





"""simple docstring"""





import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)




def 		lowerCAmelCase_     (  lowercase_       :							Tuple			,				lowercase_       :							Union[str, Any]=False			,				lowercase_       :							Optional[Any]=False			,				lowercase_       :							Dict="data2vec."		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	'''backbone.''' if is_semantic else ''''''

 __SCREAMING_SNAKE_CASE			:      Any 							=	[]
 for i in range(config.num_hidden_layers		):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (F'''{prefix}blocks.{i}.norm1.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.bias''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.weight''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.bias''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.norm2.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.bias''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.weight''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.bias''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''{hf_prefix}encoder.layer.{i}.output.dense.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''{hf_prefix}encoder.layer.{i}.output.dense.bias''')		)

 # projection layer + position embeddings
 rename_keys.extend(
     [
         (F'''{prefix}cls_token''', F'''{hf_prefix}embeddings.cls_token'''),
         (F'''{prefix}patch_embed.proj.weight''', F'''{hf_prefix}embeddings.patch_embeddings.projection.weight'''),
         (F'''{prefix}patch_embed.proj.bias''', F'''{hf_prefix}embeddings.patch_embeddings.projection.bias'''),
     ]		)

 if has_lm_head:
  # mask token + shared relative position bias + layernorm
  rename_keys.extend(
      [
          ('''mask_token''', F'''{hf_prefix}embeddings.mask_token'''),
          (
              '''rel_pos_bias.relative_position_bias_table''',
              F'''{hf_prefix}encoder.relative_position_bias.relative_position_bias_table''',
          ),
          (
              '''rel_pos_bias.relative_position_index''',
              F'''{hf_prefix}encoder.relative_position_bias.relative_position_index''',
          ),
          ('''norm.weight''', '''layernorm.weight'''),
          ('''norm.bias''', '''layernorm.bias'''),
      ]		)
 elif is_semantic:
  # semantic segmentation classification heads
  rename_keys.extend(
      [
          ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
          ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
          ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
          ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
      ]		)
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ('''fc_norm.weight''', F'''{hf_prefix}pooler.layernorm.weight'''),
          ('''fc_norm.bias''', F'''{hf_prefix}pooler.layernorm.bias'''),
          ('''head.weight''', '''classifier.weight'''),
          ('''head.bias''', '''classifier.bias'''),
      ]		)

 return rename_keys




def 		lowerCAmelCase_     (  lowercase_       :							Union[str, Any]			,				lowercase_       :							Union[str, Any]			,				lowercase_       :							Optional[Any]=False			,				lowercase_       :							Any=False			,				lowercase_       :							Dict="data2vec_vision."		):

 '''simple docstring'''






 for i in range(config.num_hidden_layers		):
  __SCREAMING_SNAKE_CASE			:      int 							=	'''backbone.''' if is_semantic else ''''''
  # queries, keys and values
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''		)
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''		)
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''		)

  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	in_proj_weight[
      : config.hidden_size, :
  ]
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	q_bias
  __SCREAMING_SNAKE_CASE			:      str 							=	in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __SCREAMING_SNAKE_CASE			:      Dict 							=	in_proj_weight[
      -config.hidden_size :, :
  ]
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''		)
  __SCREAMING_SNAKE_CASE			:      int 							=	state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''		)

  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	gamma_a
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	gamma_a

  # relative_position bias table + index
  if not has_lm_head:
   # each layer has its own relative position bias
   __SCREAMING_SNAKE_CASE			:      Dict 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''		)
   __SCREAMING_SNAKE_CASE			:      int 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''		)

   __SCREAMING_SNAKE_CASE			:      List[str] 							=	table
   __SCREAMING_SNAKE_CASE			:      Any 							=	index




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      List[str] 							=	argparse.ArgumentParser(
     '''Convert Data2VecVision to HF for image classification and pretraining'''			,				add_help=lowercase_		)
 parser.add_argument('''--hf_checkpoint_name'''			,				type=lowercase_		)
 parser.add_argument('''--input_size'''			,				default=224			,				type=lowercase_			,				help='''images input size'''		)
 parser.add_argument('''--beit_checkpoint'''			,				default=''''''			,				help='''beit checkpoint'''		)

 return parser.parse_args()




def 		lowerCAmelCase_     (  lowercase_       :							str			,				lowercase_       :							List[str]			,				lowercase_       :							Union[str, Any]		):

