


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				:				List[str]  				= logging.get_logger(__name__)

enable_full_determinism()

class 					__magic_name__		(   UpperCAmelCase__    , UpperCAmelCase__    , unittest.TestCase):
     A:						Union[str, Any]								=					UNetaDModel
     A:						Any								=					"""sample"""


     @property
     def    UpperCAmelCase__		(					self							:   Tuple	)     ->					Optional[int]:





      '''simple docstring'''






      UpperCamelCase__    :			Any      				=      4
      UpperCamelCase__    :			int      				=      3
      UpperCamelCase__    :			Optional[Any]      				=      (32, 32)

      UpperCamelCase__    :			List[Any]      				=      floats_tensor((batch_size, num_channels) + sizes	).to(SCREAMING_SNAKE_CASE__	)
      UpperCamelCase__    :			Tuple      				=      torch.tensor([10]	).to(SCREAMING_SNAKE_CASE__	)

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


     @property
     def    UpperCAmelCase__		(					self							:   int	)     ->					List[Any]:





      '''simple docstring'''






      return (3, 32, 32)


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





      '''simple docstring'''






      return (3, 32, 32)


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





      '''simple docstring'''






      UpperCamelCase__    :			Optional[Any]      				=      {
          '''block_out_channels''': (32, 64),
          '''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''': 32,
      }
      UpperCamelCase__    :			Optional[Any]      				=      self.dummy_input
      return init_dict, inputs_dict



class 					__magic_name__		(   UpperCAmelCase__    , UpperCAmelCase__    , unittest.TestCase):
     A:						Dict								=					UNetaDModel
     A:						Optional[int]								=					"""sample"""


     @property
     def    UpperCAmelCase__		(					self							:   str	)     ->					Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__    :			int      				=      4
      UpperCamelCase__    :			Optional[Any]      				=      4
      UpperCamelCase__    :			Any      				=      (32, 32)

      UpperCamelCase__    :			int      				=      floats_tensor((batch_size, num_channels) + sizes	).to(SCREAMING_SNAKE_CASE__	)
      UpperCamelCase__    :			Optional[Any]      				=      torch.tensor([10]	).to(SCREAMING_SNAKE_CASE__	)

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


     @property
     def    UpperCAmelCase__		(					self							:   int	)     ->					Dict:





      '''simple docstring'''






      return (4, 32, 32)


     @property
     def    UpperCAmelCase__		(					self							:   Optional[Any]	)     ->					str:





      '''simple docstring'''






      return (4, 32, 32)


     def    UpperCAmelCase__		(					self							:   Optional[int]	)     ->					Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__    :			Optional[int]      				=      {
          '''sample_size''': 32,
          '''in_channels''': 4,
          '''out_channels''': 4,
          '''layers_per_block''': 2,
          '''block_out_channels''': (32, 64),
          '''attention_head_dim''': 32,
          '''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
          '''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
      }
      UpperCamelCase__    :			Optional[Any]      				=      self.dummy_input
      return init_dict, inputs_dict


     def    UpperCAmelCase__		(					self							:   Dict	)     ->					Any:





      '''simple docstring'''






      UpperCamelCase__				, UpperCamelCase__    :			Any      				=      UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''							,		output_loading_info=SCREAMING_SNAKE_CASE__	)

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

      model.to(SCREAMING_SNAKE_CASE__	)
      UpperCamelCase__    :			int      				=      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[Any]	)     ->					List[str]:





      '''simple docstring'''






      UpperCamelCase__				, UpperCamelCase__    :			Tuple      				=      UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''							,		output_loading_info=SCREAMING_SNAKE_CASE__	)
      model.to(SCREAMING_SNAKE_CASE__	)
      UpperCamelCase__    :			str      				=      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							:   Optional[int]	)     ->					str:





      '''simple docstring'''






      UpperCamelCase__				, UpperCamelCase__    :			Any      				=      UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''							,		output_loading_info=SCREAMING_SNAKE_CASE__	)
      model_accelerate.to(SCREAMING_SNAKE_CASE__	)
      model_accelerate.eval()

      UpperCamelCase__    :			Dict      				=      torch.randn(
          1							,		model_accelerate.config.in_channels							,		model_accelerate.config.sample_size							,		model_accelerate.config.sample_size							,		generator=torch.manual_seed(0	)							,		)
      UpperCamelCase__    :			Optional[int]      				=      noise.to(SCREAMING_SNAKE_CASE__	)
      UpperCamelCase__    :			Tuple      				=      torch.tensor([10] * noise.shape[0]	).to(SCREAMING_SNAKE_CASE__	)

