
'''simple docstring'''
import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


a_      =  {
    "vit_b32": {
        "embed_dim": 512,
        "image_resolution": 768,
        "context_length": 16,
        "vocab_size": 4_9408,
        "vision_layers": 12,
        "vision_width": 768,
        "vision_patch_size": 32,
        "transformer_width": 512,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_b16": {
        "embed_dim": 512,
        "image_resolution": 768,
        "context_length": 16,
        "vocab_size": 4_9408,
        "vision_layers": 12,
        "vision_width": 768,
        "vision_patch_size": 16,
        "transformer_width": 512,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_l14": {
        "embed_dim": 768,
        "image_resolution": 840,
        "context_length": 16,
        "vocab_size": 4_9408,
        "vision_layers": 24,
        "vision_width": 1024,
        "vision_patch_size": 14,
        "transformer_width": 768,
        "transformer_heads": 12,
        "transformer_layers": 12,
    },
}

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE="",						__SCREAMING_SNAKE_CASE="/"				):
    """simple docstring"""






    snake_case_       :			Any   	=  []

    for k, v in params.items():
        snake_case_       :			Tuple   	=  parent_key + sep + k if parent_key else k

        if isinstance(__SCREAMING_SNAKE_CASE,						collections.MutableMapping				):
            items.extend(flatten_nested_dict(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						sep=__SCREAMING_SNAKE_CASE				).items()				)
        else:
            items.append((new_key, v)				)
    return dict(__SCREAMING_SNAKE_CASE				)

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    return jax.tree_util.tree_map(lambda __SCREAMING_SNAKE_CASE				: x.astype(jnp.floataa				) if x.dtype == jnp.bfloataa else x,						__SCREAMING_SNAKE_CASE				)

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_	,       snake_case_	,       snake_case_       :			Optional[int]   	=  pt_attn_layer.in_proj_weight.chunk(3,						dim=0				)
    snake_case_	,       snake_case_	,       snake_case_       :			Any   	=  pt_attn_layer.in_proj_bias.chunk(3,						dim=0				)

    snake_case_       :			List[str]   	=  pt_attn_layer.out_proj.weight
    snake_case_       :			int   	=  pt_attn_layer.out_proj.bias

    snake_case_       :			str   	=  q_proj
    snake_case_       :			Tuple   	=  q_proj_bias

    snake_case_       :			List[Any]   	=  k_proj
    snake_case_       :			Tuple   	=  k_proj_bias

    snake_case_       :			Any   	=  v_proj
    snake_case_       :			List[str]   	=  v_proj_bias

    snake_case_       :			Optional[Any]   	=  out_proj_weights
    snake_case_       :			Optional[int]   	=  out_proj_bias

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    copy_linear(hf_mlp.fca,						pt_mlp.c_fc				)
    copy_linear(hf_mlp.fca,						pt_mlp.c_proj				)

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_       :			Dict   	=  pt_linear.weight
    snake_case_       :			int   	=  pt_linear.bias

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    copy_linear(hf_layer.layer_norma,						pt_layer.ln_a				)
    copy_linear(hf_layer.layer_norma,						pt_layer.ln_a				)

    # copy MLP
    copy_mlp(hf_layer.mlp,						pt_layer.mlp				)

    # copy attn
    copy_attn_layer(hf_layer.self_attn,						pt_layer.attn				)

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    for hf_layer, pt_layer in zip(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
        copy_layer(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				)

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_       :			Union[str, Any]   	=  pt_model.token_embedding.weight
    snake_case_       :			Optional[int]   	=  pt_model.positional_embedding

    # copy layer norm
    copy_linear(hf_encoder.final_layer_norm,						pt_model.ln_final				)

    # copy hidden layers
    copy_layers(hf_encoder.encoder.layers,						pt_model.transformer.resblocks				)

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_       :			Optional[int]   	=  pt_model.text_projection.data.T

    # copy text encoder
    copy_encoder(hf_model.text_model,						__SCREAMING_SNAKE_CASE				)

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_       :			Union[str, Any]   	=  pt_model.visual.proj.data.T

    # copy layer norms
    copy_linear(hf_model.vision_model.pre_layernorm,						pt_model.visual.ln_pre				)
    copy_linear(hf_model.vision_model.post_layernorm,						pt_model.visual.ln_post				)

    # copy embeds
    snake_case_       :			int   	=  pt_model.visual.conva.weight.data
    snake_case_       :			Any   	=  pt_model.visual.class_embedding
    snake_case_       :			int   	=  pt_model.visual.positional_embedding.data

