






'''simple docstring'''





import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
       import bitsandbytes as bnb

from copy import deepcopy


__lowerCamelCase	:		Union[str, Any]										=   logging.getLogger(__name__)







def 			A__							(      _a  :					torch.nn.Module		,  _a  :					BnbQuantizationConfig		,  _a  :					Union[str, os.PathLike] = None		,  _a  :					Optional[Dict[str, Union[int, str, torch.device]]] = None		,  _a  :					Optional[List[str]] = None		,  _a  :					Optional[Dict[Union[int, str], Union[int, str]]] = None		,  _a  :					Optional[Union[str, os.PathLike]] = None		,  _a  :					bool = False		,  ):





 '''simple docstring'''







 snake_case__  :    Union[str, Any]							       =bnb_quantization_config.load_in_abit
 snake_case__  :    str							       =bnb_quantization_config.load_in_abit

 if load_in_abit and not is_abit_bnb_available():
  raise ImportError(
      """You have a version of `bitsandbytes` that is not compatible with 8bit quantization,"""
      """ make sure you have the latest version of `bitsandbytes` installed."""	)
 if load_in_abit and not is_abit_bnb_available():
  raise ValueError(
      """You have a version of `bitsandbytes` that is not compatible with 4bit quantization,"""
      """make sure you have the latest version of `bitsandbytes` installed."""	)

 snake_case__  :    Any							       =[]
 # custom device map
 if isinstance(_a		,  _a	) and len(device_map.keys()	) > 1:
  snake_case__  :    List[str]							       =[key for key, value in device_map.items() if value in ["""disk""", """cpu"""]]

 # We keep some modules such as the lm_head in their original dtype for numerical stability reasons
 if bnb_quantization_config.skip_modules is None:
  snake_case__  :    int							       =get_keys_to_not_convert(_a	)

 # add cpu modules to skip modules only for 4-bit modules
 if load_in_abit:
  bnb_quantization_config.skip_modules.extend(_a	)
 snake_case__  :    Any							       =bnb_quantization_config.skip_modules

 # We add the modules we want to keep in full precision
 if bnb_quantization_config.keep_in_fpaa_modules is None:
  snake_case__  :    Any							       =[]
 snake_case__  :    Dict							       =bnb_quantization_config.keep_in_fpaa_modules
 modules_to_not_convert.extend(_a	)

 # compatibility with peft
 snake_case__  :    Optional[Any]							       =load_in_abit
 snake_case__  :    Tuple							       =load_in_abit

 snake_case__  :    Tuple							       =get_parameter_device(_a	)
 if model_device.type != "meta":
  # quantization of an already loaded model
  logger.warning(
      """It is not recommended to quantize a loaded model. """
      """The model should be instantiated under the `init_empty_weights` context manager."""	)
  snake_case__  :    Optional[Any]							       =replace_with_bnb_layers(_a		,  _a		,  modules_to_not_convert=_a	)
  # convert param to the right dtype
  snake_case__  :    Tuple							       =bnb_quantization_config.torch_dtype
  for name, param in model.state_dict().items():
   if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules	):
    param.to(torch.floataa	)
    if param.dtype != torch.floataa:
     snake_case__  :    Optional[int]							       =name.replace(""".weight"""		,  """"""	).replace(""".bias"""		,  """"""	)
     snake_case__  :    Dict							       =getattr(_a		,  _a		,  _a	)
     if param is not None:
      param.to(torch.floataa	)
   elif torch.is_floating_point(_a	):
    param.to(_a	)
  if model_device.type == "cuda":
   # move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
   model.cuda(torch.cuda.current_device()	)
   torch.cuda.empty_cache()
  elif torch.cuda.is_available():
   model.to(torch.cuda.current_device()	)
  else:
   raise RuntimeError("""No GPU found. A GPU is needed for quantization."""	)
  logger.info(
      f"The model device type is {model_device.type}. However, cuda is needed for quantization."
      """We move the model to cuda."""	)
  return model

 elif weights_location is None:
  raise RuntimeError(
      f"`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} "	)

