





"""simple docstring"""




import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


UpperCamelCase_	 =logging.get_logger(__name__)  # pylint: disable=invalid-name





def 			a_							(				self			,		_lowercase			,		_lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Optional[int]      =       image.to(device=_lowercase  )
						_UpperCamelCase     :   int      =       image  # take image as image_embeddings
						_UpperCamelCase     :   Optional[Any]      =       image_embeddings.unsqueeze(1  )

						# duplicate image embeddings for each generation per prompt, using mps friendly method
						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   str      =       image_embeddings.shape
						_UpperCamelCase     :   List[Any]      =       image_embeddings.repeat(1			,		_lowercase			,		1  )
						_UpperCamelCase     :   Optional[Any]      =       image_embeddings.view(bs_embed * num_images_per_prompt			,		_lowercase			,		-1  )

						if do_classifier_free_guidance:
												_UpperCamelCase     :   Dict      =       torch.zeros_like(_lowercase  )

												# For classifier free guidance, we need to do two forward passes.
												# Here we concatenate the unconditional and text embeddings into a single batch
												# to avoid doing two forward passes
												_UpperCamelCase     :   Any      =       torch.cat([uncond_embeddings, image_embeddings]  )

						return image_embeddings






class 					_a		( _lowerCAmelCase							):






				def __init__(					self						:       str,					lowerCAmelCase__						:       PriorTransformer,					lowerCAmelCase__						:       CLIPTokenizer,					lowerCAmelCase__						:       CLIPTextModelWithProjection,					lowerCAmelCase__						:       UnCLIPScheduler,					lowerCAmelCase__						:       Optional[dict] = None,					)      -> int:




										'''simple docstring'''



										super().__init__()

										_UpperCamelCase     :   Any      =       {'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

										_UpperCamelCase     :   Union[str, Any]      =       decoder_pipe_kwargs.get('''torch_dtype''',					lowerCAmelCase__				) or prior.dtype

										_UpperCamelCase     :   Dict      =       StableDiffusionImageVariationPipeline.from_pretrained(
										    '''lambdalabs/sd-image-variations-diffusers''',					**lowerCAmelCase__				)

										# replace `_encode_image` method
										_UpperCamelCase     :   Optional[Any]      =       types.MethodType(_encode_image,					self.decoder_pipe				)

										self.register_modules(
										    prior=lowerCAmelCase__,					tokenizer=lowerCAmelCase__,					text_encoder=lowerCAmelCase__,					prior_scheduler=lowerCAmelCase__,					)






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       str,					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Optional[Union[CLIPTextModelOutput, Tuple]] = None,					lowerCAmelCase__						:       Optional[torch.Tensor] = None,					)      -> Tuple:




										'''simple docstring'''



										if text_model_output is None:
																_UpperCamelCase     :   List[Any]      =       len(lowerCAmelCase__				) if isinstance(lowerCAmelCase__,					lowerCAmelCase__				) else 1
																# get prompt text embeddings
																_UpperCamelCase     :   Any      =       self.tokenizer(
																    lowerCAmelCase__,					padding='''max_length''',					max_length=self.tokenizer.model_max_length,					return_tensors='''pt''',					)
																_UpperCamelCase     :   List[str]      =       text_inputs.input_ids
																_UpperCamelCase     :   List[str]      =       text_inputs.attention_mask.bool().to(lowerCAmelCase__				)

																if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																						_UpperCamelCase     :   str      =       self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]				)
																						logger.warning(
																						    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
																						    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""				)
																						_UpperCamelCase     :   Optional[Any]      =       text_input_ids[:, : self.tokenizer.model_max_length]

																_UpperCamelCase     :   Dict      =       self.text_encoder(text_input_ids.to(lowerCAmelCase__				)				)

																_UpperCamelCase     :   Optional[int]      =       text_encoder_output.text_embeds
																_UpperCamelCase     :   List[Any]      =       text_encoder_output.last_hidden_state

										else:
																_UpperCamelCase     :   Any      =       text_model_output[0].shape[0]
																_UpperCamelCase  ,				_UpperCamelCase     :   Any      =       text_model_output[0], text_model_output[1]
																_UpperCamelCase     :   Optional[Any]      =       text_attention_mask