    # copy encoder
    copy_layers(hf_model.vision_model.encoder.layers,						pt_model.visual.transformer.resblocks				)

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_       :			Optional[Any]   	=  flatten_nested_dict(flax_params["backbone"]["merged_class_token"]				)

    snake_case_       :			Tuple   	=  torch.from_numpy(flax_class_token_params["scale"]				)
    snake_case_       :			Any   	=  torch.from_numpy(flax_class_token_params["bias"]				)
    snake_case_       :			Tuple   	=  nn.Parameter(__SCREAMING_SNAKE_CASE				)
    snake_case_       :			Dict   	=  nn.Parameter(__SCREAMING_SNAKE_CASE				)

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_       :			Any   	=  hf_model.state_dict()
    snake_case_       :			Any   	=  {}

    # Rename class prediction head flax params to pytorch HF
    snake_case_       :			Any   	=  flatten_nested_dict(flax_params["class_head"]				)

    for flax_key, v in flax_class_params.items():
        snake_case_       :			Tuple   	=  flax_key.replace("/",						"."				)
        snake_case_       :			List[str]   	=  torch_key.replace(".kernel",						".weight"				)
        snake_case_       :			Any   	=  torch_key.replace("Dense_0",						"dense0"				)
        snake_case_       :			List[Any]   	=  "class_head." + torch_key

        if "weight" in torch_key and v.ndim == 2:
            snake_case_       :			Optional[Any]   	=  v.T

        snake_case_       :			Tuple   	=  nn.Parameter(torch.from_numpy(__SCREAMING_SNAKE_CASE				)				)

    # Rename box prediction box flax params to pytorch HF
    snake_case_       :			int   	=  flatten_nested_dict(flax_params["obj_box_head"]				)

    for flax_key, v in flax_box_params.items():
        snake_case_       :			Any   	=  flax_key.replace("/",						"."				)
        snake_case_       :			List[str]   	=  torch_key.replace(".kernel",						".weight"				)
        snake_case_       :			Optional[Any]   	=  torch_key.replace("_",						""				).lower()
        snake_case_       :			str   	=  "box_head." + torch_key

        if "weight" in torch_key and v.ndim == 2:
            snake_case_       :			Tuple   	=  v.T

        snake_case_       :			List[str]   	=  nn.Parameter(torch.from_numpy(__SCREAMING_SNAKE_CASE				)				)

    # Copy flax params to PyTorch params
    for name, param in new_params.items():
        if name in pt_params.keys():
            pt_params[name].copy_(__SCREAMING_SNAKE_CASE				)

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    for k, v in flax_attn_params.items():
        if k.startswith("transformer"				):
            snake_case_       :			Any   	=  k.replace("transformer.resblocks",						"text_model.encoder.layers"				)
        else:
            snake_case_       :			List[str]   	=  k.replace("visual.transformer.resblocks",						"vision_model.encoder.layers"				)

        snake_case_       :			Dict   	=  torch_key.replace("attn",						"self_attn"				)
        snake_case_       :			Optional[int]   	=  torch_key.replace("key",						"k_proj"				)
        snake_case_       :			Any   	=  torch_key.replace("value",						"v_proj"				)
        snake_case_       :			Optional[Any]   	=  torch_key.replace("query",						"q_proj"				)
        snake_case_       :			List[Any]   	=  torch_key.replace("out",						"out_proj"				)

        if "bias" in torch_key and v.ndim == 2:
            snake_case_       :			Any   	=  v.shape[0] * v.shape[1]
            snake_case_       :			List[str]   	=  v.reshape(__SCREAMING_SNAKE_CASE				)

        if "weight" in torch_key and "out" in torch_key:
            snake_case_       :			Any   	=  (v.shape[0] * v.shape[1], v.shape[2])
            snake_case_       :			Optional[Any]   	=  v.reshape(__SCREAMING_SNAKE_CASE				).T

        if "weight" in torch_key and "out" not in torch_key:
            snake_case_       :			Tuple   	=  (v.shape[0], v.shape[1] * v.shape[2])
            snake_case_       :			Optional[Any]   	=  v.reshape(__SCREAMING_SNAKE_CASE				).T

        # Copy flax CLIP attn params to HF PyTorch params
        snake_case_       :			List[str]   	=  torch.from_numpy(__SCREAMING_SNAKE_CASE				)
        hf_backbone.state_dict()[torch_key].copy_(__SCREAMING_SNAKE_CASE				)