 else:
  with init_empty_weights():
   snake_case__  :    int							       =replace_with_bnb_layers(
       _a		,  _a		,  modules_to_not_convert=_a	)

  snake_case__  :    Union[str, Any]							       =get_quantized_model_device_map(
      _a		,  _a		,  _a		,  max_memory=_a		,  no_split_module_classes=_a		,  )
  if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
   snake_case__  :    Optional[Any]							       =True

  snake_case__  :    Optional[Any]							       =any(x in list(device_map.values()	) for x in ["""cpu""", """disk"""]	)

  load_checkpoint_in_model(
      _a		,  _a		,  _a		,  dtype=bnb_quantization_config.torch_dtype		,  offload_folder=_a		,  offload_state_dict=_a		,  keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules		,  offload_abit_bnb=load_in_abit and offload		,  )
  return dispatch_model(_a		,  device_map=_a		,  offload_dir=_a	)







def 			A__							(      _a  :					Tuple		,  _a  :					int		,  _a  :					List[str]=None		,  _a  :					List[Any]=None		,  _a  :					Any=None	):





 '''simple docstring'''







 if device_map is None:
  if torch.cuda.is_available():
   snake_case__  :    Union[str, Any]							       ={"""""": torch.cuda.current_device()}
  else:
   raise RuntimeError("""No GPU found. A GPU is needed for quantization."""	)
  logger.info("""The device_map was not initialized.""" """Setting device_map to `{'':torch.cuda.current_device()}`."""	)

 if isinstance(_a		,  _a	):
  if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
   raise ValueError(
       """If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or """
       """'sequential'."""	)

  snake_case__  :    Optional[int]							       ={}
  special_dtypes.update(
      {
          name: bnb_quantization_config.torch_dtype
          for name, _ in model.named_parameters()
          if any(m in name for m in bnb_quantization_config.skip_modules	)
      }	)
  special_dtypes.update(
      {
          name: torch.floataa
          for name, _ in model.named_parameters()
          if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules	)
      }	)

  snake_case__  :    str							       ={}
  snake_case__  :    Optional[Any]							       =special_dtypes
  snake_case__  :    Union[str, Any]							       =no_split_module_classes
  snake_case__  :    Dict							       =bnb_quantization_config.target_dtype

  # get max_memory for each device.
  if device_map != "sequential":
   snake_case__  :    Optional[int]							       =get_balanced_memory(
       _a		,  low_zero=(device_map == """balanced_low_0""")		,  max_memory=_a		,  **_a		,  )

  snake_case__  :    List[str]							       =max_memory
  snake_case__  :    str							       =infer_auto_device_map(_a		,  **_a	)

 if isinstance(_a		,  _a	):
  # check if don't have any quantized module on the cpu
  snake_case__  :    Optional[Any]							       =bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

  snake_case__  :    Tuple							       ={
      key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
  }
  for device in ["cpu", "disk"]:
   if device in device_map_without_some_modules.values():
    if bnb_quantization_config.load_in_abit:
     raise ValueError(
         """
                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
                        these modules in `torch_dtype`, you need to pass a custom `device_map` to
                        `load_and_quantize_model`. Check
                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
                        for more details.
                        """	)
    else:
     logger.info(
         """Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit"""	)
  del device_map_without_some_modules
 return device_map







def 			A__							(      _a  :					Tuple		,  _a  :					List[str]		,  _a  :					Optional[int]=None		,  _a  :					Optional[Any]=None	):





 '''simple docstring'''







 if modules_to_not_convert is None:
  snake_case__  :    int							       =[]

 snake_case__  :    Optional[Any]							       =_replace_with_bnb_layers(
     _a		,  _a		,  _a		,  _a	)
 if not has_been_replaced:
  logger.warning(
      """You are loading your model in 8bit or 4bit but no linear modules were found in your model."""
      """ this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers."""
      """ Please double check your model architecture, or submit an issue on github if you think this is"""
      """ a bug."""	)
 return model







def 			A__							(      _a  :					Any		,  _a  :					Optional[Any]		,  _a  :					Optional[int]=None		,  _a  :					str=None		,  ):





