


"""simple docstring"""
import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

SCREAMING_SNAKE_CASE					:       Any							   =							logging.get_logger(__name__)






def 						lowercase				(	_snake_case		: List[str]	,  _snake_case		: bool = True	)	->str:
	"""simple docstring"""





	__snake_case      :		List[str]         =							{}
	for name in model.attn_processors.keys():
		__snake_case      :		str         =							name.endswith('''attn2.processor'''	) and not (
		    name.startswith('''transformer_in'''	) or '''temp_attentions''' in name.split('''.'''	)
		)
		__snake_case      :		Tuple         =							model.config.cross_attention_dim if has_cross_attention else None
		if name.startswith('''mid_block'''	):
			__snake_case      :		Dict         =							model.config.block_out_channels[-1]
		elif name.startswith('''up_blocks'''	):
			__snake_case      :		Any         =							int(name[len('''up_blocks.'''	)]	)
			__snake_case      :		List[str]         =							list(reversed(model.config.block_out_channels	)	)[block_id]
		elif name.startswith('''down_blocks'''	):
			__snake_case      :		Dict         =							int(name[len('''down_blocks.'''	)]	)
			__snake_case      :		Optional[Any]         =							model.config.block_out_channels[block_id]
		elif name.startswith('''transformer_in'''	):
			# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
			__snake_case      :		int         =							8 * model.config.attention_head_dim

		__snake_case      :		Tuple         =							LoRAAttnProcessor(hidden_size=_snake_case	,  cross_attention_dim=_snake_case	)
		__snake_case      :		List[str]         =							lora_attn_procs[name].to(model.device	)

		if mock_weights:
			# add 1 to weights to mock trained weights
			with torch.no_grad():
				lora_attn_procs[name].to_q_lora.up.weight += 1
				lora_attn_procs[name].to_k_lora.up.weight += 1
				lora_attn_procs[name].to_v_lora.up.weight += 1
				lora_attn_procs[name].to_out_lora.up.weight += 1

	return lora_attn_procs



@skip_mps
class      _UpperCAmelCase					(				__snake_case,							__snake_case,							unittest.TestCase							):






		'''simple docstring'''

		lowerCamelCase__           =UNetaDConditionModel
		lowerCamelCase__           ='sample'



		@property
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		str         =							4
			__snake_case      :		List[Any]         =							4
			__snake_case      :		List[str]         =							4
			__snake_case      :		Any         =							(32, 32)

			__snake_case      :		Tuple         =							floats_tensor((batch_size, num_channels, num_frames) + sizes   ).to(a_   )
			__snake_case      :		Union[str, Any]         =							torch.tensor([10]   ).to(a_   )
			__snake_case      :		str         =							floats_tensor((batch_size, 4, 32)   ).to(a_   )

			return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}



		@property
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			return (4, 4, 32, 32)



		@property
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			return (4, 4, 32, 32)



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Any         =							{
			    '''block_out_channels''': (32, 64),
			    '''down_block_types''': (
			        '''CrossAttnDownBlock3D''',
			        '''DownBlock3D''',
			    ),
			    '''up_block_types''': ('''UpBlock3D''', '''CrossAttnUpBlock3D'''),
			    '''cross_attention_dim''': 32,
			    '''attention_head_dim''': 8,
			    '''out_channels''': 4,
			    '''in_channels''': 4,
			    '''layers_per_block''': 1,
			    '''sample_size''': 32,
			}
			__snake_case      :		Dict         =							self.dummy_input
			return init_dict, inputs_dict



		@unittest.skipIf(
		    torch_device != '''cuda''' or not is_xformers_available()							,			reason='''XFormers attention is only available with CUDA and `xformers` installed'''							,			)
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      ,				__snake_case      :		int         =							self.prepare_init_args_and_inputs_for_common()
			__snake_case      :		Tuple         =							self.model_class(**a_   )

			model.enable_xformers_memory_efficient_attention()

			assert (
			    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
			    == "XFormersAttnProcessor"
			), "xformers is not enabled"



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      ,				__snake_case      :		int         =							self.prepare_init_args_and_inputs_for_common()

