
'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					Union[str, Any]       =					DDIMPipeline
		a							:					Optional[Any]       =					UNCONDITIONAL_IMAGE_GENERATION_PARAMS
		a							:					Tuple       =					PipelineTesterMixin.required_optional_params - {
		    """num_images_per_prompt""",
		    """latents""",
		    """callback""",
		    """callback_steps""",
		}
		a							:					Any       =					UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
		a							:					Tuple       =					False




		def     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= 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")					,						)
								_SCREAMING_SNAKE_CASE				= DDIMScheduler()
								_SCREAMING_SNAKE_CASE				= {"unet": unet, "scheduler": scheduler}
								return components




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase=0      ):
								if str(UpperCamelCase      ).startswith("mps"      ):
														_SCREAMING_SNAKE_CASE				= torch.manual_seed(UpperCamelCase      )
								else:
														_SCREAMING_SNAKE_CASE				= torch.Generator(device=UpperCamelCase      ).manual_seed(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= {
								    "batch_size": 1,
								    "generator": generator,
								    "num_inference_steps": 2,
								    "output_type": "numpy",
								}
								return inputs




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= "cpu"

								_SCREAMING_SNAKE_CASE				= self.get_dummy_components()
								_SCREAMING_SNAKE_CASE				= self.pipeline_class(**UpperCamelCase      )
								pipe.to(UpperCamelCase      )
								pipe.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= self.get_dummy_inputs(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= pipe(**UpperCamelCase      ).images
								_SCREAMING_SNAKE_CASE				= image[0, -3:, -3:, -1]

								self.assertEqual(image.shape					,						(1, 32, 32, 3)      )
								_SCREAMING_SNAKE_CASE				= np.array(
								    [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]      )
								_SCREAMING_SNAKE_CASE				= np.abs(image_slice.flatten() - expected_slice      ).max()
								self.assertLessEqual(UpperCamelCase					,						1e-3      )




		def     lowercase       (				self      ):
								super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3      )




		def     lowercase       (				self      ):
								super().test_save_load_local(expected_max_difference=3e-3      )




		def     lowercase       (				self      ):
								super().test_save_load_optional_components(expected_max_difference=3e-3      )




		def     lowercase       (				self      ):
								super().test_inference_batch_single_identical(expected_max_diff=3e-3      )


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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= "google/ddpm-cifar10-32"

								_SCREAMING_SNAKE_CASE				= UNetaDModel.from_pretrained(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= DDIMScheduler()

								_SCREAMING_SNAKE_CASE				= DDIMPipeline(unet=UpperCamelCase					,						scheduler=UpperCamelCase      )
								ddim.to(UpperCamelCase      )
								ddim.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= ddim(generator=UpperCamelCase					,						eta=0.0					,						output_type="numpy"      ).images

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

								assert image.shape == (1, 32, 32, 3)
								_SCREAMING_SNAKE_CASE				= np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53]      )

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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= "google/ddpm-ema-bedroom-256"

								_SCREAMING_SNAKE_CASE				= UNetaDModel.from_pretrained(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= DDIMScheduler.from_pretrained(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= DDIMPipeline(unet=UpperCamelCase					,						scheduler=UpperCamelCase      )
								ddpm.to(UpperCamelCase      )
								ddpm.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= ddpm(generator=UpperCamelCase					,						output_type="numpy"      ).images

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

								assert image.shape == (1, 256, 256, 3)
								_SCREAMING_SNAKE_CASE				= np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69]      )

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

'''simple docstring'''







from typing import TYPE_CHECKING

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


_snake_case				:   int     =				{
    """configuration_longformer""": [
        """LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LongformerConfig""",
        """LongformerOnnxConfig""",
    ],
    """tokenization_longformer""": ["""LongformerTokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Optional[int]     =				["""LongformerTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   int     =				[
						    """LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """LongformerForMaskedLM""",
						    """LongformerForMultipleChoice""",
						    """LongformerForQuestionAnswering""",
						    """LongformerForSequenceClassification""",
						    """LongformerForTokenClassification""",
						    """LongformerModel""",
						    """LongformerPreTrainedModel""",
						    """LongformerSelfAttention""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Tuple     =				[
						    """TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFLongformerForMaskedLM""",
						    """TFLongformerForMultipleChoice""",
						    """TFLongformerForQuestionAnswering""",
						    """TFLongformerForSequenceClassification""",
						    """TFLongformerForTokenClassification""",
						    """TFLongformerModel""",
						    """TFLongformerPreTrainedModel""",
						    """TFLongformerSelfAttention""",
						]


if TYPE_CHECKING:
						from .configuration_longformer import (
						    LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    LongformerConfig,
						    LongformerOnnxConfig,
						)
						from .tokenization_longformer import LongformerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_longformer_fast import LongformerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_longformer import (
												    LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LongformerForMaskedLM,
												    LongformerForMultipleChoice,
												    LongformerForQuestionAnswering,
												    LongformerForSequenceClassification,
												    LongformerForTokenClassification,
												    LongformerModel,
												    LongformerPreTrainedModel,
												    LongformerSelfAttention,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_longformer import (
												    TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFLongformerForMaskedLM,
												    TFLongformerForMultipleChoice,
												    TFLongformerForQuestionAnswering,
												    TFLongformerForSequenceClassification,
												    TFLongformerForTokenClassification,
												    TFLongformerModel,
												    TFLongformerPreTrainedModel,
												    TFLongformerSelfAttention,
												)

else:
						import sys

						_snake_case				:   Dict     =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)