
"""simple docstring"""


import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate



class 			lowerCAmelCase_			( snake_case__      ):



						'''simple docstring'''


						_lowerCamelCase:      Union[str, Any] =      CONFIG_NAME


						@register_to_config
						def __init__(	self       : int					,A_       : bool = True					,A_       : int = 8					,A_       : str = "lanczos"					,A_       : bool = True					,A_       : bool = False					,)       ->				Tuple:
							super().__init__()


						@staticmethod
						def   _SCREAMING_SNAKE_CASE (	A_       : np.ndarray )       ->				Any:
							if images.ndim == 3:
								A  				=				images[None, ...]
							A  				=				(images * 255).round().astype('uint8' )
							if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								A  				=				[Image.fromarray(image.squeeze()					,mode='L' ) for image in images]
							else:
								A  				=				[Image.fromarray(A_ ) for image in images]

							return pil_images


						@staticmethod
						def   _SCREAMING_SNAKE_CASE (	A_       : Union[List[PIL.Image.Image], PIL.Image.Image] )       ->				List[Any]:
							if not isinstance(A_					,A_ ):
								A  				=				[images]
							A  				=				[np.array(A_ ).astype(np.floataa ) / 2_55.0 for image in images]
							A  				=				np.stack(A_					,axis=0 )

							return images


						@staticmethod
						def   _SCREAMING_SNAKE_CASE (	A_       : np.ndarray )       ->				Optional[int]:
							if images.ndim == 3:
								A  				=				images[..., None]

							A  				=				torch.from_numpy(images.transpose(0					,3					,1					,2 ) )
							return images


						@staticmethod
						def   _SCREAMING_SNAKE_CASE (	A_       : torch.FloatTensor )       ->				Optional[int]:
							A  				=				images.cpu().permute(0					,2					,3					,1 ).float().numpy()
							return images


						@staticmethod
						def   _SCREAMING_SNAKE_CASE (	A_       : str )       ->				Tuple:
							return 2.0 * images - 1.0


						@staticmethod
						def   _SCREAMING_SNAKE_CASE (	A_       : Any )       ->				Dict:
							return (images / 2 + 0.5).clamp(0					,1 )


						@staticmethod
						def   _SCREAMING_SNAKE_CASE (	A_       : PIL.Image.Image )       ->				List[Any]:
							A  				=				image.convert('RGB' )
							return image


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any]					,A_       : PIL.Image.Image					,A_       : Optional[int] = None					,A_       : Optional[int] = None					,)       ->				Union[str, Any]:
							if height is None:
								A  				=				image.height
							if width is None:
								A  				=				image.width

							A  				=				(
							    x - x % self.config.vae_scale_factor for x in (width, height)
							)  # resize to integer multiple of vae_scale_factor
							A  				=				image.resize((width, height)					,resample=PIL_INTERPOLATION[self.config.resample] )
							return image


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any]					,A_       : Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]					,A_       : Optional[int] = None					,A_       : Optional[int] = None					,)       ->				Any:
							A  				=				(PIL.Image.Image, np.ndarray, torch.Tensor)
							if isinstance(A_					,A_ ):
								A  				=				[image]
							elif not (isinstance(A_					,A_ ) and all(isinstance(A_					,A_ ) for i in image )):
								raise ValueError(
								    F'Input is in incorrect format: {[type(A_ ) for i in image]}. Currently, we only support {", ".join(A_ )}' )

							if isinstance(image[0]					,PIL.Image.Image ):
								if self.config.do_convert_rgb:
									A  				=				[self.convert_to_rgb(A_ ) for i in image]
								if self.config.do_resize:
									A  				=				[self.resize(A_					,A_					,A_ ) for i in image]
								A  				=				self.pil_to_numpy(A_ )  # to np
								A  				=				self.numpy_to_pt(A_ )  # to pt

							elif isinstance(image[0]					,np.ndarray ):
								A  				=				np.concatenate(A_					,axis=0 ) if image[0].ndim == 4 else np.stack(A_					,axis=0 )
								A  				=				self.numpy_to_pt(A_ )
								A  				=				image.shape
								if self.config.do_resize and (
								    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
								):
									raise ValueError(
									    F'Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'
									    F'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor' )

							elif isinstance(image[0]					,torch.Tensor ):
								A  				=				torch.cat(A_					,axis=0 ) if image[0].ndim == 4 else torch.stack(A_					,axis=0 )
								A  				=				image.shape

