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

import numpy as np
import torch
from scipy import integrate

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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							_snake_case  		=	42
							_snake_case  		=	None





def 						A_			(				snake_case				: List[Any]			,   snake_case				: List[Any]=0.999			,   snake_case				: Union[str, Any]="cosine"			,   )   ->     Dict:



						'''simple docstring'''

						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

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

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






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






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



							@register_to_config
							def __init__(	self  ,						SCREAMING_SNAKE_CASE_ = 1000  ,						SCREAMING_SNAKE_CASE_ = 0.0_0_0_1  ,						SCREAMING_SNAKE_CASE_ = 0.0_2  ,						SCREAMING_SNAKE_CASE_ = "linear"  ,						SCREAMING_SNAKE_CASE_ = None  ,						SCREAMING_SNAKE_CASE_ = False  ,						SCREAMING_SNAKE_CASE_ = "epsilon"  ,						SCREAMING_SNAKE_CASE_ = "linspace"  ,						SCREAMING_SNAKE_CASE_ = 0  ,						)-> str:





													'''simple docstring'''






													if trained_betas is not None:
																			__UpperCamelCase  	=      torch.tensor(SCREAMING_SNAKE_CASE_  ,						dtype=torch.floataa      )
													elif beta_schedule == "linear":
																			__UpperCamelCase  	=      torch.linspace(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						dtype=torch.floataa      )
													elif beta_schedule == "scaled_linear":
																			# this schedule is very specific to the latent diffusion model.
																			__UpperCamelCase  	=      (
																			    torch.linspace(beta_start**0.5  ,						beta_end**0.5  ,						SCREAMING_SNAKE_CASE_  ,						dtype=torch.floataa      ) ** 2
																			)
													elif beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			__UpperCamelCase  	=      betas_for_alpha_bar(SCREAMING_SNAKE_CASE_      )
													else:
																			raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"      )

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

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

													# setable values
													__UpperCamelCase  	=      None
													__UpperCamelCase  	=      use_karras_sigmas
													self.set_timesteps(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      []
													__UpperCamelCase  	=      False



							@property
							def      A__		(	self      )-> Any:





													'''simple docstring'''






													if self.config.timestep_spacing in ["linspace", "trailing"]:
																			return self.sigmas.max()

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



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> torch.FloatTensor:





													'''simple docstring'''






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



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> List[str]:





													'''simple docstring'''






													def lms_derivative(SCREAMING_SNAKE_CASE_      ):
																			__UpperCamelCase  	=      1.0
																			for k in range(SCREAMING_SNAKE_CASE_      ):
																									if current_order == k:
																															continue
																									prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
																			return prod

													__UpperCamelCase  	=      integrate.quad(SCREAMING_SNAKE_CASE_  ,						self.sigmas[t]  ,						self.sigmas[t + 1]  ,						epsrel=1E-4      )[0]

													return integrated_coeff



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = None      )-> List[str]:





													'''simple docstring'''






													__UpperCamelCase  	=      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":
																			__UpperCamelCase  	=      np.linspace(0  ,						self.config.num_train_timesteps - 1  ,						SCREAMING_SNAKE_CASE_  ,						dtype=SCREAMING_SNAKE_CASE_      )[
																			    ::-1
																			].copy()
													elif self.config.timestep_spacing == "leading":
																			__UpperCamelCase  	=      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
																			__UpperCamelCase  	=      (np.arange(0  ,						SCREAMING_SNAKE_CASE_      ) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_      )
																			timesteps += self.config.steps_offset
													elif self.config.timestep_spacing == "trailing":
																			__UpperCamelCase  	=      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
																			__UpperCamelCase  	=      (np.arange(self.config.num_train_timesteps  ,						0  ,						-step_ratio      )).round().copy().astype(SCREAMING_SNAKE_CASE_      )
																			timesteps -= 1
													else:
																			raise ValueError(
																			    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."      )

													__UpperCamelCase  	=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
													__UpperCamelCase  	=      np.log(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      np.interp(SCREAMING_SNAKE_CASE_  ,						np.arange(0  ,						len(SCREAMING_SNAKE_CASE_      )      )  ,						SCREAMING_SNAKE_CASE_      )

													if self.use_karras_sigmas:
																			__UpperCamelCase  	=      self._convert_to_karras(in_sigmas=SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      np.array([self._sigma_to_t(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) for sigma in sigmas]      )

													__UpperCamelCase  	=      np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )

													__UpperCamelCase  	=      torch.from_numpy(SCREAMING_SNAKE_CASE_      ).to(device=SCREAMING_SNAKE_CASE_      )
													if str(SCREAMING_SNAKE_CASE_      ).startswith('''mps'''      ):
																			# mps does not support float64
																			__UpperCamelCase  	=      torch.from_numpy(SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_  ,						dtype=torch.floataa      )
													else:
																			__UpperCamelCase  	=      torch.from_numpy(SCREAMING_SNAKE_CASE_      ).to(device=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      []



