




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 SchedulerMixin


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






@dataclass
class    __lowerCAmelCase			(	UpperCAmelCase__			):
			snake_case_				:			torch.FloatTensor






class    __lowerCAmelCase			(	UpperCAmelCase__ ,		UpperCAmelCase__			):
			snake_case_				:			List[str]								=					1







			@register_to_config
			def __init__(	self   :      Union[str, Any]   ,   snake_case__   :      int = 40   ,   snake_case__   :      float = 0.002   ,   snake_case__   :      float = 80.0   ,   snake_case__   :      float = 0.5   ,   snake_case__   :      float = 1.0   ,   snake_case__   :      float = 7.0   ,   snake_case__   :      bool = True   ,   ):




									"""simple docstring"""


									_UpperCAmelCase													=					sigma_max

									_UpperCAmelCase													=					np.linspace(0   ,   1   ,   snake_case__				)
									_UpperCAmelCase													=					self._convert_to_karras(snake_case__				)
									_UpperCAmelCase													=					self.sigma_to_t(snake_case__				)

									# setable values
									_UpperCAmelCase													=					None
									_UpperCAmelCase													=					torch.from_numpy(snake_case__				)
									_UpperCAmelCase													=					torch.from_numpy(snake_case__				)
									_UpperCAmelCase													=					False
									_UpperCAmelCase													=					False







			def UpperCamelCase      (	self   :      Any   ,   snake_case__   :      List[Any]   ,   snake_case__   :      List[str]=None				):




									"""simple docstring"""


									if schedule_timesteps is None:
															_UpperCAmelCase													=					self.timesteps

									_UpperCAmelCase													=					(schedule_timesteps == timestep).nonzero()
									return indices.item()







			def UpperCamelCase      (	self   :      Union[str, Any]   ,   snake_case__   :      torch.FloatTensor   ,   snake_case__   :      Union[float, torch.FloatTensor]				):




									"""simple docstring"""


									if isinstance(snake_case__   ,   torch.Tensor				):
															_UpperCAmelCase													=					timestep.to(self.timesteps.device				)
									_UpperCAmelCase													=					self.index_for_timestep(snake_case__				)
									_UpperCAmelCase													=					self.sigmas[step_idx]

									_UpperCAmelCase													=					sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

									_UpperCAmelCase													=					True
									return sample







			def UpperCamelCase      (	self   :      int   ,   snake_case__   :      Union[float, np.ndarray]				):




									"""simple docstring"""


									if not isinstance(snake_case__   ,   np.ndarray				):
															_UpperCAmelCase													=					np.array(snake_case__   ,   dtype=np.floataa				)

									_UpperCAmelCase													=					1_000 * 0.25 * np.log(sigmas + 1e-44				)

									return timesteps







			def UpperCamelCase      (	self   :      List[Any]   ,   snake_case__   :      Optional[int] = None   ,   snake_case__   :      Union[str, torch.device] = None   ,   snake_case__   :      Optional[List[int]] = None   ,   ):




									"""simple docstring"""


									if num_inference_steps is None and timesteps is None:
															raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."				)

									if num_inference_steps is not None and timesteps is not None:
															raise ValueError("Can only pass one of `num_inference_steps` or `timesteps`."				)

									# Follow DDPMScheduler custom timesteps logic
									if timesteps is not None:
															for i in range(1   ,   len(snake_case__				)				):
																					if timesteps[i] >= timesteps[i - 1]:
																											raise ValueError("`timesteps` must be in descending order."				)

															if timesteps[0] >= self.config.num_train_timesteps:
																					raise ValueError(
																					    F"""`timesteps` must start before `self.config.train_timesteps`:"""
																					    F""" {self.config.num_train_timesteps}."""				)

															_UpperCAmelCase													=					np.array(snake_case__   ,   dtype=np.intaa				)
															_UpperCAmelCase													=					True
									else:
															if num_inference_steps > self.config.num_train_timesteps:
																					raise ValueError(
																					    F"""`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"""
																					    F""" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"""
																					    F""" maximal {self.config.num_train_timesteps} timesteps."""				)

