



'''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  UpperCamelCase__(     unittest.TestCase ):




				@property
				def   a__(	self    :				Optional[int]   )->  Optional[Any]:

										"""simple docstring"""




										torch.manual_seed(0   )
										UpperCAmelCase         =			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   a__(	self    :				int   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			self.dummy_uncond_unet
										UpperCAmelCase         =			PNDMScheduler()

										UpperCAmelCase         =			PNDMPipeline(unet=lowerCAmelCase   ,					scheduler=lowerCAmelCase   )
										pndm.to(lowerCAmelCase   )
										pndm.set_progress_bar_config(disable=lowerCAmelCase   )

										UpperCAmelCase         =			torch.manual_seed(0   )
										UpperCAmelCase         =			pndm(generator=lowerCAmelCase   ,					num_inference_steps=20   ,					output_type='''numpy'''   ).images

										UpperCAmelCase         =			torch.manual_seed(0   )
										UpperCAmelCase         =			pndm(generator=lowerCAmelCase   ,					num_inference_steps=20   ,					output_type='''numpy'''   ,					return_dict=lowerCAmelCase   )[0]

										UpperCAmelCase         =			image[0, -3:, -3:, -1]
										UpperCAmelCase         =			image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										UpperCAmelCase         =			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  UpperCamelCase__(     unittest.TestCase ):




				def   a__(	self    :				List[Any]   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			'''google/ddpm-cifar10-32'''

										UpperCAmelCase         =			UNetaDModel.from_pretrained(lowerCAmelCase   )
										UpperCAmelCase         =			PNDMScheduler()

										UpperCAmelCase         =			PNDMPipeline(unet=lowerCAmelCase   ,					scheduler=lowerCAmelCase   )
										pndm.to(lowerCAmelCase   )
										pndm.set_progress_bar_config(disable=lowerCAmelCase   )
										UpperCAmelCase         =			torch.manual_seed(0   )
										UpperCAmelCase         =			pndm(generator=lowerCAmelCase   ,					output_type='''numpy'''   ).images

										UpperCAmelCase         =			image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										UpperCAmelCase         =			np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125]   )

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









'''simple docstring'''



from typing import TYPE_CHECKING

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


_lowercase    :    Any			     =  {
    """configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Optional[int]			     =  [
	    """GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTBigCodeForSequenceClassification""",
	    """GPTBigCodeForTokenClassification""",
	    """GPTBigCodeForCausalLM""",
	    """GPTBigCodeModel""",
	    """GPTBigCodePreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_bigcode import (
		    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTBigCodeForCausalLM,
		    GPTBigCodeForSequenceClassification,
		    GPTBigCodeForTokenClassification,
		    GPTBigCodeModel,
		    GPTBigCodePreTrainedModel,
		)


else:
	import sys

	_lowercase    :    Tuple			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