 '''simple docstring'''






 def load_state_dict(lowercase_       :							Optional[Any]			,				lowercase_       :							int			,				lowercase_       :							Union[str, Any]=""			,				lowercase_       :							Optional[int]="relative_position_index"		):
  __SCREAMING_SNAKE_CASE			:      Any 							=	[]
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	[]
  __SCREAMING_SNAKE_CASE			:      Dict 							=	[]
  # copy state_dict so _load_from_state_dict can modify it
  __SCREAMING_SNAKE_CASE			:      List[str] 							=	getattr(lowercase_			,				'''_metadata'''			,				lowercase_		)
  __SCREAMING_SNAKE_CASE			:      str 							=	state_dict.copy()
  if metadata is not None:
   __SCREAMING_SNAKE_CASE			:      int 							=	metadata

  def load(lowercase_       :							Any			,				lowercase_       :							Optional[Any]=""		):
   __SCREAMING_SNAKE_CASE			:      Dict 							=	{} if metadata is None else metadata.get(prefix[:-1]			,				{}		)
   module._load_from_state_dict(
       lowercase_			,				lowercase_			,				lowercase_			,				lowercase_			,				lowercase_			,				lowercase_			,				lowercase_		)
   for name, child in module._modules.items():
    if child is not None:
     load(lowercase_			,				prefix + name + '''.'''		)

  load(lowercase_			,				prefix=lowercase_		)

  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	[]
  __SCREAMING_SNAKE_CASE			:      Dict 							=	[]
  for key in missing_keys:
   __SCREAMING_SNAKE_CASE			:      Dict 							=	True
   for ignore_key in ignore_missing.split('''|'''		):
    if ignore_key in key:
     __SCREAMING_SNAKE_CASE			:      Dict 							=	False
     break
   if keep_flag:
    warn_missing_keys.append(lowercase_		)
   else:
    ignore_missing_keys.append(lowercase_		)

  __SCREAMING_SNAKE_CASE			:      Tuple 							=	warn_missing_keys

  if len(lowercase_		) > 0:
   print(
       '''Weights of {} not initialized from pretrained model: {}'''.format(
           model.__class__.__name__			,				lowercase_		)		)
  if len(lowercase_		) > 0:
   print('''Weights from pretrained model not used in {}: {}'''.format(model.__class__.__name__			,				lowercase_		)		)
  if len(lowercase_		) > 0:
   print(
       '''Ignored weights of {} not initialized from pretrained model: {}'''.format(
           model.__class__.__name__			,				lowercase_		)		)
  if len(lowercase_		) > 0:
   print('''\n'''.join(lowercase_		)		)

 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	{
     '''pretrained''': False,
     '''use_shared_rel_pos_bias''': True,
     '''use_abs_pos_emb''': False,
     '''init_values''': 0.1,
 }

 if is_finetuned:
  model_kwargs.update(
      {
          '''num_classes''': 1000,
          '''use_mean_pooling''': True,
          '''init_scale''': 0.001,
          '''use_rel_pos_bias''': True,
      }		)

 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	create_model(
     '''beit_large_patch16_224''' if is_large else '''beit_base_patch16_224'''			,				**lowercase_			,				)
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	model.patch_embed.patch_size
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	(args.input_size // patch_size[0], args.input_size // patch_size[1])
 __SCREAMING_SNAKE_CASE			:      Dict 							=	torch.load(args.beit_checkpoint			,				map_location='''cpu'''		)

 print(F'''Load ckpt from {args.beit_checkpoint}'''		)
 __SCREAMING_SNAKE_CASE			:      Any 							=	None
 for model_key in ("model", "module"):
  if model_key in checkpoint:
   __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	checkpoint[model_key]
   print(F'''Load state_dict by model_key = {model_key}'''		)
   break

 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	list(checkpoint_model.keys()		)
 for key in all_keys:
  if "relative_position_index" in key:
   checkpoint_model.pop(lowercase_		)

  if "relative_position_bias_table" in key:
   __SCREAMING_SNAKE_CASE			:      str 							=	checkpoint_model[key]
   __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	rel_pos_bias.size()
   __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	model.state_dict()[key].size()
   __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	model.patch_embed.patch_shape
   if dst_patch_shape[0] != dst_patch_shape[1]:
    raise NotImplementedError()

 load_state_dict(lowercase_			,				lowercase_			,				prefix=''''''		)

 return model




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	get_args()

