"""simple docstring"""





import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


_UpperCamelCase     :		Tuple											=					logging.get_logger(__name__)

enable_full_determinism()


class   UpperCAmelCase_			(					_a				,							_a				,							unittest.TestCase):
      lowerCamelCase__	:					str										=						UNetaDModel
      lowerCamelCase__	:					int										=						"sample"


      @property
      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	Union[str, Any]     					=			4
             lowercase__    :	List[str]     					=			3
             lowercase__    :	Union[str, Any]     					=			(3_2, 3_2)

             lowercase__    :	Tuple     					=			floats_tensor((batch_size, num_channels) + sizes							).to(a							)
             lowercase__    :	List[str]     					=			torch.tensor([1_0]							).to(a							)

             return {"sample": noise, "timestep": time_step}


      @property
      def 	_UpperCAmelCase (     self							) ->      List[str]:
             return (3, 3_2, 3_2)


      @property
      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             return (3, 3_2, 3_2)


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__    :	int     					=			{
                 'block_out_channels': (3_2, 6_4),
                 'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
                 'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
                 'attention_head_dim': 3,
                 'out_channels': 3,
                 'in_channels': 3,
                 'layers_per_block': 2,
                 'sample_size': 3_2,
             }
             lowercase__    :	str     					=			self.dummy_input
             return init_dict, inputs_dict





class   UpperCAmelCase_			(					_a				,							_a				,							unittest.TestCase):
      lowerCamelCase__	:					Optional[int]										=						UNetaDModel
      lowerCamelCase__	:					int										=						"sample"


      @property
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__    :	Optional[int]     					=			4
             lowercase__    :	Tuple     					=			4
             lowercase__    :	Tuple     					=			(3_2, 3_2)

             lowercase__    :	Union[str, Any]     					=			floats_tensor((batch_size, num_channels) + sizes							).to(a							)
             lowercase__    :	Any     					=			torch.tensor([1_0]							).to(a							)

             return {"sample": noise, "timestep": time_step}


      @property
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             return (4, 3_2, 3_2)


      @property
      def 	_UpperCAmelCase (     self							) ->      List[Any]:
             return (4, 3_2, 3_2)


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Tuple     					=			{
                 'sample_size': 3_2,
                 'in_channels': 4,
                 'out_channels': 4,
                 'layers_per_block': 2,
                 'block_out_channels': (3_2, 6_4),
                 'attention_head_dim': 3_2,
                 'down_block_types': ('DownBlock2D', 'DownBlock2D'),
                 'up_block_types': ('UpBlock2D', 'UpBlock2D'),
             }
             lowercase__    :	str     					=			self.dummy_input
             return init_dict, inputs_dict


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__					,   lowercase__    :	Optional[int]     					=			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'	,    output_loading_info=a							)

             self.assertIsNotNone(a							)
             self.assertEqual(len(loading_info['missing_keys']							)	,    0							)

             model.to(a							)
             lowercase__    :	Dict     					=			model(**self.dummy_input							).sample

             assert image is not None, "Make sure output is not None"


      @unittest.skipIf(torch_device != 'cuda'	,    'This test is supposed to run on GPU'							)
      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__					,   lowercase__    :	int     					=			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'	,    output_loading_info=a							)
             model.to(a							)
             lowercase__    :	Tuple     					=			model(**self.dummy_input							).sample

             assert image is not None, "Make sure output is not None"


      @unittest.skipIf(torch_device != 'cuda'	,    'This test is supposed to run on GPU'							)
      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
             lowercase__					,   lowercase__    :	Optional[Any]     					=			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'	,    output_loading_info=a							)
             model_accelerate.to(a							)
             model_accelerate.eval()

             lowercase__    :	List[str]     					=			torch.randn(
                 1	,    model_accelerate.config.in_channels	,    model_accelerate.config.sample_size	,    model_accelerate.config.sample_size	,    generator=torch.manual_seed(0							)	,    )
             lowercase__    :	Any     					=			noise.to(a							)
             lowercase__    :	Optional[Any]     					=			torch.tensor([1_0] * noise.shape[0]							).to(a							)

             lowercase__    :	int     					=			model_accelerate(a	,    a							)['sample']

             # two models don't need to stay in the device at the same time
             del model_accelerate
             torch.cuda.empty_cache()
             gc.collect()

             lowercase__					,   lowercase__    :	Any     					=			UNetaDModel.from_pretrained(
                 'fusing/unet-ldm-dummy-update'	,    output_loading_info=a	,    low_cpu_mem_usage=a							)
             model_normal_load.to(a							)
             model_normal_load.eval()
             lowercase__    :	Tuple     					=			model_normal_load(a	,    a							)['sample']

             assert torch_all_close(a	,    a	,    rtol=1e-3							)