			__snake_case      :		Union[str, Any]         =							32

			__snake_case      :		Union[str, Any]         =							self.model_class(**a_   )
			model.to(a_   )
			model.eval()

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

				if isinstance(a_							,			a_   ):
					__snake_case      :		Optional[Any]         =							output.sample

			self.assertIsNotNone(a_   )
			__snake_case      :		str         =							inputs_dict['''sample'''].shape
			self.assertEqual(output.shape							,			a_							,			'''Input and output shapes do not match'''   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      ,				__snake_case      :		Optional[Any]         =							self.prepare_init_args_and_inputs_for_common()
			__snake_case      :		List[Any]         =							self.model_class(**a_   )
			model.to(a_   )
			model.eval()

			with torch.no_grad():
				# Warmup pass when using mps (see #372)
				if torch_device == "mps" and isinstance(a_							,			a_   ):
					model(**self.dummy_input   )

				__snake_case      :		Tuple         =							model(**a_   )
				if isinstance(a_							,			a_   ):
					__snake_case      :		int         =							first.sample

				__snake_case      :		str         =							model(**a_   )
				if isinstance(a_							,			a_   ):
					__snake_case      :		Optional[int]         =							second.sample

			__snake_case      :		Union[str, Any]         =							first.cpu().numpy()
			__snake_case      :		Tuple         =							second.cpu().numpy()
			__snake_case      :		int         =							out_a[~np.isnan(a_   )]
			__snake_case      :		int         =							out_a[~np.isnan(a_   )]
			__snake_case      :		Optional[int]         =							np.amax(np.abs(out_a - out_a   )   )
			self.assertLessEqual(a_							,			1E-5   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      ,				__snake_case      :		Union[str, Any]         =							self.prepare_init_args_and_inputs_for_common()

			__snake_case      :		Optional[Any]         =							8

			__snake_case      :		Union[str, Any]         =							self.model_class(**a_   )
			model.to(a_   )
			model.eval()

			model.set_attention_slice('''auto'''   )
			with torch.no_grad():
				__snake_case      :		Any         =							model(**a_   )
			assert output is not None

			model.set_attention_slice('''max'''   )
			with torch.no_grad():
				__snake_case      :		str         =							model(**a_   )
			assert output is not None

			model.set_attention_slice(2   )
			with torch.no_grad():
				__snake_case      :		Any         =							model(**a_   )
			assert output is not None



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      ,				__snake_case      :		Optional[Any]         =							self.prepare_init_args_and_inputs_for_common()

			__snake_case      :		Optional[Any]         =							8

			__snake_case      :		str         =							self.model_class(**a_   )
			model.to(a_   )

			with torch.no_grad():
				__snake_case      :		Optional[int]         =							model(**a_   ).sample

			__snake_case      :		List[str]         =							create_lora_layers(a_   )

			# make sure we can set a list of attention processors
			model.set_attn_processor(a_   )
			model.to(a_   )

			# test that attn processors can be set to itself
			model.set_attn_processor(model.attn_processors   )

			with torch.no_grad():
				__snake_case      :		Any         =							model(**a_							,			cross_attention_kwargs={'''scale''': 0.0}   ).sample
				__snake_case      :		Dict         =							model(**a_							,			cross_attention_kwargs={'''scale''': 0.5}   ).sample
				__snake_case      :		Union[str, Any]         =							model(**a_							,			cross_attention_kwargs={'''scale''': 0.5}   ).sample

			assert (samplea - samplea).abs().max() < 3E-3
			assert (samplea - samplea).abs().max() < 3E-3

			# sample 2 and sample 3 should be different
			assert (samplea - samplea).abs().max() > 3E-3



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      ,				__snake_case      :		Optional[Any]         =							self.prepare_init_args_and_inputs_for_common()

			__snake_case      :		Union[str, Any]         =							8

			torch.manual_seed(0   )
			__snake_case      :		Any         =							self.model_class(**a_   )
			model.to(a_   )

			with torch.no_grad():
				__snake_case      :		Any         =							model(**a_   ).sample