 '''simple docstring'''







 snake_case__  :    Any							       =False
 for name, module in model.named_children():
  if current_key_name is None:
   snake_case__  :    List[str]							       =[]
  current_key_name.append(_a	)
  if isinstance(_a		,  nn.Linear	) and name not in modules_to_not_convert:
   # Check if the current key is not in the `modules_to_not_convert`
   snake_case__  :    List[Any]							       =""".""".join(_a	)
   snake_case__  :    List[Any]							       =True
   for key in modules_to_not_convert:
    if (
        (key in current_key_name_str) and (key + "." in current_key_name_str)
    ) or key == current_key_name_str:
     snake_case__  :    Optional[int]							       =False
     break
   if proceed:
    # Load bnb module with empty weight and replace ``nn.Linear` module
    if bnb_quantization_config.load_in_abit:
     snake_case__  :    Tuple							       =bnb.nn.LinearabitLt(
         module.in_features		,  module.out_features		,  module.bias is not None		,  has_fpaa_weights=_a		,  threshold=bnb_quantization_config.llm_inta_threshold		,  )
    elif bnb_quantization_config.load_in_abit:
     snake_case__  :    List[Any]							       =bnb.nn.Linearabit(
         module.in_features		,  module.out_features		,  module.bias is not None		,  bnb_quantization_config.bnb_abit_compute_dtype		,  compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant		,  quant_type=bnb_quantization_config.bnb_abit_quant_type		,  )
    else:
     raise ValueError("""load_in_8bit and load_in_4bit can't be both False"""	)
    snake_case__  :    Optional[int]							       =module.weight.data
    if module.bias is not None:
     snake_case__  :    Any							       =module.bias.data
    bnb_module.requires_grad_(_a	)
    setattr(_a		,  _a		,  _a	)
    snake_case__  :    Optional[Any]							       =True
  if len(list(module.children()	)	) > 0:
   snake_case__  :    Dict							       =_replace_with_bnb_layers(
       _a		,  _a		,  _a		,  _a	)
   snake_case__  :    Optional[Any]							       =has_been_replaced | _has_been_replaced
  # Remove the last key for recursion
  current_key_name.pop(-1	)
 return model, has_been_replaced







def 			A__							(      _a  :					List[str]	):





 '''simple docstring'''







 with init_empty_weights():
  snake_case__  :    List[Any]							       =deepcopy(_a	)  # this has 0 cost since it is done inside `init_empty_weights` context manager`

 snake_case__  :    Dict							       =find_tied_parameters(_a	)
 # For compatibility with Accelerate < 0.18
 if isinstance(_a		,  _a	):
  snake_case__  :    int							       =sum(list(tied_params.values()	)		,  []	) + list(tied_params.keys()	)
 else:
  snake_case__  :    List[str]							       =sum(_a		,  []	)
 snake_case__  :    Optional[Any]							       =len(_a	) > 0

 # Check if it is a base model
 snake_case__  :    str							       =False
 if hasattr(_a		,  """base_model_prefix"""	):
  snake_case__  :    Tuple							       =not hasattr(_a		,  model.base_model_prefix	)

 # Ignore this for base models (BertModel, GPT2Model, etc.)
 if (not has_tied_params) and is_base_model:
  return []

 # otherwise they have an attached head
 snake_case__  :    Optional[Any]							       =list(model.named_children()	)
 snake_case__  :    Optional[Any]							       =[list_modules[-1][0]]

 # add last module together with tied weights
 snake_case__  :    Tuple							       =set(_a	) - set(_a	)
 snake_case__  :    Any							       =list(set(_a	)	) + list(_a	)

 # remove ".weight" from the keys
 snake_case__  :    Optional[int]							       =[""".weight""", """.bias"""]
 snake_case__  :    List[Any]							       =[]
 for name in list_untouched:
  for name_to_remove in names_to_remove:
   if name_to_remove in name:
    snake_case__  :    str							       =name.replace(_a		,  """"""	)
  filtered_module_names.append(_a	)

 return filtered_module_names







def 			A__							(      _a  :					Dict	):





