


"""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": 5_12,
        "image_resolution": 7_68,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 12,
        "vision_width": 7_68,
        "vision_patch_size": 32,
        "transformer_width": 5_12,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_b16": {
        "embed_dim": 5_12,
        "image_resolution": 7_68,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 12,
        "vision_width": 7_68,
        "vision_patch_size": 16,
        "transformer_width": 5_12,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_l14": {
        "embed_dim": 7_68,
        "image_resolution": 8_40,
        "context_length": 16,
        "vocab_size": 4_94_08,
        "vision_layers": 24,
        "vision_width": 10_24,
        "vision_patch_size": 14,
        "transformer_width": 7_68,
        "transformer_heads": 12,
        "transformer_layers": 12,
    },
}






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=""	,			_UpperCamelCase="/" ):





						"""simple docstring"""

						UpperCAmelCase__		=    []

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

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






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

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






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__	,				UpperCAmelCase__	,				UpperCAmelCase__		=    pt_attn_layer.in_proj_weight.chunk(3	,			dim=0 )
						UpperCAmelCase__	,				UpperCAmelCase__	,				UpperCAmelCase__		=    pt_attn_layer.in_proj_bias.chunk(3	,			dim=0 )

						UpperCAmelCase__		=    pt_attn_layer.out_proj.weight
						UpperCAmelCase__		=    pt_attn_layer.out_proj.bias

						UpperCAmelCase__		=    q_proj
						UpperCAmelCase__		=    q_proj_bias

						UpperCAmelCase__		=    k_proj
						UpperCAmelCase__		=    k_proj_bias

						UpperCAmelCase__		=    v_proj
						UpperCAmelCase__		=    v_proj_bias

						UpperCAmelCase__		=    out_proj_weights
						UpperCAmelCase__		=    out_proj_bias






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

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






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    pt_linear.weight
						UpperCAmelCase__		=    pt_linear.bias






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""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 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

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






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    pt_model.token_embedding.weight
						UpperCAmelCase__		=    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 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    pt_model.text_projection.data.T

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






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    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
						UpperCAmelCase__		=    pt_model.visual.conva.weight.data
						UpperCAmelCase__		=    pt_model.visual.class_embedding
						UpperCAmelCase__		=    pt_model.visual.positional_embedding.data

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






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    flatten_nested_dict(flax_params['''backbone''']['''merged_class_token'''] )

						UpperCAmelCase__		=    torch.from_numpy(flax_class_token_params['''scale'''] )
						UpperCAmelCase__		=    torch.from_numpy(flax_class_token_params['''bias'''] )
						UpperCAmelCase__		=    nn.Parameter(_UpperCamelCase )
						UpperCAmelCase__		=    nn.Parameter(_UpperCamelCase )






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    hf_model.state_dict()
						UpperCAmelCase__		=    {}

						# Rename class prediction head flax params to pytorch HF
						UpperCAmelCase__		=    flatten_nested_dict(flax_params['''class_head'''] )

						for flax_key, v in flax_class_params.items():
												UpperCAmelCase__		=    flax_key.replace('''/'''	,			'''.''' )
												UpperCAmelCase__		=    torch_key.replace('''.kernel'''	,			'''.weight''' )
												UpperCAmelCase__		=    torch_key.replace('''Dense_0'''	,			'''dense0''' )
												UpperCAmelCase__		=    '''class_head.''' + torch_key

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

												UpperCAmelCase__		=    nn.Parameter(torch.from_numpy(_UpperCamelCase ) )

						# Rename box prediction box flax params to pytorch HF
						UpperCAmelCase__		=    flatten_nested_dict(flax_params['''obj_box_head'''] )

						for flax_key, v in flax_box_params.items():
												UpperCAmelCase__		=    flax_key.replace('''/'''	,			'''.''' )
												UpperCAmelCase__		=    torch_key.replace('''.kernel'''	,			'''.weight''' )
												UpperCAmelCase__		=    torch_key.replace('''_'''	,			'''''' ).lower()
												UpperCAmelCase__		=    '''box_head.''' + torch_key

