




'''simple docstring'''



import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


UpperCamelCase_    	=				get_logger(__name__)






class 							_a				(			enum.Enum ):





       '''simple docstring'''






       A						:				Tuple        =						'''all_checks'''
       A						:				Dict        =						'''basic_checks'''
       A						:				List[str]        =						'''no_checks'''






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''












class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''












class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''












class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''










def   lowercase__(			__UpperCamelCase:					Optional[dict]      ,__UpperCamelCase:					dict      ,__UpperCamelCase:					Any=None    ):

      """simple docstring"""






      if expected_checksums is None:
            logger.info('Unable to verify checksums.'    )
            return
      if len(set(__UpperCamelCase    ) - set(__UpperCamelCase    )    ) > 0:
            raise ExpectedMoreDownloadedFiles(str(set(__UpperCamelCase    ) - set(__UpperCamelCase    )    )    )
      if len(set(__UpperCamelCase    ) - set(__UpperCamelCase    )    ) > 0:
            raise UnexpectedDownloadedFile(str(set(__UpperCamelCase    ) - set(__UpperCamelCase    )    )    )
      SCREAMING_SNAKE_CASE   :      int    					=						[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						' for ' + verification_name if verification_name is not None else ''
      if len(__UpperCamelCase    ) > 0:
            raise NonMatchingChecksumError(
                f"Checksums didn't match{for_verification_name}:\n"
                f"{bad_urls}\n"
                'Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error'    )
      logger.info('All the checksums matched successfully' + for_verification_name    )






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''












class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''












class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''












class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''










def   lowercase__(			__UpperCamelCase:					Optional[dict]      ,__UpperCamelCase:					dict    ):

      """simple docstring"""






      if expected_splits is None:
            logger.info('Unable to verify splits sizes.'    )
            return
      if len(set(__UpperCamelCase    ) - set(__UpperCamelCase    )    ) > 0:
            raise ExpectedMoreSplits(str(set(__UpperCamelCase    ) - set(__UpperCamelCase    )    )    )
      if len(set(__UpperCamelCase    ) - set(__UpperCamelCase    )    ) > 0:
            raise UnexpectedSplits(str(set(__UpperCamelCase    ) - set(__UpperCamelCase    )    )    )
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						[
          {'expected': expected_splits[name], 'recorded': recorded_splits[name]}
          for name in expected_splits
          if expected_splits[name].num_examples != recorded_splits[name].num_examples
      ]
      if len(__UpperCamelCase    ) > 0:
            raise NonMatchingSplitsSizesError(str(__UpperCamelCase    )    )
      logger.info('All the splits matched successfully.'    )




def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					bool = True    ):

      """simple docstring"""






      if record_checksum:
            SCREAMING_SNAKE_CASE   :      str    					=						shaaaa()
            with open(__UpperCamelCase      ,'rb'    ) as f:
                  for chunk in iter(lambda: f.read(1 << 20    )      ,B''    ):
                        m.update(__UpperCamelCase    )
                  SCREAMING_SNAKE_CASE   :      Optional[int]    					=						m.hexdigest()
      else:
            SCREAMING_SNAKE_CASE   :      int    					=						None
      return {"num_bytes": os.path.getsize(__UpperCamelCase    ), "checksum": checksum}





def   lowercase__(			__UpperCamelCase:					Optional[int]    ):

      """simple docstring"""






      if dataset_size and config.IN_MEMORY_MAX_SIZE:
            return dataset_size < config.IN_MEMORY_MAX_SIZE
      else:
            return False






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 						_snake_case    ( unittest.TestCase				):




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       torch.manual_seed(0       )
       a		:List[Any]									=			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       ):
       a		:Union[str, Any]									=			self.dummy_uncond_unet
       a		:Any									=			PNDMScheduler()

       a		:Any									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )

       a		:Any									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''       ).images

       a		:int									=			torch.manual_seed(0       )
       a		:str									=			pndm(generator=_lowerCamelCase   ,    num_inference_steps=20   ,    output_type='''numpy'''   ,    return_dict=_lowerCamelCase       )[0]

       a		:int									=			image[0, -3:, -3:, -1]
       a		:List[str]									=			image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:str									=			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 						_snake_case    ( unittest.TestCase				):




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:int									=			'''google/ddpm-cifar10-32'''

       a		:str									=			UNetaDModel.from_pretrained(_lowerCamelCase       )
       a		:Optional[Any]									=			PNDMScheduler()

       a		:Union[str, Any]									=			PNDMPipeline(unet=_lowerCamelCase   ,    scheduler=_lowerCamelCase       )
       pndm.to(_lowerCamelCase       )
       pndm.set_progress_bar_config(disable=_lowerCamelCase       )
       a		:Dict									=			torch.manual_seed(0       )
       a		:List[str]									=			pndm(generator=_lowerCamelCase   ,    output_type='''numpy'''       ).images

       a		:List[Any]									=			image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       a		:int									=			np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

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