 __SCREAMING_SNAKE_CASE			:      int 							=	'''ft1k''' in args.hf_checkpoint_name
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	'''large''' in args.hf_checkpoint_name

 if is_finetuned:
  # To convert Beit's data2vec_vision to HF you need to copy
  # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
  # into this folder.
  import modeling_finetune  # noqa: F401
 else:
  # To convert Beit's data2vec_vision to HF you need to copy
  # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
  # into this folder
  # IMPORTANT: Note that for now we've only converted the down-stream
  # model and not the full pretrained model. This means for the integration
  # test you need to add a `return x` after the following line:
  # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
  # to make the integration test pass.
  import modeling_cyclical  # noqa: F401

 # 1. Create model config
 __SCREAMING_SNAKE_CASE			:      Dict 							=	DataaVecVisionConfig()
 if is_finetuned:
  __SCREAMING_SNAKE_CASE			:      int 							=	True
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	False
  __SCREAMING_SNAKE_CASE			:      Dict 							=	True
  __SCREAMING_SNAKE_CASE			:      int 							=	1000

  __SCREAMING_SNAKE_CASE			:      Any 							=	'''huggingface/label-files'''
  __SCREAMING_SNAKE_CASE			:      Dict 							=	'''imagenet-1k-id2label.json'''
  __SCREAMING_SNAKE_CASE			:      Dict 							=	json.load(open(hf_hub_download(lowercase_			,				lowercase_			,				repo_type='''dataset'''		)			,				'''r'''		)		)
  __SCREAMING_SNAKE_CASE			:      Dict 							=	{int(lowercase_		): v for k, v in idalabel.items()}
  __SCREAMING_SNAKE_CASE			:      List[str] 							=	idalabel
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	{v: k for k, v in idalabel.items()}
 else:
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	False
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	True
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	False

 if is_large:
  __SCREAMING_SNAKE_CASE			:      str 							=	1024
  __SCREAMING_SNAKE_CASE			:      Any 							=	4096
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	24
  __SCREAMING_SNAKE_CASE			:      Tuple 							=	16

 # 2. Load Beit model
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	load_beit_model(lowercase_			,				lowercase_			,				lowercase_		)
 orig_model.eval()

 # 3. Forward Beit model
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	BeitImageProcessor(size=config.image_size			,				do_center_crop=lowercase_		)
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	Image.open('''../../../../tests/fixtures/tests_samples/COCO/000000039769.png'''		)
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	image_processor(images=lowercase_			,				return_tensors='''pt'''		)
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	encoding['''pixel_values''']

 __SCREAMING_SNAKE_CASE			:      Tuple 							=	(pixel_values,) if is_finetuned else (pixel_values, None)
 with torch.no_grad():
  __SCREAMING_SNAKE_CASE			:      Tuple 							=	orig_model(*lowercase_		)

 # 4. Load HF Data2VecVision model
 if is_finetuned:
  __SCREAMING_SNAKE_CASE			:      Dict 							=	DataaVecVisionForImageClassification(lowercase_		)
  hf_model.eval()
  __SCREAMING_SNAKE_CASE			:      Any 							=	False
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	'''data2vec_vision.'''
 else:
  __SCREAMING_SNAKE_CASE			:      Dict 							=	DataaVecVisionModel(lowercase_		)
  hf_model.eval()
  __SCREAMING_SNAKE_CASE			:      str 							=	True
  __SCREAMING_SNAKE_CASE			:      Any 							=	''''''

 __SCREAMING_SNAKE_CASE			:      Dict 							=	create_rename_keys(lowercase_			,				hf_prefix=lowercase_			,				has_lm_head=lowercase_		)
 __SCREAMING_SNAKE_CASE			:      Dict 							=	orig_model.state_dict()
 for src, dest in rename_keys:
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	state_dict.pop(lowercase_		)
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	val

 read_in_q_k_v(lowercase_			,				lowercase_			,				hf_prefix=lowercase_			,				has_lm_head=lowercase_		)
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	hf_model.load_state_dict(lowercase_			,				strict=lowercase_		)
 print('''HF missing'''			,				lowercase_		)
 print('''HF unexpected_keys'''			,				lowercase_		)

