





'''simple docstring'''

import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


lowercase_         =			[nn.LayerNorm]

lowercase_         =			logging.get_logger(__name__)

lowercase_         =			version.parse(version.parse(torch.__version__).base_version)

lowercase_         =			parsed_torch_version_base >= version.parse("""2.0""")
lowercase_         =			parsed_torch_version_base >= version.parse("""1.12""")
lowercase_         =			parsed_torch_version_base >= version.parse("""1.11""")
lowercase_         =			parsed_torch_version_base < version.parse("""1.11""")





def lowerCamelCase						(					__lowerCamelCase			:   str ,	__lowerCamelCase			:   Optional[int] ,	__lowerCamelCase			:   Any ,	__lowerCamelCase			:   List[str] ,	self			:   List[str]   )						->Optional[int]:
    from torch import _softmax_backward_data

    if is_torch_less_than_1_11:
        return _softmax_backward_data(__lowerCamelCase ,	__lowerCamelCase ,	parent.dim ,	self   )
    else:
        return _softmax_backward_data(__lowerCamelCase ,	__lowerCamelCase ,	parent.dim ,	self.dtype   )





def lowerCamelCase						(					__lowerCamelCase			:   nn.Linear ,	__lowerCamelCase			:   torch.LongTensor ,	__lowerCamelCase			:   int = 0   )						->nn.Linear:
    _SCREAMING_SNAKE_CASE     =      index.to(layer.weight.device   )
    _SCREAMING_SNAKE_CASE     =      layer.weight.index_select(__lowerCamelCase ,	__lowerCamelCase   ).clone().detach()
    if layer.bias is not None:
        if dim == 1:
            _SCREAMING_SNAKE_CASE     =      layer.bias.clone().detach()
        else:
            _SCREAMING_SNAKE_CASE     =      layer.bias[index].clone().detach()
    _SCREAMING_SNAKE_CASE     =      list(layer.weight.size()   )
    _SCREAMING_SNAKE_CASE     =      len(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      nn.Linear(new_size[1] ,	new_size[0] ,	bias=layer.bias is not None   ).to(layer.weight.device   )
    _SCREAMING_SNAKE_CASE     =      False
    new_layer.weight.copy_(W.contiguous()   )
    _SCREAMING_SNAKE_CASE     =      True
    if layer.bias is not None:
        _SCREAMING_SNAKE_CASE     =      False
        new_layer.bias.copy_(b.contiguous()   )
        _SCREAMING_SNAKE_CASE     =      True
    return new_layer


class  a_					(    nn.Module	):

      '''simple docstring'''


      def __init__(		self			,		A			,		A	)		->       Dict:
          super().__init__()
          _SCREAMING_SNAKE_CASE     =      nf
          _SCREAMING_SNAKE_CASE     =      nn.Parameter(torch.empty(A			,		A	)	)
          _SCREAMING_SNAKE_CASE     =      nn.Parameter(torch.zeros(A	)	)
          nn.init.normal_(self.weight			,		std=0.02	)





      def       snake_case_(		self			,		A	)		->       Dict:
          _SCREAMING_SNAKE_CASE     =      x.size()[:-1] + (self.nf,)
          _SCREAMING_SNAKE_CASE     =      torch.addmm(self.bias			,		x.view(-1			,		x.size(-1	)	)			,		self.weight	)
          _SCREAMING_SNAKE_CASE     =      x.view(A	)
          return x





def lowerCamelCase						(					__lowerCamelCase			:   ConvaD ,	__lowerCamelCase			:   torch.LongTensor ,	__lowerCamelCase			:   int = 1   )						->ConvaD:
    _SCREAMING_SNAKE_CASE     =      index.to(layer.weight.device   )
    _SCREAMING_SNAKE_CASE     =      layer.weight.index_select(__lowerCamelCase ,	__lowerCamelCase   ).clone().detach()
    if dim == 0:
        _SCREAMING_SNAKE_CASE     =      layer.bias.clone().detach()
    else:
        _SCREAMING_SNAKE_CASE     =      layer.bias[index].clone().detach()
    _SCREAMING_SNAKE_CASE     =      list(layer.weight.size()   )
    _SCREAMING_SNAKE_CASE     =      len(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      ConvaD(new_size[1] ,	new_size[0]   ).to(layer.weight.device   )
    _SCREAMING_SNAKE_CASE     =      False
    new_layer.weight.copy_(W.contiguous()   )
    _SCREAMING_SNAKE_CASE     =      True
    _SCREAMING_SNAKE_CASE     =      False
    new_layer.bias.copy_(b.contiguous()   )
    _SCREAMING_SNAKE_CASE     =      True
    return new_layer





def lowerCamelCase						(					__lowerCamelCase			:   Union[nn.Linear, ConvaD] ,	__lowerCamelCase			:   torch.LongTensor ,	__lowerCamelCase			:   Optional[int] = None   )						->Union[nn.Linear, ConvaD]:
    if isinstance(__lowerCamelCase ,	nn.Linear   ):
        return prune_linear_layer(__lowerCamelCase ,	__lowerCamelCase ,	dim=0 if dim is None else dim   )
    elif isinstance(__lowerCamelCase ,	__lowerCamelCase   ):
        return prune_convad_layer(__lowerCamelCase ,	__lowerCamelCase ,	dim=1 if dim is None else dim   )
    else:
        raise ValueError(F'Can\'t prune layer of class {layer.__class__}'   )





def lowerCamelCase						(					__lowerCamelCase			:   Callable[..., torch.Tensor] ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	*__lowerCamelCase			:   Any   )						->torch.Tensor:
    assert len(__lowerCamelCase   ) > 0, F'{input_tensors} has to be a tuple/list of tensors'

    # inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
    _SCREAMING_SNAKE_CASE     =      len(inspect.signature(__lowerCamelCase   ).parameters   )
    if num_args_in_forward_chunk_fn != len(__lowerCamelCase   ):
        raise ValueError(
            F'forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(__lowerCamelCase   )} input '
            """tensors are given"""   )

    if chunk_size > 0:
        _SCREAMING_SNAKE_CASE     =      input_tensors[0].shape[chunk_dim]
        for input_tensor in input_tensors:
            if input_tensor.shape[chunk_dim] != tensor_shape:
                raise ValueError(
                    F'All input tenors have to be of the same shape: {tensor_shape}, '
                    F'found shape {input_tensor.shape[chunk_dim]}'   )

        if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
            raise ValueError(
                F'The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '
                F'size {chunk_size}'   )

        _SCREAMING_SNAKE_CASE     =      input_tensors[0].shape[chunk_dim] // chunk_size

        # chunk input tensor into tuples
        _SCREAMING_SNAKE_CASE     =      tuple(input_tensor.chunk(__lowerCamelCase ,	dim=__lowerCamelCase   ) for input_tensor in input_tensors   )
        # apply forward fn to every tuple
        _SCREAMING_SNAKE_CASE     =      tuple(forward_fn(*__lowerCamelCase   ) for input_tensors_chunk in zip(*__lowerCamelCase   )   )
        # concatenate output at same dimension
        return torch.cat(__lowerCamelCase ,	dim=__lowerCamelCase   )

    return forward_fn(*__lowerCamelCase   )





def lowerCamelCase						(					__lowerCamelCase			:   List[int] ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   Set[int]   )						->Tuple[Set[int], torch.LongTensor]:
    _SCREAMING_SNAKE_CASE     =      torch.ones(__lowerCamelCase ,	__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      set(__lowerCamelCase   ) - already_pruned_heads  # Convert to set and remove already pruned heads
    for head in heads:
        # Compute how many pruned heads are before the head and move the index accordingly
        _SCREAMING_SNAKE_CASE     =      head - sum(1 if h < head else 0 for h in already_pruned_heads   )
        _SCREAMING_SNAKE_CASE     =      0
    _SCREAMING_SNAKE_CASE     =      mask.view(-1   ).contiguous().eq(1   )
    _SCREAMING_SNAKE_CASE     =      torch.arange(len(__lowerCamelCase   )   )[mask].long()
    return heads, index





def lowerCamelCase						(					*__lowerCamelCase			:   Union[torch.Tensor, List[torch.Tensor]] ,	__lowerCamelCase			:   Optional[str] = None   )						->Tuple[torch.Tensor, ...]:
    return torch.meshgrid(*__lowerCamelCase ,	indexing=__lowerCamelCase   )






def lowerCamelCase						(					__lowerCamelCase			:   torch.Tensor   )						->Tuple[torch.device, int, int]:
    return tensor.device, storage_ptr(__lowerCamelCase   ), storage_size(__lowerCamelCase   )










'''simple docstring'''

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			(LMSDiscreteScheduler,)
    UpperCamelCase			   =			10


    def       snake_case_(		self			,		**A	)		->       str:
        _SCREAMING_SNAKE_CASE     =      {
            """num_train_timesteps""": 1100,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
        }

        config.update(**A	)
        return config


    def       snake_case_(		self	)		->       str:
        for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=A	)


    def       snake_case_(		self	)		->       Optional[Any]:
        for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]			,		[0.0002, 0.002, 0.02]	):
            self.check_over_configs(beta_start=A			,		beta_end=A	)


    def       snake_case_(		self	)		->       Tuple:
        for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=A	)


    def       snake_case_(		self	)		->       List[str]:
        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=A	)


    def       snake_case_(		self	)		->       Optional[int]:
        for t in [0, 500, 800]:
            self.check_over_forward(time_step=A	)


    def       snake_case_(		self	)		->       str:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 1006.388	) < 1e-2
        assert abs(result_mean.item() - 1.31	) < 1e-3


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config(prediction_type="""v_prediction"""	)
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 0.0017	) < 1e-2
        assert abs(result_mean.item() - 2.26_76e-06	) < 1e-3


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps			,		device=A	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        _SCREAMING_SNAKE_CASE     =      sample.to(A	)

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 1006.388	) < 1e-2
        assert abs(result_mean.item() - 1.31	) < 1e-3





    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A			,		use_karras_sigmas=A	)

        scheduler.set_timesteps(self.num_inference_steps			,		device=A	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter.to(A	) * scheduler.init_noise_sigma
        _SCREAMING_SNAKE_CASE     =      sample.to(A	)

        for t in scheduler.timesteps:
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 3812.9927	) < 2e-2
        assert abs(result_mean.item() - 4.9648	) < 1e-3



