
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def 						UpperCAmelCase			(	a_		,   a_=0.999		,   a_="cosine"		,   )		->	Optional[Any]:







				"""simple docstring"""



				if alpha_transform_type == "cosine":

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

				elif alpha_transform_type == "exp":

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

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

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





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     [e.name for e in KarrasDiffusionSchedulers]
	snake_case_					    =     2






	@register_to_config
	def __init__(       self					: Optional[Any]							,A					: int = 10_00							,A					: float = 0.0_00_85							,A					: float = 0.0_12							,A					: str = "linear"							,A					: Optional[Union[np.ndarray, List[float]]] = None							,A					: str = "epsilon"							,A					: str = "linspace"							,A					: int = 0							,):
					if trained_betas is not None:
									__A   			= torch.tensor(A							,dtype=torch.floataa )
					elif beta_schedule == "linear":
									__A   			= torch.linspace(A							,A							,A							,dtype=torch.floataa )
					elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									__A   			= (
									    torch.linspace(beta_start**0.5							,beta_end**0.5							,A							,dtype=torch.floataa ) ** 2
									)
					elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									__A   			= betas_for_alpha_bar(A )
					else:
									raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )

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

					#  set all values
					self.set_timesteps(A							,A							,A )






	def 				UpperCamelCase_  (       self					: Dict							,A					: List[str]							,A					: List[str]=None ):
					if schedule_timesteps is None:
									__A   			= self.timesteps

					__A   			= (schedule_timesteps == timestep).nonzero()

					# The sigma index that is taken for the **very** first `step`
					# is always the second index (or the last index if there is only 1)
					# This way we can ensure we don't accidentally skip a sigma in
					# case we start in the middle of the denoising schedule (e.g. for image-to-image)
					if len(self._index_counter ) == 0:
									__A   			= 1 if len(A ) > 1 else 0
					else:
									__A   			= timestep.cpu().item() if torch.is_tensor(A ) else timestep
									__A   			= self._index_counter[timestep_int]

					return indices[pos].item()






	@property
	def 				UpperCamelCase_  (       self					: Any ):
					# 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 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: torch.FloatTensor							,A					: Union[float, torch.FloatTensor]							,):
					__A   			= self.index_for_timestep(A )

					if self.state_in_first_order:
									__A   			= self.sigmas[step_index]
					else:
									__A   			= self.sigmas_interpol[step_index]

					__A   			= sample / ((sigma**2 + 1) ** 0.5)
					return sample






	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: int							,A					: Union[str, torch.device] = None							,A					: Optional[int] = None							,):
					__A   			= num_inference_steps

					__A   			= num_train_timesteps or self.config.num_train_timesteps

					# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
					if self.config.timestep_spacing == "linspace":
									__A   			= np.linspace(0							,num_train_timesteps - 1							,A							,dtype=A )[::-1].copy()
					elif self.config.timestep_spacing == "leading":
									__A   			= 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
									__A   			= (np.arange(0							,A ) * step_ratio).round()[::-1].copy().astype(A )
									timesteps += self.config.steps_offset
					elif self.config.timestep_spacing == "trailing":
									__A   			= 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
									__A   			= (np.arange(A							,0							,-step_ratio )).round().copy().astype(A )
									timesteps -= 1
					else:
									raise ValueError(
									    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''' )

					__A   			= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
					__A   			= torch.from_numpy(np.log(A ) ).to(A )

					__A   			= np.interp(A							,np.arange(0							,len(A ) )							,A )
					__A   			= np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
					__A   			= torch.from_numpy(A ).to(device=A )

					# interpolate sigmas
					__A   			= sigmas.log().lerp(sigmas.roll(1 ).log()							,0.5 ).exp()

					__A   			= torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
					__A   			= torch.cat(
					    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )

					if str(A ).startswith("mps" ):
									# mps does not support float64
									__A   			= torch.from_numpy(A ).to(A							,dtype=torch.floataa )
					else:
									__A   			= torch.from_numpy(A ).to(A )

					# interpolate timesteps
					__A   			= self.sigma_to_t(A ).to(A							,dtype=timesteps.dtype )
					__A   			= torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])							,dim=-1 ).flatten()

					__A   			= torch.cat([timesteps[:1], interleaved_timesteps] )

					__A   			= None

					# for exp beta schedules, such as the one for `pipeline_shap_e.py`
					# we need an index counter
					__A   			= defaultdict(A )






	def 				UpperCamelCase_  (       self					: Any							,A					: Any ):
					# get log sigma
					__A   			= sigma.log()

					# get distribution
					__A   			= log_sigma - self.log_sigmas[:, None]

					# get sigmas range
					__A   			= dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
					__A   			= low_idx + 1

					__A   			= self.log_sigmas[low_idx]
					__A   			= self.log_sigmas[high_idx]

					# interpolate sigmas
					__A   			= (low - log_sigma) / (low - high)
					__A   			= w.clamp(0							,1 )

					# transform interpolation to time range
					__A   			= (1 - w) * low_idx + w * high_idx
					__A   			= t.view(sigma.shape )
					return t






	@property
	def 				UpperCamelCase_  (       self					: Dict ):
					return self.sample is None