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

												UpperCAmelCase__		=    nn.Parameter(torch.from_numpy(_UpperCamelCase ) )

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






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						for k, v in flax_attn_params.items():
												if k.startswith('''transformer''' ):
																		UpperCAmelCase__		=    k.replace('''transformer.resblocks'''	,			'''text_model.encoder.layers''' )
												else:
																		UpperCAmelCase__		=    k.replace('''visual.transformer.resblocks'''	,			'''vision_model.encoder.layers''' )

												UpperCAmelCase__		=    torch_key.replace('''attn'''	,			'''self_attn''' )
												UpperCAmelCase__		=    torch_key.replace('''key'''	,			'''k_proj''' )
												UpperCAmelCase__		=    torch_key.replace('''value'''	,			'''v_proj''' )
												UpperCAmelCase__		=    torch_key.replace('''query'''	,			'''q_proj''' )
												UpperCAmelCase__		=    torch_key.replace('''out'''	,			'''out_proj''' )

												if "bias" in torch_key and v.ndim == 2:
																		UpperCAmelCase__		=    v.shape[0] * v.shape[1]
																		UpperCAmelCase__		=    v.reshape(_UpperCamelCase )

												if "weight" in torch_key and "out" in torch_key:
																		UpperCAmelCase__		=    (v.shape[0] * v.shape[1], v.shape[2])
																		UpperCAmelCase__		=    v.reshape(_UpperCamelCase ).T

												if "weight" in torch_key and "out" not in torch_key:
																		UpperCAmelCase__		=    (v.shape[0], v.shape[1] * v.shape[2])
																		UpperCAmelCase__		=    v.reshape(_UpperCamelCase ).T

												# Copy flax CLIP attn params to HF PyTorch params
												UpperCAmelCase__		=    torch.from_numpy(_UpperCamelCase )
												hf_backbone.state_dict()[torch_key].copy_(_UpperCamelCase )






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    {}
						UpperCAmelCase__		=    []

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

																		processed_attn_layers.append(_UpperCamelCase )
																		UpperCAmelCase__		=    params[base + '''out.weight'''].shape[-1]
																		UpperCAmelCase__		=    params[base + '''out.weight'''].reshape(_UpperCamelCase	,			_UpperCamelCase ).T
																		UpperCAmelCase__		=    params[base + '''out.bias''']
												else:
																		UpperCAmelCase__		=    v
						return new_params






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    CLIP(**_UpperCamelCase )
						torch_model.eval()
						UpperCAmelCase__		=    torch_model.state_dict()

						UpperCAmelCase__		=    flatten_nested_dict(flax_params['''backbone''']['''clip'''] )
						UpperCAmelCase__		=    {}

						for flax_key, v in flax_clip_params.items():
												UpperCAmelCase__		=    flax_key.replace('''/'''	,			'''.''' )
												UpperCAmelCase__		=    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''' )
												):
																		UpperCAmelCase__		=    torch_key[5:]

												UpperCAmelCase__		=    torch_key.replace('''text_projection.kernel'''	,			'''text_projection''' )
												UpperCAmelCase__		=    torch_key.replace('''visual.proj.kernel'''	,			'''visual.proj''' )
												UpperCAmelCase__		=    torch_key.replace('''.scale'''	,			'''.weight''' )
												UpperCAmelCase__		=    torch_key.replace('''.kernel'''	,			'''.weight''' )

												if "conv" in torch_key or "downsample.0.weight" in torch_key:
																		UpperCAmelCase__		=    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
																		UpperCAmelCase__		=    v.T

												UpperCAmelCase__		=    v

						UpperCAmelCase__		=    _convert_attn_layers(_UpperCamelCase )
						new_torch_params.update(_UpperCamelCase )
						UpperCAmelCase__		=    {}

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

						return torch_clip_params, torch_model, attn_params






@torch.no_grad()
def 					a__ (       _UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase	,			_UpperCamelCase=None ):





