"""simple docstring"""



import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
   import bitsandbytes as bnb
   import torch
   import torch.nn as nn

   from ..pytorch_utils import ConvaD

if is_accelerate_available():
   from accelerate import init_empty_weights
   from accelerate.utils import find_tied_parameters

SCREAMING_SNAKE_CASE__:Tuple			=	logging.get_logger(__name__)



def 					_lowerCamelCase(					a ,  a ,  a ,  a=None ,  a=None							):
       # Recurse if needed
       if "." in tensor_name:
              __a     				=  tensor_name.split("."							)
              for split in splits[:-1]:
                     __a     				=  getattr(a ,  a							)
                     if new_module is None:
                            raise ValueError(F"{module} has no attribute {split}."							)
                     __a     				=  new_module
              __a     				=  splits[-1]

       if tensor_name not in module._parameters and tensor_name not in module._buffers:
              raise ValueError(F"{module} does not have a parameter or a buffer named {tensor_name}."							)
       __a     				=  tensor_name in module._buffers
       __a     				=  getattr(a ,  a							)

       if old_value.device == torch.device("meta"							) and device not in ["meta", torch.device("meta"							)] and value is None:
              raise ValueError(F"{tensor_name} is on the meta device, we need a `value` to put in on {device}."							)

       __a     				=  False
       __a     				=  False
       if is_buffer or not is_bitsandbytes_available():
              __a     				=  False
              __a     				=  False
       else:
              __a     				=  hasattr(bnb.nn ,  "Params4bit"							) and isinstance(module._parameters[tensor_name] ,  bnb.nn.Paramsabit							)
              __a     				=  isinstance(module._parameters[tensor_name] ,  bnb.nn.IntaParams							)

       if is_abit or is_abit:
              __a     				=  module._parameters[tensor_name]
              if param.device.type != "cuda":
                     if value is None:
                            __a     				=  old_value.to(a							)
                     elif isinstance(a ,  torch.Tensor							):
                            __a     				=  value.to("cpu"							)
                            if value.dtype == torch.inta:
                                   __a     				=  version.parse(importlib.metadata.version("bitsandbytes"							)							) > version.parse(
                                       "0.37.2"							)
                                   if not is_abit_serializable:
                                          raise ValueError(
                                              "Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. "
                                              "Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."							)
                     else:
                            __a     				=  torch.tensor(a ,  device="cpu"							)

                     # Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
                     # Since weights are saved in the correct "orientation", we skip transposing when loading.
                     if issubclass(module.source_cls ,  a							) and fpaa_statistics is None:
                            __a     				=  new_value.T

                     __a     				=  old_value.__dict__
                     if is_abit:
                            __a     				=  bnb.nn.IntaParams(a ,  requires_grad=a ,  **a							).to(a							)
                     elif is_abit:
                            __a     				=  bnb.nn.Paramsabit(a ,  requires_grad=a ,  **a							).to(a							)

                     __a     				=  new_value
                     if fpaa_statistics is not None:
                            setattr(module.weight ,  "SCB" ,  fpaa_statistics.to(a							)							)

       else:
              if value is None:
                     __a     				=  old_value.to(a							)
              elif isinstance(a ,  torch.Tensor							):
                     __a     				=  value.to(a							)
              else:
                     __a     				=  torch.tensor(a ,  device=a							)

              if is_buffer:
                     __a     				=  new_value
              else:
                     __a     				=  nn.Parameter(a ,  requires_grad=old_value.requires_grad							)
                     __a     				=  new_value



def 					_lowerCamelCase(					a ,  a=None ,  a=None ,  a=None ,  a=False							):
       for name, module in model.named_children():
              if current_key_name is None:
                     __a     				=  []
              current_key_name.append(a							)

              if (isinstance(a ,  nn.Linear							) or isinstance(a ,  a							)) and name not in modules_to_not_convert:
                     # Check if the current key is not in the `modules_to_not_convert`
                     if not any(key in ".".join(a							) for key in modules_to_not_convert							):
                            with init_empty_weights():
                                   if isinstance(a ,  a							):
                                          __a				, __a     				=  module.weight.shape
                                   else:
                                          __a     				=  module.in_features
                                          __a     				=  module.out_features

                                   if quantization_config.quantization_method() == "llm_int8":
                                          __a     				=  bnb.nn.LinearabitLt(
                                              a ,  a ,  module.bias is not None ,  has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight ,  threshold=quantization_config.llm_inta_threshold ,  )
                                          __a     				=  True
                                   else:
                                          if (
                                              quantization_config.llm_inta_skip_modules is not None
                                              and name in quantization_config.llm_inta_skip_modules
                                          ):
                                                 pass
                                          else:
                                                 __a     				=  bnb.nn.Linearabit(
                                                     a ,  a ,  module.bias is not None ,  quantization_config.bnb_abit_compute_dtype ,  compress_statistics=quantization_config.bnb_abit_use_double_quant ,  quant_type=quantization_config.bnb_abit_quant_type ,  )
                                                 __a     				=  True
                    # Store the module class in case we need to transpose the weight later
                                   __a     				=  type(a							)
                                   # Force requires grad to False to avoid unexpected errors
                                   model._modules[name].requires_grad_(a							)
              if len(list(module.children()							)							) > 0:
                     __a				, __a     				=  _replace_with_bnb_linear(
                         a ,  a ,  a ,  a ,  has_been_replaced=a ,  )
              # Remove the last key for recursion
              current_key_name.pop(-1							)
       return model, has_been_replaced



