



"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       List[Any]:

				"""simple docstring"""

				torch.manual_seed(0)
				__lowerCAmelCase      :  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 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       self.dummy_uncond_unet
				__lowerCAmelCase      :  str												=       PNDMScheduler()

				__lowerCAmelCase      :  List[str]												=       PNDMPipeline(unet=snake_case_       ,			scheduler=snake_case_)
				pndm.to(snake_case_)
				pndm.set_progress_bar_config(disable=snake_case_)

				__lowerCAmelCase      :  int												=       torch.manual_seed(0)
				__lowerCAmelCase      :  Optional[int]												=       pndm(generator=snake_case_       ,			num_inference_steps=20       ,			output_type="numpy").images

				__lowerCAmelCase      :  Union[str, Any]												=       torch.manual_seed(0)
				__lowerCAmelCase      :  Optional[Any]												=       pndm(generator=snake_case_       ,			num_inference_steps=20       ,			output_type="numpy"       ,			return_dict=snake_case_)[0]

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

				assert image.shape == (1, 32, 32, 3)
				__lowerCAmelCase      :  Union[str, Any]												=       np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0])

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






@slow
@require_torch
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  int												=       "google/ddpm-cifar10-32"

				__lowerCAmelCase      :  str												=       UNetaDModel.from_pretrained(snake_case_)
				__lowerCAmelCase      :  Tuple												=       PNDMScheduler()

				__lowerCAmelCase      :  Tuple												=       PNDMPipeline(unet=snake_case_       ,			scheduler=snake_case_)
				pndm.to(snake_case_)
				pndm.set_progress_bar_config(disable=snake_case_)
				__lowerCAmelCase      :  int												=       torch.manual_seed(0)
				__lowerCAmelCase      :  int												=       pndm(generator=snake_case_       ,			output_type="numpy").images

				__lowerCAmelCase      :  Tuple												=       image[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				__lowerCAmelCase      :  List[str]												=       np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125])

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






'''simple docstring'''




import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
    "kwargs, expected"			,     [
        ({"num_shards": 0, "max_num_jobs": 1}, []),
        ({"num_shards": 10, "max_num_jobs": 1}, [range(10       )]),
        ({"num_shards": 10, "max_num_jobs": 10}, [range(__lowercase			,     i + 1       ) for i in range(10       )]),
        ({"num_shards": 1, "max_num_jobs": 10}, [range(1       )]),
        ({"num_shards": 10, "max_num_jobs": 3}, [range(0			,     4       ), range(4			,     7       ), range(7			,     10       )]),
        ({"num_shards": 3, "max_num_jobs": 10}, [range(0			,     1       ), range(1			,     2       ), range(2			,     3       )]),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      Dict       )		->       List[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   _distribute_shards(**__lowercase       )
     assert out == expected
@pytest.mark.parametrize(
    "gen_kwargs, max_num_jobs, expected"			,     [
        ({"foo": 0}, 10, [{"foo": 0}]),
        ({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
        ({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
        ({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
        ({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Dict			,     __lowercase							:      Optional[Any]			,     __lowercase							:      int       )		->       str:


     '''simple docstring'''



     _UpperCAmelCase     							=   _split_gen_kwargs(__lowercase			,     __lowercase       )
     assert out == expected






@pytest.mark.parametrize(
    "gen_kwargs, expected"			,     [
        ({"foo": 0}, 1),
        ({"shards": [0]}, 1),
        ({"shards": [0, 1, 2, 3]}, 4),
        ({"shards": [0, 1, 2, 3], "foo": 0}, 4),
        ({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
        ({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Optional[Any]			,     __lowercase							:      List[Any]       )		->       List[Any]:


     '''simple docstring'''



     if expected is RuntimeError:
          with pytest.raises(__lowercase       ):
               _number_of_shards_in_gen_kwargs(__lowercase       )
     else:
          _UpperCAmelCase     							=   _number_of_shards_in_gen_kwargs(__lowercase       )
          assert out == expected




