




'''simple docstring'''







import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM






def 						_lowercase						(      lowercase_    ):



		'''simple docstring'''







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

		return orig_key






def 						_lowercase						(      lowercase_		,				lowercase_    ):



		'''simple docstring'''







		for key in orig_state_dict.copy().keys():
				_snake_case      			=			orig_state_dict.pop(lowercase_    )

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

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

		return orig_state_dict






def 						_lowercase						(      lowercase_		,				lowercase_		,				lowercase_    ):



		'''simple docstring'''







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

		_snake_case      			=			convert_checkpoint_helper(lowercase_		,				lowercase_    )

		model.load_state_dict(lowercase_    )
		model.eval()
		model.save_pretrained(lowercase_    )

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


if __name__ == "__main__":
						_lowerCamelCase				:		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.'
						)
						_lowerCamelCase				:		List[Any]				      =    parser.parse_args()
						convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)











'''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