										_UpperCamelCase     :   Optional[Any]      =       text_embeddings.repeat_interleave(lowerCAmelCase__,					dim=0				)
										_UpperCamelCase     :   Dict      =       text_encoder_hidden_states.repeat_interleave(lowerCAmelCase__,					dim=0				)
										_UpperCamelCase     :   int      =       text_mask.repeat_interleave(lowerCAmelCase__,					dim=0				)

										if do_classifier_free_guidance:
																_UpperCamelCase     :   int      =       [''''''] * batch_size

																_UpperCamelCase     :   Tuple      =       self.tokenizer(
																    lowerCAmelCase__,					padding='''max_length''',					max_length=self.tokenizer.model_max_length,					truncation=lowerCAmelCase__,					return_tensors='''pt''',					)
																_UpperCamelCase     :   Tuple      =       uncond_input.attention_mask.bool().to(lowerCAmelCase__				)
																_UpperCamelCase     :   str      =       self.text_encoder(uncond_input.input_ids.to(lowerCAmelCase__				)				)

																_UpperCamelCase     :   Tuple      =       uncond_embeddings_text_encoder_output.text_embeds
																_UpperCamelCase     :   List[Any]      =       uncond_embeddings_text_encoder_output.last_hidden_state

																# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

																_UpperCamelCase     :   Dict      =       uncond_embeddings.shape[1]
																_UpperCamelCase     :   Optional[Any]      =       uncond_embeddings.repeat(1,					lowerCAmelCase__				)
																_UpperCamelCase     :   Union[str, Any]      =       uncond_embeddings.view(batch_size * num_images_per_prompt,					lowerCAmelCase__				)

																_UpperCamelCase     :   Optional[Any]      =       uncond_text_encoder_hidden_states.shape[1]
																_UpperCamelCase     :   List[Any]      =       uncond_text_encoder_hidden_states.repeat(1,					lowerCAmelCase__,					1				)
																_UpperCamelCase     :   List[str]      =       uncond_text_encoder_hidden_states.view(
																    batch_size * num_images_per_prompt,					lowerCAmelCase__,					-1				)
																_UpperCamelCase     :   Any      =       uncond_text_mask.repeat_interleave(lowerCAmelCase__,					dim=0				)

																# done duplicates

																# For classifier free guidance, we need to do two forward passes.
																# Here we concatenate the unconditional and text embeddings into a single batch
																# to avoid doing two forward passes
																_UpperCamelCase     :   Any      =       torch.cat([uncond_embeddings, text_embeddings]				)
																_UpperCamelCase     :   Dict      =       torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]				)

																_UpperCamelCase     :   Optional[Any]      =       torch.cat([uncond_text_mask, text_mask]				)

										return text_embeddings, text_encoder_hidden_states, text_mask






				@property
				def 							snake_case      (					self						:       int				)      -> Optional[int]:




										'''simple docstring'''



										if self.device != torch.device('''meta'''				) or not hasattr(self.prior,					'''_hf_hook'''				):
																return self.device
										for module in self.prior.modules():
																if (
																    hasattr(lowerCAmelCase__,					'''_hf_hook'''				)
																    and hasattr(module._hf_hook,					'''execution_device'''				)
																    and module._hf_hook.execution_device is not None
																):
																						return torch.device(module._hf_hook.execution_device				)
										return self.device






				def 							snake_case      (					self						:       Tuple,					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       Optional[int]				)      -> int:




										'''simple docstring'''



										if latents is None:
																_UpperCamelCase     :   Union[str, Any]      =       randn_tensor(lowerCAmelCase__,					generator=lowerCAmelCase__,					device=lowerCAmelCase__,					dtype=lowerCAmelCase__				)
										else:
																if latents.shape != shape:
																						raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""				)
																_UpperCamelCase     :   Union[str, Any]      =       latents.to(lowerCAmelCase__				)

										_UpperCamelCase     :   List[Any]      =       latents * scheduler.init_noise_sigma
										return latents






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       Optional[Union[str, torch.device]] = None				)      -> Optional[Any]:




										'''simple docstring'''



										self.decoder_pipe.to(lowerCAmelCase__				)
										super().to(lowerCAmelCase__				)






