


'''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 					_A						:



   '''simple docstring'''






   _lowercase    						= None
   _lowercase    						= None
   _lowercase    						= None




   @classmethod
   def   __lowerCAmelCase						(	cls	:					Union[str, Any]						)->	List[str]:
          return cls()







@dataclass
class 					_A						(						UpperCamelCase							):



   '''simple docstring'''






   _lowercase    						= 42
   _lowercase    						= 42
   _lowercase    						= None







class 					_A						(						UpperCamelCase    ,  UpperCamelCase							):



   '''simple docstring'''






   @property
   def   __lowerCAmelCase						(	self	:					str						)->	str:
          return True




   @register_to_config
   def __init__(	self	:					List[Any]					, lowerCamelCase	:					int = 2_000					, lowerCamelCase	:					float = 0.15					, lowerCamelCase	:					float = 0.01					, lowerCamelCase	:					float = 1_348.0					, lowerCamelCase	:					float = 1e-5					, lowerCamelCase	:					int = 1					, )->	Any:
          pass




   def   __lowerCAmelCase						(	self	:					Dict						)->	Tuple:
          snake_case__					:	Optional[Any]          =					ScoreSdeVeSchedulerState.create()
          return self.set_sigmas(
              lowerCamelCase					, self.config.num_train_timesteps					, self.config.sigma_min					, self.config.sigma_max					, self.config.sampling_eps					, )




   def   __lowerCAmelCase						(	self	:					Any					, lowerCamelCase	:					ScoreSdeVeSchedulerState					, lowerCamelCase	:					int					, lowerCamelCase	:					Tuple = ()					, lowerCamelCase	:					float = None						)->	ScoreSdeVeSchedulerState:
          snake_case__					:	Dict          =					sampling_eps if sampling_eps is not None else self.config.sampling_eps

          snake_case__					:	int          =					jnp.linspace(1					, lowerCamelCase					, lowerCamelCase						)
          return state.replace(timesteps=lowerCamelCase						)




   def   __lowerCAmelCase						(	self	:					int					, lowerCamelCase	:					ScoreSdeVeSchedulerState					, lowerCamelCase	:					int					, lowerCamelCase	:					float = None					, lowerCamelCase	:					float = None					, lowerCamelCase	:					float = None					, )->	ScoreSdeVeSchedulerState:
          snake_case__					:	Dict          =					sigma_min if sigma_min is not None else self.config.sigma_min
          snake_case__					:	Tuple          =					sigma_max if sigma_max is not None else self.config.sigma_max
          snake_case__					:	Dict          =					sampling_eps if sampling_eps is not None else self.config.sampling_eps
          if state.timesteps is None:
                 snake_case__					:	Optional[Any]          =					self.set_timesteps(lowerCamelCase					, lowerCamelCase					, lowerCamelCase						)

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

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




   def   __lowerCAmelCase						(	self	:					str					, lowerCamelCase	:					Any					, lowerCamelCase	:					Tuple					, lowerCamelCase	:					Tuple						)->	Tuple:
          return jnp.where(timesteps == 0					, jnp.zeros_like(lowerCamelCase						)					, state.discrete_sigmas[timesteps - 1]						)




   def   __lowerCAmelCase						(	self	:					List[Any]					, lowerCamelCase	:					ScoreSdeVeSchedulerState					, lowerCamelCase	:					jnp.ndarray					, lowerCamelCase	:					int					, lowerCamelCase	:					jnp.ndarray					, lowerCamelCase	:					random.KeyArray					, lowerCamelCase	:					bool = True					, )->	Union[FlaxSdeVeOutput, Tuple]:
          if state.timesteps is None:
                 raise ValueError(
                     """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""						)

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

          snake_case__					:	Union[str, Any]          =					state.discrete_sigmas[timesteps]
          snake_case__					:	str          =					self.get_adjacent_sigma(lowerCamelCase					, lowerCamelCase					, lowerCamelCase						)
          snake_case__					:	Optional[Any]          =					jnp.zeros_like(lowerCamelCase						)
          snake_case__					:	Union[str, 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
          snake_case__					:	Optional[int]          =					diffusion.flatten()
          snake_case__					:	Optional[Any]          =					broadcast_to_shape_from_left(lowerCamelCase					, sample.shape						)
          snake_case__					:	Tuple          =					drift - diffusion**2 * model_output