 # 5. Forward HF Data2VecVision model
 with torch.no_grad():
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	hf_model(lowercase_		)

 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

 # 6. Compare
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	torch.max(torch.abs(hf_output - orig_model_output		)		).item()

 print(F'''max_absolute_diff = {max_absolute_diff}'''		)
 __SCREAMING_SNAKE_CASE			:      str 							=	torch.allclose(lowercase_			,				lowercase_			,				atol=1E-3		)
 print('''Do both models output the same tensors?'''			,				'''🔥''' if success else '''💩'''		)
 if not success:
  raise Exception('''Something went wRoNg'''		)

 # 7. Save
 print(F'''Saving to {args.hf_checkpoint_name}'''		)
 hf_model.save_pretrained(args.hf_checkpoint_name		)
 image_processor.save_pretrained(args.hf_checkpoint_name		)


if __name__ == "__main__":
    main()
    # Run the following to convert checkpoints
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./pretrained_base.pt \
    #          --hf_checkpoint_name "./data2vec-vision-base"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./finetuned_base.pt \
    #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./pretrained_large.pt \
    #          --hf_checkpoint_name "./data2vec-vision-large"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./finetuned_large.pt \
    #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"






"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_lowerCamelCase  					=     logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowerCamelCase  					=     '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''




def 		lowerCAmelCase_     (  lowercase_       :							Optional[int]			,				lowercase_       :							Optional[Any]			,				lowercase_       :							Optional[int]=8		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Tuple 							=	height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor




def 		lowerCAmelCase_     (  lowercase_       :							str			,				lowercase_       :							Tuple=512			,				lowercase_       :							Any=512		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	pil_image.resize((w, h)			,				resample=Image.BICUBIC			,				reducing_gap=1		)
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	np.array(pil_image.convert('''RGB'''		)		)
 __SCREAMING_SNAKE_CASE			:      Dict 							=	arr.astype(np.floataa		) / 127.5 - 1
 __SCREAMING_SNAKE_CASE			:      Dict 							=	np.transpose(lowercase_			,				[2, 0, 1]		)
 __SCREAMING_SNAKE_CASE			:      List[str] 							=	torch.from_numpy(lowercase_		).unsqueeze(0		)
 return image






class       snake_case		(       __UpperCAmelCase       ):

      def __init__(      self       :Dict						,							_lowerCamelCase       :UNetaDConditionModel						,							_lowerCamelCase       :DDPMScheduler						,							_lowerCamelCase       :VQModel						,							):
       super().__init__()

       self.register_modules(
           unet=_lowerCamelCase						,							scheduler=_lowerCamelCase						,							movq=_lowerCamelCase						,							)
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	2 ** (len(self.movq.config.block_out_channels						) - 1)

      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						,							_lowerCamelCase       :Union[str, Any]						,							_lowerCamelCase       :List[Any]						,							_lowerCamelCase       :List[Any]						):
       # get the original timestep using init_timestep
       __SCREAMING_SNAKE_CASE			:      str 							=	min(int(num_inference_steps * strength						)						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      Tuple 							=	max(num_inference_steps - init_timestep						,							0						)
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start

      def 						SCREAMING_SNAKE_CASE_      (      self       :Any						,							_lowerCamelCase       :str						,							_lowerCamelCase       :int						,							_lowerCamelCase       :str						,							_lowerCamelCase       :Optional[int]						,							_lowerCamelCase       :Optional[Any]						,							_lowerCamelCase       :Union[str, Any]						,							_lowerCamelCase       :Dict=None						):
       if not isinstance(_lowerCamelCase						,							(torch.Tensor, PIL.Image.Image, list)						):
        raise ValueError(
            f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowerCamelCase						)}'''						)

