




'''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						(      lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			"https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint"

		with open(lowercase_		,				"wb"    ) as f:
				with requests.get(lowercase_		,				stream=lowercase_    ) as req:
						for chunk in req.iter_content(chunk_size=2048    ):
								f.write(lowercase_    )






def 						_lowercase						(      ):



		'''simple docstring'''







		_snake_case      			=			VivitConfig()

		_snake_case      			=			400
		_snake_case      			=			"huggingface/label-files"
		_snake_case      			=			"kinetics400-id2label.json"

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






def 						_lowercase						(      ):



		'''simple docstring'''







		_snake_case      			=			hf_hub_download(
		    repo_id="hf-internal-testing/spaghetti-video"		,				filename="eating_spaghetti_32_frames.npy"		,				repo_type="dataset"    )
		_snake_case      			=			np.load(lowercase_    )
		return list(lowercase_    )






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







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

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

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






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







		return current.flatten()






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







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






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







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






def 						_lowercase						(      lowercase_		,				lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			state_dict["optimizer"]["target"]["Transformer"][f'''encoderblock_{i}''']

		_snake_case      			=			f'''encoder.layer.{i}.'''
		_snake_case      			=			{
		    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						(      lowercase_    ):



		'''simple docstring'''







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






def 						_lowercase						(      lowercase_		,				lowercase_=False    ):



		'''simple docstring'''







		_snake_case      			=			get_n_layers(lowercase_    )

		_snake_case      			=			OrderedDict()

		_snake_case      			=			state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["bias"]
		_snake_case      			=			state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["scale"]

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

		_snake_case      			=			state_dict["optimizer"]["target"]["cls"]
		_snake_case      			=			state_dict["optimizer"]["target"]["Transformer"]["posembed_input"][
		    "pos_embedding"
		]

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

		if classification_head:
				_snake_case      			=			{"vivit." + k: v for k, v in new_state.items()}
				_snake_case      			=			np.transpose(state_dict["optimizer"]["target"]["output_projection"]["kernel"]    )
				_snake_case      			=			np.transpose(state_dict["optimizer"]["target"]["output_projection"]["bias"]    )

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






def 						_lowercase						(      ):



		'''simple docstring'''







		_snake_case      			=			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						(      lowercase_    ):



		'''simple docstring'''







		_snake_case      			=			"checkpoint"

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

		_snake_case      			=			restore_checkpoint(lowercase_		,				lowercase_    )
		_snake_case      			=			transform_state(lowercase_		,				classification_head=lowercase_    )

		_snake_case      			=			get_vivit_config()

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

		_snake_case      			=			VivitForVideoClassification(lowercase_    )
		model.load_state_dict(lowercase_    )
		model.eval()

		_snake_case      			=			get_processor()

		_snake_case      			=			prepare_video()
		_snake_case      			=			extractor(lowercase_		,				return_tensors="pt"    )

		_snake_case      			=			model(**lowercase_    )

		_snake_case      			=			torch.Size([1, 400]    )
		_snake_case      			=			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]		,				lowercase_		,				atol=1e-4    ), outputs.logits[0, :5]

		model.save_pretrained(lowercase_    )
		extractor.save_pretrained(lowercase_    )


if __name__ == "__main__":
						_lowerCamelCase				:		Dict				      =    argparse.ArgumentParser()

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

						_lowerCamelCase				:		Any				      =    parser.parse_args()
						convert(args.output_model_name)











'''simple docstring'''







import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


_lowerCamelCase				:		Optional[Any]				      =    logging.get_logger(__name__)  # pylint: disable=invalid-name





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					lowercase__       =					42
					lowercase__       =					None






def 						_lowercase						(      lowercase_		,				lowercase_=0.999		,				lowercase_="cosine"		,				):



		'''simple docstring'''







		if alpha_transform_type == "cosine":

				def alpha_bar_fn(lowercase_    ):
						return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(lowercase_    ):
						return math.exp(t * -12.0    )

		else:
				raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''    )