			__snake_case      :		List[Any]         =							create_lora_layers(a_   )
			model.set_attn_processor(a_   )

			with torch.no_grad():
				__snake_case      :		Optional[Any]         =							model(**a_							,			cross_attention_kwargs={'''scale''': 0.5}   ).sample

			with tempfile.TemporaryDirectory() as tmpdirname:
				model.save_attn_procs(a_   )
				self.assertTrue(os.path.isfile(os.path.join(a_							,			'''pytorch_lora_weights.bin'''   )   )   )
				torch.manual_seed(0   )
				__snake_case      :		Union[str, Any]         =							self.model_class(**a_   )
				new_model.to(a_   )
				new_model.load_attn_procs(a_   )

			with torch.no_grad():
				__snake_case      :		List[Any]         =							new_model(**a_							,			cross_attention_kwargs={'''scale''': 0.5}   ).sample

			assert (sample - new_sample).abs().max() < 1E-3

			# LoRA and no LoRA should NOT be the same
			assert (sample - old_sample).abs().max() > 1E-4



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      ,				__snake_case      :		str         =							self.prepare_init_args_and_inputs_for_common()

			__snake_case      :		Union[str, Any]         =							8

			torch.manual_seed(0   )
			__snake_case      :		int         =							self.model_class(**a_   )
			model.to(a_   )

			with torch.no_grad():
				__snake_case      :		Tuple         =							model(**a_   ).sample

			__snake_case      :		Dict         =							create_lora_layers(a_   )
			model.set_attn_processor(a_   )

			with torch.no_grad():
				__snake_case      :		List[Any]         =							model(**a_							,			cross_attention_kwargs={'''scale''': 0.5}   ).sample

			with tempfile.TemporaryDirectory() as tmpdirname:
				model.save_attn_procs(a_							,			safe_serialization=a_   )
				self.assertTrue(os.path.isfile(os.path.join(a_							,			'''pytorch_lora_weights.safetensors'''   )   )   )
				torch.manual_seed(0   )
				__snake_case      :		Optional[int]         =							self.model_class(**a_   )
				new_model.to(a_   )
				new_model.load_attn_procs(a_   )

			with torch.no_grad():
				__snake_case      :		Optional[int]         =							new_model(**a_							,			cross_attention_kwargs={'''scale''': 0.5}   ).sample

			assert (sample - new_sample).abs().max() < 3E-3

			# LoRA and no LoRA should NOT be the same
			assert (sample - old_sample).abs().max() > 1E-4



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      ,				__snake_case      :		Dict         =							self.prepare_init_args_and_inputs_for_common()

			__snake_case      :		List[Any]         =							8

			torch.manual_seed(0   )
			__snake_case      :		Dict         =							self.model_class(**a_   )
			model.to(a_   )

			__snake_case      :		Any         =							create_lora_layers(a_							,			mock_weights=a_   )
			model.set_attn_processor(a_   )
			# Saving as torch, properly reloads with directly filename
			with tempfile.TemporaryDirectory() as tmpdirname:
				model.save_attn_procs(a_   )
				self.assertTrue(os.path.isfile(os.path.join(a_							,			'''pytorch_lora_weights.bin'''   )   )   )
				torch.manual_seed(0   )
				__snake_case      :		Union[str, Any]         =							self.model_class(**a_   )
				new_model.to(a_   )
				new_model.load_attn_procs(a_							,			weight_name='''pytorch_lora_weights.bin'''   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      ,				__snake_case      :		Tuple         =							self.prepare_init_args_and_inputs_for_common()

			__snake_case      :		List[Any]         =							8

			torch.manual_seed(0   )
			__snake_case      :		Dict         =							self.model_class(**a_   )
			model.to(a_   )

			__snake_case      :		Tuple         =							create_lora_layers(a_							,			mock_weights=a_   )
			model.set_attn_processor(a_   )
			# Saving as torch, properly reloads with directly filename
			with tempfile.TemporaryDirectory() as tmpdirname:
				model.save_attn_procs(a_   )
				self.assertTrue(os.path.isfile(os.path.join(a_							,			'''pytorch_lora_weights.bin'''   )   )   )
				torch.manual_seed(0   )
				__snake_case      :		str         =							self.model_class(**a_   )
				new_model.to(a_   )
				with self.assertRaises(a_   ) as e:
					new_model.load_attn_procs(a_							,			use_safetensors=a_   )
				self.assertIn('''Error no file named pytorch_lora_weights.safetensors'''							,			str(e.exception   )   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      ,				__snake_case      :		Any         =							self.prepare_init_args_and_inputs_for_common()