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      np.log(SCREAMING_SNAKE_CASE_      )

													# get distribution
													__UpperCamelCase  	=      log_sigma - log_sigmas[:, np.newaxis]

													# get sigmas range
													__UpperCamelCase  	=      np.cumsum((dists >= 0)  ,						axis=0      ).argmax(axis=0      ).clip(max=log_sigmas.shape[0] - 2      )
													__UpperCamelCase  	=      low_idx + 1

													__UpperCamelCase  	=      log_sigmas[low_idx]
													__UpperCamelCase  	=      log_sigmas[high_idx]

													# interpolate sigmas
													__UpperCamelCase  	=      (low - log_sigma) / (low - high)
													__UpperCamelCase  	=      np.clip(SCREAMING_SNAKE_CASE_  ,						0  ,						1      )

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



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_      )-> torch.FloatTensor:





													'''simple docstring'''






													__UpperCamelCase  	=      in_sigmas[-1].item()
													__UpperCamelCase  	=      in_sigmas[0].item()

													__UpperCamelCase  	=      7.0  # 7.0 is the value used in the paper
													__UpperCamelCase  	=      np.linspace(0  ,						1  ,						self.num_inference_steps      )
													__UpperCamelCase  	=      sigma_min ** (1 / rho)
													__UpperCamelCase  	=      sigma_max ** (1 / rho)
													__UpperCamelCase  	=      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
													return sigmas



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = 4  ,						SCREAMING_SNAKE_CASE_ = True  ,						)-> Union[LMSDiscreteSchedulerOutput, Tuple]:





													'''simple docstring'''






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

													if isinstance(SCREAMING_SNAKE_CASE_  ,						torch.Tensor      ):
																			__UpperCamelCase  	=      timestep.to(self.timesteps.device      )
													__UpperCamelCase  	=      (self.timesteps == timestep).nonzero().item()
													__UpperCamelCase  	=      self.sigmas[step_index]

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													if self.config.prediction_type == "epsilon":
																			__UpperCamelCase  	=      sample - sigma * model_output
													elif self.config.prediction_type == "v_prediction":
																			# * c_out + input * c_skip
																			__UpperCamelCase  	=      model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
													elif self.config.prediction_type == "sample":
																			__UpperCamelCase  	=      model_output
													else:
																			raise ValueError(
																			    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"      )

													# 2. Convert to an ODE derivative
													__UpperCamelCase  	=      (sample - pred_original_sample) / sigma
													self.derivatives.append(SCREAMING_SNAKE_CASE_      )
													if len(self.derivatives      ) > order:
																			self.derivatives.pop(0      )

													# 3. Compute linear multistep coefficients
													__UpperCamelCase  	=      min(step_index + 1  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      [self.get_lms_coefficient(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ) for curr_order in range(SCREAMING_SNAKE_CASE_      )]

													# 4. Compute previous sample based on the derivatives path
													__UpperCamelCase  	=      sample + sum(
													    coeff * derivative for coeff, derivative in zip(SCREAMING_SNAKE_CASE_  ,						reversed(self.derivatives      )      )      )

													if not return_dict:
																			return (prev_sample,)

													return LMSDiscreteSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_  ,						pred_original_sample=SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						)-> torch.FloatTensor:





													'''simple docstring'''






													__UpperCamelCase  	=      self.sigmas.to(device=original_samples.device  ,						dtype=original_samples.dtype      )
													if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_      ):
																			# mps does not support float64
																			__UpperCamelCase  	=      self.timesteps.to(original_samples.device  ,						dtype=torch.floataa      )
																			__UpperCamelCase  	=      timesteps.to(original_samples.device  ,						dtype=torch.floataa      )
													else:
																			__UpperCamelCase  	=      self.timesteps.to(original_samples.device      )
																			__UpperCamelCase  	=      timesteps.to(original_samples.device      )

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

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

													__UpperCamelCase  	=      original_samples + noise * sigma
													return noisy_samples



							def __len__(	self      )-> Union[str, Any]:





													'''simple docstring'''






													return self.config.num_train_timesteps






import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel




def 						A_			(				snake_case				: List[str]			,   snake_case				: int			)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      pt_attn_layer.in_proj_weight.chunk(3			,   dim=0			)
						__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      pt_attn_layer.in_proj_bias.chunk(3			,   dim=0			)

						__UpperCamelCase  	=      pt_attn_layer.out_proj.weight
						__UpperCamelCase  	=      pt_attn_layer.out_proj.bias

						__UpperCamelCase  	=      q_proj
						__UpperCamelCase  	=      q_proj_bias

						__UpperCamelCase  	=      k_proj
						__UpperCamelCase  	=      k_proj_bias

						__UpperCamelCase  	=      v_proj
						__UpperCamelCase  	=      v_proj_bias

						__UpperCamelCase  	=      out_proj_weights
						__UpperCamelCase  	=      out_proj_bias




def 						A_			(				snake_case				: Optional[Any]			,   snake_case				: int			)   ->     Any:



						'''simple docstring'''

						copy_linear(hf_mlp.fca			,   pt_mlp.c_fc			)
						copy_linear(hf_mlp.fca			,   pt_mlp.c_proj			)




def 						A_			(				snake_case				: Union[str, Any]			,   snake_case				: Optional[Any]			)   ->     Union[str, Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_linear.weight
						__UpperCamelCase  	=      pt_linear.bias




def 						A_			(				snake_case				: Tuple			,   snake_case				: int			)   ->     str:



						'''simple docstring'''

						copy_linear(hf_layer.layer_norma			,   pt_layer.ln_a			)
						copy_linear(hf_layer.layer_norma			,   pt_layer.ln_a			)

						# copy MLP
						copy_mlp(hf_layer.mlp			,   pt_layer.mlp			)

						# copy attn
						copy_attn_layer(hf_layer.self_attn			,   pt_layer.attn			)




def 						A_			(				snake_case				: List[Any]			,   snake_case				: Any			)   ->     Optional[int]:



						'''simple docstring'''

						for hf_layer, pt_layer in zip(snake_case			,   snake_case			):
												copy_layer(snake_case			,   snake_case			)




def 						A_			(				snake_case				: str			,   snake_case				: Dict			)   ->     List[str]:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_model.token_embedding.weight
						__UpperCamelCase  	=      pt_model.positional_embedding

						# copy layer norm
						copy_linear(hf_encoder.final_layer_norm			,   pt_model.ln_final			)

						# copy hidden layers
						copy_layers(hf_encoder.encoder.layers			,   pt_model.transformer.resblocks			)




def 						A_			(				snake_case				: str			,   snake_case				: List[str]			)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_model.text_projection.data.T

						# copy text encoder
						copy_encoder(hf_model.text_model			,   snake_case			)




def 						A_			(				snake_case				: List[str]			,   snake_case				: Any			)   ->     str:



						'''simple docstring'''

						__UpperCamelCase  	=      pt_model.visual.proj.data.T

						# copy layer norms
						copy_linear(hf_model.vision_model.pre_layrnorm			,   pt_model.visual.ln_pre			)
						copy_linear(hf_model.vision_model.post_layernorm			,   pt_model.visual.ln_post			)

						# copy embeds
						__UpperCamelCase  	=      pt_model.visual.conva.weight.data
						__UpperCamelCase  	=      pt_model.visual.class_embedding
						__UpperCamelCase  	=      pt_model.visual.positional_embedding.data

						# copy encoder
						copy_layers(hf_model.vision_model.encoder.layers			,   pt_model.visual.transformer.resblocks			)




@torch.no_grad()
def 						A_			(				snake_case				: List[Any]			,   snake_case				: int			,   snake_case				: Any=None			)   ->     List[Any]:



						'''simple docstring'''

						if config_path is not None:
												__UpperCamelCase  	=      CLIPConfig.from_pretrained(snake_case			)
						else:
												__UpperCamelCase  	=      CLIPConfig(projection_dim=512			,   text_config={}			,   vision_config={}			)

						__UpperCamelCase  	=      CLIPModel(snake_case			).eval()

						__UpperCamelCase	,				__UpperCamelCase  	=      load(snake_case			,   device='''cpu'''			,   jit=snake_case			)
						__UpperCamelCase  	=      pt_model.eval()

						copy_text_model_and_projection(snake_case			,   snake_case			)
						copy_vison_model_and_projection(snake_case			,   snake_case			)
						__UpperCamelCase  	=      pt_model.logit_scale

						__UpperCamelCase  	=      torch.arange(0			,   77			).unsqueeze(0			)
						__UpperCamelCase  	=      torch.randn(1			,   3			,   224			,   224			)

						__UpperCamelCase  	=      hf_model(input_ids=snake_case			,   pixel_values=snake_case			,   return_dict=snake_case			)
						__UpperCamelCase  	=      hf_outputs.logits_per_image
						__UpperCamelCase  	=      hf_outputs.logits_per_text
						__UpperCamelCase	,				__UpperCamelCase  	=      pt_model(snake_case			,   snake_case			)

						assert torch.allclose(snake_case			,   snake_case			,   atol=1e-3			)
						assert torch.allclose(snake_case			,   snake_case			,   atol=1e-3			)

						hf_model.save_pretrained(snake_case			)


if __name__ == "__main__":
				lowercase__				:				Union[str, 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 fairseq checkpoint")
				parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
				lowercase__				:				str						=      parser.parse_args()

				convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




