






'''simple docstring'''


import os
from typing import List






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 if name != "training_function" and name != "main":
  raise ValueError(f'''Incorrect function name passed: {name}, choose either \'main\' or \'training_function\''''		)
 __a    :	Any									=						[], False
 for line in lines:
  if not found_start and f'''def {name}''' in line:
   __a    :	Optional[Any]									=						True
   good_lines.append(SCREAMING_SNAKE_CASE__		)
   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(SCREAMING_SNAKE_CASE__		)






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






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__ = None		):
 with open(SCREAMING_SNAKE_CASE__       ,		'r'		) as f:
  __a    :	List[str]									=						f.readlines()
 with open(os.path.abspath(os.path.join('examples'       ,		'nlp_example.py'		)		)       ,		'r'		) as f:
  __a    :	Any									=						f.readlines()
 with open(SCREAMING_SNAKE_CASE__       ,		'r'		) as f:
  __a    :	str									=						f.readlines()
 if secondary_filename is not None:
  with open(SCREAMING_SNAKE_CASE__       ,		'r'		) as f:
   __a    :	Dict									=						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:
  __a    :	int									=						clean_lines(get_function_contents_by_name(SCREAMING_SNAKE_CASE__       ,		'main'		)		)
  __a    :	Optional[int]									=						clean_lines(get_function_contents_by_name(SCREAMING_SNAKE_CASE__       ,		'main'		)		)
  __a    :	List[str]									=						clean_lines(get_function_contents_by_name(SCREAMING_SNAKE_CASE__       ,		'main'		)		)
  if secondary_filename is not None:
   __a    :	Tuple									=						clean_lines(get_function_contents_by_name(SCREAMING_SNAKE_CASE__       ,		'main'		)		)
 else:
  __a    :	int									=						clean_lines(get_function_contents_by_name(SCREAMING_SNAKE_CASE__       ,		'training_function'		)		)
  __a    :	Optional[Any]									=						clean_lines(get_function_contents_by_name(SCREAMING_SNAKE_CASE__       ,		'training_function'		)		)
  __a    :	List[str]									=						clean_lines(get_function_contents_by_name(SCREAMING_SNAKE_CASE__       ,		'training_function'		)		)
  if secondary_filename is not None:
   __a    :	str									=						clean_lines(
       get_function_contents_by_name(SCREAMING_SNAKE_CASE__       ,		'training_function'		)		)

 __a    :	int									=						'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
 __a    :	Optional[int]									=						[]
 __a    :	Tuple									=						[]  # We keep track of the idxs just in case it's a repeated statement
 __a    :	Dict									=						iter(SCREAMING_SNAKE_CASE__		)
 for i in range(len(SCREAMING_SNAKE_CASE__		) - 1		):
  if i not in passed_idxs:
   __a    :	Optional[Any]									=						next(SCREAMING_SNAKE_CASE__		)
   if (line not in full_file_func) and (line.lstrip() != _dl_line):
    if "TESTING_MOCKED_DATALOADERS" not in line:
     new_feature_code.append(SCREAMING_SNAKE_CASE__		)
     passed_idxs.append(SCREAMING_SNAKE_CASE__		)
    else:
     # Skip over the `config['num_epochs'] = 2` statement
     __a    :	Optional[int]									=						next(SCREAMING_SNAKE_CASE__		)

    # Extract out just the new parts from the full_file_training_func
 __a    :	List[Any]									=						[]
 __a    :	List[Any]									=						[]  # We keep track of the idxs just in case it's a repeated statement
 for i, line in enumerate(SCREAMING_SNAKE_CASE__		):
  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(SCREAMING_SNAKE_CASE__		)
     passed_idxs.append(SCREAMING_SNAKE_CASE__		)

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

 return diff_from_example












'''simple docstring'''


import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   @property
   def    __magic_name__    ( self			)    ->   Any:
    torch.manual_seed(0			)
    __a    :	Optional[Any]									=						UNetaDModel(
        block_out_channels=(32, 64)   ,   layers_per_block=2   ,   sample_size=32   ,   in_channels=3   ,   out_channels=3   ,   down_block_types=('DownBlock2D', 'AttnDownBlock2D')   ,   up_block_types=('AttnUpBlock2D', 'UpBlock2D')   ,   )
    return model



   def    __magic_name__    ( self			)    ->   Tuple:
    __a    :	Union[str, Any]									=						'cpu'
    __a    :	Tuple									=						self.dummy_uncond_unet
    __a    :	str									=						DDPMScheduler()

    __a    :	int									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	str									=						torch.Generator(device=_A			).manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			).images

    __a    :	Union[str, Any]									=						torch.Generator(device=_A			).manual_seed(0			)
    __a    :	Optional[int]									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'   ,   return_dict=_A			)[0]

    __a    :	List[str]									=						image[0, -3:, -3:, -1]
    __a    :	List[str]									=						image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	List[Any]									=						np.array(
        [9.9_5_6E-0_1, 5.7_8_5E-0_1, 4.6_7_5E-0_1, 9.9_3_0E-0_1, 0.0, 1.000, 1.1_9_9E-0_3, 2.6_4_8E-0_4, 5.1_0_1E-0_4]			)

    assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2



   def    __magic_name__    ( self			)    ->   Tuple:
    __a    :	Optional[Any]									=						self.dummy_uncond_unet
    __a    :	List[str]									=						DDPMScheduler(prediction_type='sample'			)

    __a    :	List[str]									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	Optional[Any]									=						torch.manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			).images

    __a    :	Union[str, Any]									=						torch.manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			)[0]

    __a    :	Dict									=						image[0, -3:, -3:, -1]
    __a    :	int									=						image_eps[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	int									=						1E-2 if torch_device != 'mps' else 3E-2
    assert np.abs(image_slice.flatten() - image_eps_slice.flatten()			).max() < tolerance




@slow
@require_torch_gpu
class 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   def    __magic_name__    ( self			)    ->   List[str]:
    __a    :	Union[str, Any]									=						'google/ddpm-cifar10-32'

    __a    :	str									=						UNetaDModel.from_pretrained(_A			)
    __a    :	List[str]									=						DDPMScheduler.from_pretrained(_A			)

    __a    :	List[str]									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	int									=						torch.manual_seed(0			)
    __a    :	Optional[Any]									=						ddpm(generator=_A   ,   output_type='numpy'			).images

    __a    :	int									=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	Optional[Any]									=						np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]			)
    assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2




