
"""simple docstring"""


from typing import List, Optional, Union

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

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

_lowercase											=			'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''






def  _snake_case			(						snake_case__				:       Tuple		, snake_case__				:       Union[str, Any]		, snake_case__				:       Any=8       ):
	A  				=				height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	A  				=				width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor






def  _snake_case			(						snake_case__				:       Dict		, snake_case__				:       int=512		, snake_case__				:       Dict=512       ):
	A  				=				pil_image.resize((w, h)		, resample=Image.BICUBIC		, reducing_gap=1       )
	A  				=				np.array(pil_image.convert('RGB'       )       )
	A  				=				arr.astype(np.floataa       ) / 127.5 - 1
	A  				=				np.transpose(A_		, [2, 0, 1]       )
	A  				=				torch.from_numpy(A_       ).unsqueeze(0       )
	return image



class 			lowerCAmelCase_			( UpperCamelCase__      ):



						'''simple docstring'''


						def __init__(	self       : Any					,A_       : UNetaDConditionModel					,A_       : DDPMScheduler					,A_       : VQModel					,)       ->				int:
							super().__init__()

							self.register_modules(
							    unet=__lowerCamelCase					,scheduler=__lowerCamelCase					,movq=__lowerCamelCase					,)
							A  				=				2 ** (len(self.movq.config.block_out_channels ) - 1)


						def   _SCREAMING_SNAKE_CASE (	self       : str					,A_       : Any					,A_       : Optional[int]					,A_       : List[str] )       ->				Tuple:
							A  				=				min(int(num_inference_steps * strength )					,__lowerCamelCase )

							A  				=				max(num_inference_steps - init_timestep					,0 )
							A  				=				self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start


						def   _SCREAMING_SNAKE_CASE (	self       : List[str]					,A_       : str					,A_       : List[str]					,A_       : List[str]					,A_       : List[Any]					,A_       : Tuple					,A_       : Union[str, Any]					,A_       : List[Any]=None )       ->				Tuple:


							if not isinstance(__lowerCamelCase					,(torch.Tensor, PIL.Image.Image, list) ):
								raise ValueError(
								    F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__lowerCamelCase )}' )

							A  				=				image.to(device=__lowerCamelCase					,dtype=__lowerCamelCase )

							A  				=				batch_size * num_images_per_prompt

							if image.shape[1] == 4:
								A  				=				image

							else:
								if isinstance(__lowerCamelCase					,__lowerCamelCase ) and len(__lowerCamelCase ) != batch_size:
									raise ValueError(
									    F'You have passed a list of generators of length {len(__lowerCamelCase )}, but requested an effective batch'
									    F' size of {batch_size}. Make sure the batch size matches the length of the generators.' )

								elif isinstance(__lowerCamelCase					,__lowerCamelCase ):
									A  				=				[
									    self.movq.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(__lowerCamelCase )
									]
									A  				=				torch.cat(__lowerCamelCase					,dim=0 )
								else:
									A  				=				self.movq.encode(__lowerCamelCase ).latent_dist.sample(__lowerCamelCase )

								A  				=				self.movq.config.scaling_factor * init_latents

							A  				=				torch.cat([init_latents]					,dim=0 )

							A  				=				init_latents.shape
							A  				=				randn_tensor(__lowerCamelCase					,generator=__lowerCamelCase					,device=__lowerCamelCase					,dtype=__lowerCamelCase )

							# get latents
							A  				=				self.scheduler.add_noise(__lowerCamelCase					,__lowerCamelCase					,__lowerCamelCase )

							A  				=				init_latents

							return latents


						def   _SCREAMING_SNAKE_CASE (	self       : List[str]					,A_       : Dict=0 )       ->				Any:


							if is_accelerate_available():
								from accelerate import cpu_offload
							else:
								raise ImportError('Please install accelerate via `pip install accelerate`' )

							A  				=				torch.device(F'cuda:{gpu_id}' )

							A  				=				[
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
								if cpu_offloaded_model is not None:
									cpu_offload(__lowerCamelCase					,__lowerCamelCase )


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int]					,A_       : Optional[int]=0 )       ->				Dict:


							if is_accelerate_available() and is_accelerate_version('>='					,'0.17.0.dev0' ):
								from accelerate import cpu_offload_with_hook
							else:
								raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.' )

							A  				=				torch.device(F'cuda:{gpu_id}' )

							if self.device.type != "cpu":
								self.to('cpu'					,silence_dtype_warnings=__lowerCamelCase )
								torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							A  				=				None
							for cpu_offloaded_model in [self.unet, self.movq]:
								A  				=				cpu_offload_with_hook(__lowerCamelCase					,__lowerCamelCase					,prev_module_hook=__lowerCamelCase )

							# We'll offload the last model manually.
							A  				=				hook


						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any] )       ->				Optional[int]:


							if not hasattr(self.unet					,'_hf_hook' ):
								return self.device
							for module in self.unet.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