 '''simple docstring'''







 for m in model.modules():
  if isinstance(_a		,  bnb.nn.Linearabit	):
   return True
 return False







def 			A__							(      _a  :					nn.Module	):





 '''simple docstring'''







 return next(parameter.parameters()	).device







def 			A__							(      _a  :					int		,  _a  :					List[Any]		,  _a  :					Union[str, Any]		,  _a  :					List[str]		,  _a  :					Union[str, Any]		,  _a  :					List[str]		,  _a  :					int	):





 '''simple docstring'''







 if fpaa_statistics is None:
  set_module_tensor_to_device(_a		,  _a		,  0		,  dtype=_a		,  value=_a	)
  snake_case__  :    str							       =param_name
  snake_case__  :    List[Any]							       =model
  if "." in tensor_name:
   snake_case__  :    List[Any]							       =tensor_name.split("""."""	)
   for split in splits[:-1]:
    snake_case__  :    Any							       =getattr(_a		,  _a	)
    if new_module is None:
     raise ValueError(f"{module} has no attribute {split}."	)
    snake_case__  :    List[str]							       =new_module
   snake_case__  :    str							       =splits[-1]
  # offload weights
  snake_case__  :    Any							       =False
  offload_weight(module._parameters[tensor_name]		,  _a		,  _a		,  index=_a	)
  if hasattr(module._parameters[tensor_name]		,  """SCB"""	):
   offload_weight(
       module._parameters[tensor_name].SCB		,  param_name.replace("""weight"""		,  """SCB"""	)		,  _a		,  index=_a		,  )
 else:
  offload_weight(_a		,  _a		,  _a		,  index=_a	)
  offload_weight(_a		,  param_name.replace("""weight"""		,  """SCB"""	)		,  _a		,  index=_a	)

 set_module_tensor_to_device(_a		,  _a		,  """meta"""		,  dtype=_a		,  value=torch.empty(*param.size()	)	)













import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class  _lowercase   (   _A						,				unittest.TestCase						):
      _a							:						Optional[Any]										=							UnCLIPImageVariationPipeline
      _a							:						Optional[int]										=							IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
      _a							:						Optional[Any]										=							IMAGE_VARIATION_BATCH_PARAMS

      _a							:						str										=							[
          'generator',
          'return_dict',
          'decoder_num_inference_steps',
          'super_res_num_inference_steps',
      ]
      _a							:						Any										=							False
      @property
      def 						lowercase__					(     self   ):
       return 3_2
      @property
      def 						lowercase__					(     self   ):
       return 3_2
      @property
      def 						lowercase__					(     self   ):
       return self.time_input_dim
      @property
      def 						lowercase__					(     self   ):
       return self.time_input_dim * 4
      @property
      def 						lowercase__					(     self   ):
       return 1_0_0
      @property
      def 						lowercase__					(     self   ):
       snake_case__  :    Dict							       =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )
       return tokenizer
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )
       snake_case__  :    int							       =CLIPTextConfig(
           bos_token_id=0				,			eos_token_id=2				,			hidden_size=self.text_embedder_hidden_size				,			projection_dim=self.text_embedder_hidden_size				,			intermediate_size=3_7				,			layer_norm_eps=1e-05				,			num_attention_heads=4				,			num_hidden_layers=5				,			pad_token_id=1				,			vocab_size=1_0_0_0				,			)
       return CLIPTextModelWithProjection(a   )
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )
       snake_case__  :    Optional[int]							       =CLIPVisionConfig(
           hidden_size=self.text_embedder_hidden_size				,			projection_dim=self.text_embedder_hidden_size				,			num_hidden_layers=5				,			num_attention_heads=4				,			image_size=3_2				,			intermediate_size=3_7				,			patch_size=1				,			)
       return CLIPVisionModelWithProjection(a   )
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )

       snake_case__  :    int							       ={
           """clip_embeddings_dim""": self.text_embedder_hidden_size,
           """time_embed_dim""": self.time_embed_dim,
           """cross_attention_dim""": self.cross_attention_dim,
       }

       snake_case__  :    Optional[int]							       =UnCLIPTextProjModel(**a   )
       return model
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )

       snake_case__  :    List[str]							       ={
           """sample_size""": 3_2,
           # RGB in channels
           """in_channels""": 3,
           # Out channels is double in channels because predicts mean and variance
           """out_channels""": 6,
           """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
           """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
           """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
           """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
           """layers_per_block""": 1,
           """cross_attention_dim""": self.cross_attention_dim,
           """attention_head_dim""": 4,
           """resnet_time_scale_shift""": """scale_shift""",
           """class_embed_type""": """identity""",
       }

       snake_case__  :    List[str]							       =UNetaDConditionModel(**a   )
       return model
      @property
      def 						lowercase__					(     self   ):
       return {
           "sample_size": 6_4,
           "layers_per_block": 1,
           "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
           "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
           "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
           "in_channels": 6,
           "out_channels": 3,
       }
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )

       snake_case__  :    Union[str, Any]							       =UNetaDModel(**self.dummy_super_res_kwargs   )
       return model
      @property
      def 						lowercase__					(     self   ):
       # seeded differently to get different unet than `self.dummy_super_res_first`
       torch.manual_seed(1   )

       snake_case__  :    int							       =UNetaDModel(**self.dummy_super_res_kwargs   )
       return model
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =self.dummy_decoder
       snake_case__  :    Any							       =self.dummy_text_proj
       snake_case__  :    Optional[int]							       =self.dummy_text_encoder
       snake_case__  :    Optional[Any]							       =self.dummy_tokenizer
       snake_case__  :    List[str]							       =self.dummy_super_res_first
       snake_case__  :    str							       =self.dummy_super_res_last

       snake_case__  :    List[Any]							       =UnCLIPScheduler(
           variance_type="""learned_range"""				,			prediction_type="""epsilon"""				,			num_train_timesteps=1_0_0_0				,			)

       snake_case__  :    Any							       =UnCLIPScheduler(
           variance_type="""fixed_small_log"""				,			prediction_type="""epsilon"""				,			num_train_timesteps=1_0_0_0				,			)

       snake_case__  :    str							       =CLIPImageProcessor(crop_size=3_2				,			size=3_2   )

       snake_case__  :    List[Any]							       =self.dummy_image_encoder

       return {
           "decoder": decoder,
           "text_encoder": text_encoder,
           "tokenizer": tokenizer,
           "text_proj": text_proj,
           "feature_extractor": feature_extractor,
           "image_encoder": image_encoder,
           "super_res_first": super_res_first,
           "super_res_last": super_res_last,
           "decoder_scheduler": decoder_scheduler,
           "super_res_scheduler": super_res_scheduler,
       }
      def 						lowercase__					(     self				,			a				,			a=0				,			a=True   ):
       snake_case__  :    str							       =floats_tensor((1, 3, 3_2, 3_2)				,			rng=random.Random(a   )   ).to(a   )
       if str(a   ).startswith("""mps"""   ):
        snake_case__  :    str							       =torch.manual_seed(a   )
       else:
        snake_case__  :    List[Any]							       =torch.Generator(device=a   ).manual_seed(a   )

       if pil_image:
        snake_case__  :    Optional[int]							       =input_image * 0.5 + 0.5
        snake_case__  :    Tuple							       =input_image.clamp(0				,			1   )
        snake_case__  :    Union[str, Any]							       =input_image.cpu().permute(0				,			2				,			3				,			1   ).float().numpy()
        snake_case__  :    Tuple							       =DiffusionPipeline.numpy_to_pil(a   )[0]

       return {
           "image": input_image,
           "generator": generator,
           "decoder_num_inference_steps": 2,
           "super_res_num_inference_steps": 2,
           "output_type": "np",
       }
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       ="""cpu"""

       snake_case__  :    Dict							       =self.get_dummy_components()

