





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

__A							:			List[Any]         =			logging.get_logger(__name__)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=None							,     _SCREAMING_SNAKE_CASE=None				)							-> Dict:




    """simple docstring"""




    if "." in tensor_name:
        _A     =							tensor_name.split('.'				)
        for split in splits[:-1]:
            _A     =							getattr(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)
            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(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)

    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(_SCREAMING_SNAKE_CASE				)
            elif isinstance(_SCREAMING_SNAKE_CASE							,     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(_SCREAMING_SNAKE_CASE							,     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							,     _SCREAMING_SNAKE_CASE				) and fpaa_statistics is None:
                _A     =							new_value.T

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

            _A     =							new_value
            if fpaa_statistics is not None:
                setattr(module.weight							,     'SCB'							,     fpaa_statistics.to(_SCREAMING_SNAKE_CASE				)				)

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

        if is_buffer:
            _A     =							new_value
        else:
            _A     =							nn.Parameter(_SCREAMING_SNAKE_CASE							,     requires_grad=old_value.requires_grad				)
            _A     =							new_value





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=None							,     _SCREAMING_SNAKE_CASE=None							,     _SCREAMING_SNAKE_CASE=None							,     _SCREAMING_SNAKE_CASE=False				)							-> Dict:




    """simple docstring"""




    for name, module in model.named_children():
        if current_key_name is None:
            _A     =							[]
        current_key_name.append(_SCREAMING_SNAKE_CASE				)

        if (isinstance(_SCREAMING_SNAKE_CASE							,     nn.Linear				) or isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)) 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(_SCREAMING_SNAKE_CASE				) for key in modules_to_not_convert				):
                with init_empty_weights():
                    if isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
                        _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(
                            _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     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(
                                _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     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(_SCREAMING_SNAKE_CASE				)
                    # Force requires grad to False to avoid unexpected errors
                    model._modules[name].requires_grad_(_SCREAMING_SNAKE_CASE				)
        if len(list(module.children()				)				) > 0:
            _A,			_A     =							_replace_with_bnb_linear(
                _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     has_been_replaced=_SCREAMING_SNAKE_CASE							,     )
        # Remove the last key for recursion
        current_key_name.pop(-1				)
    return model, has_been_replaced





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=None							,     _SCREAMING_SNAKE_CASE=None							,     _SCREAMING_SNAKE_CASE=None				)							-> Tuple:




    """simple docstring"""




    _A     =							['lm_head'] if modules_to_not_convert is None else modules_to_not_convert
    _A,			_A     =							_replace_with_bnb_linear(
        _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				)

    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(       *_SCREAMING_SNAKE_CASE							,     **_SCREAMING_SNAKE_CASE				)							-> Tuple:




    """simple docstring"""




    warnings.warn(
        '`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead'							,     _SCREAMING_SNAKE_CASE							,     )
    return replace_with_bnb_linear(*_SCREAMING_SNAKE_CASE							,     **_SCREAMING_SNAKE_CASE				)





def 				__lowerCAmelCase(       *_SCREAMING_SNAKE_CASE							,     **_SCREAMING_SNAKE_CASE				)							-> Optional[Any]:




    """simple docstring"""




    warnings.warn(
        '`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead'							,     _SCREAMING_SNAKE_CASE							,     )
    return set_module_quantized_tensor_to_device(*_SCREAMING_SNAKE_CASE							,     **_SCREAMING_SNAKE_CASE				)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> Optional[int]:




    """simple docstring"""




    _A     =							deepcopy(_SCREAMING_SNAKE_CASE				)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
    tied_model.tie_weights()

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

    # Check if it is a base model
    _A     =							not hasattr(_SCREAMING_SNAKE_CASE							,     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(_SCREAMING_SNAKE_CASE				) - set(_SCREAMING_SNAKE_CASE				)
    _A     =							list(set(_SCREAMING_SNAKE_CASE				)				) + list(_SCREAMING_SNAKE_CASE				)

    # 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(_SCREAMING_SNAKE_CASE							,     ''				)
        filtered_module_names.append(_SCREAMING_SNAKE_CASE				)

    return filtered_module_names






'''simple docstring'''


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						(DDPMParallelScheduler,)




  def 		lowerCamelCase__  (		self      :       Optional[Any]   ,				**lowercase_      :       str      ):







    '''simple docstring'''


    lowercase_     =       {
        """num_train_timesteps""": 1_000,
        """beta_start""": 0.0_0_0_1,
        """beta_end""": 0.0_2,
        """beta_schedule""": """linear""",
        """variance_type""": """fixed_small""",
        """clip_sample""": True,
    }

    config.update(**lowercase_      )
    return config




  def 		lowerCamelCase__  (		self      :       Optional[Any]      ):







    '''simple docstring'''


    for timesteps in [1, 5, 100, 1_000]:
      self.check_over_configs(num_train_timesteps=lowercase_      )




  def 		lowerCamelCase__  (		self      :       List[Any]      ):







    '''simple docstring'''


    for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]   ,				[0.0_0_2, 0.0_2, 0.2, 2]      ):
      self.check_over_configs(beta_start=lowercase_   ,				beta_end=lowercase_      )




  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


    for schedule in ["linear", "squaredcos_cap_v2"]:
      self.check_over_configs(beta_schedule=lowercase_      )




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    for variance in ["fixed_small", "fixed_large", "other"]:
      self.check_over_configs(variance_type=lowercase_      )