				@torch.no_grad()
				def __call__(					self						:       Tuple,					lowerCAmelCase__						:       Optional[Union[str, List[str]]] = None,					lowerCAmelCase__						:       Optional[int] = None,					lowerCAmelCase__						:       Optional[int] = None,					lowerCAmelCase__						:       int = 1,					lowerCAmelCase__						:       int = 2_5,					lowerCAmelCase__						:       Optional[torch.Generator] = None,					lowerCAmelCase__						:       Optional[torch.FloatTensor] = None,					lowerCAmelCase__						:       Optional[Union[CLIPTextModelOutput, Tuple]] = None,					lowerCAmelCase__						:       Optional[torch.Tensor] = None,					lowerCAmelCase__						:       float = 4.0,					lowerCAmelCase__						:       float = 8.0,					lowerCAmelCase__						:       int = 5_0,					lowerCAmelCase__						:       Optional[int] = 1,					lowerCAmelCase__						:       float = 0.0,					lowerCAmelCase__						:       Optional[str] = "pil",					lowerCAmelCase__						:       bool = True,					)      -> Tuple:




										'''simple docstring'''



										if prompt is not None:
																if isinstance(lowerCAmelCase__,					lowerCAmelCase__				):
																						_UpperCamelCase     :   Optional[int]      =       1
																elif isinstance(lowerCAmelCase__,					lowerCAmelCase__				):
																						_UpperCamelCase     :   List[Any]      =       len(lowerCAmelCase__				)
																else:
																						raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__				)}"""				)
										else:
																_UpperCamelCase     :   Any      =       text_model_output[0].shape[0]

										_UpperCamelCase     :   Tuple      =       self._execution_device

										_UpperCamelCase     :   Optional[int]      =       batch_size * num_images_per_prompt

										_UpperCamelCase     :   List[Any]      =       prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

										_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Optional[Any]      =       self._encode_prompt(
										    lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)

										# prior

										self.prior_scheduler.set_timesteps(lowerCAmelCase__,					device=lowerCAmelCase__				)
										_UpperCamelCase     :   Tuple      =       self.prior_scheduler.timesteps

										_UpperCamelCase     :   str      =       self.prior.config.embedding_dim

										_UpperCamelCase     :   Dict      =       self.prepare_latents(
										    (batch_size, embedding_dim),					text_embeddings.dtype,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					self.prior_scheduler,					)

										for i, t in enumerate(self.progress_bar(lowerCAmelCase__				)				):
																# expand the latents if we are doing classifier free guidance
																_UpperCamelCase     :   Tuple      =       torch.cat([prior_latents] * 2				) if do_classifier_free_guidance else prior_latents

																_UpperCamelCase     :   Any      =       self.prior(
																    lowerCAmelCase__,					timestep=lowerCAmelCase__,					proj_embedding=lowerCAmelCase__,					encoder_hidden_states=lowerCAmelCase__,					attention_mask=lowerCAmelCase__,					).predicted_image_embedding

																if do_classifier_free_guidance:
																						_UpperCamelCase  ,				_UpperCamelCase     :   Any      =       predicted_image_embedding.chunk(2				)
																						_UpperCamelCase     :   List[str]      =       predicted_image_embedding_uncond + prior_guidance_scale * (
																						    predicted_image_embedding_text - predicted_image_embedding_uncond
																						)

																if i + 1 == prior_timesteps_tensor.shape[0]:
																						_UpperCamelCase     :   Union[str, Any]      =       None
																else:
																						_UpperCamelCase     :   Union[str, Any]      =       prior_timesteps_tensor[i + 1]

																_UpperCamelCase     :   Dict      =       self.prior_scheduler.step(
																    lowerCAmelCase__,					timestep=lowerCAmelCase__,					sample=lowerCAmelCase__,					generator=lowerCAmelCase__,					prev_timestep=lowerCAmelCase__,					).prev_sample

										_UpperCamelCase     :   str      =       self.prior.post_process_latents(lowerCAmelCase__				)

										_UpperCamelCase     :   List[Any]      =       prior_latents

										_UpperCamelCase     :   Tuple      =       self.decoder_pipe(
										    image=lowerCAmelCase__,					height=lowerCAmelCase__,					width=lowerCAmelCase__,					num_inference_steps=lowerCAmelCase__,					guidance_scale=lowerCAmelCase__,					generator=lowerCAmelCase__,					output_type=lowerCAmelCase__,					return_dict=lowerCAmelCase__,					num_images_per_prompt=lowerCAmelCase__,					eta=lowerCAmelCase__,					)
										return output





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 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Any				      = TimesformerConfig()

    if "large" in model_name:
        SCREAMING_SNAKE_CASE				:				Dict				      = 96

    if "hr" in model_name:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = 16
        SCREAMING_SNAKE_CASE				:				str				      = 448

