




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      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__=False						,			UpperCamelCase__=False						,			UpperCamelCase__="data2vec."   )     ->     List[str]:







				'''simple docstring'''


				__lowerCAmelCase					=  """backbone.""" if is_semantic else """"""

				__lowerCAmelCase					=  []
				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      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__=False						,			UpperCamelCase__=False						,			UpperCamelCase__="data2vec_vision."   )     ->     Optional[Any]:







				'''simple docstring'''


				for i in range(config.num_hidden_layers   ):
								__lowerCAmelCase					=  """backbone.""" if is_semantic else """"""
								# queries, keys and values
								__lowerCAmelCase					=  state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''   )
								__lowerCAmelCase					=  state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''   )
								__lowerCAmelCase					=  state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''   )

								__lowerCAmelCase					=  in_proj_weight[
								    : config.hidden_size, :
								]
								__lowerCAmelCase					=  q_bias
								__lowerCAmelCase					=  in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								__lowerCAmelCase					=  in_proj_weight[
								    -config.hidden_size :, :
								]
								__lowerCAmelCase					=  v_bias

								# gamma_1 and gamma_2
								# we call them lambda because otherwise they are renamed when using .from_pretrained
								__lowerCAmelCase					=  state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''   )
								__lowerCAmelCase					=  state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''   )

								__lowerCAmelCase					=  gamma_a
								__lowerCAmelCase					=  gamma_a

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

												__lowerCAmelCase					=  table
												__lowerCAmelCase					=  index





def      UpperCAmelCase  (						)     ->     Optional[Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  argparse.ArgumentParser(
				    """Convert Data2VecVision to HF for image classification and pretraining"""						,			add_help=UpperCamelCase__   )
				parser.add_argument("""--hf_checkpoint_name"""						,			type=UpperCamelCase__   )
				parser.add_argument("""--input_size"""						,			default=2_24						,			type=UpperCamelCase__						,			help="""images input size"""   )
				parser.add_argument("""--beit_checkpoint"""						,			default=""""""						,			help="""beit checkpoint"""   )

				return parser.parse_args()





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     Dict:







				'''simple docstring'''


				def load_state_dict(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__=""						,			UpperCamelCase__="relative_position_index"   ):
								__lowerCAmelCase					=  []
								__lowerCAmelCase					=  []
								__lowerCAmelCase					=  []
								# copy state_dict so _load_from_state_dict can modify it
								__lowerCAmelCase					=  getattr(UpperCamelCase__						,			"""_metadata"""						,			UpperCamelCase__   )
								__lowerCAmelCase					=  state_dict.copy()
								if metadata is not None:
												__lowerCAmelCase					=  metadata

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

								load(UpperCamelCase__						,			prefix=UpperCamelCase__   )

								__lowerCAmelCase					=  []
								__lowerCAmelCase					=  []
								for key in missing_keys:
												__lowerCAmelCase					=  True
												for ignore_key in ignore_missing.split("""|"""   ):
																if ignore_key in key:
																				__lowerCAmelCase					=  False
																				break
												if keep_flag:
																warn_missing_keys.append(UpperCamelCase__   )
												else:
																ignore_missing_keys.append(UpperCamelCase__   )

								__lowerCAmelCase					=  warn_missing_keys

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

				__lowerCAmelCase					=  {
				    """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""": 10_00,
								        """use_mean_pooling""": True,
								        """init_scale""": 0.001,
								        """use_rel_pos_bias""": True,
								    }   )

				__lowerCAmelCase					=  create_model(
				    """beit_large_patch16_224""" if is_large else """beit_base_patch16_224"""						,			**UpperCamelCase__						,			)
				__lowerCAmelCase					=  model.patch_embed.patch_size
				__lowerCAmelCase					=  (args.input_size // patch_size[0], args.input_size // patch_size[1])
				__lowerCAmelCase					=  torch.load(args.beit_checkpoint						,			map_location="""cpu"""   )

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

				__lowerCAmelCase					=  list(checkpoint_model.keys()   )
				for key in all_keys:
								if "relative_position_index" in key:
												checkpoint_model.pop(UpperCamelCase__   )

								if "relative_position_bias_table" in key:
												__lowerCAmelCase					=  checkpoint_model[key]
												__lowerCAmelCase	,				__lowerCAmelCase					=  rel_pos_bias.size()
												__lowerCAmelCase	,				__lowerCAmelCase					=  model.state_dict()[key].size()
												__lowerCAmelCase					=  model.patch_embed.patch_shape
												if dst_patch_shape[0] != dst_patch_shape[1]:
																raise NotImplementedError()

				load_state_dict(UpperCamelCase__						,			UpperCamelCase__						,			prefix=""""""   )

				return model





def      UpperCAmelCase  (						)     ->     List[str]:







				'''simple docstring'''


				__lowerCAmelCase					=  get_args()

				__lowerCAmelCase					=  """ft1k""" in args.hf_checkpoint_name
				__lowerCAmelCase					=  """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
				__lowerCAmelCase					=  DataaVecVisionConfig()
				if is_finetuned:
								__lowerCAmelCase					=  True
								__lowerCAmelCase					=  False
								__lowerCAmelCase					=  True
								__lowerCAmelCase					=  10_00

								__lowerCAmelCase					=  """huggingface/label-files"""
								__lowerCAmelCase					=  """imagenet-1k-id2label.json"""
								__lowerCAmelCase					=  json.load(open(hf_hub_download(UpperCamelCase__						,			UpperCamelCase__						,			repo_type="""dataset"""   )						,			"""r"""   )   )
								__lowerCAmelCase					=  {int(UpperCamelCase__   ): v for k, v in idalabel.items()}
								__lowerCAmelCase					=  idalabel
								__lowerCAmelCase					=  {v: k for k, v in idalabel.items()}
				else:
								__lowerCAmelCase					=  False
								__lowerCAmelCase					=  True
								__lowerCAmelCase					=  False

				if is_large:
								__lowerCAmelCase					=  10_24
								__lowerCAmelCase					=  40_96
								__lowerCAmelCase					=  24
								__lowerCAmelCase					=  16

				# 2. Load Beit model
				__lowerCAmelCase					=  load_beit_model(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )
				orig_model.eval()

				# 3. Forward Beit model
				__lowerCAmelCase					=  BeitImageProcessor(size=config.image_size						,			do_center_crop=UpperCamelCase__   )
				__lowerCAmelCase					=  Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""   )
				__lowerCAmelCase					=  image_processor(images=UpperCamelCase__						,			return_tensors="""pt"""   )
				__lowerCAmelCase					=  encoding["""pixel_values"""]

				__lowerCAmelCase					=  (pixel_values,) if is_finetuned else (pixel_values, None)
				with torch.no_grad():
								__lowerCAmelCase					=  orig_model(*UpperCamelCase__   )

				# 4. Load HF Data2VecVision model
				if is_finetuned:
								__lowerCAmelCase					=  DataaVecVisionForImageClassification(UpperCamelCase__   )
								hf_model.eval()
								__lowerCAmelCase					=  False
								__lowerCAmelCase					=  """data2vec_vision."""
				else:
								__lowerCAmelCase					=  DataaVecVisionModel(UpperCamelCase__   )
								hf_model.eval()
								__lowerCAmelCase					=  True
								__lowerCAmelCase					=  """"""

				__lowerCAmelCase					=  create_rename_keys(UpperCamelCase__						,			hf_prefix=UpperCamelCase__						,			has_lm_head=UpperCamelCase__   )
				__lowerCAmelCase					=  orig_model.state_dict()
				for src, dest in rename_keys:
								__lowerCAmelCase					=  state_dict.pop(UpperCamelCase__   )
								__lowerCAmelCase					=  val

				read_in_q_k_v(UpperCamelCase__						,			UpperCamelCase__						,			hf_prefix=UpperCamelCase__						,			has_lm_head=UpperCamelCase__   )
				__lowerCAmelCase	,				__lowerCAmelCase					=  hf_model.load_state_dict(UpperCamelCase__						,			strict=UpperCamelCase__   )
				print("""HF missing"""						,			UpperCamelCase__   )
				print("""HF unexpected_keys"""						,			UpperCamelCase__   )

				# 5. Forward HF Data2VecVision model
				with torch.no_grad():
								__lowerCAmelCase					=  hf_model(UpperCamelCase__   )

				__lowerCAmelCase					=  hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

				# 6. Compare
				__lowerCAmelCase					=  torch.max(torch.abs(hf_output - orig_model_output   )   ).item()

				print(F'''max_absolute_diff = {max_absolute_diff}'''   )
				__lowerCAmelCase					=  torch.allclose(UpperCamelCase__						,			UpperCamelCase__						,			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"











