
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 snake_case_    (				lowercase__      ,					lowercase__=False      ,					lowercase__=False      ,					lowercase__="data2vec."		):
  UpperCAmelCase__			:				List[str]												=       '''backbone.''' if is_semantic else ''''''

  UpperCAmelCase__			:				int												=       []
  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 snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__=False      ,					lowercase__=False      ,					lowercase__="data2vec_vision."		):






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

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

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

    UpperCAmelCase__			:				Optional[Any]												=       gamma_a
    UpperCAmelCase__			:				Optional[Any]												=       gamma_a

    # relative_position bias table + index
    if not has_lm_head:
      # each layer has its own relative position bias
      UpperCAmelCase__			:				Optional[int]												=       state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_bias_table"""		)
      UpperCAmelCase__			:				Optional[Any]												=       state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_index"""		)

      UpperCAmelCase__			:				Union[str, Any]												=       table
      UpperCAmelCase__			:				List[str]												=       index







def snake_case_    (				):
  UpperCAmelCase__			:				Optional[Any]												=       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_2_4      ,					type=UpperCAmelCase_      ,					help="images input size"		)
  parser.add_argument("--beit_checkpoint"      ,					default=""      ,					help="beit checkpoint"		)

  return parser.parse_args()







def snake_case_    (				lowercase__      ,					lowercase__      ,					lowercase__		):






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

    def load(lowercase__      ,					lowercase__=""		):
      UpperCAmelCase__			:				Union[str, Any]												=       {} 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_		)

    UpperCAmelCase__			:				List[str]												=       []
    UpperCAmelCase__			:				Union[str, Any]												=       []
    for key in missing_keys:
      UpperCAmelCase__			:				Union[str, Any]												=       True
      for ignore_key in ignore_missing.split("|"		):
        if ignore_key in key:
          UpperCAmelCase__			:				int												=       False
          break
      if keep_flag:
        warn_missing_keys.append(UpperCAmelCase_		)
      else:
        ignore_missing_keys.append(UpperCAmelCase_		)

    UpperCAmelCase__			:				List[Any]												=       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_		)		)

  UpperCAmelCase__			:				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": 1_0_0_0,
            "use_mean_pooling": True,
            "init_scale": 0.001,
            "use_rel_pos_bias": True,
        }		)

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

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

  UpperCAmelCase__			:				int												=       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:
      UpperCAmelCase__			:				List[str]												=       checkpoint_model[key]
      UpperCAmelCase__			:				Optional[Any]												=       rel_pos_bias.size()
      UpperCAmelCase__			:				Union[str, Any]												=       model.state_dict()[key].size()
      UpperCAmelCase__			:				int												=       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 snake_case_    (				):
  UpperCAmelCase__			:				Optional[Any]												=       get_args()

  UpperCAmelCase__			:				Optional[int]												=       '''ft1k''' in args.hf_checkpoint_name
  UpperCAmelCase__			:				Any												=       '''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
  UpperCAmelCase__			:				List[Any]												=       DataaVecVisionConfig()
  if is_finetuned:
    UpperCAmelCase__			:				Optional[Any]												=       True
    UpperCAmelCase__			:				Optional[int]												=       False
    UpperCAmelCase__			:				List[str]												=       True
    UpperCAmelCase__			:				str												=       1_0_0_0

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

  if is_large:
    UpperCAmelCase__			:				Tuple												=       1_0_2_4
    UpperCAmelCase__			:				Optional[int]												=       4_0_9_6
    UpperCAmelCase__			:				Optional[int]												=       2_4
    UpperCAmelCase__			:				Any												=       1_6

  # 2. Load Beit model
  UpperCAmelCase__			:				Tuple												=       load_beit_model(UpperCAmelCase_      ,					UpperCAmelCase_      ,					UpperCAmelCase_		)
  orig_model.eval()

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

  UpperCAmelCase__			:				Optional[Any]												=       (pixel_values,) if is_finetuned else (pixel_values, None)
  with torch.no_grad():
    UpperCAmelCase__			:				Any												=       orig_model(*UpperCAmelCase_		)