								# don't need any preprocess if the image is latents
								if channel == 4:
									return image

								if self.config.do_resize and (
								    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
								):
									raise ValueError(
									    F'Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'
									    F'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor' )

        # expected range [0,1], normalize to [-1,1]
							A  				=				self.config.do_normalize
							if image.min() < 0:
								warnings.warn(
								    'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
								    F'when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'					,A_					,)
								A  				=				False

							if do_normalize:
								A  				=				self.normalize(A_ )

							return image




						def   _SCREAMING_SNAKE_CASE (	self       : Any					,A_       : torch.FloatTensor					,A_       : str = "pil"					,A_       : Optional[List[bool]] = None					,)       ->				Any:
							if not isinstance(A_					,torch.Tensor ):
								raise ValueError(
								    F'Input for postprocessing is in incorrect format: {type(A_ )}. We only support pytorch tensor' )
							if output_type not in ["latent", "pt", "np", "pil"]:
								A  				=				(
								    F'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
								    "`pil`, `np`, `pt`, `latent`"
								)
								deprecate('Unsupported output_type'					,'1.0.0'					,A_					,standard_warn=A_ )
								A  				=				"np"

							if output_type == "latent":
								return image

							if do_denormalize is None:
								A  				=				[self.config.do_normalize] * image.shape[0]

							A  				=				torch.stack(
							    [self.denormalize(image[i] ) if do_denormalize[i] else image[i] for i in range(image.shape[0] )] )

							if output_type == "pt":
								return image

							A  				=				self.pt_to_numpy(A_ )

							if output_type == "np":
								return image

							if output_type == "pil":
								return self.numpy_to_pil(A_ )



class 			lowerCAmelCase_			( snake_case__      ):



						'''simple docstring'''


						_lowerCamelCase:      str =      CONFIG_NAME


						@register_to_config
						def __init__(	self       : Any					,A_       : bool = True					,A_       : int = 8					,A_       : str = "lanczos"					,A_       : bool = True					,)       ->				Union[str, Any]:
							super().__init__()


						@staticmethod
						def   _SCREAMING_SNAKE_CASE (	A_       : int )       ->				Optional[int]:
							if images.ndim == 3:
								A  				=				images[None, ...]
							A  				=				(images * 255).round().astype('uint8' )
							if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								A  				=				[Image.fromarray(image.squeeze()					,mode='L' ) for image in images]
							else:
								A  				=				[Image.fromarray(image[:, :, :3] ) for image in images]

							return pil_images


						@staticmethod
						def   _SCREAMING_SNAKE_CASE (	A_       : Union[str, Any] )       ->				int:
							return image[:, :, 1] * 2**8 + image[:, :, 2]


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any]					,A_       : List[str] )       ->				int:
							if images.ndim == 3:
								A  				=				images[None, ...]
							A  				=				images[:, :, :, 3:]
							if images.shape[-1] == 6:
								A  				=				(images_depth * 255).round().astype('uint8' )
								A  				=				[
								    Image.fromarray(self.rgblike_to_depthmap(A_ )					,mode='I;16' ) for image_depth in images_depth
								]
							elif images.shape[-1] == 4:
								A  				=				(images_depth * 6_5535.0).astype(np.uintaa )
								A  				=				[Image.fromarray(A_					,mode='I;16' ) for image_depth in images_depth]
							else:
								raise Exception('Not supported' )

							return pil_images




						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int]					,A_       : torch.FloatTensor					,A_       : str = "pil"					,A_       : Optional[List[bool]] = None					,)       ->				int:
							if not isinstance(A_					,torch.Tensor ):
								raise ValueError(
								    F'Input for postprocessing is in incorrect format: {type(A_ )}. We only support pytorch tensor' )
							if output_type not in ["latent", "pt", "np", "pil"]:
								A  				=				(
								    F'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
								    "`pil`, `np`, `pt`, `latent`"
								)
								deprecate('Unsupported output_type'					,'1.0.0'					,A_					,standard_warn=A_ )
								A  				=				"np"

							if do_denormalize is None:
								A  				=				[self.config.do_normalize] * image.shape[0]