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_       :			Dict   	=  {}
    snake_case_       :			Optional[int]   	=  []

    for k, v in params.items():
        if "attn." in k:
            snake_case_       :			Optional[Any]   	=  k[: k.rindex("attn."				) + 5]
            if base in processed_attn_layers:
                continue

            processed_attn_layers.append(__SCREAMING_SNAKE_CASE				)
            snake_case_       :			List[str]   	=  params[base + "out.weight"].shape[-1]
            snake_case_       :			str   	=  params[base + "out.weight"].reshape(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				).T
            snake_case_       :			Optional[Any]   	=  params[base + "out.bias"]
        else:
            snake_case_       :			Any   	=  v
    return new_params

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_       :			Dict   	=  CLIP(**__SCREAMING_SNAKE_CASE				)
    torch_model.eval()
    snake_case_       :			Optional[int]   	=  torch_model.state_dict()

    snake_case_       :			int   	=  flatten_nested_dict(flax_params["backbone"]["clip"]				)
    snake_case_       :			Optional[int]   	=  {}

    for flax_key, v in flax_clip_params.items():
        snake_case_       :			Dict   	=  flax_key.replace("/",						"."				)
        snake_case_       :			Optional[int]   	=  torch_key.replace("text.token_embedding.embedding",						"token_embedding.kernel"				)

        if (
            torch_key.startswith("text.transformer"				)
            or torch_key.startswith("text.text_projection"				)
            or torch_key.startswith("text.ln_final"				)
            or torch_key.startswith("text.positional_embedding"				)
        ):
            snake_case_       :			Any   	=  torch_key[5:]

        snake_case_       :			Dict   	=  torch_key.replace("text_projection.kernel",						"text_projection"				)
        snake_case_       :			int   	=  torch_key.replace("visual.proj.kernel",						"visual.proj"				)
        snake_case_       :			Optional[Any]   	=  torch_key.replace(".scale",						".weight"				)
        snake_case_       :			Optional[Any]   	=  torch_key.replace(".kernel",						".weight"				)

        if "conv" in torch_key or "downsample.0.weight" in torch_key:
            snake_case_       :			Union[str, Any]   	=  v.transpose(3,						2,						0,						1				)

        elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
            # Fully connected layers are transposed, embeddings are not
            snake_case_       :			int   	=  v.T

        snake_case_       :			int   	=  v

    snake_case_       :			List[str]   	=  _convert_attn_layers(__SCREAMING_SNAKE_CASE				)
    new_torch_params.update(__SCREAMING_SNAKE_CASE				)
    snake_case_       :			Optional[int]   	=  {}

    # Copy flax CLIP backbone params to PyTorch params
    for name, param in new_torch_params.items():
        if name in torch_clip_params.keys():
            snake_case_       :			Dict   	=  torch.from_numpy(new_torch_params[name]				)
            torch_clip_params[name].copy_(__SCREAMING_SNAKE_CASE				)
        else:
            snake_case_       :			List[Any]   	=  param

    return torch_clip_params, torch_model, attn_params

@torch.no_grad()
def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE=None				):
    """simple docstring"""






    snake_case_       :			int   	=  Repository(__SCREAMING_SNAKE_CASE,						clone_from=f'google/{pytorch_dump_folder_path}'				)
    repo.git_pull()

    if config_path is not None:
        snake_case_       :			Union[str, Any]   	=  OwlViTConfig.from_pretrained(__SCREAMING_SNAKE_CASE				)
    else:
        snake_case_       :			Tuple   	=  OwlViTConfig()

    snake_case_       :			int   	=  OwlViTModel(__SCREAMING_SNAKE_CASE				).eval()
    snake_case_       :			Optional[int]   	=  OwlViTForObjectDetection(__SCREAMING_SNAKE_CASE				).eval()

    copy_text_model_and_projection(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				)
    copy_vision_model_and_projection(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				)
    snake_case_       :			Optional[Any]   	=  pt_backbone.logit_scale
    copy_flax_attn_params(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				)

    snake_case_       :			Any   	=  hf_backbone
    copy_class_merge_token(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				)
    copy_class_box_heads(__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE				)

    # Save HF model
    hf_model.save_pretrained(repo.local_dir				)

    # Initialize image processor
    snake_case_       :			str   	=  OwlViTImageProcessor(
        size=config.vision_config.image_size,						crop_size=config.vision_config.image_size				)
    # Initialize tokenizer
    snake_case_       :			List[Any]   	=  CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32",						pad_token="!",						model_max_length=1_6				)

