
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     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[Any]:
					torch.manual_seed(0 )
					lowerCamelCase_						=			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 )				->       Optional[Any]:
					lowerCamelCase_						=			self.dummy_uncond_unet
					lowerCamelCase_						=			PNDMScheduler()

					lowerCamelCase_						=			PNDMPipeline(unet=lowercase						,  scheduler=lowercase )
					pndm.to(lowercase )
					pndm.set_progress_bar_config(disable=lowercase )

					lowerCamelCase_						=			torch.manual_seed(0 )
					lowerCamelCase_						=			pndm(generator=lowercase						,  num_inference_steps=20						,  output_type="numpy" ).images

					lowerCamelCase_						=			torch.manual_seed(0 )
					lowerCamelCase_						=			pndm(generator=lowercase						,  num_inference_steps=20						,  output_type="numpy"						,  return_dict=lowercase )[0]

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

					assert image.shape == (1, 32, 32, 3)
					lowerCamelCase_						=			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     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):



	def     SCREAMING_SNAKE_CASE_(    self )				->       int:
					lowerCamelCase_						=			"google/ddpm-cifar10-32"

					lowerCamelCase_						=			UNetaDModel.from_pretrained(lowercase )
					lowerCamelCase_						=			PNDMScheduler()

					lowerCamelCase_						=			PNDMPipeline(unet=lowercase						,  scheduler=lowercase )
					pndm.to(lowercase )
					pndm.set_progress_bar_config(disable=lowercase )
					lowerCamelCase_						=			torch.manual_seed(0 )
					lowerCamelCase_						=			pndm(generator=lowercase						,  output_type="numpy" ).images

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

					assert image.shape == (1, 32, 32, 3)
					lowerCamelCase_						=			np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5] )

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A				={
    '''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''BloomTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''BloomForCausalLM''',
			    '''BloomModel''',
			    '''BloomPreTrainedModel''',
			    '''BloomForSequenceClassification''',
			    '''BloomForTokenClassification''',
			    '''BloomForQuestionAnswering''',
			]

if TYPE_CHECKING:
			from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bloom_fast import BloomTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bloom import (
						    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BloomForCausalLM,
						    BloomForQuestionAnswering,
						    BloomForSequenceClassification,
						    BloomForTokenClassification,
						    BloomModel,
						    BloomPreTrainedModel,
						)

else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


