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


__lowerCamelCase :     Optional[int]          =  [nn.LayerNorm]

__lowerCamelCase :     Tuple          =  logging.get_logger(__name__)

__lowerCamelCase :     Optional[Any]          =  version.parse(version.parse(torch.__version__).base_version)

__lowerCamelCase :     Dict          =  parsed_torch_version_base >= version.parse('''2.0''')
__lowerCamelCase :     Union[str, Any]          =  parsed_torch_version_base >= version.parse('''1.12''')
__lowerCamelCase :     Union[str, Any]          =  parsed_torch_version_base >= version.parse('''1.11''')
__lowerCamelCase :     List[Any]          =  parsed_torch_version_base < version.parse('''1.11''')




def 				_snake_case     (       lowerCAmelCase							:       Tuple       ,			lowerCAmelCase							:       Optional[Any]       ,			lowerCAmelCase							:       List[Any]       ,			lowerCAmelCase							:       int       ,			self							:       Tuple     ):
  """simple docstring"""







  from torch import _softmax_backward_data

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




def 				_snake_case     (       lowerCAmelCase							:       List[Any]       ,			lowerCAmelCase							:       Optional[Any]       ,			lowerCAmelCase							:       Any = 0     ):
  """simple docstring"""







  SCREAMING_SNAKE_CASE_  :		List[str]    				=   index.to(layer.weight.device     )
  SCREAMING_SNAKE_CASE_  :		Any    				=   layer.weight.index_select(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_     ).clone().detach()
  if layer.bias is not None:
    if dim == 1:
      SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   layer.bias.clone().detach()
    else:
      SCREAMING_SNAKE_CASE_  :		List[str]    				=   layer.bias[index].clone().detach()
  SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   list(layer.weight.size()     )
  SCREAMING_SNAKE_CASE_  :		Dict    				=   len(SCREAMING_SNAKE_CASE_     )
  SCREAMING_SNAKE_CASE_  :		Any    				=   nn.Linear(new_size[1]       ,			new_size[0]       ,			bias=layer.bias is not None     ).to(layer.weight.device     )
  SCREAMING_SNAKE_CASE_  :		Any    				=   False
  new_layer.weight.copy_(W.contiguous()     )
  SCREAMING_SNAKE_CASE_  :		str    				=   True
  if layer.bias is not None:
    SCREAMING_SNAKE_CASE_  :		List[Any]    				=   False
    new_layer.bias.copy_(b.contiguous()     )
    SCREAMING_SNAKE_CASE_  :		List[str]    				=   True
  return new_layer






class 		a__						( nn.Module ):

     def __init__(   self				:	str,_A				:	int,_A				:	List[Any]   ):







       """simple docstring"""






       super().__init__()
       SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   nf
       SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   nn.Parameter(torch.empty(lowercase__,lowercase__   )   )
       SCREAMING_SNAKE_CASE_  :		Any    				=   nn.Parameter(torch.zeros(lowercase__   )   )
       nn.init.normal_(self.weight,std=0.02   )






     def     __UpperCamelCase       (   self				:	Tuple,_A				:	Tuple   ):







       """simple docstring"""






       SCREAMING_SNAKE_CASE_  :		str    				=   x.size()[:-1] + (self.nf,)
       SCREAMING_SNAKE_CASE_  :		Dict    				=   torch.addmm(self.bias,x.view(-1,x.size(-1   )   ),self.weight   )
       SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   x.view(lowercase__   )
       return x







def 				_snake_case     (       lowerCAmelCase							:       List[Any]       ,			lowerCAmelCase							:       Union[str, Any]       ,			lowerCAmelCase							:       Any = 1     ):
  """simple docstring"""







  SCREAMING_SNAKE_CASE_  :		int    				=   index.to(layer.weight.device     )
  SCREAMING_SNAKE_CASE_  :		Tuple    				=   layer.weight.index_select(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_     ).clone().detach()
  if dim == 0:
    SCREAMING_SNAKE_CASE_  :		List[str]    				=   layer.bias.clone().detach()
  else:
    SCREAMING_SNAKE_CASE_  :		List[str]    				=   layer.bias[index].clone().detach()
  SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   list(layer.weight.size()     )
  SCREAMING_SNAKE_CASE_  :		Any    				=   len(SCREAMING_SNAKE_CASE_     )
  SCREAMING_SNAKE_CASE_  :		List[Any]    				=   ConvaD(new_size[1]       ,			new_size[0]     ).to(layer.weight.device     )
  SCREAMING_SNAKE_CASE_  :		Tuple    				=   False
  new_layer.weight.copy_(W.contiguous()     )
  SCREAMING_SNAKE_CASE_  :		str    				=   True
  SCREAMING_SNAKE_CASE_  :		Tuple    				=   False
  new_layer.bias.copy_(b.contiguous()     )
  SCREAMING_SNAKE_CASE_  :		Dict    				=   True
  return new_layer




def 				_snake_case     (       lowerCAmelCase							:       str       ,			lowerCAmelCase							:       Any       ,			lowerCAmelCase							:       int = None     ):
  """simple docstring"""