      UpperCamelCase__    :			Dict      				=      model_accelerate(SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__	)['''sample''']

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

      UpperCamelCase__				, UpperCamelCase__    :			Union[str, Any]      				=      UNetaDModel.from_pretrained(
          '''fusing/unet-ldm-dummy-update'''							,		output_loading_info=SCREAMING_SNAKE_CASE__							,		low_cpu_mem_usage=SCREAMING_SNAKE_CASE__	)
      model_normal_load.to(SCREAMING_SNAKE_CASE__	)
      model_normal_load.eval()
      UpperCamelCase__    :			Optional[int]      				=      model_normal_load(SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__	)['''sample''']

      assert torch_all_close(SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		rtol=1E-3	)


     def    UpperCAmelCase__		(					self							:   Any	)     ->					str:





      '''simple docstring'''






      UpperCamelCase__    :			str      				=      UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''	)
      model.eval()
      model.to(SCREAMING_SNAKE_CASE__	)

      UpperCamelCase__    :			Optional[Any]      				=      torch.randn(
          1							,		model.config.in_channels							,		model.config.sample_size							,		model.config.sample_size							,		generator=torch.manual_seed(0	)							,		)
      UpperCamelCase__    :			str      				=      noise.to(SCREAMING_SNAKE_CASE__	)
      UpperCamelCase__    :			str      				=      torch.tensor([10] * noise.shape[0]	).to(SCREAMING_SNAKE_CASE__	)

      with torch.no_grad():
       UpperCamelCase__    :			Tuple      				=      model(SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__	).sample

      UpperCamelCase__    :			Any      				=      output[0, -1, -3:, -3:].flatten().cpu()
      # fmt: off
      UpperCamelCase__    :			Union[str, Any]      				=      torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]	)
      # fmt: on

      self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		rtol=1E-3	)	)



class 					__magic_name__		(   UpperCAmelCase__    , UpperCAmelCase__    , unittest.TestCase):
     A:						Any								=					UNetaDModel
     A:						Optional[int]								=					"""sample"""


     @property
     def    UpperCAmelCase__		(					self							:   Union[str, Any]							,		lowerCamelCase__							:   Optional[Any]=(32, 32)	)     ->					List[Any]:





      '''simple docstring'''






      UpperCamelCase__    :			Optional[Any]      				=      4
      UpperCamelCase__    :			int      				=      3

      UpperCamelCase__    :			Optional[Any]      				=      floats_tensor((batch_size, num_channels) + sizes	).to(SCREAMING_SNAKE_CASE__	)
      UpperCamelCase__    :			Tuple      				=      torch.tensor(batch_size * [10]	).to(dtype=torch.intaa							,		device=SCREAMING_SNAKE_CASE__	)

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


     @property
     def    UpperCAmelCase__		(					self							:   str	)     ->					Union[str, Any]:





      '''simple docstring'''






      return (3, 32, 32)


     @property
     def    UpperCAmelCase__		(					self							:   Any	)     ->					Any:





      '''simple docstring'''






      return (3, 32, 32)


     def    UpperCAmelCase__		(					self							:   Optional[Any]	)     ->					Optional[int]:





      '''simple docstring'''






      UpperCamelCase__    :			int      				=      {
          '''block_out_channels''': [32, 64, 64, 64],
          '''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''',
          ],
      }
      UpperCamelCase__    :			Dict      				=      self.dummy_input
      return init_dict, inputs_dict


     @slow
     def    UpperCAmelCase__		(					self							:   str	)     ->					Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__				, UpperCamelCase__    :			str      				=      UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''							,		output_loading_info=SCREAMING_SNAKE_CASE__	)
      self.assertIsNotNone(SCREAMING_SNAKE_CASE__	)
      self.assertEqual(len(loading_info['''missing_keys''']	)							,		0	)

      model.to(SCREAMING_SNAKE_CASE__	)
      UpperCamelCase__    :			Optional[Any]      				=      self.dummy_input
      UpperCamelCase__    :			Optional[Any]      				=      floats_tensor((4, 3) + (256, 256)	).to(SCREAMING_SNAKE_CASE__	)
      UpperCamelCase__    :			Optional[int]      				=      noise
      UpperCamelCase__    :			Optional[Any]      				=      model(**SCREAMING_SNAKE_CASE__	)