			__snake_case      :		List[str]         =							8

			torch.manual_seed(0   )
			__snake_case      :		str         =							self.model_class(**a_   )
			model.to(a_   )

			with torch.no_grad():
				__snake_case      :		str         =							model(**a_   ).sample

			__snake_case      :		Optional[int]         =							create_lora_layers(a_   )
			model.set_attn_processor(a_   )

			with torch.no_grad():
				__snake_case      :		Any         =							model(**a_							,			cross_attention_kwargs={'''scale''': 0.0}   ).sample

			model.set_attn_processor(AttnProcessor()   )

			with torch.no_grad():
				__snake_case      :		str         =							model(**a_   ).sample

			assert (sample - new_sample).abs().max() < 1E-4
			assert (sample - old_sample).abs().max() < 3E-3



		@unittest.skipIf(
		    torch_device != '''cuda''' or not is_xformers_available()							,			reason='''XFormers attention is only available with CUDA and `xformers` installed'''							,			)
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      ,				__snake_case      :		str         =							self.prepare_init_args_and_inputs_for_common()

			__snake_case      :		int         =							4

			torch.manual_seed(0   )
			__snake_case      :		int         =							self.model_class(**a_   )
			model.to(a_   )
			__snake_case      :		Optional[int]         =							create_lora_layers(a_   )
			model.set_attn_processor(a_   )

			# default
			with torch.no_grad():
				__snake_case      :		Optional[Any]         =							model(**a_   ).sample

				model.enable_xformers_memory_efficient_attention()
				__snake_case      :		str         =							model(**a_   ).sample

				model.disable_xformers_memory_efficient_attention()
				__snake_case      :		Optional[Any]         =							model(**a_   ).sample

			assert (sample - on_sample).abs().max() < 1E-4
			assert (sample - off_sample).abs().max() < 1E-4



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      ,				__snake_case      :		int         =							self.prepare_init_args_and_inputs_for_common()
			__snake_case      :		Any         =							32

			__snake_case      :		Union[str, Any]         =							self.model_class(**a_   )
			model.to(a_   )
			model.eval()

			with torch.no_grad():
				__snake_case      :		Union[str, Any]         =							model(**a_   )[0]

			model.enable_forward_chunking()
			with torch.no_grad():
				__snake_case      :		Optional[int]         =							model(**a_   )[0]

			self.assertEqual(output.shape							,			output_a.shape							,			'''Shape doesn\'t match'''   )
			assert np.abs(output.cpu() - output_a.cpu()   ).max() < 1E-2


# (todo: sayakpaul) implement SLOW tests.









"""simple docstring"""
import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling






def 						lowercase				(	_snake_case		: Optional[Any]	)	->Union[str, Any]:
	"""simple docstring"""





	__snake_case      :		str         =							'''https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'''

	with open(_snake_case	,  '''wb'''	) as f:
		with requests.get(_snake_case	,  stream=_snake_case	) as req:
			for chunk in req.iter_content(chunk_size=2_048	):
				f.write(_snake_case	)






def 						lowercase				(	)	->VivitConfig:
	"""simple docstring"""





	__snake_case      :		int         =							VivitConfig()

	__snake_case      :		Tuple         =							400
	__snake_case      :		Tuple         =							'''huggingface/label-files'''
	__snake_case      :		List[str]         =							'''kinetics400-id2label.json'''

	__snake_case      :		Dict         =							json.load(open(hf_hub_download(_snake_case	,  _snake_case	,  repo_type='''dataset'''	)	,  '''r'''	)	)
	__snake_case      :		List[str]         =							{int(_snake_case	): v for k, v in idalabel.items()}
	__snake_case      :		Tuple         =							idalabel
	__snake_case      :		Optional[int]         =							{v: k for k, v in idalabel.items()}
	return config






def 						lowercase				(	)	->Optional[int]:
	"""simple docstring"""