						"""simple docstring"""

						UpperCAmelCase__		=    Repository(_UpperCamelCase	,			clone_from=F"""google/{pytorch_dump_folder_path}""" )
						repo.git_pull()

						if config_path is not None:
												UpperCAmelCase__		=    OwlViTConfig.from_pretrained(_UpperCamelCase )
						else:
												UpperCAmelCase__		=    OwlViTConfig()

						UpperCAmelCase__		=    OwlViTModel(_UpperCamelCase ).eval()
						UpperCAmelCase__		=    OwlViTForObjectDetection(_UpperCamelCase ).eval()

						copy_text_model_and_projection(_UpperCamelCase	,			_UpperCamelCase )
						copy_vision_model_and_projection(_UpperCamelCase	,			_UpperCamelCase )
						UpperCAmelCase__		=    pt_backbone.logit_scale
						copy_flax_attn_params(_UpperCamelCase	,			_UpperCamelCase )

						UpperCAmelCase__		=    hf_backbone
						copy_class_merge_token(_UpperCamelCase	,			_UpperCamelCase )
						copy_class_box_heads(_UpperCamelCase	,			_UpperCamelCase )

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

						# Initialize image processor
						UpperCAmelCase__		=    OwlViTImageProcessor(
						    size=config.vision_config.image_size	,			crop_size=config.vision_config.image_size )
						# Initialize tokenizer
						UpperCAmelCase__		=    CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''	,			pad_token='''!'''	,			model_max_length=16 )

						# Initialize processor
						UpperCAmelCase__		=    OwlViTProcessor(image_processor=_UpperCamelCase	,			tokenizer=_UpperCamelCase )
						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)



"""simple docstring"""







import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


__a			 =   logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      42
							SCREAMING_SNAKE_CASE										=      None






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=0.999	,			_UpperCamelCase="cosine"	,			):





						"""simple docstring"""

						if alpha_transform_type == "cosine":

												def alpha_bar_fn(_UpperCamelCase ):
																		return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(_UpperCamelCase ):
																		return math.exp(t * -12.0 )

						else:
												raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )

						UpperCAmelCase__		=    []
						for i in range(_UpperCamelCase ):
												UpperCAmelCase__		=    i / num_diffusion_timesteps
												UpperCAmelCase__		=    (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(_UpperCamelCase ) / alpha_bar_fn(_UpperCamelCase )	,			_UpperCamelCase ) )
						return torch.tensor(_UpperCamelCase	,			dtype=torch.floataa )


class 							lowerCamelCase       (			__SCREAMING_SNAKE_CASE				,					__SCREAMING_SNAKE_CASE ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      [e.name for e in KarrasDiffusionSchedulers]
							SCREAMING_SNAKE_CASE										=      1






							@register_to_config
							def __init__(						self :     Dict		, lowerCamelCase_ :     int = 1000		, lowerCamelCase_ :     float = 0.0001		, lowerCamelCase_ :     float = 0.02		, lowerCamelCase_ :     str = "linear"		, lowerCamelCase_ :     Optional[Union[np.ndarray, List[float]]] = None		, lowerCamelCase_ :     str = "epsilon"		, lowerCamelCase_ :     str = "linspace"		, lowerCamelCase_ :     int = 0		, ):
													if trained_betas is not None:
																			UpperCAmelCase__		=    torch.tensor(lowerCamelCase_		, dtype=torch.floataa				)
													elif beta_schedule == "linear":
																			UpperCAmelCase__		=    torch.linspace(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, dtype=torch.floataa				)
													elif beta_schedule == "scaled_linear":
																			# this schedule is very specific to the latent diffusion model.
																			UpperCAmelCase__		=    (
																			    torch.linspace(beta_start**0.5		, beta_end**0.5		, lowerCamelCase_		, dtype=torch.floataa				) ** 2
																			)
													elif beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			UpperCAmelCase__		=    betas_for_alpha_bar(lowerCamelCase_				)
													else:
																			raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""				)