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




   def   __lowerCAmelCase						(	self	:					str					, lowerCamelCase	:					ScoreSdeVeSchedulerState					, lowerCamelCase	:					jnp.ndarray					, lowerCamelCase	:					jnp.ndarray					, lowerCamelCase	:					random.KeyArray					, lowerCamelCase	:					bool = True					, )->	Union[FlaxSdeVeOutput, Tuple]:
          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
          snake_case__					:	List[str]          =					random.split(lowerCamelCase					, num=1						)
          snake_case__					:	List[str]          =					random.normal(key=lowerCamelCase					, shape=sample.shape						)

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

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

          if not return_dict:
                 return (prev_sample, state)

          return FlaxSdeVeOutput(prev_sample=lowerCamelCase					, state=lowerCamelCase						)




   def __len__(	self	:					Tuple						)->	List[str]:
          return self.config.num_train_timesteps








'''simple docstring'''
import argparse
import json

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

from transformers import TimesformerConfig, TimesformerForVideoClassification, VideoMAEImageProcessor



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Dict          =					TimesformerConfig()

       if "large" in model_name:
              snake_case__					:	int          =					96

       if "hr" in model_name:
              snake_case__					:	int          =					16
              snake_case__					:	int          =					448

       snake_case__					:	int          =					"""huggingface/label-files"""
       if "k400" in model_name:
              snake_case__					:	List[Any]          =					400
              snake_case__					:	List[str]          =					"""kinetics400-id2label.json"""
       elif "k600" in model_name:
              snake_case__					:	Optional[int]          =					600
              snake_case__					:	int          =					"""kinetics600-id2label.json"""
       elif "ssv2" in model_name:
              snake_case__					:	Any          =					174
              snake_case__					:	Union[str, Any]          =					"""something-something-v2-id2label.json"""
       else:
              raise ValueError("""Model name should either contain 'k400', 'k600' or 'ssv2'."""     )
       snake_case__					:	List[str]          =					json.load(open(hf_hub_download(UpperCAmelCase    ,       UpperCAmelCase    ,       repo_type="""dataset"""     )    ,       """r"""     )     )
       snake_case__					:	Tuple          =					{int(UpperCAmelCase     ): v for k, v in idalabel.items()}
       snake_case__					:	int          =					idalabel
       snake_case__					:	Optional[int]          =					{v: k for k, v in idalabel.items()}

       return config



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       if "encoder." in name:
              snake_case__					:	Union[str, Any]          =					name.replace("""encoder."""    ,       """"""     )
       if "cls_token" in name:
              snake_case__					:	Any          =					name.replace("""cls_token"""    ,       """timesformer.embeddings.cls_token"""     )
       if "pos_embed" in name:
              snake_case__					:	Tuple          =					name.replace("""pos_embed"""    ,       """timesformer.embeddings.position_embeddings"""     )
       if "time_embed" in name:
              snake_case__					:	Optional[int]          =					name.replace("""time_embed"""    ,       """timesformer.embeddings.time_embeddings"""     )
       if "patch_embed.proj" in name:
              snake_case__					:	Optional[int]          =					name.replace("""patch_embed.proj"""    ,       """timesformer.embeddings.patch_embeddings.projection"""     )
       if "patch_embed.norm" in name:
              snake_case__					:	Tuple          =					name.replace("""patch_embed.norm"""    ,       """timesformer.embeddings.norm"""     )
       if "blocks" in name:
              snake_case__					:	Optional[int]          =					name.replace("""blocks"""    ,       """timesformer.encoder.layer"""     )
       if "attn.proj" in name:
              snake_case__					:	Optional[int]          =					name.replace("""attn.proj"""    ,       """attention.output.dense"""     )
       if "attn" in name and "bias" not in name and "temporal" not in name:
              snake_case__					:	Any          =					name.replace("""attn"""    ,       """attention.self"""     )
       if "attn" in name and "temporal" not in name:
              snake_case__					:	Optional[Any]          =					name.replace("""attn"""    ,       """attention.attention"""     )
       if "temporal_norm1" in name:
              snake_case__					:	Optional[int]          =					name.replace("""temporal_norm1"""    ,       """temporal_layernorm"""     )
       if "temporal_attn.proj" in name:
              snake_case__					:	Any          =					name.replace("""temporal_attn"""    ,       """temporal_attention.output.dense"""     )
       if "temporal_fc" in name:
              snake_case__					:	Union[str, Any]          =					name.replace("""temporal_fc"""    ,       """temporal_dense"""     )
       if "norm1" in name and "temporal" not in name:
              snake_case__					:	Any          =					name.replace("""norm1"""    ,       """layernorm_before"""     )
       if "norm2" in name:
              snake_case__					:	Union[str, Any]          =					name.replace("""norm2"""    ,       """layernorm_after"""     )
       if "mlp.fc1" in name:
              snake_case__					:	List[str]          =					name.replace("""mlp.fc1"""    ,       """intermediate.dense"""     )
       if "mlp.fc2" in name:
              snake_case__					:	Tuple          =					name.replace("""mlp.fc2"""    ,       """output.dense"""     )
       if "norm.weight" in name and "fc" not in name and "temporal" not in name:
              snake_case__					:	Optional[int]          =					name.replace("""norm.weight"""    ,       """timesformer.layernorm.weight"""     )
       if "norm.bias" in name and "fc" not in name and "temporal" not in name:
              snake_case__					:	Tuple          =					name.replace("""norm.bias"""    ,       """timesformer.layernorm.bias"""     )
       if "head" in name:
              snake_case__					:	str          =					name.replace("""head"""    ,       """classifier"""     )