def 					_lowerCamelCase(					a ,  a=None ,  a=None ,  a=None							):
       __a     				=  ["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
       __a				, __a     				=  _replace_with_bnb_linear(
           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."
                  " Please double check your model architecture, or submit an issue on github if you think this is"
                  " a bug."							)

       return model



def 					_lowerCamelCase(					*a ,  **a							):
       warnings.warn(
           "`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead" ,  a ,  )
       return replace_with_bnb_linear(*a ,  **a							)



def 					_lowerCamelCase(					*a ,  **a							):
       warnings.warn(
           "`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead" ,  a ,  )
       return set_module_quantized_tensor_to_device(*a ,  **a							)




def 					_lowerCamelCase(					a							):
       __a     				=  deepcopy(a							)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
       tied_model.tie_weights()

       __a     				=  find_tied_parameters(a							)
       # For compatibility with Accelerate < 0.18
       if isinstance(a ,  a							):
              __a     				=  sum(list(tied_params.values()							) ,  []							) + list(tied_params.keys()							)
       else:
              __a     				=  sum(a ,  []							)
       __a     				=  len(a							) > 0

       # Check if it is a base model
       __a     				=  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
       __a     				=  list(model.named_children()							)
       __a     				=  [list_modules[-1][0]]

       # add last module together with tied weights
       __a     				=  set(a							) - set(a							)
       __a     				=  list(set(a							)							) + list(a							)

       # remove ".weight" from the keys
       __a     				=  [".weight", ".bias"]
       __a     				=  []
       for name in list_untouched:
              for name_to_remove in names_to_remove:
                     if name_to_remove in name:
                            __a     				=  name.replace(a ,  ""							)
              filtered_module_names.append(a							)

       return filtered_module_names

"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			UNetaDModel
 _snake_case   :			KarrasVeScheduler


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        super().__init__()
        self.register_modules(unet=lowerCamelCase  ,						scheduler=lowerCamelCase		)






 @torch.no_grad()
 def __call__(				self  ,						lowerCamelCase = 1  ,						lowerCamelCase = 50  ,						lowerCamelCase = None  ,						lowerCamelCase = "pil"  ,						lowerCamelCase = True  ,						**lowerCamelCase  ,						):
        __a     				=  self.unet.config.sample_size
        __a     				=  (batch_size, 3, img_size, img_size)

        __a     				=  self.unet

        # sample x_0 ~ N(0, sigma_0^2 * I)
        __a     				=  randn_tensor(lowerCamelCase  ,						generator=lowerCamelCase  ,						device=self.device		) * self.scheduler.init_noise_sigma

        self.scheduler.set_timesteps(lowerCamelCase		)

        for t in self.progress_bar(self.scheduler.timesteps		):
               # here sigma_t == t_i from the paper
               __a     				=  self.scheduler.schedule[t]
               __a     				=  self.scheduler.schedule[t - 1] if t > 0 else 0

               # 1. Select temporarily increased noise level sigma_hat
               # 2. Add new noise to move from sample_i to sample_hat
               __a				, __a     				=  self.scheduler.add_noise_to_input(lowerCamelCase  ,						lowerCamelCase  ,						generator=lowerCamelCase		)

               # 3. Predict the noise residual given the noise magnitude `sigma_hat`
               # The model inputs and output are adjusted by following eq. (213) in [1].
               __a     				=  (sigma_hat / 2) * model((sample_hat + 1) / 2  ,						sigma_hat / 2		).sample

               # 4. Evaluate dx/dt at sigma_hat
               # 5. Take Euler step from sigma to sigma_prev
               __a     				=  self.scheduler.step(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)

               if sigma_prev != 0:
                      # 6. Apply 2nd order correction
                      # The model inputs and output are adjusted by following eq. (213) in [1].
                      __a     				=  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2  ,						sigma_prev / 2		).sample
                      __a     				=  self.scheduler.step_correct(
                          lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						step_output.prev_sample  ,						step_output["derivative"]  ,						)
               __a     				=  step_output.prev_sample

        __a     				=  (sample / 2 + 0.5).clamp(0  ,						1		)
        __a     				=  sample.cpu().permute(0  ,						2  ,						3  ,						1		).numpy()
        if output_type == "pil":
               __a     				=  self.numpy_to_pil(lowerCamelCase		)

        if not return_dict:
               return (image,)

        return ImagePipelineOutput(images=lowerCamelCase		)