  if isinstance(SCREAMING_SNAKE_CASE_       ,			nn.Linear     ):
    return prune_linear_layer(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			dim=0 if dim is None else dim     )
  elif isinstance(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_     ):
    return prune_convad_layer(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_       ,			dim=1 if dim is None else dim     )
  else:
    raise ValueError(f'Can\'t prune layer of class {layer.__class__}'     )




def 				_snake_case     (       lowerCAmelCase							:       Any       ,			lowerCAmelCase							:       str       ,			lowerCAmelCase							:       Tuple       ,			*lowerCAmelCase							:       Union[str, Any]     ):
  """simple docstring"""







  assert len(SCREAMING_SNAKE_CASE_     ) > 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_  :		Any    				=   len(inspect.signature(SCREAMING_SNAKE_CASE_     ).parameters     )
  if num_args_in_forward_chunk_fn != len(SCREAMING_SNAKE_CASE_     ):
    raise ValueError(
        f'forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(SCREAMING_SNAKE_CASE_     )} input '
        "tensors are given"     )

  if chunk_size > 0:
    SCREAMING_SNAKE_CASE_  :		List[str]    				=   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_  :		Optional[int]    				=   input_tensors[0].shape[chunk_dim] // chunk_size

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

  return forward_fn(*SCREAMING_SNAKE_CASE_     )




def 				_snake_case     (       lowerCAmelCase							:       List[str]       ,			lowerCAmelCase							:       Optional[Any]       ,			lowerCAmelCase							:       Tuple       ,			lowerCAmelCase							:       List[Any]     ):
  """simple docstring"""







  SCREAMING_SNAKE_CASE_  :		Any    				=   torch.ones(SCREAMING_SNAKE_CASE_       ,			SCREAMING_SNAKE_CASE_     )
  SCREAMING_SNAKE_CASE_  :		Dict    				=   set(SCREAMING_SNAKE_CASE_     ) - 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_  :		Any    				=   head - sum(1 if h < head else 0 for h in already_pruned_heads     )
    SCREAMING_SNAKE_CASE_  :		str    				=   0
  SCREAMING_SNAKE_CASE_  :		Tuple    				=   mask.view(-1     ).contiguous().eq(1     )
  SCREAMING_SNAKE_CASE_  :		List[Any]    				=   torch.arange(len(SCREAMING_SNAKE_CASE_     )     )[mask].long()
  return heads, index




def 				_snake_case     (       *lowerCAmelCase							:       List[Any]       ,			lowerCAmelCase							:       Optional[int] = None     ):
  """simple docstring"""







  return torch.meshgrid(*SCREAMING_SNAKE_CASE_       ,			indexing=SCREAMING_SNAKE_CASE_     )







def 				_snake_case     (       lowerCAmelCase							:       Optional[Any]     ):
  """simple docstring"""







  return tensor.device, storage_ptr(SCREAMING_SNAKE_CASE_     ), storage_size(SCREAMING_SNAKE_CASE_     )

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__lowerCamelCase :     List[Any]          =  False






@nightly
@require_torch_gpu
class 		a__						( unittest.TestCase ):

					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()

					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int    				=   VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
							# remove text_unet
							pipe.remove_unused_weights()
							pipe.to(_A   )
							pipe.set_progress_bar_config(disable=_A   )

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   torch.manual_seed(0   )
							SCREAMING_SNAKE_CASE_  :		int    				=   pipe(
							    prompt="first prompt",image=_A,text_to_image_strength=0.75,generator=_A,guidance_scale=7.5,num_inference_steps=2,output_type="numpy",).images

							with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(_A   )
									SCREAMING_SNAKE_CASE_  :		Dict    				=   VersatileDiffusionDualGuidedPipeline.from_pretrained(_A   )

							pipe.to(_A   )
							pipe.set_progress_bar_config(disable=_A   )

							SCREAMING_SNAKE_CASE_  :		List[str]    				=   generator.manual_seed(0   )
							SCREAMING_SNAKE_CASE_  :		str    				=   pipe(
							    prompt="first prompt",image=_A,text_to_image_strength=0.75,generator=_A,guidance_scale=7.5,num_inference_steps=2,output_type="numpy",).images

							assert np.abs(image - new_image   ).sum() < 1E-5, "Models don't have the same forward pass"






					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
							pipe.remove_unused_weights()
							pipe.to(_A   )
							pipe.set_progress_bar_config(disable=_A   )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   "cyberpunk 2077"
							SCREAMING_SNAKE_CASE_  :		Dict    				=   load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   torch.manual_seed(0   )
							SCREAMING_SNAKE_CASE_  :		Dict    				=   pipe(
							    prompt=_A,image=_A,text_to_image_strength=0.75,generator=_A,guidance_scale=7.5,num_inference_steps=50,output_type="numpy",).images

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   image[0, 253:256, 253:256, -1]

							assert image.shape == (1, 512, 512, 3)
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]   )

							assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