       return name



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       for key in orig_state_dict.copy().keys():
              snake_case__					:	Any          =					orig_state_dict.pop(UpperCAmelCase     )

              if key.startswith("""model."""     ):
                     snake_case__					:	Tuple          =					key.replace("""model."""    ,       """"""     )

              if "qkv" in key:
                     snake_case__					:	int          =					key.split("""."""     )
                     snake_case__					:	Union[str, Any]          =					int(key_split[1]     )
                     snake_case__					:	int          =					"""timesformer.encoder.layer."""
                     if "temporal" in key:
                            snake_case__					:	Any          =					""".temporal_attention.attention.qkv."""
                     else:
                            snake_case__					:	Any          =					""".attention.attention.qkv."""
                     if "weight" in key:
                            snake_case__					:	Dict          =					val
                     else:
                            snake_case__					:	str          =					val
              else:
                     snake_case__					:	Any          =					val

       return orig_state_dict



def 				lowerCAmelCase__	(       ):






       """simple docstring"""





       snake_case__					:	Dict          =					hf_hub_download(
           repo_id="""hf-internal-testing/spaghetti-video"""    ,       filename="""eating_spaghetti.npy"""    ,       repo_type="""dataset"""     )
       snake_case__					:	Any          =					np.load(UpperCAmelCase     )
       return list(UpperCAmelCase     )



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Any          =					get_timesformer_config(UpperCAmelCase     )

       snake_case__					:	Optional[int]          =					TimesformerForVideoClassification(UpperCAmelCase     )

       # download original checkpoint, hosted on Google Drive
       snake_case__					:	str          =					"""pytorch_model.bin"""
       gdown.cached_download(UpperCAmelCase    ,       UpperCAmelCase    ,       quiet=UpperCAmelCase     )
       snake_case__					:	Dict          =					torch.load(UpperCAmelCase    ,       map_location="""cpu"""     )
       if "model" in files:
              snake_case__					:	Optional[Any]          =					files["""model"""]
       elif "module" in files:
              snake_case__					:	List[str]          =					files["""module"""]
       else:
              snake_case__					:	str          =					files["""model_state"""]
       snake_case__					:	List[Any]          =					convert_state_dict(UpperCAmelCase    ,       UpperCAmelCase     )

       model.load_state_dict(UpperCAmelCase     )
       model.eval()