    # Initialize processor
    snake_case_       :			Tuple   	=  OwlViTProcessor(image_processor=__SCREAMING_SNAKE_CASE,						tokenizer=__SCREAMING_SNAKE_CASE				)
    image_processor.save_pretrained(repo.local_dir				)
    processor.save_pretrained(repo.local_dir				)

    repo.git_add()
    repo.git_commit("Upload model and processor"				)
    repo.git_push()


if __name__ == "__main__":
  a_      =  argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--owlvit_version",
      default=None,
      type=str,
      required=True,
      help="OWL-ViT model name [clip_b16, clip_b32, clip_l14].",
  )
  parser.add_argument(
      "--owlvit_checkpoint", default=None, type=str, required=True, help="Path to flax model checkpoint."
  )
  parser.add_argument("--hf_config", default=None, type=str, required=True, help="Path to HF model config.")
  parser.add_argument(
      "--pytorch_dump_folder_path", default="hf_model", type=str, help="Path to the output PyTorch model."
  )
  a_      =  parser.parse_args()

  # Initialize PyToch clip model
  a_      =  args.owlvit_version
  if model_name == "clip_b16":
    a_      =  CONFIGS["vit_b16"]
  elif model_name == "clip_b32":
    a_      =  CONFIGS["vit_b32"]
  elif model_name == "clip_l14":
    a_      =  CONFIGS["vit_l14"]

  # Load from checkpoint and convert params to float-32
  a_      =  checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["optimizer"]["target"]
  a_      =  jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
  del variables

  # Convert CLIP backbone
  a_    ,a_    ,a_      =  convert_clip_backbone(flax_params, torch_config)

  convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)








from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


UpperCAmelCase			=    """
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = \"cuda\"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = \"openai/diffusers-cd_imagenet64_l2\"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save(\"cd_imagenet64_l2_onestep_sample.png\")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save(\"cd_imagenet64_l2_onestep_sample_penguin.png\")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save(\"cd_imagenet64_l2_multistep_sample_penguin.png\")
        ```
"""







class lowerCAmelCase_						( lowerCamelCase__       ):

 '''simple docstring'''

 def __init__(	self		,      _UpperCAmelCase		,      _UpperCAmelCase					):
       super().__init__()

       self.register_modules(
           unet=_UpperCAmelCase		,      scheduler=_UpperCAmelCase		,      )

       snake_case_       =      None



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase=0					):
       if is_accelerate_available() and is_accelerate_version('''>='''		,      '''0.14.0'''					):
             from accelerate import cpu_offload
       else:
             raise ImportError('''`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'''					)

       snake_case_       =      torch.device(F'''cuda:{gpu_id}'''					)

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

       for cpu_offloaded_model in [self.unet]:
             cpu_offload(_UpperCAmelCase		,      _UpperCAmelCase					)

       if self.safety_checker is not None:
             cpu_offload(self.safety_checker		,      execution_device=_UpperCAmelCase		,      offload_buffers=_UpperCAmelCase					)



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase=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.'''					)

       snake_case_       =      torch.device(F'''cuda:{gpu_id}'''					)

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

       snake_case_       =      None
       for cpu_offloaded_model in [self.unet]:
             snake_case_       ,    snake_case_       =      cpu_offload_with_hook(_UpperCAmelCase		,      _UpperCAmelCase		,      prev_module_hook=_UpperCAmelCase					)

       if self.safety_checker is not None:
             snake_case_       ,    snake_case_       =      cpu_offload_with_hook(self.safety_checker		,      _UpperCAmelCase		,      prev_module_hook=_UpperCAmelCase					)

       # We'll offload the last model manually.
       snake_case_       =      hook



 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 						UpperCamelCase__     (	self					):
       if not hasattr(self.unet		,      '''_hf_hook'''					):
             return self.device
       for module in self.unet.modules():
             if (
                 hasattr(_UpperCAmelCase		,      '''_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



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=None					):
       snake_case_       =      (batch_size, num_channels, height, width)
       if isinstance(_UpperCAmelCase		,      _UpperCAmelCase					) and len(_UpperCAmelCase					) != batch_size:
             raise ValueError(
                 F'''You have passed a list of generators of length {len(_UpperCAmelCase					)}, but requested an effective batch'''
                 F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''					)

       if latents is None:
             snake_case_       =      randn_tensor(_UpperCAmelCase		,      generator=_UpperCAmelCase		,      device=_UpperCAmelCase		,      dtype=_UpperCAmelCase					)
       else:
             snake_case_       =      latents.to(device=_UpperCAmelCase		,      dtype=_UpperCAmelCase					)

       # scale the initial noise by the standard deviation required by the scheduler
       snake_case_       =      latents * self.scheduler.init_noise_sigma
       return latents



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase = "pil"					):
       if output_type not in ["pt", "np", "pil"]:
             raise ValueError(
                 F'''output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'''					)

       # Equivalent to diffusers.VaeImageProcessor.denormalize
       snake_case_       =      (sample / 2 + 0.5).clamp(0		,      1					)
       if output_type == "pt":
             return sample

       # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
       snake_case_       =      sample.cpu().permute(0		,      2		,      3		,      1					).numpy()
       if output_type == "np":
             return sample

       # Output_type must be 'pil'
       snake_case_       =      self.numpy_to_pil(_UpperCAmelCase					)
       return sample



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=None					):
       if self.unet.config.num_class_embeds is not None:
             if isinstance(_UpperCAmelCase		,      _UpperCAmelCase					):
                   snake_case_       =      torch.tensor(_UpperCAmelCase		,      dtype=torch.int					)
             elif isinstance(_UpperCAmelCase		,      _UpperCAmelCase					):
                   assert batch_size == 1, "Batch size must be 1 if classes is an int"
                   snake_case_       =      torch.tensor([class_labels]		,      dtype=torch.int					)
             elif class_labels is None:
                   # Randomly generate batch_size class labels
                   # TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
                   snake_case_       =      torch.randint(0		,      self.unet.config.num_class_embeds		,      size=(batch_size,)					)
             snake_case_       =      class_labels.to(_UpperCAmelCase					)
       else:
             snake_case_       =      None
       return class_labels



 def 						UpperCamelCase__     (	self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					):
       if num_inference_steps is None and timesteps is None:
             raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''					)

       if num_inference_steps is not None and timesteps is not None:
             logger.warning(
                 F'''Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'''
                 ''' `timesteps` will be used over `num_inference_steps`.'''					)

       if latents is not None:
             snake_case_       =      (batch_size, 3, img_size, img_size)
             if latents.shape != expected_shape:
                   raise ValueError(F'''The shape of latents is {latents.shape} but is expected to be {expected_shape}.'''					)

       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(_UpperCAmelCase		,      _UpperCAmelCase					) or callback_steps <= 0)
       ):
             raise ValueError(
                 F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                 F''' {type(_UpperCAmelCase					)}.'''					)





 @torch.no_grad()
 @replace_example_docstring(_UpperCAmelCase					)
 def __call__(	self		,      _UpperCAmelCase = 1		,      _UpperCAmelCase = None		,      _UpperCAmelCase = 1		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = "pil"		,      _UpperCAmelCase = True		,      _UpperCAmelCase = None		,      _UpperCAmelCase = 1		,      ):
       snake_case_       =      self.unet.config.sample_size
       snake_case_       =      self._execution_device

       # 1. Check inputs
       self.check_inputs(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)

       # 2. Prepare image latents
       # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
       snake_case_       =      self.prepare_latents(
           batch_size=_UpperCAmelCase		,      num_channels=self.unet.config.in_channels		,      height=_UpperCAmelCase		,      width=_UpperCAmelCase		,      dtype=self.unet.dtype		,      device=_UpperCAmelCase		,      generator=_UpperCAmelCase		,      latents=_UpperCAmelCase		,      )

       # 3. Handle class_labels for class-conditional models
       snake_case_       =      self.prepare_class_labels(_UpperCAmelCase		,      _UpperCAmelCase		,      class_labels=_UpperCAmelCase					)

       # 4. Prepare timesteps
       if timesteps is not None:
             self.scheduler.set_timesteps(timesteps=_UpperCAmelCase		,      device=_UpperCAmelCase					)
             snake_case_       =      self.scheduler.timesteps
             snake_case_       =      len(_UpperCAmelCase					)
       else:
             self.scheduler.set_timesteps(_UpperCAmelCase					)
             snake_case_       =      self.scheduler.timesteps

       # 5. Denoising loop
       # Multistep sampling: implements Algorithm 1 in the paper
       with self.progress_bar(total=_UpperCAmelCase					) as progress_bar:
             for i, t in enumerate(_UpperCAmelCase					):
                   snake_case_       =      self.scheduler.scale_model_input(_UpperCAmelCase		,      _UpperCAmelCase					)
                   snake_case_       =      self.unet(_UpperCAmelCase		,      _UpperCAmelCase		,      class_labels=_UpperCAmelCase		,      return_dict=_UpperCAmelCase					)[0]

                   snake_case_       =      self.scheduler.step(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      generator=_UpperCAmelCase					)[0]

                   # call the callback, if provided
                   progress_bar.update()
                   if callback is not None and i % callback_steps == 0:
                         callback(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)

        # 6. Post-process image sample
       snake_case_       =      self.postprocess_image(_UpperCAmelCase		,      output_type=_UpperCAmelCase					)

       # Offload last model to CPU
       if hasattr(self		,      '''final_offload_hook'''					) and self.final_offload_hook is not None:
             self.final_offload_hook.offload()

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_UpperCAmelCase					)