





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    __lowerCAmelCase			(     unittest.TestCase	):






			@property
			def      lowerCamelCase    (self       )	->  Any:


							'''simple docstring'''



							torch.manual_seed(0       )
							snake_case_		:       str           =    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      lowerCamelCase    (self       )	->  Union[str, Any]:


							'''simple docstring'''



							snake_case_		:       Dict           =    self.dummy_uncond_unet
							snake_case_		:       Optional[Any]           =    PNDMScheduler()

							snake_case_		:       Union[str, Any]           =    PNDMPipeline(unet=__UpperCAmelCase   ,				scheduler=__UpperCAmelCase       )
							pndm.to(__UpperCAmelCase       )
							pndm.set_progress_bar_config(disable=__UpperCAmelCase       )

							snake_case_		:       Tuple           =    torch.manual_seed(0       )
							snake_case_		:       Tuple           =    pndm(generator=__UpperCAmelCase   ,				num_inference_steps=20   ,				output_type='''numpy'''       ).images

							snake_case_		:       Optional[int]           =    torch.manual_seed(0       )
							snake_case_		:       Tuple           =    pndm(generator=__UpperCAmelCase   ,				num_inference_steps=20   ,				output_type='''numpy'''   ,				return_dict=__UpperCAmelCase       )[0]

							snake_case_		:       str           =    image[0, -3:, -3:, -1]
							snake_case_		:       int           =    image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_		:       int           =    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    __lowerCAmelCase			(     unittest.TestCase	):






			def      lowerCamelCase    (self       )	->  Any:


							'''simple docstring'''



							snake_case_		:       Dict           =    "google/ddpm-cifar10-32"

							snake_case_		:       List[Any]           =    UNetaDModel.from_pretrained(__UpperCAmelCase       )
							snake_case_		:       Optional[Any]           =    PNDMScheduler()

							snake_case_		:       int           =    PNDMPipeline(unet=__UpperCAmelCase   ,				scheduler=__UpperCAmelCase       )
							pndm.to(__UpperCAmelCase       )
							pndm.set_progress_bar_config(disable=__UpperCAmelCase       )
							snake_case_		:       Union[str, Any]           =    torch.manual_seed(0       )
							snake_case_		:       Union[str, Any]           =    pndm(generator=__UpperCAmelCase   ,				output_type='''numpy'''       ).images

							snake_case_		:       List[Any]           =    image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_		:       Dict           =    np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]       )

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowerCamelCase		       =      {
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TrOCRForCausalLM""",
						    """TrOCRPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
						from .processing_trocr import TrOCRProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
						import sys

						lowerCamelCase		       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