							A  				=				torch.stack(
							    [self.denormalize(image[i] ) if do_denormalize[i] else image[i] for i in range(image.shape[0] )] )

							A  				=				self.pt_to_numpy(A_ )

							if output_type == "np":
								if image.shape[-1] == 6:
									A  				=				np.stack([self.rgblike_to_depthmap(im[:, :, 3:] ) for im in image]					,axis=0 )
								else:
									A  				=				image[:, :, :, 3:]
								return image[:, :, :, :3], image_depth

							if output_type == "pil":
								return self.numpy_to_pil(A_ ), self.numpy_to_depth(A_ )
							else:
								raise Exception(F'This type {output_type} is not supported' )

"""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  _snake_case			(						snake_case__				:       Tuple       ):
	A  				=				'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=2048       ):
				f.write(snake_case__       )






def  _snake_case			(						):
	A  				=				VivitConfig()

	A  				=				400
	A  				=				'huggingface/label-files'
	A  				=				'kinetics400-id2label.json'

	A  				=				json.load(open(hf_hub_download(snake_case__		, snake_case__		, repo_type='dataset'       )		, 'r'       )       )
	A  				=				{int(snake_case__       ): v for k, v in idalabel.items()}
	A  				=				idalabel
	A  				=				{v: k for k, v in idalabel.items()}
	return config






def  _snake_case			(						):
	A  				=				hf_hub_download(
	    repo_id='hf-internal-testing/spaghetti-video'		, filename='eating_spaghetti_32_frames.npy'		, repo_type='dataset'       )
	A  				=				np.load(snake_case__       )
	return list(snake_case__       )






def  _snake_case			(						snake_case__				:       np.ndarray       ):
	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  _snake_case			(						snake_case__				:       np.ndarray       ):
	return current.flatten()






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






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






def  _snake_case			(						snake_case__				:       str		, snake_case__				:       List[str]       ):
	A  				=				state_dict['optimizer']['target']['Transformer'][F'encoderblock_{i}']

	A  				=				F'encoder.layer.{i}.'
	A  				=				{
	    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  _snake_case			(						snake_case__				:       Dict       ):
	return sum([1 if 'encoderblock_' in k else 0 for k in state_dict['optimizer']['target']['Transformer'].keys()]       )






def  _snake_case			(						snake_case__				:       Union[str, Any]		, snake_case__				:       Optional[int]=False       ):
	A  				=				get_n_layers(snake_case__       )

	A  				=				OrderedDict()

	A  				=				state_dict['optimizer']['target']['Transformer']['encoder_norm']['bias']
	A  				=				state_dict['optimizer']['target']['Transformer']['encoder_norm']['scale']

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

	A  				=				state_dict['optimizer']['target']['cls']
	A  				=				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:
		A  				=				{'vivit.' + k: v for k, v in new_state.items()}
		A  				=				np.transpose(state_dict['optimizer']['target']['output_projection']['kernel']       )
		A  				=				np.transpose(state_dict['optimizer']['target']['output_projection']['bias']       )

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






def  _snake_case			(						):
	A  				=				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  _snake_case			(						snake_case__				:       str       ):
	A  				=				'checkpoint'

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

	A  				=				restore_checkpoint(snake_case__		, snake_case__       )
	A  				=				transform_state(snake_case__		, classification_head=snake_case__       )

	A  				=				get_vivit_config()

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

	A  				=				VivitForVideoClassification(snake_case__       )
	model.load_state_dict(snake_case__       )
	model.eval()

	A  				=				get_processor()

	A  				=				prepare_video()
	A  				=				extractor(snake_case__		, return_tensors='pt'       )

	A  				=				model(**snake_case__       )

	A  				=				torch.Size([1, 400]       )
	A  				=				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__":
		_lowercase											=			argparse.ArgumentParser()

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

		_lowercase											=			parser.parse_args()
		convert(args.output_model_name)