       snake_case__  :    Tuple							       =self.pipeline_class(**a   )
       snake_case__  :    Optional[Any]							       =pipe.to(a   )

       pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    Optional[int]							       =self.get_dummy_inputs(a				,			pil_image=a   )

       snake_case__  :    List[str]							       =pipe(**a   )
       snake_case__  :    List[str]							       =output.images

       snake_case__  :    Any							       =self.get_dummy_inputs(a				,			pil_image=a   )

       snake_case__  :    List[Any]							       =pipe(
           **a				,			return_dict=a				,			)[0]

       snake_case__  :    Union[str, Any]							       =image[0, -3:, -3:, -1]
       snake_case__  :    Optional[int]							       =image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)

       snake_case__  :    Optional[Any]							       =np.array(
           [
               0.9997,
               0.0002,
               0.9997,
               0.9997,
               0.9969,
               0.0023,
               0.9997,
               0.9969,
               0.9970,
           ]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
      def 						lowercase__					(     self   ):
       snake_case__  :    Dict							       ="""cpu"""

       snake_case__  :    int							       =self.get_dummy_components()

       snake_case__  :    Any							       =self.pipeline_class(**a   )
       snake_case__  :    Tuple							       =pipe.to(a   )

       pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    int							       =self.get_dummy_inputs(a				,			pil_image=a   )

       snake_case__  :    Any							       =pipe(**a   )
       snake_case__  :    Optional[int]							       =output.images

       snake_case__  :    int							       =self.get_dummy_inputs(a				,			pil_image=a   )

       snake_case__  :    List[str]							       =pipe(
           **a				,			return_dict=a				,			)[0]

       snake_case__  :    List[str]							       =image[0, -3:, -3:, -1]
       snake_case__  :    List[Any]							       =image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)

       snake_case__  :    str							       =np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
      def 						lowercase__					(     self   ):
       snake_case__  :    List[str]							       ="""cpu"""

       snake_case__  :    Dict							       =self.get_dummy_components()

       snake_case__  :    Tuple							       =self.pipeline_class(**a   )
       snake_case__  :    List[Any]							       =pipe.to(a   )

       pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    Tuple							       =self.get_dummy_inputs(a				,			pil_image=a   )
       snake_case__  :    Any							       =[
           pipeline_inputs["""image"""],
           pipeline_inputs["""image"""],
       ]

       snake_case__  :    Tuple							       =pipe(**a   )
       snake_case__  :    str							       =output.images

       snake_case__  :    Tuple							       =self.get_dummy_inputs(a				,			pil_image=a   )
       snake_case__  :    str							       =[
           tuple_pipeline_inputs["""image"""],
           tuple_pipeline_inputs["""image"""],
       ]

       snake_case__  :    Union[str, Any]							       =pipe(
           **a				,			return_dict=a				,			)[0]

       snake_case__  :    List[Any]							       =image[0, -3:, -3:, -1]
       snake_case__  :    Tuple							       =image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (2, 6_4, 6_4, 3)

       snake_case__  :    int							       =np.array(
           [
               0.9997,
               0.9989,
               0.0008,
               0.0021,
               0.9960,
               0.0018,
               0.0014,
               0.0002,
               0.9933,
           ]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
      def 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =torch.device("""cpu"""   )







       class  _lowercase   :
             _a							:						Tuple										=							1




       snake_case__  :    Optional[Any]							       =self.get_dummy_components()

       snake_case__  :    List[str]							       =self.pipeline_class(**a   )
       snake_case__  :    int							       =pipe.to(a   )

       pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    List[Any]							       =torch.Generator(device=a   ).manual_seed(0   )
       snake_case__  :    List[Any]							       =pipe.decoder.dtype
       snake_case__  :    str							       =1

       snake_case__  :    Dict							       =(
           batch_size,
           pipe.decoder.config.in_channels,
           pipe.decoder.config.sample_size,
           pipe.decoder.config.sample_size,
       )
       snake_case__  :    List[Any]							       =pipe.prepare_latents(
           a				,			dtype=a				,			device=a				,			generator=a				,			latents=a				,			scheduler=DummyScheduler()   )