		_snake_case      			=			[]
		for i in range(lowercase_    ):
				_snake_case      			=			i / num_diffusion_timesteps
				_snake_case      			=			(i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(lowercase_    ) / alpha_bar_fn(lowercase_    )		,				lowercase_    )    )
		return torch.tensor(lowercase_		,				dtype=torch.floataa    )





class 	lowerCamelCase_				(			UpperCAmelCase_,			UpperCAmelCase_				):




					'''simple docstring'''






					lowercase__       =					[e.name for e in KarrasDiffusionSchedulers]
					lowercase__       =					1






					@register_to_config
					def __init__(       self,    __UpperCamelCase = 1_000,    __UpperCamelCase = 0.00_01,    __UpperCamelCase = 0.02,    __UpperCamelCase = "linear",    __UpperCamelCase = None,    __UpperCamelCase = "epsilon",    __UpperCamelCase = "linspace",    __UpperCamelCase = 0,    ):
							if trained_betas is not None:
									_snake_case      			=			torch.tensor(__UpperCamelCase,    dtype=torch.floataa					)
							elif beta_schedule == "linear":
									_snake_case      			=			torch.linspace(__UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    dtype=torch.floataa					)
							elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									_snake_case      			=			(
									    torch.linspace(beta_start**0.5,    beta_end**0.5,    __UpperCamelCase,    dtype=torch.floataa					) ** 2
									)
							elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									_snake_case      			=			betas_for_alpha_bar(__UpperCamelCase					)
							else:
									raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''					)

							_snake_case      			=			1.0 - self.betas
							_snake_case      			=			torch.cumprod(self.alphas,    dim=0					)

							_snake_case      			=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
							_snake_case      			=			np.concatenate([sigmas[::-1], [0.0]]					).astype(np.floataa					)
							_snake_case      			=			torch.from_numpy(__UpperCamelCase					)

							# setable values
							_snake_case      			=			None
							_snake_case      			=			np.linspace(0,    num_train_timesteps - 1,    __UpperCamelCase,    dtype=__UpperCamelCase					)[::-1].copy()
							_snake_case      			=			torch.from_numpy(__UpperCamelCase					)
							_snake_case      			=			False






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							# standard deviation of the initial noise distribution
							if self.config.timestep_spacing in ["linspace", "trailing"]:
									return self.sigmas.max()

							return (self.sigmas.max() ** 2 + 1) ** 0.5






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase					):
							if isinstance(__UpperCamelCase,    torch.Tensor					):
									_snake_case      			=			timestep.to(self.timesteps.device					)
							_snake_case      			=			(self.timesteps == timestep).nonzero().item()
							_snake_case      			=			self.sigmas[step_index]
							_snake_case      			=			sample / ((sigma**2 + 1) ** 0.5)
							_snake_case      			=			True
							return sample






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase = None					):
							_snake_case      			=			num_inference_steps

							# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
							if self.config.timestep_spacing == "linspace":
									_snake_case      			=			np.linspace(0,    self.config.num_train_timesteps - 1,    __UpperCamelCase,    dtype=__UpperCamelCase					)[
									    ::-1
									].copy()
							elif self.config.timestep_spacing == "leading":
									_snake_case      			=			self.config.num_train_timesteps // self.num_inference_steps
									# creates integer timesteps by multiplying by ratio
									# casting to int to avoid issues when num_inference_step is power of 3
									_snake_case      			=			(np.arange(0,    __UpperCamelCase					) * step_ratio).round()[::-1].copy().astype(__UpperCamelCase					)
									timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
									_snake_case      			=			self.config.num_train_timesteps / self.num_inference_steps
									# creates integer timesteps by multiplying by ratio
									# casting to int to avoid issues when num_inference_step is power of 3
									_snake_case      			=			(np.arange(self.config.num_train_timesteps,    0,    -step_ratio					)).round().copy().astype(__UpperCamelCase					)
									timesteps -= 1
							else:
									raise ValueError(
									    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''					)