						@torch.no_grad()
						@replace_example_docstring(__lowerCamelCase )
						def __call__(	self       : Tuple					,A_       : Union[torch.FloatTensor, List[torch.FloatTensor]]					,A_       : Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]					,A_       : Union[torch.FloatTensor, List[torch.FloatTensor]]					,A_       : torch.FloatTensor					,A_       : int = 512					,A_       : int = 512					,A_       : int = 100					,A_       : float = 4.0					,A_       : float = 0.3					,A_       : int = 1					,A_       : Optional[Union[torch.Generator, List[torch.Generator]]] = None					,A_       : Optional[str] = "pil"					,A_       : bool = True					,)       ->				Tuple:
							A  				=				self._execution_device

							A  				=				guidance_scale > 1.0

							if isinstance(__lowerCamelCase					,__lowerCamelCase ):
								A  				=				torch.cat(__lowerCamelCase					,dim=0 )
							if isinstance(__lowerCamelCase					,__lowerCamelCase ):
								A  				=				torch.cat(__lowerCamelCase					,dim=0 )
							if isinstance(__lowerCamelCase					,__lowerCamelCase ):
								A  				=				torch.cat(__lowerCamelCase					,dim=0 )

							A  				=				image_embeds.shape[0]

							if do_classifier_free_guidance:
								A  				=				image_embeds.repeat_interleave(__lowerCamelCase					,dim=0 )
								A  				=				negative_image_embeds.repeat_interleave(__lowerCamelCase					,dim=0 )
								A  				=				hint.repeat_interleave(__lowerCamelCase					,dim=0 )

							A  				=				torch.cat([negative_image_embeds, image_embeds]					,dim=0 ).to(dtype=self.unet.dtype					,device=__lowerCamelCase )
							A  				=				torch.cat([hint, hint]					,dim=0 ).to(dtype=self.unet.dtype					,device=__lowerCamelCase )

							if not isinstance(__lowerCamelCase					,__lowerCamelCase ):
								A  				=				[image]
							if not all(isinstance(__lowerCamelCase					,(PIL.Image.Image, torch.Tensor) ) for i in image ):
								raise ValueError(
								    F'Input is in incorrect format: {[type(__lowerCamelCase ) for i in image]}. Currently, we only support  PIL image and pytorch tensor' )

							A  				=				torch.cat([prepare_image(__lowerCamelCase					,__lowerCamelCase					,__lowerCamelCase ) for i in image]					,dim=0 )
							A  				=				image.to(dtype=image_embeds.dtype					,device=__lowerCamelCase )

							A  				=				self.movq.encode(__lowerCamelCase )["latents"]
							A  				=				latents.repeat_interleave(__lowerCamelCase					,dim=0 )
							self.scheduler.set_timesteps(__lowerCamelCase					,device=__lowerCamelCase )
							A  				=				self.get_timesteps(__lowerCamelCase					,__lowerCamelCase					,__lowerCamelCase )
							A  				=				timesteps[:1].repeat(batch_size * num_images_per_prompt )
							A  				=				downscale_height_and_width(__lowerCamelCase					,__lowerCamelCase					,self.movq_scale_factor )
							A  				=				self.prepare_latents(
							    __lowerCamelCase					,__lowerCamelCase					,__lowerCamelCase					,__lowerCamelCase					,image_embeds.dtype					,__lowerCamelCase					,__lowerCamelCase )
							for i, t in enumerate(self.progress_bar(__lowerCamelCase ) ):
								# expand the latents if we are doing classifier free guidance
								A  				=				torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents

								A  				=				{"image_embeds": image_embeds, "hint": hint}
								A  				=				self.unet(
								    sample=__lowerCamelCase					,timestep=__lowerCamelCase					,encoder_hidden_states=__lowerCamelCase					,added_cond_kwargs=__lowerCamelCase					,return_dict=__lowerCamelCase					,)[0]

								if do_classifier_free_guidance:
									A  				=				noise_pred.split(latents.shape[1]					,dim=1 )
									A  				=				noise_pred.chunk(2 )
									A  				=				variance_pred.chunk(2 )
									A  				=				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
									A  				=				torch.cat([noise_pred, variance_pred_text]					,dim=1 )

								if not (
								    hasattr(self.scheduler.config					,'variance_type' )
								    and self.scheduler.config.variance_type in ["learned", "learned_range"]
								):
									A  				=				noise_pred.split(latents.shape[1]					,dim=1 )

								# compute the previous noisy sample x_t -> x_t-1

								A  				=				self.scheduler.step(
								    __lowerCamelCase					,__lowerCamelCase					,__lowerCamelCase					,generator=__lowerCamelCase					,)[0]

							# post-processing
							A  				=				self.movq.decode(__lowerCamelCase					,force_not_quantize=__lowerCamelCase )["sample"]

							if output_type not in ["pt", "np", "pil"]:
								raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}' )

							if output_type in ["np", "pil"]:
								A  				=				image * 0.5 + 0.5
								A  				=				image.clamp(0					,1 )
								A  				=				image.cpu().permute(0					,2					,3					,1 ).float().numpy()

							if output_type == "pil":
								A  				=				self.numpy_to_pil(__lowerCamelCase )

							if not return_dict:
								return (image,)

							return ImagePipelineOutput(images=__lowerCamelCase )

"""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)