	__snake_case      :		List[Any]         =							hf_hub_download(
	    repo_id='''hf-internal-testing/spaghetti-video'''	,  filename='''eating_spaghetti_32_frames.npy'''	,  repo_type='''dataset'''	)
	__snake_case      :		str         =							np.load(_snake_case	)
	return list(_snake_case	)






def 						lowercase				(	_snake_case		: np.ndarray	)	->str:
	"""simple docstring"""





	if np.ndim(_snake_case	) == 2:
		return transform_attention_bias(_snake_case	)

	elif np.ndim(_snake_case	) == 3:
		return transform_attention_kernel(_snake_case	)

	else:
		raise Exception(f"""Invalid number of dimesions: {np.ndim(_snake_case	)}"""	)






def 						lowercase				(	_snake_case		: np.ndarray	)	->Tuple:
	"""simple docstring"""





	return current.flatten()






def 						lowercase				(	_snake_case		: np.ndarray	)	->List[str]:
	"""simple docstring"""





	return np.reshape(_snake_case	,  (current.shape[0], current.shape[1] * current.shape[2])	).T






def 						lowercase				(	_snake_case		: np.ndarray	)	->Optional[Any]:
	"""simple docstring"""





	return np.reshape(_snake_case	,  (current.shape[0] * current.shape[1], current.shape[2])	).T






def 						lowercase				(	_snake_case		: Dict	,  _snake_case		: Tuple	)	->Tuple:
	"""simple docstring"""





	__snake_case      :		int         =							state_dict['''optimizer''']['''target''']['''Transformer'''][f"""encoderblock_{i}"""]

	__snake_case      :		Optional[int]         =							f"""encoder.layer.{i}."""
	__snake_case      :		Dict         =							{
	    prefix + '''intermediate.dense.bias''': state['''MlpBlock_0''']['''Dense_0''']['''bias'''],
	    prefix + '''intermediate.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_0''']['''kernel''']	),
	    prefix + '''output.dense.bias''': state['''MlpBlock_0''']['''Dense_1''']['''bias'''],
	    prefix + '''output.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_1''']['''kernel''']	),
	    prefix + '''layernorm_before.bias''': state['''LayerNorm_0''']['''bias'''],
	    prefix + '''layernorm_before.weight''': state['''LayerNorm_0''']['''scale'''],
	    prefix + '''layernorm_after.bias''': state['''LayerNorm_1''']['''bias'''],
	    prefix + '''layernorm_after.weight''': state['''LayerNorm_1''']['''scale'''],
	    prefix
	    + '''attention.attention.query.bias''': transform_attention(
	        state['''MultiHeadDotProductAttention_0''']['''query''']['''bias''']	),
	    prefix
	    + '''attention.attention.query.weight''': transform_attention(
	        state['''MultiHeadDotProductAttention_0''']['''query''']['''kernel''']	),
	    prefix
	    + '''attention.attention.key.bias''': transform_attention(state['''MultiHeadDotProductAttention_0''']['''key''']['''bias''']	),
	    prefix
	    + '''attention.attention.key.weight''': transform_attention(
	        state['''MultiHeadDotProductAttention_0''']['''key''']['''kernel''']	),
	    prefix
	    + '''attention.attention.value.bias''': transform_attention(
	        state['''MultiHeadDotProductAttention_0''']['''value''']['''bias''']	),
	    prefix
	    + '''attention.attention.value.weight''': transform_attention(
	        state['''MultiHeadDotProductAttention_0''']['''value''']['''kernel''']	),
	    prefix + '''attention.output.dense.bias''': state['''MultiHeadDotProductAttention_0''']['''out''']['''bias'''],
	    prefix
	    + '''attention.output.dense.weight''': transform_attention_output_weight(
	        state['''MultiHeadDotProductAttention_0''']['''out''']['''kernel''']	),
	}

	return new_state






def 						lowercase				(	_snake_case		: int	)	->List[str]:
	"""simple docstring"""





	return sum([1 if '''encoderblock_''' in k else 0 for k in state_dict['''optimizer''']['''target''']['''Transformer'''].keys()]	)






def 						lowercase				(	_snake_case		: Optional[Any]	,  _snake_case		: Optional[int]=False	)	->Union[str, Any]:
	"""simple docstring"""