  # 4. Load HF Data2VecVision model
  if is_finetuned:
    UpperCAmelCase__			:				Dict												=       DataaVecVisionForImageClassification(UpperCAmelCase_		)
    hf_model.eval()
    UpperCAmelCase__			:				List[str]												=       False
    UpperCAmelCase__			:				int												=       '''data2vec_vision.'''
  else:
    UpperCAmelCase__			:				List[str]												=       DataaVecVisionModel(UpperCAmelCase_		)
    hf_model.eval()
    UpperCAmelCase__			:				Optional[int]												=       True
    UpperCAmelCase__			:				int												=       ''''''

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

  read_in_q_k_v(UpperCAmelCase_      ,					UpperCAmelCase_      ,					hf_prefix=UpperCAmelCase_      ,					has_lm_head=UpperCAmelCase_		)
  UpperCAmelCase__			:				Dict												=       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():
    UpperCAmelCase__			:				List[Any]												=       hf_model(UpperCAmelCase_		)

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

  # 6. Compare
  UpperCAmelCase__			:				Any												=       torch.max(torch.abs(hf_output - orig_model_output		)		).item()

  print(F"""max_absolute_diff = {max_absolute_diff}"""		)
  UpperCAmelCase__			:				Optional[int]												=       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"





'''simple docstring'''





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left






@flax.struct.dataclass
class        UpperCAmelCase_       :

 '''simple docstring'''
 # setable values
 lowercase_				:						Optional[jnp.ndarray]				=       None
 lowercase_				:						Optional[jnp.ndarray]				=       None
 lowercase_				:						Optional[jnp.ndarray]				=       None







 @classmethod
 def        UpperCamelCase     (				cls		:     List[str]   ):



   '''simple docstring'''




   return cls()






@dataclass
class        UpperCAmelCase_       (						A							):

 '''simple docstring'''
 lowercase_				:						ScoreSdeVeSchedulerState
 lowercase_				:						jnp.ndarray
 lowercase_				:						Optional[jnp.ndarray]				=       None






class        UpperCAmelCase_       (						A				,							A							):

 '''simple docstring'''







 @property
 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




   return True







 @register_to_config
 def __init__(				self		:     Optional[Any] ,							snake_case__		:     int = 20_00 ,							snake_case__		:     float = 0.15 ,							snake_case__		:     float = 0.01 ,							snake_case__		:     float = 1348.0 ,							snake_case__		:     float = 1e-5 ,							snake_case__		:     int = 1 ,							):



   '''simple docstring'''




   pass







 def        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Union[str, Any]												=       ScoreSdeVeSchedulerState.create()
   return self.set_sigmas(
       snake_case__ ,							self.config.num_train_timesteps ,							self.config.sigma_min ,							self.config.sigma_max ,							self.config.sampling_eps ,							)







 def        UpperCamelCase     (				self		:     List[Any] ,							snake_case__		:     ScoreSdeVeSchedulerState ,							snake_case__		:     int ,							snake_case__		:     Tuple = () ,							snake_case__		:     float = None   ):



   '''simple docstring'''




   UpperCAmelCase__			:				str												=       sampling_eps if sampling_eps is not None else self.config.sampling_eps

   UpperCAmelCase__			:				Optional[int]												=       jnp.linspace(1 ,							snake_case__ ,							snake_case__   )
   return state.replace(timesteps=snake_case__   )







 def        UpperCamelCase     (				self		:     str ,							snake_case__		:     ScoreSdeVeSchedulerState ,							snake_case__		:     int ,							snake_case__		:     float = None ,							snake_case__		:     float = None ,							snake_case__		:     float = None ,							):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       sigma_min if sigma_min is not None else self.config.sigma_min
   UpperCAmelCase__			:				Dict												=       sigma_max if sigma_max is not None else self.config.sigma_max
   UpperCAmelCase__			:				Any												=       sampling_eps if sampling_eps is not None else self.config.sampling_eps
   if state.timesteps is None:
     UpperCAmelCase__			:				Optional[int]												=       self.set_timesteps(snake_case__ ,							snake_case__ ,							snake_case__   )

   UpperCAmelCase__			:				int												=       jnp.exp(jnp.linspace(jnp.log(snake_case__   ) ,							jnp.log(snake_case__   ) ,							snake_case__   )   )
   UpperCAmelCase__			:				Optional[int]												=       jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]   )

   return state.replace(discrete_sigmas=snake_case__ ,							sigmas=snake_case__   )







 def        UpperCamelCase     (				self		:     List[str] ,							snake_case__		:     str ,							snake_case__		:     Tuple ,							snake_case__		:     Dict   ):



   '''simple docstring'''




   return jnp.where(timesteps == 0 ,							jnp.zeros_like(snake_case__   ) ,							state.discrete_sigmas[timesteps - 1]   )







 def        UpperCamelCase     (				self		:     Optional[int] ,							snake_case__		:     ScoreSdeVeSchedulerState ,							snake_case__		:     jnp.ndarray ,							snake_case__		:     int ,							snake_case__		:     jnp.ndarray ,							snake_case__		:     random.KeyArray ,							snake_case__		:     bool = True ,							):



   '''simple docstring'''




   if state.timesteps is None:
     raise ValueError(
         "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"   )

   UpperCAmelCase__			:				str												=       timestep * jnp.ones(
       sample.shape[0] ,							)
   UpperCAmelCase__			:				int												=       (timestep * (len(state.timesteps   ) - 1)).long()

   UpperCAmelCase__			:				Optional[int]												=       state.discrete_sigmas[timesteps]
   UpperCAmelCase__			:				str												=       self.get_adjacent_sigma(snake_case__ ,							snake_case__ ,							snake_case__   )
   UpperCAmelCase__			:				Optional[Any]												=       jnp.zeros_like(snake_case__   )
   UpperCAmelCase__			:				List[Any]												=       (sigma**2 - adjacent_sigma**2) ** 0.5

   # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
   # also equation 47 shows the analog from SDE models to ancestral sampling methods
   UpperCAmelCase__			:				Optional[Any]												=       diffusion.flatten()
   UpperCAmelCase__			:				List[str]												=       broadcast_to_shape_from_left(snake_case__ ,							sample.shape   )
   UpperCAmelCase__			:				Optional[Any]												=       drift - diffusion**2 * model_output

   #  equation 6: sample noise for the diffusion term of
   UpperCAmelCase__			:				List[str]												=       random.split(snake_case__ ,							num=1   )
   UpperCAmelCase__			:				Dict												=       random.normal(key=snake_case__ ,							shape=sample.shape   )
   UpperCAmelCase__			:				Optional[int]												=       sample - drift  # subtract because `dt` is a small negative timestep
   # TODO is the variable diffusion the correct scaling term for the noise?
   UpperCAmelCase__			:				int												=       prev_sample_mean + diffusion * noise  # add impact of diffusion field g

   if not return_dict:
     return (prev_sample, prev_sample_mean, state)

   return FlaxSdeVeOutput(prev_sample=snake_case__ ,							prev_sample_mean=snake_case__ ,							state=snake_case__   )







 def        UpperCamelCase     (				self		:     Any ,							snake_case__		:     ScoreSdeVeSchedulerState ,							snake_case__		:     jnp.ndarray ,							snake_case__		:     jnp.ndarray ,							snake_case__		:     random.KeyArray ,							snake_case__		:     bool = True ,							):



   '''simple docstring'''




   if state.timesteps is None:
     raise ValueError(
         "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"   )

   # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
   # sample noise for correction
   UpperCAmelCase__			:				str												=       random.split(snake_case__ ,							num=1   )
   UpperCAmelCase__			:				Tuple												=       random.normal(key=snake_case__ ,							shape=sample.shape   )

   # compute step size from the model_output, the noise, and the snr
   UpperCAmelCase__			:				Union[str, Any]												=       jnp.linalg.norm(snake_case__   )
   UpperCAmelCase__			:				List[str]												=       jnp.linalg.norm(snake_case__   )
   UpperCAmelCase__			:				Optional[Any]												=       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
   UpperCAmelCase__			:				str												=       step_size * jnp.ones(sample.shape[0]   )

   # compute corrected sample: model_output term and noise term
   UpperCAmelCase__			:				Union[str, Any]												=       step_size.flatten()
   UpperCAmelCase__			:				Optional[int]												=       broadcast_to_shape_from_left(snake_case__ ,							sample.shape   )
   UpperCAmelCase__			:				Tuple												=       sample + step_size * model_output
   UpperCAmelCase__			:				Any												=       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

   if not return_dict:
     return (prev_sample, state)

   return FlaxSdeVeOutput(prev_sample=snake_case__ ,							state=snake_case__   )







 def __len__(				self		:     List[str]   ):



   '''simple docstring'''




   return self.config.num_train_timesteps