													UpperCAmelCase__		=    1.0 - self.betas
													UpperCAmelCase__		=    torch.cumprod(self.alphas		, dim=0				)

													UpperCAmelCase__		=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
													UpperCAmelCase__		=    np.concatenate([sigmas[::-1], [0.0]]				).astype(np.floataa				)
													UpperCAmelCase__		=    torch.from_numpy(lowerCamelCase_				)

													# setable values
													UpperCAmelCase__		=    None
													UpperCAmelCase__		=    np.linspace(0		, num_train_timesteps - 1		, lowerCamelCase_		, dtype=lowerCamelCase_				)[::-1].copy()
													UpperCAmelCase__		=    torch.from_numpy(lowerCamelCase_				)
													UpperCAmelCase__		=    False






							@property
							def 					lowerCAmelCase     (						self :     Optional[int]				):
													# standard deviation of the initial noise distribution
													if self.config.timestep_spacing in ["linspace", "trailing"]:
																			return self.sigmas.max()

													return (self.sigmas.max() ** 2 + 1) ** 0.5






							def 					lowerCAmelCase     (						self :     Union[str, Any]		, lowerCamelCase_ :     torch.FloatTensor		, lowerCamelCase_ :     Union[float, torch.FloatTensor]				):
													if isinstance(lowerCamelCase_		, torch.Tensor				):
																			UpperCAmelCase__		=    timestep.to(self.timesteps.device				)
													UpperCAmelCase__		=    (self.timesteps == timestep).nonzero().item()
													UpperCAmelCase__		=    self.sigmas[step_index]
													UpperCAmelCase__		=    sample / ((sigma**2 + 1) ** 0.5)
													UpperCAmelCase__		=    True
													return sample






							def 					lowerCAmelCase     (						self :     List[Any]		, lowerCamelCase_ :     int		, lowerCamelCase_ :     Union[str, torch.device] = None				):
													UpperCAmelCase__		=    num_inference_steps

													# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
													if self.config.timestep_spacing == "linspace":
																			UpperCAmelCase__		=    np.linspace(0		, self.config.num_train_timesteps - 1		, lowerCamelCase_		, dtype=lowerCamelCase_				)[
																			    ::-1
																			].copy()
													elif self.config.timestep_spacing == "leading":
																			UpperCAmelCase__		=    self.config.num_train_timesteps // self.num_inference_steps
																			# creates integer timesteps by multiplying by ratio
																			# casting to int to avoid issues when num_inference_step is power of 3
																			UpperCAmelCase__		=    (np.arange(0		, lowerCamelCase_				) * step_ratio).round()[::-1].copy().astype(lowerCamelCase_				)
																			timesteps += self.config.steps_offset
													elif self.config.timestep_spacing == "trailing":
																			UpperCAmelCase__		=    self.config.num_train_timesteps / self.num_inference_steps
																			# creates integer timesteps by multiplying by ratio
																			# casting to int to avoid issues when num_inference_step is power of 3
																			UpperCAmelCase__		=    (np.arange(self.config.num_train_timesteps		, 0		, -step_ratio				)).round().copy().astype(lowerCamelCase_				)
																			timesteps -= 1
													else:
																			raise ValueError(
																			    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""				)

													UpperCAmelCase__		=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
													UpperCAmelCase__		=    np.interp(lowerCamelCase_		, np.arange(0		, len(lowerCamelCase_				)				)		, lowerCamelCase_				)
													UpperCAmelCase__		=    np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
													UpperCAmelCase__		=    torch.from_numpy(lowerCamelCase_				).to(device=lowerCamelCase_				)
													if str(lowerCamelCase_				).startswith('''mps'''				):
																			# mps does not support float64
																			UpperCAmelCase__		=    torch.from_numpy(lowerCamelCase_				).to(lowerCamelCase_		, dtype=torch.floataa				)
													else:
																			UpperCAmelCase__		=    torch.from_numpy(lowerCamelCase_				).to(device=lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     List[str]		, lowerCamelCase_ :     torch.FloatTensor		, lowerCamelCase_ :     Union[float, torch.FloatTensor]		, lowerCamelCase_ :     torch.FloatTensor		, lowerCamelCase_ :     Optional[torch.Generator] = None		, lowerCamelCase_ :     bool = True		, ):

