




import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM







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

					return orig_key







def       _a (		SCREAMING_SNAKE_CASE_				:							Dict , SCREAMING_SNAKE_CASE_				:							Tuple	):
					for key in orig_state_dict.copy().keys():
										__lowerCAmelCase							  =			orig_state_dict.pop(SCREAMING_SNAKE_CASE_	)

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

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

					return orig_state_dict







def       _a (		SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							Any , SCREAMING_SNAKE_CASE_				:							int	):
					__lowerCAmelCase							  =			torch.load(SCREAMING_SNAKE_CASE_ , map_location="cpu"	)["model_state_dict"]
					__lowerCAmelCase							  =			NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			NystromformerForMaskedLM(SCREAMING_SNAKE_CASE_	)

					__lowerCAmelCase							  =			convert_checkpoint_helper(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)

					model.load_state_dict(SCREAMING_SNAKE_CASE_	)
					model.eval()
					model.save_pretrained(SCREAMING_SNAKE_CASE_	)

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


if __name__ == "__main__":
					UpperCamelCase__            =     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."""
					)
					UpperCamelCase__            =     parser.parse_args()
					convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


UpperCamelCase__            =     logging.get_logger(__name__)  # pylint: disable=invalid-name
class      a__       (   snake_case__ ):
						_a				:       Tuple   =       ["""safety_checker""", """feature_extractor"""]






						def __init__(  self						,     _A						,     _A						,     _A						,     _A						,     _A						,     _A						,     _A						,     _A = True						,     ):




											"""simple docstring"""



											super().__init__()
											if hasattr(scheduler.config						,     "steps_offset"     ) and scheduler.config.steps_offset != 1:
																__lowerCAmelCase							  =			(
																    f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
																    f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
																    "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
																    " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
																    " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
																    " file"
																)
																deprecate("steps_offset!=1"						,     "1.0.0"						,     _A						,     standard_warn=_A     )
																__lowerCAmelCase							  =			dict(scheduler.config     )
																__lowerCAmelCase							  =			1
																__lowerCAmelCase							  =			FrozenDict(_A     )

											self.register_modules(
											    vae=_A						,     text_encoder=_A						,     tokenizer=_A						,     unet=_A						,     scheduler=_A						,     safety_checker=_A						,     feature_extractor=_A						,     )
											self.register_to_config(requires_safety_checker=_A     )






						@property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return {k: getattr(self						,     _A     ) for k in self.config.keys() if not k.startswith("_"     )}






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A = "auto"     ):




											"""simple docstring"""



											if slice_size == "auto":
																# half the attention head size is usually a good trade-off between
																# speed and memory
																__lowerCAmelCase							  =			self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(_A     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											self.enable_attention_slicing(_A     )






						@torch.no_grad()
						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A						,     _A = 0.8						,     _A = 5_0						,     _A = 7.5						,     _A = None						,     _A = 1						,     _A = 0.0						,     _A = None						,     _A = "pil"						,     _A = True						,     _A = None						,     _A = 1						,     ):




											"""simple docstring"""



											return StableDiffusionInpaintPipelineLegacy(**self.components     )(
											    prompt=_A						,     image=_A						,     mask_image=_A						,     strength=_A						,     num_inference_steps=_A						,     guidance_scale=_A						,     negative_prompt=_A						,     num_images_per_prompt=_A						,     eta=_A						,     generator=_A						,     output_type=_A						,     return_dict=_A						,     callback=_A						,     )






						@torch.no_grad()
						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A = 0.8						,     _A = 5_0						,     _A = 7.5						,     _A = None						,     _A = 1						,     _A = 0.0						,     _A = None						,     _A = "pil"						,     _A = True						,     _A = None						,     _A = 1						,     **_A						,     ):




											"""simple docstring"""



											return StableDiffusionImgaImgPipeline(**self.components     )(
											    prompt=_A						,     image=_A						,     strength=_A						,     num_inference_steps=_A						,     guidance_scale=_A						,     negative_prompt=_A						,     num_images_per_prompt=_A						,     eta=_A						,     generator=_A						,     output_type=_A						,     return_dict=_A						,     callback=_A						,     callback_steps=_A						,     )






						@torch.no_grad()
						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A = 5_1_2						,     _A = 5_1_2						,     _A = 5_0						,     _A = 7.5						,     _A = None						,     _A = 1						,     _A = 0.0						,     _A = None						,     _A = None						,     _A = "pil"						,     _A = True						,     _A = None						,     _A = 1						,     ):




											"""simple docstring"""



											return StableDiffusionPipeline(**self.components     )(
											    prompt=_A						,     height=_A						,     width=_A						,     num_inference_steps=_A						,     guidance_scale=_A						,     negative_prompt=_A						,     num_images_per_prompt=_A						,     eta=_A						,     generator=_A						,     latents=_A						,     output_type=_A						,     return_dict=_A						,     callback=_A						,     callback_steps=_A						,     )