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

    return config







def 				UpperCAmelCase_(    a__   ):



    """simple docstring"""



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

    return name







def 				UpperCAmelCase_(    a__ ,						a__   ):



    """simple docstring"""



    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = orig_state_dict.pop(a__   )

        if key.startswith('''model.'''   ):
            SCREAMING_SNAKE_CASE				:				Dict				      = key.replace('''model.''' ,						''''''   )

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

    return orig_state_dict







def 				UpperCAmelCase_(    ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Optional[int]				      = hf_hub_download(
        repo_id='''hf-internal-testing/spaghetti-video''' ,						filename='''eating_spaghetti.npy''' ,						repo_type='''dataset'''   )
    SCREAMING_SNAKE_CASE				:				int				      = np.load(a__   )
    return list(a__   )







def 				UpperCAmelCase_(    a__ ,						a__ ,						a__ ,						a__   ):



    """simple docstring"""



    SCREAMING_SNAKE_CASE				:				Dict				      = get_timesformer_config(a__   )

    SCREAMING_SNAKE_CASE				:				Tuple				      = TimesformerForVideoClassification(a__   )

    # download original checkpoint, hosted on Google Drive
    SCREAMING_SNAKE_CASE				:				Tuple				      = '''pytorch_model.bin'''
    gdown.cached_download(a__ ,						a__ ,						quiet=a__   )
    SCREAMING_SNAKE_CASE				:				int				      = torch.load(a__ ,						map_location='''cpu'''   )
    if "model" in files:
        SCREAMING_SNAKE_CASE				:				Tuple				      = files['''model''']
    elif "module" in files:
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = files['''module''']
    else:
        SCREAMING_SNAKE_CASE				:				Any				      = files['''model_state''']
    SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = convert_state_dict(a__ ,						a__   )

    model.load_state_dict(a__   )
    model.eval()

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

    SCREAMING_SNAKE_CASE				:				Any				      = model(**a__   )
    SCREAMING_SNAKE_CASE				:				List[str]				      = outputs.logits

    SCREAMING_SNAKE_CASE				:				Optional[Any]				      = [
        # 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":
        SCREAMING_SNAKE_CASE				:				Tuple				      = torch.Size([1, 400]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([-0.3_016, -0.7_713, -0.4_205]   )
    elif model_name == "timesformer-base-finetuned-k600":
        SCREAMING_SNAKE_CASE				:				Tuple				      = torch.Size([1, 600]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([-0.7_267, -0.7_466, 3.2_404]   )
    elif model_name == "timesformer-base-finetuned-ssv2":
        SCREAMING_SNAKE_CASE				:				Dict				      = torch.Size([1, 174]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([-0.9_059, 0.6_433, -3.1_457]   )
    elif model_name == "timesformer-large-finetuned-k400":
        SCREAMING_SNAKE_CASE				:				Any				      = torch.Size([1, 400]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([0, 0, 0]   )
    elif model_name == "timesformer-large-finetuned-k600":
        SCREAMING_SNAKE_CASE				:				str				      = torch.Size([1, 600]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([0, 0, 0]   )
    elif model_name == "timesformer-large-finetuned-ssv2":
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.Size([1, 174]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([0, 0, 0]   )
    elif model_name == "timesformer-hr-finetuned-k400":
        SCREAMING_SNAKE_CASE				:				List[str]				      = torch.Size([1, 400]   )
        SCREAMING_SNAKE_CASE				:				int				      = torch.tensor([-0.9_617, -3.7_311, -3.7_708]   )
    elif model_name == "timesformer-hr-finetuned-k600":
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.Size([1, 600]   )
        SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = torch.tensor([2.5_273, 0.7_127, 1.8_848]   )
    elif model_name == "timesformer-hr-finetuned-ssv2":
        SCREAMING_SNAKE_CASE				:				Optional[Any]				      = torch.Size([1, 174]   )
        SCREAMING_SNAKE_CASE				:				str				      = torch.tensor([-3.6_756, -0.7_513, 0.7_180]   )
    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] ,						a__ ,						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(a__   )
        model.save_pretrained(a__   )

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


if __name__ == "__main__":
      a__				:      Optional[int]    		=     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.'''
      )

      a__				:      str    		=     parser.parse_args()
      convert_timesformer_checkpoint(
          args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub
      )