       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	image.to(device=_lowerCamelCase						,							dtype=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	batch_size * num_images_per_prompt

       if image.shape[1] == 4:
        __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	image

       else:
        if isinstance(_lowerCamelCase						,							_lowerCamelCase						) and len(_lowerCamelCase						) != batch_size:
         raise ValueError(
             f'''You have passed a list of generators of length {len(_lowerCamelCase						)}, but requested an effective batch'''
             f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''						)

        elif isinstance(_lowerCamelCase						,							_lowerCamelCase						):
         __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	[
             self.movq.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(_lowerCamelCase						)
         ]
         __SCREAMING_SNAKE_CASE			:      List[Any] 							=	torch.cat(_lowerCamelCase						,							dim=0						)
        else:
         __SCREAMING_SNAKE_CASE			:      Dict 							=	self.movq.encode(_lowerCamelCase						).latent_dist.sample(_lowerCamelCase						)

        __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	self.movq.config.scaling_factor * init_latents

       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	torch.cat([init_latents]						,							dim=0						)

       __SCREAMING_SNAKE_CASE			:      int 							=	init_latents.shape
       __SCREAMING_SNAKE_CASE			:      int 							=	randn_tensor(_lowerCamelCase						,							generator=_lowerCamelCase						,							device=_lowerCamelCase						,							dtype=_lowerCamelCase						)

       # get latents
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	self.scheduler.add_noise(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      List[str] 							=	init_latents

       return latents

      def 						SCREAMING_SNAKE_CASE_      (      self       :str						,							_lowerCamelCase       :List[Any]=0						):
       if is_accelerate_available():
        from accelerate import cpu_offload
       else:
        raise ImportError('''Please install accelerate via `pip install accelerate`'''						)

       __SCREAMING_SNAKE_CASE			:      List[str] 							=	torch.device(f'''cuda:{gpu_id}'''						)

       __SCREAMING_SNAKE_CASE			:      Tuple 							=	[
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
        if cpu_offloaded_model is not None:
         cpu_offload(_lowerCamelCase						,							_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						,							_lowerCamelCase       :Optional[Any]=0						):
       if is_accelerate_available() and is_accelerate_version('''>='''						,							'''0.17.0.dev0'''						):
        from accelerate import cpu_offload_with_hook
       else:
        raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''						)

       __SCREAMING_SNAKE_CASE			:      Dict 							=	torch.device(f'''cuda:{gpu_id}'''						)

       if self.device.type != "cpu":
        self.to('''cpu'''						,							silence_dtype_warnings=_lowerCamelCase						)
        torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	None
       for cpu_offloaded_model in [self.unet, self.movq]:
        __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Optional[int] 							=	cpu_offload_with_hook(_lowerCamelCase						,							_lowerCamelCase						,							prev_module_hook=_lowerCamelCase						)

       # We'll offload the last model manually.
       __SCREAMING_SNAKE_CASE			:      Any 							=	hook

      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[int]						):
       if not hasattr(self.unet						,							'''_hf_hook'''						):
        return self.device
       for module in self.unet.modules():
        if (
            hasattr(_lowerCamelCase						,							'''_hf_hook'''						)
            and hasattr(module._hf_hook						,							'''execution_device'''						)
            and module._hf_hook.execution_device is not None
        ):
         return torch.device(module._hf_hook.execution_device						)
       return self.device



      @torch.no_grad()
      @replace_example_docstring(_lowerCamelCase						)
      def __call__(      self       :List[str]						,							_lowerCamelCase       :Union[torch.FloatTensor, List[torch.FloatTensor]]						,							_lowerCamelCase       :Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]						,							_lowerCamelCase       :Union[torch.FloatTensor, List[torch.FloatTensor]]						,							_lowerCamelCase       :torch.FloatTensor						,							_lowerCamelCase       :int = 5_1_2						,							_lowerCamelCase       :int = 5_1_2						,							_lowerCamelCase       :int = 1_0_0						,							_lowerCamelCase       :float = 4.0						,							_lowerCamelCase       :float = 0.3						,							_lowerCamelCase       :int = 1						,							_lowerCamelCase       :Optional[Union[torch.Generator, List[torch.Generator]]] = None						,							_lowerCamelCase       :Optional[str] = "pil"						,							_lowerCamelCase       :bool = True						,							):
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	self._execution_device

       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	guidance_scale > 1.0

       if isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	torch.cat(_lowerCamelCase						,							dim=0						)
       if isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE			:      List[str] 							=	torch.cat(_lowerCamelCase						,							dim=0						)
       if isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE			:      Dict 							=	torch.cat(_lowerCamelCase						,							dim=0						)

