
"""simple docstring"""




import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)



def 							a__ (				lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       Union[str, Any]		):






     '''simple docstring'''
     UpperCAmelCase__							: Any 							=						XCLIPTextConfig()

     # derive patch size from model name
     UpperCAmelCase__							: List[Any] 							=						model_name.find("patch"		)
     UpperCAmelCase__							: Optional[int] 							=						int(model_name[start_idx + len("patch"		) : start_idx + len("patch"		) + 2]		)
     UpperCAmelCase__							: Optional[Any] 							=						XCLIPVisionConfig(patch_size=lowerCAmelCase       ,    num_frames=lowerCAmelCase		)

     if "large" in model_name:
          UpperCAmelCase__							: Any 							=						768
          UpperCAmelCase__							: int 							=						3072
          UpperCAmelCase__							: Dict 							=						12

          UpperCAmelCase__							: Optional[int] 							=						1024
          UpperCAmelCase__							: Optional[Any] 							=						4096
          UpperCAmelCase__							: Optional[Any] 							=						16
          UpperCAmelCase__							: Tuple 							=						24
          UpperCAmelCase__							: List[Any] 							=						768
          UpperCAmelCase__							: int 							=						3072

     if model_name == "xclip-large-patch14-16-frames":
          UpperCAmelCase__							: str 							=						336

     UpperCAmelCase__							: int 							=						XCLIPConfig.from_text_vision_configs(lowerCAmelCase       ,    lowerCAmelCase		)

     if "large" in model_name:
          UpperCAmelCase__							: Optional[Any] 							=						768

     return config



def 							a__ (				lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     # text encoder
     if name == "token_embedding.weight":
          UpperCAmelCase__							: Optional[Any] 							=						name.replace("token_embedding.weight"       ,    "text_model.embeddings.token_embedding.weight"		)
     if name == "positional_embedding":
          UpperCAmelCase__							: Tuple 							=						name.replace("positional_embedding"       ,    "text_model.embeddings.position_embedding.weight"		)
     if "ln_1" in name:
          UpperCAmelCase__							: Any 							=						name.replace("ln_1"       ,    "layer_norm1"		)
     if "ln_2" in name:
          UpperCAmelCase__							: Dict 							=						name.replace("ln_2"       ,    "layer_norm2"		)
     if "c_fc" in name:
          UpperCAmelCase__							: int 							=						name.replace("c_fc"       ,    "fc1"		)
     if "c_proj" in name:
          UpperCAmelCase__							: int 							=						name.replace("c_proj"       ,    "fc2"		)
     if name.startswith("transformer.resblocks"		):
          UpperCAmelCase__							: List[str] 							=						name.replace("transformer.resblocks"       ,    "text_model.encoder.layers"		)
     if "attn.out_proj" in name and "message" not in name:
          UpperCAmelCase__							: str 							=						name.replace("attn.out_proj"       ,    "self_attn.out_proj"		)
     if "ln_final" in name:
          UpperCAmelCase__							: Tuple 							=						name.replace("ln_final"       ,    "text_model.final_layer_norm"		)
     # visual encoder
     if name == "visual.class_embedding":
          UpperCAmelCase__							: Optional[Any] 							=						name.replace("visual.class_embedding"       ,    "vision_model.embeddings.class_embedding"		)
     if name == "visual.positional_embedding":
          UpperCAmelCase__							: Any 							=						name.replace("visual.positional_embedding"       ,    "vision_model.embeddings.position_embedding.weight"		)
     if name.startswith("visual.transformer.resblocks"		):
          UpperCAmelCase__							: Optional[Any] 							=						name.replace("visual.transformer.resblocks"       ,    "vision_model.encoder.layers"		)
     if "visual.conv1" in name:
          UpperCAmelCase__							: Union[str, Any] 							=						name.replace("visual.conv1"       ,    "vision_model.embeddings.patch_embedding"		)
     if "visual.ln_pre" in name:
          UpperCAmelCase__							: Optional[Any] 							=						name.replace("visual.ln_pre"       ,    "vision_model.pre_layernorm"		)
     if "visual.ln_post" in name:
          UpperCAmelCase__							: Union[str, Any] 							=						name.replace("visual.ln_post"       ,    "vision_model.post_layernorm"		)
     if "visual.proj" in name:
          UpperCAmelCase__							: List[Any] 							=						name.replace("visual.proj"       ,    "visual_projection.weight"		)
     if "text_projection" in name:
          UpperCAmelCase__							: int 							=						name.replace("text_projection"       ,    "text_projection.weight"		)
     # things on top
     if "prompts_visual_proj" in name:
          UpperCAmelCase__							: Tuple 							=						name.replace("prompts_visual_proj"       ,    "prompts_visual_projection"		)
     if "prompts_visual_ln" in name:
          UpperCAmelCase__							: List[str] 							=						name.replace("prompts_visual_ln"       ,    "prompts_visual_layernorm"		)
     # mit
     if name == "mit.positional_embedding":
          UpperCAmelCase__							: Optional[int] 							=						name.replace("positional"       ,    "position"		)
     if name.startswith("mit.resblocks"		):
          UpperCAmelCase__							: List[str] 							=						name.replace("mit.resblocks"       ,    "mit.encoder.layers"		)
     # prompts generator
     if name.startswith("prompts_generator.norm"		):
          UpperCAmelCase__							: int 							=						name.replace("prompts_generator.norm"       ,    "prompts_generator.layernorm"		)

     return name



def 							a__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       Tuple		):






     '''simple docstring'''
     for key in orig_state_dict.copy().keys():
          UpperCAmelCase__							: Union[str, Any] 							=						orig_state_dict.pop(lowerCAmelCase		)

          if "attn.in_proj" in key:
               UpperCAmelCase__							: Optional[Any] 							=						key.split("."		)
               if key.startswith("visual"		):
                    UpperCAmelCase__							: Union[str, Any] 							=						key_split[3]
                    UpperCAmelCase__							: int 							=						config.vision_config.hidden_size
                    if "message_attn" in key:
                         if "weight" in key:
                              UpperCAmelCase__							: Dict 							=						val[
                                  :dim, :
                              ]
                              UpperCAmelCase__							: Optional[int] 							=						val[
                                  dim : dim * 2, :
                              ]
                              UpperCAmelCase__							: Any 							=						val[
                                  -dim:, :
                              ]
                         else:
                              UpperCAmelCase__							: Optional[int] 							=						val[
                                  :dim
                              ]
                              UpperCAmelCase__							: List[Any] 							=						val[
                                  dim : dim * 2
                              ]
                              UpperCAmelCase__							: int 							=						val[
                                  -dim:
                              ]
                    else:
                         if "weight" in key:
                              UpperCAmelCase__							: int 							=						val[
                                  :dim, :
                              ]
                              UpperCAmelCase__							: Optional[Any] 							=						val[
                                  dim : dim * 2, :
                              ]
                              UpperCAmelCase__							: Union[str, Any] 							=						val[
                                  -dim:, :
                              ]
                         else:
                              UpperCAmelCase__							: List[Any] 							=						val[:dim]
                              UpperCAmelCase__							: List[Any] 							=						val[
                                  dim : dim * 2
                              ]
                              UpperCAmelCase__							: Tuple 							=						val[-dim:]
               elif key.startswith("mit"		):
                    UpperCAmelCase__							: Optional[Any] 							=						key_split[2]
                    UpperCAmelCase__							: str 							=						config.vision_config.mit_hidden_size
                    if "weight" in key:
                         UpperCAmelCase__							: Optional[Any] 							=						val[:dim, :]
                         UpperCAmelCase__							: List[Any] 							=						val[dim : dim * 2, :]
                         UpperCAmelCase__							: Tuple 							=						val[-dim:, :]
                    else:
                         UpperCAmelCase__							: Optional[int] 							=						val[:dim]
                         UpperCAmelCase__							: Any 							=						val[dim : dim * 2]
                         UpperCAmelCase__							: Tuple 							=						val[-dim:]
               else:
                    UpperCAmelCase__							: Any 							=						key_split[2]
                    UpperCAmelCase__							: List[str] 							=						config.text_config.hidden_size
                    if "weight" in key:
                         UpperCAmelCase__							: List[Any] 							=						val[:dim, :]
                         UpperCAmelCase__							: List[str] 							=						val[
                             dim : dim * 2, :
                         ]
                         UpperCAmelCase__							: List[Any] 							=						val[-dim:, :]
                    else:
                         UpperCAmelCase__							: List[str] 							=						val[:dim]
                         UpperCAmelCase__							: List[str] 							=						val[
                             dim : dim * 2
                         ]
                         UpperCAmelCase__							: List[Any] 							=						val[-dim:]
          else:
               UpperCAmelCase__							: List[str] 							=						rename_key(lowerCAmelCase		)
               if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
                    UpperCAmelCase__							: Optional[int] 							=						val.T
               UpperCAmelCase__							: List[str] 							=						val

     return orig_state_dict



def 							a__ (				lowerCAmelCase	:       List[str]		):






     '''simple docstring'''
     if num_frames == 8:
          UpperCAmelCase__							: int 							=						"eating_spaghetti_8_frames.npy"
     elif num_frames == 16:
          UpperCAmelCase__							: Optional[int] 							=						"eating_spaghetti.npy"
     elif num_frames == 32:
          UpperCAmelCase__							: Optional[int] 							=						"eating_spaghetti_32_frames.npy"
     UpperCAmelCase__							: Any 							=						hf_hub_download(
         repo_id="hf-internal-testing/spaghetti-video"       ,    filename=lowerCAmelCase       ,    repo_type="dataset"       ,    )
     UpperCAmelCase__							: Union[str, Any] 							=						np.load(lowerCAmelCase		)
     return list(lowerCAmelCase		)



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       str=None       ,    lowerCAmelCase	:       Any=False		):






     '''simple docstring'''
     UpperCAmelCase__							: Optional[int] 							=						{
         # fully supervised kinetics-400 checkpoints
         "xclip-base-patch32": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth",
         "xclip-base-patch32-16-frames": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"
         ),
         "xclip-base-patch16": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth",
         "xclip-base-patch16-16-frames": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"
         ),
         "xclip-large-patch14": "https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb",
         "xclip-large-patch14-16-frames": "https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f",
         # fully supervised kinetics-600 checkpoints
         "xclip-base-patch16-kinetics-600": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"
         ),
         "xclip-base-patch16-kinetics-600-16-frames": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"
         ),
         "xclip-large-patch14-kinetics-600": "https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be",
         # few shot
         "xclip-base-patch16-hmdb-2-shot": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"
         ),
         "xclip-base-patch16-hmdb-4-shot": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"
         ),
         "xclip-base-patch16-hmdb-8-shot": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"
         ),
         "xclip-base-patch16-hmdb-16-shot": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"
         ),
         "xclip-base-patch16-ucf-2-shot": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"
         ),
         "xclip-base-patch16-ucf-4-shot": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"
         ),
         "xclip-base-patch16-ucf-8-shot": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"
         ),
         "xclip-base-patch16-ucf-16-shot": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"
         ),
         # zero shot
         "xclip-base-patch16-zero-shot": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth",
     }

     UpperCAmelCase__							: Dict 							=						model_to_url[model_name]
     UpperCAmelCase__							: int 							=						8
     if "16-frames" in model_name:
          UpperCAmelCase__							: Optional[Any] 							=						16
     elif "shot" in model_name:
          UpperCAmelCase__							: Union[str, Any] 							=						32

     UpperCAmelCase__							: str 							=						get_xclip_config(lowerCAmelCase       ,    lowerCAmelCase		)
     UpperCAmelCase__							: List[str] 							=						XCLIPModel(lowerCAmelCase		)
     model.eval()

     if "drive" in checkpoint_url:
          UpperCAmelCase__							: int 							=						"pytorch_model.bin"
          gdown.cached_download(lowerCAmelCase       ,    lowerCAmelCase       ,    quiet=lowerCAmelCase		)
          UpperCAmelCase__							: Any 							=						torch.load(lowerCAmelCase       ,    map_location="cpu"		)["model"]
     else:
          UpperCAmelCase__							: Tuple 							=						torch.hub.load_state_dict_from_url(lowerCAmelCase		)["model"]

     UpperCAmelCase__							: Any 							=						convert_state_dict(lowerCAmelCase       ,    lowerCAmelCase		)

     UpperCAmelCase__							: Tuple 							=						XCLIPModel(lowerCAmelCase		)
     UpperCAmelCase__							: Union[str, Any] 							=						model.load_state_dict(lowerCAmelCase       ,    strict=lowerCAmelCase		)
     assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
     model.eval()

     UpperCAmelCase__							: Dict 							=						336 if model_name == "xclip-large-patch14-16-frames" else 224
     UpperCAmelCase__							: str 							=						VideoMAEImageProcessor(size=lowerCAmelCase		)
     UpperCAmelCase__							: int 							=						CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"		)
     UpperCAmelCase__							: int 							=						CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32"		)
     UpperCAmelCase__							: Dict 							=						XCLIPProcessor(image_processor=lowerCAmelCase       ,    tokenizer=lowerCAmelCase		)

     UpperCAmelCase__							: Optional[int] 							=						prepare_video(lowerCAmelCase		)
     UpperCAmelCase__							: Dict 							=						processor(
         text=["playing sports", "eating spaghetti", "go shopping"]       ,    videos=lowerCAmelCase       ,    return_tensors="pt"       ,    padding=lowerCAmelCase		)

     print("Shape of pixel values:"       ,    inputs.pixel_values.shape		)

     with torch.no_grad():
          UpperCAmelCase__							: Union[str, Any] 							=						model(**lowerCAmelCase		)

     # Verify outputs
     UpperCAmelCase__							: Union[str, Any] 							=						outputs.logits_per_video
     UpperCAmelCase__							: List[str] 							=						logits_per_video.softmax(dim=1		)
     print("Probs:"       ,    lowerCAmelCase		)
     # kinetics-400
     if model_name == "xclip-base-patch32":
          UpperCAmelCase__							: Dict 							=						torch.tensor([[0.0019, 0.9951, 0.0030]]		)
     elif model_name == "xclip-base-patch32-16-frames":
          UpperCAmelCase__							: Dict 							=						torch.tensor([[7.0999E-04, 9.9883E-01, 4.5580E-04]]		)
     elif model_name == "xclip-base-patch16":
          UpperCAmelCase__							: int 							=						torch.tensor([[0.0083, 0.9681, 0.0236]]		)
     elif model_name == "xclip-base-patch16-16-frames":
          UpperCAmelCase__							: Any 							=						torch.tensor([[7.6937E-04, 9.9728E-01, 1.9473E-03]]		)
     elif model_name == "xclip-large-patch14":
          UpperCAmelCase__							: Optional[int] 							=						torch.tensor([[0.0062, 0.9864, 0.0075]]		)
     elif model_name == "xclip-large-patch14-16-frames":
          UpperCAmelCase__							: Tuple 							=						torch.tensor([[3.3877E-04, 9.9937E-01, 2.8888E-04]]		)
     # kinetics-600
     elif model_name == "xclip-base-patch16-kinetics-600":
          UpperCAmelCase__							: Optional[int] 							=						torch.tensor([[0.0555, 0.8914, 0.0531]]		)
     elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
          UpperCAmelCase__							: str 							=						torch.tensor([[3.8554E-04, 9.9929E-01, 3.2754E-04]]		)
     elif model_name == "xclip-large-patch14-kinetics-600":
          UpperCAmelCase__							: List[Any] 							=						torch.tensor([[0.0036, 0.9920, 0.0045]]		)
     # few shot
     elif model_name == "xclip-base-patch16-hmdb-2-shot":
          UpperCAmelCase__							: int 							=						torch.tensor([[7.1890E-06, 9.9994E-01, 5.6559E-05]]		)
     elif model_name == "xclip-base-patch16-hmdb-4-shot":
          UpperCAmelCase__							: List[str] 							=						torch.tensor([[1.0320E-05, 9.9993E-01, 6.2435E-05]]		)
     elif model_name == "xclip-base-patch16-hmdb-8-shot":
          UpperCAmelCase__							: Dict 							=						torch.tensor([[4.1377E-06, 9.9990E-01, 9.8386E-05]]		)
     elif model_name == "xclip-base-patch16-hmdb-16-shot":
          UpperCAmelCase__							: Optional[int] 							=						torch.tensor([[4.1347E-05, 9.9962E-01, 3.3411E-04]]		)
     elif model_name == "xclip-base-patch16-ucf-2-shot":
          UpperCAmelCase__							: Tuple 							=						torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]		)
     elif model_name == "xclip-base-patch16-ucf-4-shot":
          UpperCAmelCase__							: Tuple 							=						torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]		)
     elif model_name == "xclip-base-patch16-ucf-8-shot":
          UpperCAmelCase__							: Any 							=						torch.tensor([[0.0027, 0.9904, 0.0070]]		)
     elif model_name == "xclip-base-patch16-ucf-16-shot":
          UpperCAmelCase__							: Optional[int] 							=						torch.tensor([[9.8219E-04, 9.9593E-01, 3.0863E-03]]		)
     # zero shot
     elif model_name == "xclip-base-patch16-zero-shot":
          UpperCAmelCase__							: Dict 							=						torch.tensor([[3.5082E-04, 9.9785E-01, 1.7966E-03]]		)
     else:
          raise ValueError(F"Model name {model_name} not supported"		)
     assert torch.allclose(lowerCAmelCase       ,    lowerCAmelCase       ,    atol=1E-3		)
     print("Looks ok!"		)

     if pytorch_dump_folder_path is not None:
          print(F"Saving model {model_name} to {pytorch_dump_folder_path}"		)
          model.save_pretrained(lowerCAmelCase		)

     if push_to_hub:
          print("Pushing model, processor and slow tokenizer files to the hub..."		)
          model.push_to_hub(lowerCAmelCase       ,    organization="nielsr"		)
          processor.push_to_hub(lowerCAmelCase       ,    organization="nielsr"		)
          slow_tokenizer.push_to_hub(lowerCAmelCase       ,    organization="nielsr"		)


if __name__ == "__main__":
    A__      :	Union[str, Any]          =    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--model_name""",
        default="""xclip-base-patch32""",
        type=str,
        help="""Name of the model.""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
    )
    parser.add_argument(
        """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
    )

    A__      :	Optional[int]          =    parser.parse_args()
    convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""




# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)







@flax.struct.dataclass
class   _lowercase						:


     '''simple docstring'''





     _A								=							42

     # setable values
     _A								=							42
     _A								=							42
     _A								=							None



     @classmethod
     def  lowerCAmelCase__       (      cls ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Union[str, Any]:
          return cls(common=__UpperCamelCase ,			init_noise_sigma=__UpperCamelCase ,			timesteps=__UpperCamelCase  )







@dataclass
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							42







class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							[e.name for e in FlaxKarrasDiffusionSchedulers]

     _A								=							42



     @property
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          return True



     @register_to_config
     def __init__(      self ,			__UpperCamelCase = 10_00 ,			__UpperCamelCase = 0.0001 ,			__UpperCamelCase = 0.02 ,			__UpperCamelCase = "linear" ,			__UpperCamelCase = None ,			__UpperCamelCase = "fixed_small" ,			__UpperCamelCase = True ,			__UpperCamelCase = "epsilon" ,			__UpperCamelCase = jnp.floataa ,			)->					List[str]:
          UpperCAmelCase__							: int 							=						dtype



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase = None  )->					DDPMSchedulerState:
          if common is None:
               UpperCAmelCase__							: int 							=						CommonSchedulerState.create(self  )

          # standard deviation of the initial noise distribution
          UpperCAmelCase__							: Tuple 							=						jnp.array(1.0 ,			dtype=self.dtype  )

          UpperCAmelCase__							: Tuple 							=						jnp.arange(0 ,			self.config.num_train_timesteps  ).round()[::-1]

          return DDPMSchedulerState.create(
              common=__UpperCamelCase ,			init_noise_sigma=__UpperCamelCase ,			timesteps=__UpperCamelCase ,			)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					jnp.ndarray:
          return sample



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = ()  )->					DDPMSchedulerState:
          UpperCAmelCase__							: Dict 							=						self.config.num_train_timesteps // num_inference_steps
          # creates integer timesteps by multiplying by ratio
          # rounding to avoid issues when num_inference_step is power of 3
          UpperCAmelCase__							: Optional[int] 							=						(jnp.arange(0 ,			__UpperCamelCase  ) * step_ratio).round()[::-1]

          return state.replace(
              num_inference_steps=__UpperCamelCase ,			timesteps=__UpperCamelCase ,			)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase=None ,			__UpperCamelCase=None  )->					Union[str, Any]:
          UpperCAmelCase__							: Optional[Any] 							=						state.common.alphas_cumprod[t]
          UpperCAmelCase__							: int 							=						jnp.where(t > 0 ,			state.common.alphas_cumprod[t - 1] ,			jnp.array(1.0 ,			dtype=self.dtype  )  )

          # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
          # and sample from it to get previous sample
          # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
          UpperCAmelCase__							: Any 							=						(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

          if variance_type is None:
               UpperCAmelCase__							: Union[str, Any] 							=						self.config.variance_type

          # hacks - were probably added for training stability
          if variance_type == "fixed_small":
               UpperCAmelCase__							: Dict 							=						jnp.clip(__UpperCamelCase ,			a_min=1E-20  )
          # for rl-diffuser https://arxiv.org/abs/2205.09991
          elif variance_type == "fixed_small_log":
               UpperCAmelCase__							: Tuple 							=						jnp.log(jnp.clip(__UpperCamelCase ,			a_min=1E-20  )  )
          elif variance_type == "fixed_large":
               UpperCAmelCase__							: Union[str, Any] 							=						state.common.betas[t]
          elif variance_type == "fixed_large_log":
               # Glide max_log
               UpperCAmelCase__							: Optional[int] 							=						jnp.log(state.common.betas[t]  )
          elif variance_type == "learned":
               return predicted_variance
          elif variance_type == "learned_range":
               UpperCAmelCase__							: List[str] 							=						variance
               UpperCAmelCase__							: Union[str, Any] 							=						state.common.betas[t]
               UpperCAmelCase__							: Optional[int] 							=						(predicted_variance + 1) / 2
               UpperCAmelCase__							: Any 							=						frac * max_log + (1 - frac) * min_log

          return variance



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = True ,			)->					Union[FlaxDDPMSchedulerOutput, Tuple]:
          UpperCAmelCase__							: List[str] 							=						timestep

          if key is None:
               UpperCAmelCase__							: int 							=						jax.random.PRNGKey(0  )

          if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
               UpperCAmelCase__							,	UpperCAmelCase__							: List[str] 							=						jnp.split(__UpperCamelCase ,			sample.shape[1] ,			axis=1  )
          else:
               UpperCAmelCase__							: Optional[Any] 							=						None

          # 1. compute alphas, betas
          UpperCAmelCase__							: Union[str, Any] 							=						state.common.alphas_cumprod[t]
          UpperCAmelCase__							: Tuple 							=						jnp.where(t > 0 ,			state.common.alphas_cumprod[t - 1] ,			jnp.array(1.0 ,			dtype=self.dtype  )  )
          UpperCAmelCase__							: Union[str, Any] 							=						1 - alpha_prod_t
          UpperCAmelCase__							: Tuple 							=						1 - alpha_prod_t_prev

          # 2. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
          if self.config.prediction_type == "epsilon":
               UpperCAmelCase__							: List[Any] 							=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          elif self.config.prediction_type == "sample":
               UpperCAmelCase__							: Any 							=						model_output
          elif self.config.prediction_type == "v_prediction":
               UpperCAmelCase__							: Union[str, Any] 							=						(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
          else:
               raise ValueError(
                   F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
                   " for the FlaxDDPMScheduler."  )

          # 3. Clip "predicted x_0"
          if self.config.clip_sample:
               UpperCAmelCase__							: List[Any] 							=						jnp.clip(__UpperCamelCase ,			-1 ,			1  )

          # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
          # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
          UpperCAmelCase__							: List[str] 							=						(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
          UpperCAmelCase__							: List[Any] 							=						state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

          # 5. Compute predicted previous sample µ_t
          # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
          UpperCAmelCase__							: Tuple 							=						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

          # 6. Add noise
          def random_variance():
               UpperCAmelCase__							: Any 							=						jax.random.split(__UpperCamelCase ,			num=1  )
               UpperCAmelCase__							: int 							=						jax.random.normal(__UpperCamelCase ,			shape=model_output.shape ,			dtype=self.dtype  )
               return (self._get_variance(__UpperCamelCase ,			__UpperCamelCase ,			predicted_variance=__UpperCamelCase  ) ** 0.5) * noise

          UpperCAmelCase__							: Dict 							=						jnp.where(t > 0 ,			random_variance() ,			jnp.zeros(model_output.shape ,			dtype=self.dtype  )  )

          UpperCAmelCase__							: Any 							=						pred_prev_sample + variance

          if not return_dict:
               return (pred_prev_sample, state)

          return FlaxDDPMSchedulerOutput(prev_sample=__UpperCamelCase ,			state=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					jnp.ndarray:
          return add_noise_common(state.common ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					jnp.ndarray:
          return get_velocity_common(state.common ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def __len__(      self  )->					Tuple:
          return self.config.num_train_timesteps