       # verify model on basic input
       snake_case__					:	Any          =					VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]    ,       image_std=[0.5, 0.5, 0.5]     )
       snake_case__					:	Union[str, Any]          =					prepare_video()
       snake_case__					:	str          =					image_processor(video[:8]    ,       return_tensors="""pt"""     )

       snake_case__					:	int          =					model(**UpperCAmelCase     )
       snake_case__					:	Tuple          =					outputs.logits

       snake_case__					:	List[str]          =					[
           # Kinetics-400 checkpoints (hr = high resolution input of 448px instead of 224px)
           """timesformer-base-finetuned-k400""",
           """timesformer-large-finetuned-k400""",
           """timesformer-hr-finetuned-k400""",
           # Kinetics-600 checkpoints (hr = high resolution input of 448px instead of 224px)
           """timesformer-base-finetuned-k600""",
           """timesformer-large-finetuned-k600""",
           """timesformer-hr-finetuned-k600""",
           # Something-Something-v2 checkpoints (hr = high resolution input of 448px instead of 224px)
           """timesformer-base-finetuned-ssv2""",
           """timesformer-large-finetuned-ssv2""",
           """timesformer-hr-finetuned-ssv2""",
       ]

       # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
       if model_name == "timesformer-base-finetuned-k400":
              snake_case__					:	str          =					torch.Size([1, 400]     )
              snake_case__					:	str          =					torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]     )
       elif model_name == "timesformer-base-finetuned-k600":
              snake_case__					:	Dict          =					torch.Size([1, 600]     )
              snake_case__					:	str          =					torch.tensor([-0.7_2_6_7, -0.7_4_6_6, 3.2_4_0_4]     )
       elif model_name == "timesformer-base-finetuned-ssv2":
              snake_case__					:	List[Any]          =					torch.Size([1, 174]     )
              snake_case__					:	Union[str, Any]          =					torch.tensor([-0.9_0_5_9, 0.6_4_3_3, -3.1_4_5_7]     )
       elif model_name == "timesformer-large-finetuned-k400":
              snake_case__					:	Optional[Any]          =					torch.Size([1, 400]     )
              snake_case__					:	Tuple          =					torch.tensor([0, 0, 0]     )
       elif model_name == "timesformer-large-finetuned-k600":
              snake_case__					:	Optional[int]          =					torch.Size([1, 600]     )
              snake_case__					:	int          =					torch.tensor([0, 0, 0]     )
       elif model_name == "timesformer-large-finetuned-ssv2":
              snake_case__					:	Union[str, Any]          =					torch.Size([1, 174]     )
              snake_case__					:	List[Any]          =					torch.tensor([0, 0, 0]     )
       elif model_name == "timesformer-hr-finetuned-k400":
              snake_case__					:	str          =					torch.Size([1, 400]     )
              snake_case__					:	List[Any]          =					torch.tensor([-0.9_6_1_7, -3.7_3_1_1, -3.7_7_0_8]     )
       elif model_name == "timesformer-hr-finetuned-k600":
              snake_case__					:	Optional[int]          =					torch.Size([1, 600]     )
              snake_case__					:	str          =					torch.tensor([2.5_2_7_3, 0.7_1_2_7, 1.8_8_4_8]     )
       elif model_name == "timesformer-hr-finetuned-ssv2":
              snake_case__					:	int          =					torch.Size([1, 174]     )
              snake_case__					:	Optional[Any]          =					torch.tensor([-3.6_7_5_6, -0.7_5_1_3, 0.7_1_8_0]     )
       else:
              raise ValueError(f"""Model name not supported. Should be one of {model_names}"""     )

       # verify logits
       assert logits.shape == expected_shape
       assert torch.allclose(logits[0, :3]    ,       UpperCAmelCase    ,       atol=1E-4     )
       print("""Logits ok!"""     )

       if pytorch_dump_folder_path is not None:
              print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""     )
              image_processor.save_pretrained(UpperCAmelCase     )
              model.save_pretrained(UpperCAmelCase     )

       if push_to_hub:
              print("""Pushing to the hub..."""     )
              model.push_to_hub(f"""fcakyon/{model_name}"""     )


if __name__ == "__main__":
     lowerCAmelCase__  =     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--checkpoint_url',
         default='https://drive.google.com/u/1/uc?id=17yvuYp9L4mn-HpIcK5Zo6K3UoOy1kA5l&export=download',
         type=str,
         help=(
             'URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'
             ' download link.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_folder_path',
         default='',
         type=str,
         help='Path to the output PyTorch model directory.',
     )
     parser.add_argument('--model_name', default='timesformer-base-finetuned-k400', type=str, help='Name of the model.')
     parser.add_argument(
         '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
     )

     lowerCAmelCase__  =     parser.parse_args()
     convert_timesformer_checkpoint(
         args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
     )




