




from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)






class    __lowerCAmelCase			(	UpperCAmelCase__			):







			def __init__(	self   :      Any   ,   snake_case__   :      AutoencoderKL   ,   snake_case__   :      CLIPTextModel   ,   snake_case__   :      CLIPTokenizer   ,   snake_case__   :      UNetaDConditionModel   ,   snake_case__   :      DDIMScheduler   ,   snake_case__   :      float = 0.93   ,   snake_case__   :      bool = True   ,   ):




									"""simple docstring"""


									_UpperCAmelCase													=					mixing_coeff
									_UpperCAmelCase													=					leapfrog_steps

									super().__init__()
									self.register_modules(
									    vae=snake_case__   ,   text_encoder=snake_case__   ,   tokenizer=snake_case__   ,   unet=snake_case__   ,   scheduler=snake_case__   ,   )

									_UpperCAmelCase													=					2 ** (len(self.vae.config.block_out_channels				) - 1)
									_UpperCAmelCase													=					VaeImageProcessor(vae_scale_factor=self.vae_scale_factor				)







			def UpperCamelCase      (	self   :      Optional[Any]   ,   snake_case__   :      str   ,   snake_case__   :      Optional[str] = None   ,   snake_case__   :      bool = False				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.tokenizer(
									    snake_case__   ,   padding="max_length"   ,   max_length=self.tokenizer.model_max_length   ,   truncation=snake_case__   ,   return_tensors="pt"   ,   )

									_UpperCAmelCase													=					self.text_encoder(text_inputs.input_ids.to(self.device				)				).last_hidden_state

									_UpperCAmelCase													=					prompt_embeds.to(dtype=self.text_encoder.dtype   ,   device=self.device				)

									if do_classifier_free_guidance:
															_UpperCAmelCase													=					"" if negative_prompt is None else negative_prompt

															_UpperCAmelCase													=					self.tokenizer(
															    snake_case__   ,   padding="max_length"   ,   max_length=self.tokenizer.model_max_length   ,   truncation=snake_case__   ,   return_tensors="pt"   ,   )

															_UpperCAmelCase													=					self.text_encoder(uncond_input.input_ids.to(self.device				)				).last_hidden_state

															_UpperCAmelCase													=					torch.cat([negative_prompt_embeds, prompt_embeds]				)

									return prompt_embeds







			def UpperCamelCase      (	self   :      Any   ,   snake_case__   :      torch.Tensor   ,   snake_case__   :      torch.Tensor				):




									"""simple docstring"""


									_UpperCAmelCase													=					self.mixing_coeff * x + (1 - self.mixing_coeff) * y
									_UpperCAmelCase													=					self.mixing_coeff * y + (1 - self.mixing_coeff) * x

									return [x, y]







			def UpperCamelCase      (	self   :      int   ,   snake_case__   :      torch.Tensor   ,   snake_case__   :      torch.Tensor				):




									"""simple docstring"""


									_UpperCAmelCase													=					(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
									_UpperCAmelCase													=					(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

									return [x, y]







			def UpperCamelCase      (	self   :      int   ,   snake_case__   :      torch.Tensor				):




									"""simple docstring"""


									_UpperCAmelCase													=					int(snake_case__				)

									_UpperCAmelCase													=					self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

									return alpha_prod, 1 - alpha_prod







			def UpperCamelCase      (	self   :      List[Any]   ,   snake_case__   :      torch.Tensor   ,   snake_case__   :      torch.Tensor   ,   snake_case__   :      torch.Tensor   ,   snake_case__   :      torch.Tensor   ,   ):




									"""simple docstring"""


									_UpperCAmelCase													=					timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

									_UpperCAmelCase							,			_UpperCAmelCase													=					self._get_alpha_and_beta(snake_case__				)
									_UpperCAmelCase							,			_UpperCAmelCase													=					self._get_alpha_and_beta(snake_case__				)

									_UpperCAmelCase													=					(alpha_prod_t_prev / alpha_prod_t) ** 0.5
									_UpperCAmelCase													=					-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

									_UpperCAmelCase													=					(base - b_t * model_output) / a_t

									return model_input, next_model_input.to(base.dtype				)







			def UpperCamelCase      (	self   :      List[str]   ,   snake_case__   :      torch.Tensor   ,   snake_case__   :      torch.Tensor   ,   snake_case__   :      torch.Tensor   ,   snake_case__   :      torch.Tensor   ,   ):




									"""simple docstring"""


									_UpperCAmelCase													=					timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

									_UpperCAmelCase							,			_UpperCAmelCase													=					self._get_alpha_and_beta(snake_case__				)
									_UpperCAmelCase							,			_UpperCAmelCase													=					self._get_alpha_and_beta(snake_case__				)

									_UpperCAmelCase													=					(alpha_prod_t_prev / alpha_prod_t) ** 0.5
									_UpperCAmelCase													=					-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
									_UpperCAmelCase													=					a_t * base + b_t * model_output

									return model_input, next_model_input.to(base.dtype				)







			@torch.no_grad()
			def UpperCamelCase      (	self   :      Union[str, Any]   ,   snake_case__   :      torch.Tensor				):




									"""simple docstring"""


									_UpperCAmelCase													=					1 / self.vae.config.scaling_factor * latents
									_UpperCAmelCase													=					self.vae.decode(snake_case__				).sample
									_UpperCAmelCase													=					(image / 2 + 0.5).clamp(0   ,   1				)
									return image







