





'''simple docstring'''

from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
  import timm


if is_torch_available():
  from torch import Tensor






class    UpperCAmelCase	(       UpperCamelCase__			,       UpperCamelCase__				):
 __lowercase   		=				"""pixel_values"""
 __lowercase   		=				False
 __lowercase   		=				TimmBackboneConfig
 def __init__(				self      :Optional[Any]						,       lowercase_      :Optional[Any]						,       **lowercase_      :Any							)->							int:
     requires_backends(self						,       "timm"							)
     super().__init__(__lowerCamelCase							)
     A__				=			config

     if config.backbone is None:
         raise ValueError("backbone is not set in the config. Please set it to a timm model name."							)

     if config.backbone not in timm.list_models():
         raise ValueError(F"backbone {config.backbone} is not supported by timm."							)

     if hasattr(__lowerCamelCase						,       "out_features"							) and config.out_features is not None:
         raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead."							)

     A__				=			getattr(__lowerCamelCase						,       "use_pretrained_backbone"						,       __lowerCamelCase							)
     if pretrained is None:
         raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False."							)

     # We just take the final layer by default. This matches the default for the transformers models.
     A__				=			config.out_indices if getattr(__lowerCamelCase						,       "out_indices"						,       __lowerCamelCase							) is not None else (-1,)

     A__				=			timm.create_model(
         config.backbone						,       pretrained=__lowerCamelCase						,       features_only=config.features_only						,       in_chans=config.num_channels						,       out_indices=__lowerCamelCase						,       **__lowerCamelCase						,       )
     # These are used to control the output of the model when called. If output_hidden_states is True, then
     # return_layers is modified to include all layers.
     A__				=			self._backbone.return_layers
     A__				=			{layer["module"]: str(__lowerCamelCase							) for i, layer in enumerate(self._backbone.feature_info.info							)}
     super()._init_backbone(__lowerCamelCase							)
 @classmethod
 def      UpperCAmelCase_							(				cls      :Union[str, Any]						,       lowercase_      :Union[str, Any]						,       *lowercase_      :Any						,       **lowercase_      :Dict							)->							List[str]:
     requires_backends(cls						,       ["vision", "timm"]							)
     from ...models.timm_backbone import TimmBackboneConfig

     A__				=			kwargs.pop("config"						,       TimmBackboneConfig()							)

     A__				=			kwargs.pop("use_timm_backbone"						,       __lowerCamelCase							)
     if not use_timm:
         raise ValueError("use_timm_backbone must be True for timm backbones"							)

     A__				=			kwargs.pop("num_channels"						,       config.num_channels							)
     A__				=			kwargs.pop("features_only"						,       config.features_only							)
     A__				=			kwargs.pop("use_pretrained_backbone"						,       config.use_pretrained_backbone							)
     A__				=			kwargs.pop("out_indices"						,       config.out_indices							)
     A__				=			TimmBackboneConfig(
         backbone=__lowerCamelCase						,       num_channels=__lowerCamelCase						,       features_only=__lowerCamelCase						,       use_pretrained_backbone=__lowerCamelCase						,       out_indices=__lowerCamelCase						,       )
     return super()._from_config(__lowerCamelCase						,       **__lowerCamelCase							)
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :Optional[Any]							)->							Dict:
     pass



 def      UpperCAmelCase_							(				self      :Tuple						,       lowercase_      :Any						,       lowercase_      :Optional[Any]=None						,       lowercase_      :Optional[int]=None						,       lowercase_      :List[Any]=None						,       **lowercase_      :str							)->							Optional[int]:
     A__				=			return_dict if return_dict is not None else self.config.use_return_dict
     A__				=			(
         output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
     )
     A__				=			output_attentions if output_attentions is not None else self.config.output_attentions

     if output_attentions:
         raise ValueError("Cannot output attentions for timm backbones at the moment"							)

     if output_hidden_states:
         # We modify the return layers to include all the stages of the backbone
         A__				=			self._all_layers
         A__				=			self._backbone(__lowerCamelCase						,       **__lowerCamelCase							)
         A__				=			self._return_layers
         A__				=			tuple(hidden_states[i] for i in self.out_indices							)
     else:
         A__				=			self._backbone(__lowerCamelCase						,       **__lowerCamelCase							)
         A__				=			None

     A__				=			tuple(__lowerCamelCase							)
     A__				=			tuple(__lowerCamelCase							) if hidden_states is not None else None

     if not return_dict:
         A__				=			(feature_maps,)
         if output_hidden_states:
             A__				=			output + (hidden_states,)
         return output

     return BackboneOutput(feature_maps=__lowerCamelCase						,       hidden_states=__lowerCamelCase						,       attentions=__lowerCamelCase							)










import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


a__:      List[str]									=		False




class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):
 pass




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



 def 				UpperCamelCase			(							self ):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



 def 				UpperCamelCase			(							self ):
        A__											=						VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						'''A painting of a squirrel eating a burger '''
        A__											=						torch.manual_seed(0 )
        A__											=						pipe(
            prompt=__lowerCamelCase,generator=__lowerCamelCase,guidance_scale=7.5,num_inference_steps=2,output_type='''numpy''' ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__lowerCamelCase )
               A__											=						VersatileDiffusionTextToImagePipeline.from_pretrained(__lowerCamelCase )
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						generator.manual_seed(0 )
        A__											=						pipe(
            prompt=__lowerCamelCase,generator=__lowerCamelCase,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 ):
        A__											=						VersatileDiffusionTextToImagePipeline.from_pretrained(
            '''shi-labs/versatile-diffusion''',torch_dtype=torch.floataa )
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						'''A painting of a squirrel eating a burger '''
        A__											=						torch.manual_seed(0 )
        A__											=						pipe(
            prompt=__lowerCamelCase,generator=__lowerCamelCase,guidance_scale=7.5,num_inference_steps=50,output_type='''numpy''' ).images

        A__											=						image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        A__											=						np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )

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