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


     @slow
     def    UpperCAmelCase__		(					self							:   List[str]	)     ->					int:





      '''simple docstring'''






      UpperCamelCase__    :			Tuple      				=      UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''	)
      model.to(SCREAMING_SNAKE_CASE__	)

      UpperCamelCase__    :			Optional[Any]      				=      4
      UpperCamelCase__    :			Optional[int]      				=      3
      UpperCamelCase__    :			int      				=      (256, 256)

      UpperCamelCase__    :			Union[str, Any]      				=      torch.ones((batch_size, num_channels) + sizes	).to(SCREAMING_SNAKE_CASE__	)
      UpperCamelCase__    :			int      				=      torch.tensor(batch_size * [1E-4]	).to(SCREAMING_SNAKE_CASE__	)

      with torch.no_grad():
       UpperCamelCase__    :			int      				=      model(SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__	).sample

      UpperCamelCase__    :			Any      				=      output[0, -3:, -3:, -1].flatten().cpu()
      # fmt: off
      UpperCamelCase__    :			Dict      				=      torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10980.7129, -20028.8535, 8148.2822, 2342.2905, 567.7608]	)
      # fmt: on

      self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		rtol=1E-2	)	)


     def    UpperCAmelCase__		(					self							:   Tuple	)     ->					Any:





      '''simple docstring'''






      UpperCamelCase__    :			Union[str, Any]      				=      UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''	)
      model.to(SCREAMING_SNAKE_CASE__	)

      UpperCamelCase__    :			List[Any]      				=      4
      UpperCamelCase__    :			Tuple      				=      3
      UpperCamelCase__    :			int      				=      (32, 32)

      UpperCamelCase__    :			Any      				=      torch.ones((batch_size, num_channels) + sizes	).to(SCREAMING_SNAKE_CASE__	)
      UpperCamelCase__    :			List[str]      				=      torch.tensor(batch_size * [1E-4]	).to(SCREAMING_SNAKE_CASE__	)

      with torch.no_grad():
       UpperCamelCase__    :			Optional[Any]      				=      model(SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__	).sample

      UpperCamelCase__    :			Union[str, Any]      				=      output[0, -3:, -3:, -1].flatten().cpu()
      # fmt: off
      UpperCamelCase__    :			List[str]      				=      torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]	)
      # fmt: on

      self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		rtol=1E-2	)	)


     def    UpperCAmelCase__		(					self							:   str	)     ->					int:





      '''simple docstring'''






      pass



'''simple docstring'''

import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast







@dataclass
class 				_lowercase     (				datasets.BuilderConfig			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Optional[datasets.Features]      	=					None







class 				_lowercase     (				datasets.ArrowBasedBuilder			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					PandasConfig
 def a					(      self   :	Union[str, Any]							)  ->    int:
  return datasets.DatasetInfo(features=self.config.features							)
 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]							)  ->    str:
  if not self.config.data_files:
   raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""							)
  __lowerCAmelCase											=						dl_manager.download_and_extract(self.config.data_files							)
  if isinstance(SCREAMING_SNAKE_CASE__      ,       (str, list, tuple)							):
   __lowerCAmelCase											=						data_files
   if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase											=						[files]
   # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
   __lowerCAmelCase											=						[dl_manager.iter_files(SCREAMING_SNAKE_CASE__							) for file in files]
   return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,       gen_kwargs={"""files""": files}							)]
  __lowerCAmelCase											=						[]
  for split_name, files in data_files.items():
   if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase											=						[files]
   # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
   __lowerCAmelCase											=						[dl_manager.iter_files(SCREAMING_SNAKE_CASE__							) for file in files]
   splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE__      ,       gen_kwargs={"""files""": files}							)							)
  return splits
 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	pa.Table							)  ->    pa.Table:
  if self.config.features is not None:
   # more expensive cast to support nested features with keys in a different order
   # allows str <-> int/float or str to Audio for example
   __lowerCAmelCase											=						table_cast(SCREAMING_SNAKE_CASE__      ,       self.config.features.arrow_schema							)
  return pa_table





 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]							)  ->    List[Any]:
  for i, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE__							)							):
   with open(SCREAMING_SNAKE_CASE__      ,       """rb"""							) as f:
    __lowerCAmelCase											=						pa.Table.from_pandas(pd.read_pickle(SCREAMING_SNAKE_CASE__							)							)
    yield i, self._cast_table(SCREAMING_SNAKE_CASE__							)