			@torch.no_grad()
			def UpperCamelCase      (	self   :      Optional[Any]   ,   snake_case__   :      Image.Image   ,   snake_case__   :      torch.Tensor   ,   snake_case__   :      torch.Tensor   ,   snake_case__   :      float   ,   snake_case__   :      Optional[torch.Generator] = None   ,   ):




									"""simple docstring"""


									_UpperCAmelCase													=					guidance_scale > 1.0

									_UpperCAmelCase													=					image.to(device=self.device   ,   dtype=text_embeds.dtype				)
									_UpperCAmelCase													=					self.vae.encode(snake_case__				).latent_dist.sample(snake_case__				)

									_UpperCAmelCase													=					self.vae.config.scaling_factor * latent

									_UpperCAmelCase													=					[latent.clone(), latent.clone()]

									for i, t in tqdm(enumerate(snake_case__				)   ,   total=len(snake_case__				)				):
															_UpperCAmelCase													=					self.noise_mixing_layer(x=coupled_latents[0]   ,   y=coupled_latents[1]				)

															# j - model_input index, k - base index
															for j in range(2				):
																					_UpperCAmelCase													=					j ^ 1

																					if self.leapfrog_steps:
																											if i % 2 == 0:
																																	_UpperCAmelCase							,			_UpperCAmelCase													=					j, k

																					_UpperCAmelCase													=					coupled_latents[j]
																					_UpperCAmelCase													=					coupled_latents[k]

																					_UpperCAmelCase													=					torch.cat([model_input] * 2				) if do_classifier_free_guidance else model_input

																					_UpperCAmelCase													=					self.unet(snake_case__   ,   snake_case__   ,   encoder_hidden_states=snake_case__				).sample

																					if do_classifier_free_guidance:
																											_UpperCAmelCase							,			_UpperCAmelCase													=					noise_pred.chunk(2				)
																											_UpperCAmelCase													=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																					_UpperCAmelCase							,			_UpperCAmelCase													=					self.noise_step(
																					    base=snake_case__   ,   model_input=snake_case__   ,   model_output=snake_case__   ,   timestep=snake_case__   ,   )

																					_UpperCAmelCase													=					model_input

									return coupled_latents







			@torch.no_grad()
			def __call__(	self   :      Tuple   ,   snake_case__   :      str   ,   snake_case__   :      str   ,   snake_case__   :      Image.Image   ,   snake_case__   :      float = 3.0   ,   snake_case__   :      int = 50   ,   snake_case__   :      float = 0.8   ,   snake_case__   :      Optional[str] = None   ,   snake_case__   :      Optional[torch.Generator] = None   ,   snake_case__   :      Optional[str] = "pil"   ,   ):




									"""simple docstring"""


									_UpperCAmelCase													=					guidance_scale > 1.0

									_UpperCAmelCase													=					self.image_processor.preprocess(snake_case__				)

									_UpperCAmelCase													=					self._encode_prompt(snake_case__   ,   snake_case__   ,   snake_case__				)
									_UpperCAmelCase													=					self._encode_prompt(snake_case__   ,   snake_case__   ,   snake_case__				)

									self.scheduler.set_timesteps(snake_case__   ,   self.device				)

									_UpperCAmelCase													=					num_inference_steps - int(num_inference_steps * strength				)
									_UpperCAmelCase													=					self.scheduler.timesteps[t_limit:]
									_UpperCAmelCase													=					fwd_timesteps.flip(0				)

									_UpperCAmelCase													=					self.prepare_latents(snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__				)

									for i, t in tqdm(enumerate(snake_case__				)   ,   total=len(snake_case__				)				):
															# j - model_input index, k - base index
															for k in range(2				):
																					_UpperCAmelCase													=					k ^ 1

																					if self.leapfrog_steps:
																											if i % 2 == 1:
																																	_UpperCAmelCase							,			_UpperCAmelCase													=					j, k

																					_UpperCAmelCase													=					coupled_latents[j]
																					_UpperCAmelCase													=					coupled_latents[k]

																					_UpperCAmelCase													=					torch.cat([model_input] * 2				) if do_classifier_free_guidance else model_input

																					_UpperCAmelCase													=					self.unet(snake_case__   ,   snake_case__   ,   encoder_hidden_states=snake_case__				).sample

																					if do_classifier_free_guidance:
																											_UpperCAmelCase							,			_UpperCAmelCase													=					noise_pred.chunk(2				)
																											_UpperCAmelCase													=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																					_UpperCAmelCase							,			_UpperCAmelCase													=					self.denoise_step(
																					    base=snake_case__   ,   model_input=snake_case__   ,   model_output=snake_case__   ,   timestep=snake_case__   ,   )

																					_UpperCAmelCase													=					model_input

															_UpperCAmelCase													=					self.denoise_mixing_layer(x=coupled_latents[0]   ,   y=coupled_latents[1]				)

									# either one is fine
									_UpperCAmelCase													=					coupled_latents[0]

									if output_type not in ["latent", "pt", "np", "pil"]:
															_UpperCAmelCase													=					(
															    F"""the output_type {output_type} is outdated. Please make sure to set it to one of these instead: """
															    "`pil`, `np`, `pt`, `latent`"
															)
															deprecate("Unsupported output_type"   ,   "1.0.0"   ,   snake_case__   ,   standard_warn=snake_case__				)
															_UpperCAmelCase													=					"np"

									if output_type == "latent":
															_UpperCAmelCase													=					final_latent
									else:
															_UpperCAmelCase													=					self.decode_latents(snake_case__				)
															_UpperCAmelCase													=					self.image_processor.postprocess(snake_case__   ,   output_type=snake_case__				)

									return image










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)