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	List[Any]     					=			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'							)
             model.eval()
             model.to(a							)

             lowercase__    :	Dict     					=			torch.randn(
                 1	,    model.config.in_channels	,    model.config.sample_size	,    model.config.sample_size	,    generator=torch.manual_seed(0							)	,    )
             lowercase__    :	str     					=			noise.to(a							)
             lowercase__    :	Union[str, Any]     					=			torch.tensor([1_0] * noise.shape[0]							).to(a							)

             with torch.no_grad():
                    lowercase__    :	Any     					=			model(a	,    a							).sample

             lowercase__    :	Optional[int]     					=			output[0, -1, -3:, -3:].flatten().cpu()
             # fmt: off
             lowercase__    :	Union[str, Any]     					=			torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]							)
             # fmt: on

             self.assertTrue(torch_all_close(a	,    a	,    rtol=1e-3							)							)





class   UpperCAmelCase_			(					_a				,							_a				,							unittest.TestCase):
      lowerCamelCase__	:					Any										=						UNetaDModel
      lowerCamelCase__	:					str										=						"sample"


      @property
      def 	_UpperCAmelCase (     self	,    a=(3_2, 3_2)							) ->      Tuple:
             lowercase__    :	List[Any]     					=			4
             lowercase__    :	Dict     					=			3

             lowercase__    :	List[Any]     					=			floats_tensor((batch_size, num_channels) + sizes							).to(a							)
             lowercase__    :	Optional[int]     					=			torch.tensor(batch_size * [1_0]							).to(dtype=torch.intaa	,    device=a							)

             return {"sample": noise, "timestep": time_step}


      @property
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             return (3, 3_2, 3_2)


      @property
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             return (3, 3_2, 3_2)


      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Tuple     					=			{
                 'block_out_channels': [3_2, 6_4, 6_4, 6_4],
                 'in_channels': 3,
                 'layers_per_block': 1,
                 'out_channels': 3,
                 'time_embedding_type': 'fourier',
                 'norm_eps': 1e-6,
                 'mid_block_scale_factor': math.sqrt(2.0							),
                 'norm_num_groups': None,
                 'down_block_types': [
                     'SkipDownBlock2D',
                     'AttnSkipDownBlock2D',
                     'SkipDownBlock2D',
                     'SkipDownBlock2D',
                 ],
                 'up_block_types': [
                     'SkipUpBlock2D',
                     'SkipUpBlock2D',
                     'AttnSkipUpBlock2D',
                     'SkipUpBlock2D',
                 ],
             }
             lowercase__    :	Optional[Any]     					=			self.dummy_input
             return init_dict, inputs_dict


      @slow
      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__					,   lowercase__    :	str     					=			UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'	,    output_loading_info=a							)
             self.assertIsNotNone(a							)
             self.assertEqual(len(loading_info['missing_keys']							)	,    0							)

             model.to(a							)
             lowercase__    :	str     					=			self.dummy_input
             lowercase__    :	Dict     					=			floats_tensor((4, 3) + (2_5_6, 2_5_6)							).to(a							)
             lowercase__    :	str     					=			noise
             lowercase__    :	Optional[Any]     					=			model(**a							)

             assert image is not None, "Make sure output is not None"


      @slow
      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	Any     					=			UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'							)
             model.to(a							)

             lowercase__    :	Optional[Any]     					=			4
             lowercase__    :	int     					=			3
             lowercase__    :	Optional[Any]     					=			(2_5_6, 2_5_6)

             lowercase__    :	Dict     					=			torch.ones((batch_size, num_channels) + sizes							).to(a							)
             lowercase__    :	Union[str, Any]     					=			torch.tensor(batch_size * [1e-4]							).to(a							)

             with torch.no_grad():
                    lowercase__    :	Tuple     					=			model(a	,    a							).sample

             lowercase__    :	List[Any]     					=			output[0, -3:, -3:, -1].flatten().cpu()
             # fmt: off
             lowercase__    :	List[str]     					=			torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608]							)
             # fmt: on

             self.assertTrue(torch_all_close(a	,    a	,    rtol=1e-2							)							)


      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__    :	Any     					=			UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'							)
             model.to(a							)

             lowercase__    :	Dict     					=			4
             lowercase__    :	List[str]     					=			3
             lowercase__    :	List[str]     					=			(3_2, 3_2)

             lowercase__    :	Union[str, Any]     					=			torch.ones((batch_size, num_channels) + sizes							).to(a							)
             lowercase__    :	Union[str, Any]     					=			torch.tensor(batch_size * [1e-4]							).to(a							)

             with torch.no_grad():
                    lowercase__    :	List[str]     					=			model(a	,    a							).sample

             lowercase__    :	int     					=			output[0, -3:, -3:, -1].flatten().cpu()
             # fmt: off
             lowercase__    :	Dict     					=			torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]							)
             # fmt: on

             self.assertTrue(torch_all_close(a	,    a	,    rtol=1e-2							)							)


      def 	_UpperCAmelCase (     self							) ->      Any:
             # not required for this model
             pass


"""simple docstring"""





from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments

