





"""simple docstring"""



import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
a_ =			logging.get_logger(__name__)







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  Optional[int]=False						)   ->Dict:



    '''simple docstring'''







    __A    :						Optional[int]						 =     []

    # fmt: off
    # stem:
    rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''')						)
    rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''')						)

    rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''')						)
    rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''')						)

    # backbone
    rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''')						)
    rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''')						)
    rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''')						)

    for stage_idx in range(len(config.backbone_config.depths						)						):
        for layer_idx in range(config.backbone_config.depths[stage_idx]						):
            rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")						)
            rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")						)
            rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")						)
            rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")						)
            rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")						)
            rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")						)
            rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")						)
            rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")						)
            rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")						)

        rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")						)
        rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")						)
        rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")						)

    # transformer encoder
    for i in range(config.num_hidden_layers						):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""")						)
        rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""")						)
        rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""")						)
        rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""")						)
        rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""")						)
        rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""")						)
        rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""")						)
        rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""")						)
        rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""")						)
        rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""")						)

    if base_model:
        # layernorm + pooler
        rename_keys.extend(
            [
                ('''norm.weight''', '''layernorm.weight'''),
                ('''norm.bias''', '''layernorm.bias'''),
                ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
                ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
            ]						)

        # if just the base model, we should remove "vit" from all keys that start with "vit"
        __A    :						Union[str, Any]						 =     [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''						) else pair for pair in rename_keys]
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ('''norm.weight''', '''vit.layernorm.weight'''),
                ('''norm.bias''', '''vit.layernorm.bias'''),
                ('''head.weight''', '''classifier.weight'''),
                ('''head.bias''', '''classifier.bias'''),
            ]						)
    # fmt: on

    return rename_keys







def 		__lowercase     (   snake_case_  :  Union[str, Any]				,snake_case_  :  Any				,snake_case_  :  Any=False						)   ->List[str]:



    '''simple docstring'''







    for i in range(config.num_hidden_layers						):
        if base_model:
            __A    :						List[str]						 =     ''''''
        else:
            __A    :						str						 =     '''vit.'''
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        __A    :						Tuple						 =     state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""						)
        __A    :						Dict						 =     state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""						)
        # next, add query, keys and values (in that order) to the state dict
        __A    :						str						 =     in_proj_weight[
            : config.hidden_size, :
        ]
        __A    :						List[str]						 =     in_proj_bias[: config.hidden_size]
        __A    :						str						 =     in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        __A    :						Dict						 =     in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        __A    :						List[str]						 =     in_proj_weight[
            -config.hidden_size :, :
        ]
        __A    :						List[Any]						 =     in_proj_bias[-config.hidden_size :]







def 		__lowercase     (   snake_case_  :  List[Any]						)   ->Optional[Any]:



    '''simple docstring'''







    __A    :						Optional[Any]						 =     ['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(snake_case_				,snake_case_						)







def 		__lowercase     (   snake_case_  :  str				,snake_case_  :  str				,snake_case_  :  Dict						)   ->List[str]:



    '''simple docstring'''







    __A    :						Union[str, Any]						 =     dct.pop(snake_case_						)
    __A    :						Union[str, Any]						 =     val







def 		__lowercase     (   )   ->Optional[Any]:



    '''simple docstring'''







    __A    :						Tuple						 =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __A    :						Union[str, Any]						 =     Image.open(requests.get(snake_case_				,stream=snake_case_						).raw						)
    return im







@torch.no_grad()
def 		__lowercase     (   snake_case_  :  List[Any]				,snake_case_  :  Optional[Any]				,snake_case_  :  Dict=False						)   ->List[str]:



    '''simple docstring'''







    __A    :						Dict						 =     BitConfig(
        global_padding='''same'''				,layer_type='''bottleneck'''				,depths=(3, 4, 9)				,out_features=['''stage3''']				,embedding_dynamic_padding=snake_case_				,)
    __A    :						Dict						 =     ViTHybridConfig(backbone_config=snake_case_				,image_size=384				,num_labels=1000						)
    __A    :						Optional[Any]						 =     False

    # load original model from timm
    __A    :						Dict						 =     timm.create_model(snake_case_				,pretrained=snake_case_						)
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    __A    :						int						 =     timm_model.state_dict()
    if base_model:
        remove_classification_head_(snake_case_						)
    __A    :						Any						 =     create_rename_keys(snake_case_				,snake_case_						)
    for src, dest in rename_keys:
        rename_key(snake_case_				,snake_case_				,snake_case_						)
    read_in_q_k_v(snake_case_				,snake_case_				,snake_case_						)

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

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        __A    :						Optional[int]						 =     ViTHybridModel(snake_case_						).eval()
    else:
        __A    :						Any						 =     ViTHybridForImageClassification(snake_case_						).eval()
    model.load_state_dict(snake_case_						)

    # create image processor
    __A    :						Dict						 =     create_transform(**resolve_data_config({}				,model=snake_case_						)						)
    __A    :						Dict						 =     transform.transforms

    __A    :						Dict						 =     {
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

    __A    :						Optional[Any]						 =     ViTHybridImageProcessor(
        do_resize=snake_case_				,size={'''shortest_edge''': timm_transforms[0].size}				,resample=pillow_resamplings[timm_transforms[0].interpolation.value]				,do_center_crop=snake_case_				,crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}				,do_normalize=snake_case_				,image_mean=timm_transforms[-1].mean.tolist()				,image_std=timm_transforms[-1].std.tolist()				,)

    __A    :						Optional[Any]						 =     prepare_img()
    __A    :						str						 =     transform(snake_case_						).unsqueeze(0						)
    __A    :						str						 =     processor(snake_case_				,return_tensors='''pt'''						).pixel_values

    # verify pixel values
    assert torch.allclose(snake_case_				,snake_case_						)

    # verify logits
    with torch.no_grad():
        __A    :						Dict						 =     model(snake_case_						)
        __A    :						Union[str, Any]						 =     outputs.logits

    print('''Predicted class:'''				,logits.argmax(-1						).item()						)
    if base_model:
        __A    :						List[str]						 =     timm_model.forward_features(snake_case_						)
        assert timm_pooled_output.shape == outputs.pooler_output.shape
        assert torch.allclose(snake_case_				,outputs.pooler_output				,atol=1e-3						)
    else:
        __A    :						Tuple						 =     timm_model(snake_case_						)
        assert timm_logits.shape == outputs.logits.shape
        assert torch.allclose(snake_case_				,outputs.logits				,atol=1e-3						)
    print('''Looks ok!'''						)

    if pytorch_dump_folder_path is not None:
        Path(snake_case_						).mkdir(exist_ok=snake_case_						)
        print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}"""						)
        model.save_pretrained(snake_case_						)
        print(F"""Saving processor to {pytorch_dump_folder_path}"""						)
        processor.save_pretrained(snake_case_						)

    if push_to_hub:
        print(F"""Pushing model and processor to the hub {vit_name}"""						)
        model.push_to_hub(F"""ybelkada/{vit_name}"""						)
        processor.push_to_hub(F"""ybelkada/{vit_name}"""						)


if __name__ == "__main__":
       a_ =			argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--vit_name""",
           default="""vit_base_r50_s16_384""",
           type=str,
           help="""Name of the hybrid ViT timm model you'd like to convert.""",
       )
       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 to upload the model to the HuggingFace hub."""
       )

       a_ =			parser.parse_args()
       convert_vit_checkpoint(args.vit_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 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     42

 # setable values
 _lowerCamelCase        =     42
 _lowerCamelCase        =     42
 _lowerCamelCase        =     None

 @classmethod
 def 					UpperCamelCase__(    cls					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     return cls(common=__lowerCamelCase					,						init_noise_sigma=__lowerCamelCase					,						timesteps=__lowerCamelCase  )






@dataclass
class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     42






class 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     [e.name for e in FlaxKarrasDiffusionSchedulers]

 _lowerCamelCase        =     42

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return True

 @register_to_config
 def __init__(    self					,						__lowerCamelCase = 1000					,						__lowerCamelCase = 0.0_0_0_1					,						__lowerCamelCase = 0.0_2					,						__lowerCamelCase = "linear"					,						__lowerCamelCase = None					,						__lowerCamelCase = "fixed_small"					,						__lowerCamelCase = True					,						__lowerCamelCase = "epsilon"					,						__lowerCamelCase = jnp.floataa					,						):



     '''simple docstring'''




     __A    :						Tuple						 =     dtype

 def 					UpperCamelCase__(    self					,						__lowerCamelCase = None  ):



     '''simple docstring'''




     if common is None:
         __A    :						Tuple						 =     CommonSchedulerState.create(self  )

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

     __A    :						Optional[int]						 =     jnp.arange(0					,						self.config.num_train_timesteps  ).round()[::-1]

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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = None  ):



     '''simple docstring'''




     return sample

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = ()  ):



     '''simple docstring'''




     __A    :						Optional[Any]						 =     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
     __A    :						Optional[Any]						 =     (jnp.arange(0					,						__lowerCamelCase  ) * step_ratio).round()[::-1]

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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase=None					,						__lowerCamelCase=None  ):



     '''simple docstring'''




     __A    :						int						 =     state.common.alphas_cumprod[t]
     __A    :						List[str]						 =     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
     __A    :						str						 =     (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

     if variance_type is None:
         __A    :						Dict						 =     self.config.variance_type

     # hacks - were probably added for training stability
     if variance_type == "fixed_small":
         __A    :						List[Any]						 =     jnp.clip(__lowerCamelCase					,						a_min=1e-2_0  )
     # for rl-diffuser https://arxiv.org/abs/2205.09991
     elif variance_type == "fixed_small_log":
         __A    :						Optional[Any]						 =     jnp.log(jnp.clip(__lowerCamelCase					,						a_min=1e-2_0  )  )
     elif variance_type == "fixed_large":
         __A    :						Tuple						 =     state.common.betas[t]
     elif variance_type == "fixed_large_log":
         # Glide max_log
         __A    :						Union[str, Any]						 =     jnp.log(state.common.betas[t]  )
     elif variance_type == "learned":
         return predicted_variance
     elif variance_type == "learned_range":
         __A    :						Optional[Any]						 =     variance
         __A    :						Optional[Any]						 =     state.common.betas[t]
         __A    :						Any						 =     (predicted_variance + 1) / 2
         __A    :						Union[str, Any]						 =     frac * max_log + (1 - frac) * min_log

     return variance

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = None					,						__lowerCamelCase = True					,						):



     '''simple docstring'''




     __A    :						Optional[int]						 =     timestep

     if key is None:
         __A    :						List[Any]						 =     jax.random.PRNGKey(0  )

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

     # 1. compute alphas, betas
     __A    :						Dict						 =     state.common.alphas_cumprod[t]
     __A    :						int						 =     jnp.where(t > 0					,						state.common.alphas_cumprod[t - 1]					,						jnp.array(1.0					,						dtype=self.dtype  )  )
     __A    :						Tuple						 =     1 - alpha_prod_t
     __A    :						Optional[int]						 =     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":
         __A    :						Optional[Any]						 =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif self.config.prediction_type == "sample":
         __A    :						Any						 =     model_output
     elif self.config.prediction_type == "v_prediction":
         __A    :						str						 =     (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:
         __A    :						str						 =     jnp.clip(__lowerCamelCase					,						-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
     __A    :						Optional[Any]						 =     (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
     __A    :						Union[str, 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
     __A    :						Optional[Any]						 =     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

     # 6. Add noise
     def random_variance():
         __A    :						List[Any]						 =     jax.random.split(__lowerCamelCase					,						num=1  )
         __A    :						List[str]						 =     jax.random.normal(__lowerCamelCase					,						shape=model_output.shape					,						dtype=self.dtype  )
         return (self._get_variance(__lowerCamelCase					,						__lowerCamelCase					,						predicted_variance=__lowerCamelCase  ) ** 0.5) * noise

     __A    :						Optional[Any]						 =     jnp.where(t > 0					,						random_variance()					,						jnp.zeros(model_output.shape					,						dtype=self.dtype  )  )

     __A    :						Any						 =     pred_prev_sample + variance

     if not return_dict:
         return (pred_prev_sample, state)

     return FlaxDDPMSchedulerOutput(prev_sample=__lowerCamelCase					,						state=__lowerCamelCase  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




     return add_noise_common(state.common					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




     return get_velocity_common(state.common					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  )







 def __len__(    self  ):



     '''simple docstring'''




     return self.config.num_train_timesteps