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						:		Union[str, Any]          =						logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= 42
					__UpperCamelCase     	= None





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__=0.999						,			UpperCamelCase__="cosine"						,			)     ->     Optional[int]:







				'''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}'''   )

				__lowerCAmelCase					=  []
				for i in range(UpperCamelCase__   ):
								__lowerCAmelCase					=  i / num_diffusion_timesteps
								__lowerCAmelCase					=  (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 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__				):
					__UpperCamelCase     	= [e.name for e in KarrasDiffusionSchedulers]
					__UpperCamelCase     	= 1




					@register_to_config
					def __init__(   self:		Any,     _lowercase:		int = 1000,     _lowercase:		float = 0.0_001,     _lowercase:		float = 0.02,     _lowercase:		str = "linear",     _lowercase:		Optional[Union[np.ndarray, List[float]]] = None,     _lowercase:		str = "epsilon",     _lowercase:		str = "linear",     _lowercase:		Optional[bool] = False,     _lowercase:		str = "linspace",     _lowercase:		int = 0,     ):


									'''simple docstring'''


									if trained_betas is not None:
													__lowerCAmelCase					=  torch.tensor(_lowercase,     dtype=torch.floataa)
									elif beta_schedule == "linear":
													__lowerCAmelCase					=  torch.linspace(_lowercase,     _lowercase,     _lowercase,     dtype=torch.floataa)
									elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													__lowerCAmelCase					=  (
													    torch.linspace(beta_start**0.5,     beta_end**0.5,     _lowercase,     dtype=torch.floataa) ** 2
													)
									elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													__lowerCAmelCase					=  betas_for_alpha_bar(_lowercase)
									else:
													raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''')

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

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

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




					@property
					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

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




					def     _lowercase						(   self:		Tuple,     _lowercase:		torch.FloatTensor,     _lowercase:		Union[float, torch.FloatTensor]):


									'''simple docstring'''


									if isinstance(_lowercase,     torch.Tensor):
													__lowerCAmelCase					=  timestep.to(self.timesteps.device)
									__lowerCAmelCase					=  (self.timesteps == timestep).nonzero().item()
									__lowerCAmelCase					=  self.sigmas[step_index]

									__lowerCAmelCase					=  sample / ((sigma**2 + 1) ** 0.5)

									__lowerCAmelCase					=  True
									return sample




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		int,     _lowercase:		Union[str, torch.device] = None):


									'''simple docstring'''


									__lowerCAmelCase					=  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":
													__lowerCAmelCase					=  np.linspace(0,     self.config.num_train_timesteps - 1,     _lowercase,     dtype=_lowercase)[
													    ::-1
													].copy()
									elif self.config.timestep_spacing == "leading":
													__lowerCAmelCase					=  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
													__lowerCAmelCase					=  (np.arange(0,     _lowercase) * step_ratio).round()[::-1].copy().astype(_lowercase)
													timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
													__lowerCAmelCase					=  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
													__lowerCAmelCase					=  (np.arange(self.config.num_train_timesteps,     0,     -step_ratio)).round().copy().astype(_lowercase)
													timesteps -= 1
									else:
													raise ValueError(
													    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''')

									__lowerCAmelCase					=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
									__lowerCAmelCase					=  np.log(_lowercase)

									if self.config.interpolation_type == "linear":
													__lowerCAmelCase					=  np.interp(_lowercase,     np.arange(0,     len(_lowercase)),     _lowercase)
									elif self.config.interpolation_type == "log_linear":
													__lowerCAmelCase					=  torch.linspace(np.log(sigmas[-1]),     np.log(sigmas[0]),     num_inference_steps + 1).exp()
									else:
													raise ValueError(
													    f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
													    """ 'linear' or 'log_linear'""")

									if self.use_karras_sigmas:
													__lowerCAmelCase					=  self._convert_to_karras(in_sigmas=_lowercase,     num_inference_steps=self.num_inference_steps)
													__lowerCAmelCase					=  np.array([self._sigma_to_t(_lowercase,     _lowercase) for sigma in sigmas])

									__lowerCAmelCase					=  np.concatenate([sigmas, [0.0]]).astype(np.floataa)
									__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(device=_lowercase)
									if str(_lowercase).startswith("""mps"""):
													# mps does not support float64
													__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(_lowercase,     dtype=torch.floataa)
									else:
													__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(device=_lowercase)




					def     _lowercase						(   self:		List[Any],     _lowercase:		Optional[int],     _lowercase:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  np.log(_lowercase)

									# get distribution
									__lowerCAmelCase					=  log_sigma - log_sigmas[:, np.newaxis]

									# get sigmas range
									__lowerCAmelCase					=  np.cumsum((dists >= 0),     axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
									__lowerCAmelCase					=  low_idx + 1

									__lowerCAmelCase					=  log_sigmas[low_idx]
									__lowerCAmelCase					=  log_sigmas[high_idx]

									# interpolate sigmas
									__lowerCAmelCase					=  (low - log_sigma) / (low - high)
									__lowerCAmelCase					=  np.clip(_lowercase,     0,     1)

									# transform interpolation to time range
									__lowerCAmelCase					=  (1 - w) * low_idx + w * high_idx
									__lowerCAmelCase					=  t.reshape(sigma.shape)
									return t




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		torch.FloatTensor,     _lowercase:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  in_sigmas[-1].item()
									__lowerCAmelCase					=  in_sigmas[0].item()

									__lowerCAmelCase					=  7.0  # 7.0 is the value used in the paper
									__lowerCAmelCase					=  np.linspace(0,     1,     _lowercase)
									__lowerCAmelCase					=  sigma_min ** (1 / rho)
									__lowerCAmelCase					=  sigma_max ** (1 / rho)
									__lowerCAmelCase					=  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas




					def     _lowercase						(   self:		Optional[int],     _lowercase:		torch.FloatTensor,     _lowercase:		Union[float, torch.FloatTensor],     _lowercase:		torch.FloatTensor,     _lowercase:		float = 0.0,     _lowercase:		float = 0.0,     _lowercase:		float = float("""inf"""),     _lowercase:		float = 1.0,     _lowercase:		Optional[torch.Generator] = None,     _lowercase:		bool = True,     ):


									'''simple docstring'''


									if (
									    isinstance(_lowercase,     _lowercase)
									    or isinstance(_lowercase,     torch.IntTensor)
									    or isinstance(_lowercase,     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(_lowercase,     torch.Tensor):
													__lowerCAmelCase					=  timestep.to(self.timesteps.device)

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

									__lowerCAmelCase					=  min(s_churn / (len(self.sigmas) - 1),     2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0

									__lowerCAmelCase					=  randn_tensor(
									    model_output.shape,     dtype=model_output.dtype,     device=model_output.device,     generator=_lowercase)

									__lowerCAmelCase					=  noise * s_noise
									__lowerCAmelCase					=  sigma * (gamma + 1)

									if gamma > 0:
													__lowerCAmelCase					=  sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									# NOTE: "original_sample" should not be an expected prediction_type but is left in for
									# backwards compatibility
									if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
													__lowerCAmelCase					=  model_output
									elif self.config.prediction_type == "epsilon":
													__lowerCAmelCase					=  sample - sigma_hat * model_output
									elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													__lowerCAmelCase					=  model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									else:
													raise ValueError(
													    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''')

									# 2. Convert to an ODE derivative
									__lowerCAmelCase					=  (sample - pred_original_sample) / sigma_hat

									__lowerCAmelCase					=  self.sigmas[step_index + 1] - sigma_hat

									__lowerCAmelCase					=  sample + derivative * dt

									if not return_dict:
													return (prev_sample,)

									return EulerDiscreteSchedulerOutput(prev_sample=_lowercase,     pred_original_sample=_lowercase)




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		torch.FloatTensor,     _lowercase:		torch.FloatTensor,     _lowercase:		torch.FloatTensor,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  self.sigmas.to(device=original_samples.device,     dtype=original_samples.dtype)
									if original_samples.device.type == "mps" and torch.is_floating_point(_lowercase):
													# mps does not support float64
													__lowerCAmelCase					=  self.timesteps.to(original_samples.device,     dtype=torch.floataa)
													__lowerCAmelCase					=  timesteps.to(original_samples.device,     dtype=torch.floataa)
									else:
													__lowerCAmelCase					=  self.timesteps.to(original_samples.device)
													__lowerCAmelCase					=  timesteps.to(original_samples.device)

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

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

									__lowerCAmelCase					=  original_samples + noise * sigma
									return noisy_samples







					def __len__(   self:		str):


									'''simple docstring'''


									return self.config.num_train_timesteps