							_snake_case      			=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
							_snake_case      			=			np.interp(__UpperCamelCase,    np.arange(0,    len(__UpperCamelCase					)					),    __UpperCamelCase					)
							_snake_case      			=			np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
							_snake_case      			=			torch.from_numpy(__UpperCamelCase					).to(device=__UpperCamelCase					)
							if str(__UpperCamelCase					).startswith("mps"					):
									# mps does not support float64
									_snake_case      			=			torch.from_numpy(__UpperCamelCase					).to(__UpperCamelCase,    dtype=torch.floataa					)
							else:
									_snake_case      			=			torch.from_numpy(__UpperCamelCase					).to(device=__UpperCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase = None,    __UpperCamelCase = True,    ):

							if (
							    isinstance(__UpperCamelCase,    __UpperCamelCase					)
							    or isinstance(__UpperCamelCase,    torch.IntTensor					)
							    or isinstance(__UpperCamelCase,    torch.LongTensor					)
							):
									raise ValueError(
									    (
									        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
									        " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
									        " one of the `scheduler.timesteps` as a timestep."
									    ),    )

							if not self.is_scale_input_called:
									logger.warning(
									    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
									    "See `StableDiffusionPipeline` for a usage example."					)

							if isinstance(__UpperCamelCase,    torch.Tensor					):
									_snake_case      			=			timestep.to(self.timesteps.device					)

							_snake_case      			=			(self.timesteps == timestep).nonzero().item()
							_snake_case      			=			self.sigmas[step_index]

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							if self.config.prediction_type == "epsilon":
									_snake_case      			=			sample - sigma * model_output
							elif self.config.prediction_type == "v_prediction":
									# * c_out + input * c_skip
									_snake_case      			=			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
							elif self.config.prediction_type == "sample":
									raise NotImplementedError("prediction_type not implemented yet: sample"					)
							else:
									raise ValueError(
									    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''					)

							_snake_case      			=			self.sigmas[step_index]
							_snake_case      			=			self.sigmas[step_index + 1]
							_snake_case      			=			(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
							_snake_case      			=			(sigma_to**2 - sigma_up**2) ** 0.5

							# 2. Convert to an ODE derivative
							_snake_case      			=			(sample - pred_original_sample) / sigma

							_snake_case      			=			sigma_down - sigma

							_snake_case      			=			sample + derivative * dt

							_snake_case      			=			model_output.device
							_snake_case      			=			randn_tensor(model_output.shape,    dtype=model_output.dtype,    device=__UpperCamelCase,    generator=__UpperCamelCase					)

							_snake_case      			=			prev_sample + noise * sigma_up

							if not return_dict:
									return (prev_sample,)

							return EulerAncestralDiscreteSchedulerOutput(
							    prev_sample=__UpperCamelCase,    pred_original_sample=__UpperCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    ):
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							_snake_case      			=			self.sigmas.to(device=original_samples.device,    dtype=original_samples.dtype					)
							if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCamelCase					):
									# mps does not support float64
									_snake_case      			=			self.timesteps.to(original_samples.device,    dtype=torch.floataa					)
									_snake_case      			=			timesteps.to(original_samples.device,    dtype=torch.floataa					)
							else:
									_snake_case      			=			self.timesteps.to(original_samples.device					)
									_snake_case      			=			timesteps.to(original_samples.device					)

							_snake_case      			=			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

							_snake_case      			=			sigmas[step_indices].flatten()
							while len(sigma.shape					) < len(original_samples.shape					):
									_snake_case      			=			sigma.unsqueeze(-1					)

							_snake_case      			=			original_samples + noise * sigma
							return noisy_samples






					def __len__(       self					):
							return self.config.num_train_timesteps