	__snake_case      :		Tuple         =							get_n_layers(_snake_case	)

	__snake_case      :		str         =							OrderedDict()

	__snake_case      :		List[Any]         =							state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''bias''']
	__snake_case      :		List[str]         =							state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''scale''']

	__snake_case      :		Optional[Any]         =							np.transpose(
	    state_dict['''optimizer''']['''target''']['''embedding''']['''kernel''']	,  (4, 3, 0, 1, 2)	)
	__snake_case      :		Optional[Any]         =							state_dict['''optimizer''']['''target''']['''embedding''']['''bias''']

	__snake_case      :		Union[str, Any]         =							state_dict['''optimizer''']['''target''']['''cls''']
	__snake_case      :		List[str]         =							state_dict['''optimizer''']['''target''']['''Transformer''']['''posembed_input'''][
	    '''pos_embedding'''
	]

	for i in range(_snake_case	):
		new_state.update(transform_state_encoder_block(_snake_case	,  _snake_case	)	)

	if classification_head:
		__snake_case      :		List[Any]         =							{'''vivit.''' + k: v for k, v in new_state.items()}
		__snake_case      :		str         =							np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''kernel''']	)
		__snake_case      :		int         =							np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''bias''']	)

	return {k: torch.tensor(_snake_case	) for k, v in new_state.items()}






def 						lowercase				(	)	->VivitImageProcessor:
	"""simple docstring"""





	__snake_case      :		List[str]         =							VivitImageProcessor()

	assert extractor.do_resize is True
	assert extractor.size == {"shortest_edge": 256}
	assert extractor.do_center_crop is True
	assert extractor.crop_size == {"width": 224, "height": 224}
	assert extractor.resample == PILImageResampling.BILINEAR

	# here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
	# one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
	# which effectively means no normalization (and ViViT does not overwrite those when calling this func)
	assert extractor.do_normalize is False
	assert extractor.do_rescale is True
	assert extractor.rescale_factor == 1 / 255

	# zero-centering = True in original implementation
	assert extractor.do_zero_centering is True

	return extractor






def 						lowercase				(	_snake_case		: str	)	->Optional[Any]:
	"""simple docstring"""





	__snake_case      :		Tuple         =							'''checkpoint'''

	if not os.path.exists(_snake_case	):
		download_checkpoint(_snake_case	)

	__snake_case      :		List[Any]         =							restore_checkpoint(_snake_case	,  _snake_case	)
	__snake_case      :		Union[str, Any]         =							transform_state(_snake_case	,  classification_head=_snake_case	)

	__snake_case      :		Optional[Any]         =							get_vivit_config()

	assert config.image_size == 224
	assert config.num_frames == 32

	__snake_case      :		List[str]         =							VivitForVideoClassification(_snake_case	)
	model.load_state_dict(_snake_case	)
	model.eval()

	__snake_case      :		Tuple         =							get_processor()

	__snake_case      :		Any         =							prepare_video()
	__snake_case      :		List[str]         =							extractor(_snake_case	,  return_tensors='''pt'''	)

	__snake_case      :		Union[str, Any]         =							model(**_snake_case	)

	__snake_case      :		List[str]         =							torch.Size([1, 400]	)
	__snake_case      :		Optional[int]         =							torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]	)

	assert outputs.logits.shape == expected_shape
	assert torch.allclose(outputs.logits[0, :5]	,  _snake_case	,  atol=1e-4	), outputs.logits[0, :5]

	model.save_pretrained(_snake_case	)
	extractor.save_pretrained(_snake_case	)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							argparse.ArgumentParser()

			parser.add_argument("""--output_model_name""", """-o""", type=str, help="""Output path for the converted HuggingFace model""")

			SCREAMING_SNAKE_CASE					:       List[str]							   =							parser.parse_args()
			convert(args.output_model_name)