       snake_case__  :    Optional[int]							       =(
           batch_size,
           pipe.super_res_first.config.in_channels // 2,
           pipe.super_res_first.config.sample_size,
           pipe.super_res_first.config.sample_size,
       )
       snake_case__  :    Dict							       =pipe.prepare_latents(
           a				,			dtype=a				,			device=a				,			generator=a				,			latents=a				,			scheduler=DummyScheduler()   )

       snake_case__  :    Union[str, Any]							       =self.get_dummy_inputs(a				,			pil_image=a   )

       snake_case__  :    List[str]							       =pipe(
           **a				,			decoder_latents=a				,			super_res_latents=a   ).images

       snake_case__  :    Any							       =self.get_dummy_inputs(a				,			pil_image=a   )
       # Don't pass image, instead pass embedding
       snake_case__  :    Optional[Any]							       =pipeline_inputs.pop("""image"""   )
       snake_case__  :    Optional[int]							       =pipe.image_encoder(a   ).image_embeds

       snake_case__  :    Dict							       =pipe(
           **a				,			decoder_latents=a				,			super_res_latents=a				,			image_embeddings=a				,			).images

       # make sure passing text embeddings manually is identical
       assert np.abs(img_out_a - img_out_a   ).max() < 1e-4
      @skip_mps
      def 						lowercase__					(     self   ):
       snake_case__  :    List[str]							       =torch_device == """cpu"""

       # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
       snake_case__  :    Tuple							       =1e-2

       self._test_attention_slicing_forward_pass(
           test_max_difference=a				,			expected_max_diff=a   )
      @skip_mps
      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =torch_device == """cpu"""
       snake_case__  :    List[Any]							       =True
       snake_case__  :    List[Any]							       =[
           """decoder_num_inference_steps""",
           """super_res_num_inference_steps""",
       ]

       self._test_inference_batch_single_identical(
           test_max_difference=a				,			relax_max_difference=a				,			additional_params_copy_to_batched_inputs=a				,			)
      def 						lowercase__					(     self   ):
       snake_case__  :    Dict							       =[
           """decoder_num_inference_steps""",
           """super_res_num_inference_steps""",
       ]

       if torch_device == "mps":
        # TODO: MPS errors with larger batch sizes
        snake_case__  :    List[Any]							       =[2, 3]
        self._test_inference_batch_consistent(
            batch_sizes=a				,			additional_params_copy_to_batched_inputs=a				,			)
       else:
        self._test_inference_batch_consistent(
            additional_params_copy_to_batched_inputs=a   )
      @skip_mps
      def 						lowercase__					(     self   ):
       return super().test_dict_tuple_outputs_equivalent()
      @skip_mps
      def 						lowercase__					(     self   ):
       return super().test_save_load_local()



      @skip_mps
      def 						lowercase__					(     self   ):
       return super().test_save_load_optional_components()







@slow
@require_torch_gpu
class  _lowercase   (   unittest.TestCase						):
      def 						lowercase__					(     self   ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



      def 						lowercase__					(     self   ):
       snake_case__  :    str							       =load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"""   )
       snake_case__  :    Union[str, Any]							       =load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/unclip/karlo_v1_alpha_cat_variation_fp16.npy"""   )

       snake_case__  :    List[Any]							       =UnCLIPImageVariationPipeline.from_pretrained(
           """kakaobrain/karlo-v1-alpha-image-variations"""				,			torch_dtype=torch.floataa   )
       snake_case__  :    Optional[int]							       =pipeline.to(a   )
       pipeline.set_progress_bar_config(disable=a   )

       snake_case__  :    Optional[Any]							       =torch.Generator(device="""cpu"""   ).manual_seed(0   )
       snake_case__  :    Tuple							       =pipeline(
           a				,			generator=a				,			output_type="""np"""				,			)

       snake_case__  :    Union[str, Any]							       =output.images[0]

       assert image.shape == (2_5_6, 2_5_6, 3)

       assert_mean_pixel_difference(a				,			a				,			1_5   )