															_UpperCAmelCase													=					num_inference_steps

															_UpperCAmelCase													=					self.config.num_train_timesteps // self.num_inference_steps
															_UpperCAmelCase													=					(np.arange(0   ,   snake_case__				) * step_ratio).round()[::-1].copy().astype(np.intaa				)
															_UpperCAmelCase													=					False

									# Map timesteps to Karras sigmas directly for multistep sampling
									# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
									_UpperCAmelCase													=					self.config.num_train_timesteps
									_UpperCAmelCase													=					timesteps[::-1].copy()
									_UpperCAmelCase													=					ramp / (num_train_timesteps - 1)
									_UpperCAmelCase													=					self._convert_to_karras(snake_case__				)
									_UpperCAmelCase													=					self.sigma_to_t(snake_case__				)

									_UpperCAmelCase													=					np.concatenate([sigmas, [self.sigma_min]]				).astype(np.floataa				)
									_UpperCAmelCase													=					torch.from_numpy(snake_case__				).to(device=snake_case__				)

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







			def UpperCamelCase      (	self   :      Tuple   ,   snake_case__   :      int				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.config.sigma_min
									_UpperCAmelCase													=					self.config.sigma_max

									_UpperCAmelCase													=					self.config.rho
									_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 UpperCamelCase      (	self   :      Tuple   ,   snake_case__   :      Optional[Any]				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.config.sigma_data

									_UpperCAmelCase													=					sigma_data**2 / (sigma**2 + sigma_data**2)
									_UpperCAmelCase													=					sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out







			def UpperCamelCase      (	self   :      Optional[Any]   ,   snake_case__   :      Union[str, Any]				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.config.sigma_min
									_UpperCAmelCase													=					self.config.sigma_data

									_UpperCAmelCase													=					sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
									_UpperCAmelCase													=					(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out







			def UpperCamelCase      (	self   :      int   ,   snake_case__   :      torch.FloatTensor   ,   snake_case__   :      Union[float, torch.FloatTensor]   ,   snake_case__   :      torch.FloatTensor   ,   snake_case__   :      Optional[torch.Generator] = None   ,   snake_case__   :      bool = True   ,   ):




									"""simple docstring"""


									if (
									    isinstance(snake_case__   ,   snake_case__				)
									    or isinstance(snake_case__   ,   torch.IntTensor				)
									    or isinstance(snake_case__   ,   torch.LongTensor				)
									):
															raise ValueError(
															    (
															        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
															        F""" `{self.__class__}.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(snake_case__   ,   torch.Tensor				):
															_UpperCAmelCase													=					timestep.to(self.timesteps.device				)

									_UpperCAmelCase													=					self.config.sigma_min
									_UpperCAmelCase													=					self.config.sigma_max

									_UpperCAmelCase													=					self.index_for_timestep(snake_case__				)

									# sigma_next corresponds to next_t in original implementation
									_UpperCAmelCase													=					self.sigmas[step_index]
									if step_index + 1 < self.config.num_train_timesteps:
															_UpperCAmelCase													=					self.sigmas[step_index + 1]
									else:
															# Set sigma_next to sigma_min
															_UpperCAmelCase													=					self.sigmas[-1]

									# Get scalings for boundary conditions
									_UpperCAmelCase							,			_UpperCAmelCase													=					self.get_scalings_for_boundary_condition(snake_case__				)

									# 1. Denoise model output using boundary conditions
									_UpperCAmelCase													=					c_out * model_output + c_skip * sample
									if self.config.clip_denoised:
															_UpperCAmelCase													=					denoised.clamp(-1   ,   1				)

									# 2. Sample z ~ N(0, s_noise^2 * I)
									# Noise is not used for onestep sampling.
									if len(self.timesteps				) > 1:
															_UpperCAmelCase													=					randn_tensor(
															    model_output.shape   ,   dtype=model_output.dtype   ,   device=model_output.device   ,   generator=snake_case__				)
									else:
															_UpperCAmelCase													=					torch.zeros_like(snake_case__				)
									_UpperCAmelCase													=					noise * self.config.s_noise

