any]												=	scheduler_class(**_UpperCAmelCase						)

             assert torch.sum(torch.abs(scheduler._get_variance(0  ,		0						) - 0.0						)						) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(420  ,		400						) - 0.1_47_71						)						) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(980  ,		960						) - 0.3_24_60						)						) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(0  ,		0						) - 0.0						)						) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(487  ,		486						) - 0.0_09_79						)						) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(999  ,		998						) - 0.02						)						) < 1E-5




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   str												=	self.full_loop()

             UpperCAmelCase__	:   Dict												=	torch.sum(torch.abs(_UpperCAmelCase						)						)
             UpperCAmelCase__	:   str												=	torch.mean(torch.abs(_UpperCAmelCase						)						)

             assert abs(result_sum.item() - 1_72.00_67						) < 1E-2
             assert abs(result_mean.item() - 0.22_39_67						) < 1E-3




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Any												=	self.full_loop(prediction_type='''v_prediction'''						)

             UpperCAmelCase__	:   Union[str, Any]												=	torch.sum(torch.abs(_UpperCAmelCase						)						)
             UpperCAmelCase__	:   Tuple												=	torch.mean(torch.abs(_UpperCAmelCase						)						)

             assert abs(result_sum.item() - 52.53_02						) < 1E-2
             assert abs(result_mean.item() - 0.06_84						) < 1E-3




      def 							lowerCamelCase     (					self						):
             # We specify different beta, so that the first alpha is 0.99
             UpperCAmelCase__	:   str												=	self.full_loop(set_alpha_to_one=_UpperCAmelCase  ,		beta_start=0.01						)
             UpperCAmelCase__	:   List[str]												=	torch.sum(torch.abs(_UpperCAmelCase						)						)
             UpperCAmelCase__	:   Union[str, Any]												=	torch.mean(torch.abs(_UpperCAmelCase						)						)

             assert abs(result_sum.item() - 1_49.82_95						) < 1E-2
             assert abs(result_mean.item() - 0.19_51						) < 1E-3




      def 							lowerCamelCase     (					self						):
             # We specify different beta, so that the first alpha is 0.99
             UpperCAmelCase__	:   str												=	self.full_loop(set_alpha_to_one=_UpperCAmelCase  ,		beta_start=0.01						)
             UpperCAmelCase__	:   Optional[Any]												=	torch.sum(torch.abs(_UpperCAmelCase						)						)
             UpperCAmelCase__	:   int												=	torch.mean(torch.abs(_UpperCAmelCase						)						)

             assert abs(result_sum.item() - 1_49.07_84						) < 1E-2
             assert abs(result_mean.item() - 0.19_41						) < 1E-3