	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: Union[torch.FloatTensor, np.ndarray]							,A					: Union[float, torch.FloatTensor]							,A					: Union[torch.FloatTensor, np.ndarray]							,A					: bool = True							,):
					__A   			= self.index_for_timestep(A )

					# advance index counter by 1
					__A   			= timestep.cpu().item() if torch.is_tensor(A ) else timestep
					self._index_counter[timestep_int] += 1

					if self.state_in_first_order:
									__A   			= self.sigmas[step_index]
									__A   			= self.sigmas_interpol[step_index + 1]
									__A   			= self.sigmas[step_index + 1]
					else:
									# 2nd order / KDPM2's method
									__A   			= self.sigmas[step_index - 1]
									__A   			= self.sigmas_interpol[step_index]
									__A   			= self.sigmas[step_index]

					# currently only gamma=0 is supported. This usually works best anyways.
					# We can support gamma in the future but then need to scale the timestep before
					# passing it to the model which requires a change in API
					__A   			= 0
					__A   			= sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

					# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
					if self.config.prediction_type == "epsilon":
									__A   			= sigma_hat if self.state_in_first_order else sigma_interpol
									__A   			= sample - sigma_input * model_output
					elif self.config.prediction_type == "v_prediction":
									__A   			= sigma_hat if self.state_in_first_order else sigma_interpol
									__A   			= model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
									    sample / (sigma_input**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`''' )

					if self.state_in_first_order:
									# 2. Convert to an ODE derivative for 1st order
									__A   			= (sample - pred_original_sample) / sigma_hat
									# 3. delta timestep
									__A   			= sigma_interpol - sigma_hat

									# store for 2nd order step
									__A   			= sample
					else:
									# DPM-Solver-2
									# 2. Convert to an ODE derivative for 2nd order
									__A   			= (sample - pred_original_sample) / sigma_interpol

									# 3. delta timestep
									__A   			= sigma_next - sigma_hat

									__A   			= self.sample
									__A   			= None

					__A   			= sample + derivative * dt

					if not return_dict:
									return (prev_sample,)

					return SchedulerOutput(prev_sample=A )






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

					__A   			= [self.index_for_timestep(A							,A ) for t in timesteps]

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

					__A   			= original_samples + noise * sigma
					return noisy_samples







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


import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint


def 						UpperCAmelCase			(	a_						)		->	List[str]:







				"""simple docstring"""



				return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()						)


def 						UpperCAmelCase			(	a_		,   a_						)		->	Tuple:







				"""simple docstring"""



				__A   			= {}

				for key, value in state_dict.items():
								if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
												continue

								__A   			= key.replace("heads.cmd.mim_head.cls.predictions"		,   "mmm_image_head"						)
								__A   			= key.replace("heads.cmd.mlm_head.cls.predictions"		,   "mmm_text_head"						)
								__A   			= key.replace("heads.cmd.itm_head.cls"		,   "itm_head"						)
								__A   			= key.replace("heads.cmd.itm_head.pooler"		,   "itm_head.pooler"						)
								__A   			= key.replace("heads.cmd.clip_head.logit_scale"		,   "flava.logit_scale"						)
								__A   			= key.replace("heads.fairseq_mlm.cls.predictions"		,   "mlm_head"						)
								__A   			= key.replace("heads.imagenet.mim_head.cls.predictions"		,   "mim_head"						)
								__A   			= key.replace("mm_text_projection"		,   "flava.text_to_mm_projection"						)
								__A   			= key.replace("mm_image_projection"		,   "flava.image_to_mm_projection"						)
								__A   			= key.replace("image_encoder.module"		,   "flava.image_model"						)
								__A   			= key.replace("text_encoder.module"		,   "flava.text_model"						)
								__A   			= key.replace("mm_encoder.module.encoder.cls_token"		,   "flava.multimodal_model.cls_token"						)
								__A   			= key.replace("mm_encoder.module"		,   "flava.multimodal_model"						)
								__A   			= key.replace("text_projection"		,   "flava.text_projection"						)
								__A   			= key.replace("image_projection"		,   "flava.image_projection"						)

								__A   			= value.float()

				for key, value in codebook_state_dict.items():
								__A   			= value

				return upgrade


@torch.no_grad()
def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_=None						)		->	Tuple:







				"""simple docstring"""



				if config_path is not None:
								__A   			= FlavaConfig.from_pretrained(a_						)
				else:
								__A   			= FlavaConfig()

				__A   			= FlavaForPreTraining(a_						).eval()

				__A   			= convert_dalle_checkpoint(a_		,   a_		,   save_checkpoint=a_						)

				if os.path.exists(a_						):
								__A   			= torch.load(a_		,   map_location="cpu"						)
				else:
								__A   			= torch.hub.load_state_dict_from_url(a_		,   map_location="cpu"						)

				__A   			= upgrade_state_dict(a_		,   a_						)
				hf_model.load_state_dict(a_						)
				__A   			= hf_model.state_dict()
				__A   			= count_parameters(a_						)
				__A   			= count_parameters(a_						) + count_parameters(a_						)

				assert torch.allclose(a_		,   a_		,   atol=1E-3						)

				hf_model.save_pretrained(a_						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Any							 =				argparse.ArgumentParser()
					parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
					parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
					parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				parser.parse_args()

					convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