									_UpperCAmelCase													=					sigma_next.clamp(min=snake_case__   ,   max=snake_case__				)

									# 3. Return noisy sample
									# tau = sigma_hat, eps = sigma_min
									_UpperCAmelCase													=					denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

									if not return_dict:
															return (prev_sample,)

									return CMStochasticIterativeSchedulerOutput(prev_sample=snake_case__				)







			def UpperCamelCase      (	self   :      str   ,   snake_case__   :      torch.FloatTensor   ,   snake_case__   :      torch.FloatTensor   ,   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(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   :      Tuple				):




									"""simple docstring"""


									return self.config.num_train_timesteps










import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM


def 							__SCREAMING_SNAKE_CASE  (      snake_case_						):

						'''simple docstring'''


						if "model" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("model."     ,   ""						)
						if "norm1" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("norm1"     ,   "attention.output.LayerNorm"						)
						if "norm2" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("norm2"     ,   "output.LayerNorm"						)
						if "norm" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("norm"     ,   "LayerNorm"						)
						if "transformer" in orig_key:
												_UpperCAmelCase													=					orig_key.split("."						)[0].split("_"						)[-1]
												_UpperCAmelCase													=					orig_key.replace(f"""transformer_{layer_num}"""     ,   f"""encoder.layer.{layer_num}"""						)
						if "mha.attn" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("mha.attn"     ,   "attention.self"						)
						if "mha" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("mha"     ,   "attention"						)
						if "W_q" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("W_q"     ,   "self.query"						)
						if "W_k" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("W_k"     ,   "self.key"						)
						if "W_v" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("W_v"     ,   "self.value"						)
						if "ff1" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("ff1"     ,   "intermediate.dense"						)
						if "ff2" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("ff2"     ,   "output.dense"						)
						if "ff" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("ff"     ,   "output.dense"						)
						if "mlm_class" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("mlm.mlm_class"     ,   "cls.predictions.decoder"						)
						if "mlm" in orig_key:
												_UpperCAmelCase													=					orig_key.replace("mlm"     ,   "cls.predictions.transform"						)
						if "cls" not in orig_key:
												_UpperCAmelCase													=					"nystromformer." + orig_key

						return orig_key


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_						):

						'''simple docstring'''


						for key in orig_state_dict.copy().keys():
												_UpperCAmelCase													=					orig_state_dict.pop(snake_case_						)

												if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
																		continue
												else:
																		_UpperCAmelCase													=					val

						_UpperCAmelCase													=					orig_state_dict["cls.predictions.decoder.bias"]
						_UpperCAmelCase													=					(
						    torch.arange(config.max_position_embeddings						).expand((1, -1)						) + 2
						)

						return orig_state_dict


def 							__SCREAMING_SNAKE_CASE  (      snake_case_     ,   snake_case_     ,   snake_case_						):

						'''simple docstring'''


						_UpperCAmelCase													=					torch.load(snake_case_     ,   map_location="cpu"						)["model_state_dict"]
						_UpperCAmelCase													=					NystromformerConfig.from_json_file(snake_case_						)
						_UpperCAmelCase													=					NystromformerForMaskedLM(snake_case_						)

						_UpperCAmelCase													=					convert_checkpoint_helper(snake_case_     ,   snake_case_						)

						model.load_state_dict(snake_case_						)
						model.eval()
						model.save_pretrained(snake_case_						)

						print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""						)


if __name__ == "__main__":
				lowercase_     :    List[str]      =			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--pytorch_model_path', default=None, type=str, required=True, help='Path to Nystromformer pytorch checkpoint.'
				)
				parser.add_argument(
				    '--config_file',
				    default=None,
				    type=str,
				    required=True,
				    help='The json file for Nystromformer model config.',
				)
				parser.add_argument(
				    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
				)
				lowercase_     :    int      =			parser.parse_args()
				convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