  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    for clip_sample in [True, False]:
      self.check_over_configs(clip_sample=lowercase_      )




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    self.check_over_configs(thresholding=lowercase_      )
    for threshold in [0.5, 1.0, 2.0]:
      for prediction_type in ["epsilon", "sample", "v_prediction"]:
        self.check_over_configs(
            thresholding=lowercase_   ,				prediction_type=lowercase_   ,				sample_max_value=lowercase_   ,				)




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    for prediction_type in ["epsilon", "sample", "v_prediction"]:
      self.check_over_configs(prediction_type=lowercase_      )




  def 		lowerCamelCase__  (		self      :       List[str]      ):







    '''simple docstring'''


    for t in [0, 500, 999]:
      self.check_over_forward(time_step=lowercase_      )




  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1e-5
    assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.0_0_9_7_9      )      ) < 1e-5
    assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.0_2      )      ) < 1e-5




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       len(lowercase_      )

    lowercase_     =       self.dummy_model()
    lowercase_     =       self.dummy_sample_deter
    lowercase_     =       self.dummy_sample_deter + 0.1
    lowercase_     =       self.dummy_sample_deter - 0.1

    lowercase_     =       samplea.shape[0]
    lowercase_     =       torch.stack([samplea, samplea, samplea]   ,				dim=0      )
    lowercase_     =       torch.arange(lowercase_      )[0:3, None].repeat(1   ,				lowercase_      )

    lowercase_     =       model(samples.flatten(0   ,				1      )   ,				timesteps.flatten(0   ,				1      )      )
    lowercase_     =       scheduler.batch_step_no_noise(lowercase_   ,				timesteps.flatten(0   ,				1      )   ,				samples.flatten(0   ,				1      )      )

    lowercase_     =       torch.sum(torch.abs(lowercase_      )      )
    lowercase_     =       torch.mean(torch.abs(lowercase_      )      )

    assert abs(result_sum.item() - 1_1_5_3.1_8_3_3      ) < 1e-2
    assert abs(result_mean.item() - 0.5_0_0_5      ) < 1e-3




  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       len(lowercase_      )

    lowercase_     =       self.dummy_model()
    lowercase_     =       self.dummy_sample_deter
    lowercase_     =       torch.manual_seed(0      )

    for t in reversed(range(lowercase_      )      ):
      # 1. predict noise residual
      lowercase_     =       model(lowercase_   ,				lowercase_      )

      # 2. predict previous mean of sample x_t-1
      lowercase_     =       scheduler.step(lowercase_   ,				lowercase_   ,				lowercase_   ,				generator=lowercase_      ).prev_sample

      lowercase_     =       pred_prev_sample

    lowercase_     =       torch.sum(torch.abs(lowercase_      )      )
    lowercase_     =       torch.mean(torch.abs(lowercase_      )      )

    assert abs(result_sum.item() - 2_5_8.9_6_0_6      ) < 1e-2
    assert abs(result_mean.item() - 0.3_3_7_2      ) < 1e-3




  def 		lowerCamelCase__  (		self      :       str      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config(prediction_type="""v_prediction"""      )
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       len(lowercase_      )

    lowercase_     =       self.dummy_model()
    lowercase_     =       self.dummy_sample_deter
    lowercase_     =       torch.manual_seed(0      )

    for t in reversed(range(lowercase_      )      ):
      # 1. predict noise residual
      lowercase_     =       model(lowercase_   ,				lowercase_      )

      # 2. predict previous mean of sample x_t-1
      lowercase_     =       scheduler.step(lowercase_   ,				lowercase_   ,				lowercase_   ,				generator=lowercase_      ).prev_sample

      lowercase_     =       pred_prev_sample

    lowercase_     =       torch.sum(torch.abs(lowercase_      )      )
    lowercase_     =       torch.mean(torch.abs(lowercase_      )      )

    assert abs(result_sum.item() - 2_0_2.0_2_9_6      ) < 1e-2
    assert abs(result_mean.item() - 0.2_6_3_1      ) < 1e-3




  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       [100, 87, 50, 1, 0]

    scheduler.set_timesteps(timesteps=lowercase_      )

    lowercase_     =       scheduler.timesteps

    for i, timestep in enumerate(lowercase_      ):
      if i == len(lowercase_      ) - 1:
        lowercase_     =       -1
      else:
        lowercase_     =       timesteps[i + 1]

      lowercase_     =       scheduler.previous_timestep(lowercase_      )
      lowercase_     =       prev_t.item()

      self.assertEqual(lowercase_   ,				lowercase_      )




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       [100, 87, 50, 51, 0]

    with self.assertRaises(lowercase_   ,				msg="""`custom_timesteps` must be in descending order."""      ):
      scheduler.set_timesteps(timesteps=lowercase_      )




  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       [100, 87, 50, 1, 0]
    lowercase_     =       len(lowercase_      )

    with self.assertRaises(lowercase_   ,				msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""      ):
      scheduler.set_timesteps(num_inference_steps=lowercase_   ,				timesteps=lowercase_      )





  def 		lowerCamelCase__  (		self      :       Optional[Any]      ):







    '''simple docstring'''


    lowercase_     =       self.scheduler_classes[0]
    lowercase_     =       self.get_scheduler_config()
    lowercase_     =       scheduler_class(**lowercase_      )

    lowercase_     =       [scheduler.config.num_train_timesteps]

    with self.assertRaises(
        lowercase_   ,				msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""   ,				):
      scheduler.set_timesteps(timesteps=lowercase_      )