													if (
													    isinstance(lowerCamelCase_		, lowerCamelCase_				)
													    or isinstance(lowerCamelCase_		, torch.IntTensor				)
													    or isinstance(lowerCamelCase_		, torch.LongTensor				)
													):
																			raise ValueError(
																			    (
																			        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
																			        ''' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'''
																			        ''' one of the `scheduler.timesteps` as a timestep.'''
																			    )		, )

													if not self.is_scale_input_called:
																			logger.warning(
																			    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
																			    '''See `StableDiffusionPipeline` for a usage example.'''				)

													if isinstance(lowerCamelCase_		, torch.Tensor				):
																			UpperCAmelCase__		=    timestep.to(self.timesteps.device				)

													UpperCAmelCase__		=    (self.timesteps == timestep).nonzero().item()
													UpperCAmelCase__		=    self.sigmas[step_index]

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													if self.config.prediction_type == "epsilon":
																			UpperCAmelCase__		=    sample - sigma * model_output
													elif self.config.prediction_type == "v_prediction":
																			# * c_out + input * c_skip
																			UpperCAmelCase__		=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
													elif self.config.prediction_type == "sample":
																			raise NotImplementedError('''prediction_type not implemented yet: sample'''				)
													else:
																			raise ValueError(
																			    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""				)

													UpperCAmelCase__		=    self.sigmas[step_index]
													UpperCAmelCase__		=    self.sigmas[step_index + 1]
													UpperCAmelCase__		=    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
													UpperCAmelCase__		=    (sigma_to**2 - sigma_up**2) ** 0.5

													# 2. Convert to an ODE derivative
													UpperCAmelCase__		=    (sample - pred_original_sample) / sigma

													UpperCAmelCase__		=    sigma_down - sigma

													UpperCAmelCase__		=    sample + derivative * dt

													UpperCAmelCase__		=    model_output.device
													UpperCAmelCase__		=    randn_tensor(model_output.shape		, dtype=model_output.dtype		, device=lowerCamelCase_		, generator=lowerCamelCase_				)

													UpperCAmelCase__		=    prev_sample + noise * sigma_up

													if not return_dict:
																			return (prev_sample,)

													return EulerAncestralDiscreteSchedulerOutput(
													    prev_sample=lowerCamelCase_		, pred_original_sample=lowerCamelCase_				)






							def 					lowerCAmelCase     (						self :     int		, lowerCamelCase_ :     torch.FloatTensor		, lowerCamelCase_ :     torch.FloatTensor		, lowerCamelCase_ :     torch.FloatTensor		, ):
													# Make sure sigmas and timesteps have the same device and dtype as original_samples
													UpperCAmelCase__		=    self.sigmas.to(device=original_samples.device		, dtype=original_samples.dtype				)
													if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase_				):
																			# mps does not support float64
																			UpperCAmelCase__		=    self.timesteps.to(original_samples.device		, dtype=torch.floataa				)
																			UpperCAmelCase__		=    timesteps.to(original_samples.device		, dtype=torch.floataa				)
													else:
																			UpperCAmelCase__		=    self.timesteps.to(original_samples.device				)
																			UpperCAmelCase__		=    timesteps.to(original_samples.device				)

													UpperCAmelCase__		=    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

													UpperCAmelCase__		=    sigmas[step_indices].flatten()
													while len(sigma.shape				) < len(original_samples.shape				):
																			UpperCAmelCase__		=    sigma.unsqueeze(-1				)

													UpperCAmelCase__		=    original_samples + noise * sigma
													return noisy_samples






							def __len__(						self :     Optional[int]				):
													return self.config.num_train_timesteps