       __SCREAMING_SNAKE_CASE			:      Tuple 							=	image_embeds.shape[0]

       if do_classifier_free_guidance:
        __SCREAMING_SNAKE_CASE			:      Tuple 							=	image_embeds.repeat_interleave(_lowerCamelCase						,							dim=0						)
        __SCREAMING_SNAKE_CASE			:      Any 							=	negative_image_embeds.repeat_interleave(_lowerCamelCase						,							dim=0						)
        __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	hint.repeat_interleave(_lowerCamelCase						,							dim=0						)

       __SCREAMING_SNAKE_CASE			:      Tuple 							=	torch.cat([negative_image_embeds, image_embeds]						,							dim=0						).to(dtype=self.unet.dtype						,							device=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	torch.cat([hint, hint]						,							dim=0						).to(dtype=self.unet.dtype						,							device=_lowerCamelCase						)

       if not isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	[image]
       if not all(isinstance(_lowerCamelCase						,							(PIL.Image.Image, torch.Tensor)						) for i in image						):
        raise ValueError(
            f'''Input is in incorrect format: {[type(_lowerCamelCase						) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''						)

       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	torch.cat([prepare_image(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						) for i in image]						,							dim=0						)
       __SCREAMING_SNAKE_CASE			:      Dict 							=	image.to(dtype=image_embeds.dtype						,							device=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.movq.encode(_lowerCamelCase						)['''latents''']
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	latents.repeat_interleave(_lowerCamelCase						,							dim=0						)
       self.scheduler.set_timesteps(_lowerCamelCase						,							device=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Optional[int] 							=	self.get_timesteps(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	timesteps[:1].repeat(batch_size * num_images_per_prompt						)
       __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Dict 							=	downscale_height_and_width(_lowerCamelCase						,							_lowerCamelCase						,							self.movq_scale_factor						)
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	self.prepare_latents(
           _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							image_embeds.dtype						,							_lowerCamelCase						,							_lowerCamelCase						)
       for i, t in enumerate(self.progress_bar(_lowerCamelCase						)						):
        # expand the latents if we are doing classifier free guidance
        __SCREAMING_SNAKE_CASE			:      int 							=	torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

        __SCREAMING_SNAKE_CASE			:      Any 							=	{'''image_embeds''': image_embeds, '''hint''': hint}
        __SCREAMING_SNAKE_CASE			:      int 							=	self.unet(
            sample=_lowerCamelCase						,							timestep=_lowerCamelCase						,							encoder_hidden_states=_lowerCamelCase						,							added_cond_kwargs=_lowerCamelCase						,							return_dict=_lowerCamelCase						,							)[0]

        if do_classifier_free_guidance:
         __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Tuple 							=	noise_pred.split(latents.shape[1]						,							dim=1						)
         __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Optional[int] 							=	noise_pred.chunk(2						)
         __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Any 							=	variance_pred.chunk(2						)
         __SCREAMING_SNAKE_CASE			:      int 							=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
         __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	torch.cat([noise_pred, variance_pred_text]						,							dim=1						)

        if not (
            hasattr(self.scheduler.config						,							'''variance_type'''						)
            and self.scheduler.config.variance_type in ["learned", "learned_range"]
        ):
         __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Any 							=	noise_pred.split(latents.shape[1]						,							dim=1						)

        # compute the previous noisy sample x_t -> x_t-1

        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	self.scheduler.step(
            _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							generator=_lowerCamelCase						,							)[0]

       # post-processing
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	self.movq.decode(_lowerCamelCase						,							force_not_quantize=_lowerCamelCase						)['''sample''']

       if output_type not in ["pt", "np", "pil"]:
        raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''						)

       if output_type in ["np", "pil"]:
        __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	image * 0.5 + 0.5
        __SCREAMING_SNAKE_CASE			:      str 							=	image.clamp(0						,							1						)
        __SCREAMING_SNAKE_CASE			:      int 							=	image.cpu().permute(0						,							2						,							3						,							1						).float().numpy()

       if output_type == "pil":
        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	self.numpy_to_pil(_lowerCamelCase						)

       if not return_dict:
        return (image,)

       return ImagePipelineOutput(images=_lowerCamelCase						)

