
'''simple docstring'''







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

from .. import config
from .logging import get_logger


__lowerCAmelCase         =    get_logger(__name__)







class UpperCAmelCase__	( enum.Enum	):





  """simple docstring"""

  __UpperCAmelCase				:				Any   =					'''all_checks'''
  __UpperCAmelCase				:				Dict   =					'''basic_checks'''
  __UpperCAmelCase				:				Optional[Any]   =					'''no_checks'''







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""








class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""








class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""








class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""






def 					UpperCAmelCase_		(__a      :    Optional[dict] ,	__a      :    dict ,	__a      :    int=None ):






   """simple docstring"""

   if expected_checksums is None:
      logger.info('Unable to verify checksums.' )
      return
   if len(set(__a ) - set(__a ) ) > 0:
      raise ExpectedMoreDownloadedFiles(str(set(__a ) - set(__a ) ) )
   if len(set(__a ) - set(__a ) ) > 0:
      raise UnexpectedDownloadedFile(str(set(__a ) - set(__a ) ) )
   _a				:	Tuple			=   [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
   _a				:	Optional[Any]			=   ' for ' + verification_name if verification_name is not None else ''
   if len(__a ) > 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 UpperCAmelCase__	( lowercase__	):





  """simple docstring"""








class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""








class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""








class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""






def 					UpperCAmelCase_		(__a      :    Optional[dict] ,	__a      :    dict ):






   """simple docstring"""

   if expected_splits is None:
      logger.info('Unable to verify splits sizes.' )
      return
   if len(set(__a ) - set(__a ) ) > 0:
      raise ExpectedMoreSplits(str(set(__a ) - set(__a ) ) )
   if len(set(__a ) - set(__a ) ) > 0:
      raise UnexpectedSplits(str(set(__a ) - set(__a ) ) )
   _a				:	Optional[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(__a ) > 0:
      raise NonMatchingSplitsSizesError(str(__a ) )
   logger.info('All the splits matched successfully.' )





def 					UpperCAmelCase_		(__a      :    str ,	__a      :    bool = True ):






   """simple docstring"""

   if record_checksum:
      _a				:	int			=   shaaaa()
      with open(__a ,	'rb' ) as f:
         for chunk in iter(lambda: f.read(1 << 2_0 ) ,	b'' ):
            m.update(__a )
         _a				:	Tuple			=   m.hexdigest()
   else:
      _a				:	List[Any]			=   None
   return {"num_bytes": os.path.getsize(__a ), "checksum": checksum}





def 					UpperCAmelCase_		(__a      :    Union[str, Any] ):






   """simple docstring"""

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





'''simple docstring'''







import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  @property
  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	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


  @property
  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	Union[str, Any]			=   VQModel(
         block_out_channels=[32, 64]							,in_channels=3							,out_channels=3							,down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,latent_channels=3							,)
     return model


  @property
  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	Any			=   CLIPTextConfig(
         bos_token_id=0							,eos_token_id=2							,hidden_size=32							,intermediate_size=37							,layer_norm_eps=1E-05							,num_attention_heads=4							,num_hidden_layers=5							,pad_token_id=1							,vocab_size=1000							,)
     return CLIPTextModel(_a       )


  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	Dict			=   self.dummy_uncond_unet
     _a				:	List[Any]			=   DDIMScheduler()
     _a				:	List[Any]			=   self.dummy_vq_model

     _a				:	str			=   LDMPipeline(unet=_a							,vqvae=_a							,scheduler=_a       )
     ldm.to(_a       )
     ldm.set_progress_bar_config(disable=_a       )

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	List[str]			=   ldm(generator=_a							,num_inference_steps=2							,output_type='numpy'       ).images

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	Union[str, Any]			=   ldm(generator=_a							,num_inference_steps=2							,output_type='numpy'							,return_dict=_a       )[0]

     _a				:	Tuple			=   image[0, -3:, -3:, -1]
     _a				:	Optional[Any]			=   image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     _a				:	int			=   np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]       )
     _a				:	Any			=   1E-2 if torch_device != 'mps' else 3E-2

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < tolerance







@slow
@require_torch
class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	List[str]			=   LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'       )
     ldm.to(_a       )
     ldm.set_progress_bar_config(disable=_a       )

     _a				:	Optional[int]			=   torch.manual_seed(0       )
     _a				:	Dict			=   ldm(generator=_a							,num_inference_steps=5							,output_type='numpy'       ).images

     _a				:	str			=   image[0, -3:, -3:, -1]

     assert image.shape == (1, 256, 256, 3)
     _a				:	Optional[int]			=   np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447]       )
     _a				:	int			=   1E-2 if torch_device != 'mps' else 3E-2

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance



