




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




@skip_mps
class       UpperCAmelCase_   ( A		,   A		,   A		,   unittest.TestCase							):




							'''simple docstring'''



							a__    	=  StableDiffusionModelEditingPipeline
							a__    	=  TEXT_TO_IMAGE_PARAMS
							a__    	=  TEXT_TO_IMAGE_BATCH_PARAMS
							a__    	=  TEXT_TO_IMAGE_IMAGE_PARAMS
							a__    	=  TEXT_TO_IMAGE_IMAGE_PARAMS





							def  _UpperCAmelCase  (       self							:						Tuple      )      ->       Tuple:
										torch.manual_seed(0      )
										SCREAMING_SNAKE_CASE     =       UNetaDConditionModel(
										    block_out_channels=(32, 64)							,  layers_per_block=2							,  sample_size=32							,  in_channels=4							,  out_channels=4							,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")							,  up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")							,  cross_attention_dim=32							,  )
										SCREAMING_SNAKE_CASE     =       DDIMScheduler()
										torch.manual_seed(0      )
										SCREAMING_SNAKE_CASE     =       AutoencoderKL(
										    block_out_channels=[32, 64]							,  in_channels=3							,  out_channels=3							,  down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]							,  up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]							,  latent_channels=4							,  )
										torch.manual_seed(0      )
										SCREAMING_SNAKE_CASE     =       CLIPTextConfig(
										    bos_token_id=0							,  eos_token_id=2							,  hidden_size=32							,  intermediate_size=37							,  layer_norm_eps=1E-0_5							,  num_attention_heads=4							,  num_hidden_layers=5							,  pad_token_id=1							,  vocab_size=1_000							,  )
										SCREAMING_SNAKE_CASE     =       CLIPTextModel(a      )
										SCREAMING_SNAKE_CASE     =       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

										SCREAMING_SNAKE_CASE     =       {
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """vae""": vae,
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """safety_checker""": None,
										    """feature_extractor""": None,
										}
										return components





							def  _UpperCAmelCase  (       self							:						List[Any]							,  a							:						Dict							,  a							:						List[str]=0      )      ->       List[Any]:
										SCREAMING_SNAKE_CASE     =       torch.manual_seed(a      )
										SCREAMING_SNAKE_CASE     =       {
										    """prompt""": """A field of roses""",
										    """generator""": generator,
										    # Setting height and width to None to prevent OOMs on CPU.
										    """height""": None,
										    """width""": None,
										    """num_inference_steps""": 2,
										    """guidance_scale""": 6.0,
										    """output_type""": """numpy""",
										}
										return inputs





							def  _UpperCAmelCase  (       self							:						List[Any]      )      ->       List[str]:
										SCREAMING_SNAKE_CASE     =       """cpu"""  # ensure determinism for the device-dependent torch.Generator
										SCREAMING_SNAKE_CASE     =       self.get_dummy_components()
										SCREAMING_SNAKE_CASE     =       StableDiffusionModelEditingPipeline(**a      )
										SCREAMING_SNAKE_CASE     =       sd_pipe.to(a      )
										sd_pipe.set_progress_bar_config(disable=a      )

										SCREAMING_SNAKE_CASE     =       self.get_dummy_inputs(a      )
										SCREAMING_SNAKE_CASE     =       sd_pipe(**a      ).images
										SCREAMING_SNAKE_CASE     =       image[0, -3:, -3:, -1]
										assert image.shape == (1, 64, 64, 3)

										SCREAMING_SNAKE_CASE     =       np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889]      )

										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2





							def  _UpperCAmelCase  (       self							:						int      )      ->       str:
										SCREAMING_SNAKE_CASE     =       """cpu"""  # ensure determinism for the device-dependent torch.Generator
										SCREAMING_SNAKE_CASE     =       self.get_dummy_components()
										SCREAMING_SNAKE_CASE     =       StableDiffusionModelEditingPipeline(**a      )
										SCREAMING_SNAKE_CASE     =       sd_pipe.to(a      )
										sd_pipe.set_progress_bar_config(disable=a      )

										SCREAMING_SNAKE_CASE     =       self.get_dummy_inputs(a      )
										SCREAMING_SNAKE_CASE     =       """french fries"""
										SCREAMING_SNAKE_CASE     =       sd_pipe(**a							,  negative_prompt=a      )
										SCREAMING_SNAKE_CASE     =       output.images
										SCREAMING_SNAKE_CASE     =       image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										SCREAMING_SNAKE_CASE     =       np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913]      )

										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2





							def  _UpperCAmelCase  (       self							:						List[Any]      )      ->       List[Any]:
										SCREAMING_SNAKE_CASE     =       """cpu"""  # ensure determinism for the device-dependent torch.Generator
										SCREAMING_SNAKE_CASE     =       self.get_dummy_components()
										SCREAMING_SNAKE_CASE     =       EulerAncestralDiscreteScheduler(
										    beta_start=0.0_0085							,  beta_end=0.012							,  beta_schedule="""scaled_linear"""      )
										SCREAMING_SNAKE_CASE     =       StableDiffusionModelEditingPipeline(**a      )
										SCREAMING_SNAKE_CASE     =       sd_pipe.to(a      )
										sd_pipe.set_progress_bar_config(disable=a      )

										SCREAMING_SNAKE_CASE     =       self.get_dummy_inputs(a      )
										SCREAMING_SNAKE_CASE     =       sd_pipe(**a      ).images
										SCREAMING_SNAKE_CASE     =       image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										SCREAMING_SNAKE_CASE     =       np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027]      )

										assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2





							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       str:
										SCREAMING_SNAKE_CASE     =       """cpu"""  # ensure determinism for the device-dependent torch.Generator
										SCREAMING_SNAKE_CASE     =       self.get_dummy_components()
										SCREAMING_SNAKE_CASE     =       PNDMScheduler()
										SCREAMING_SNAKE_CASE     =       StableDiffusionModelEditingPipeline(**a      )
										SCREAMING_SNAKE_CASE     =       sd_pipe.to(a      )
										sd_pipe.set_progress_bar_config(disable=a      )

										SCREAMING_SNAKE_CASE     =       self.get_dummy_inputs(a      )
										# the pipeline does not expect pndm so test if it raises error.
										with self.assertRaises(a      ):
													SCREAMING_SNAKE_CASE     =       sd_pipe(**a      ).images





							def  _UpperCAmelCase  (       self							:						Optional[int]      )      ->       Optional[int]:
										super().test_inference_batch_single_identical(expected_max_diff=5E-3      )







							def  _UpperCAmelCase  (       self							:						int      )      ->       str:
										super().test_attention_slicing_forward_pass(expected_max_diff=5E-3      )





@slow
@require_torch_gpu
class       UpperCAmelCase_   ( unittest.TestCase							):




							'''simple docstring'''



							def  _UpperCAmelCase  (       self							:						str      )      ->       Optional[int]:
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()





							def  _UpperCAmelCase  (       self							:						Tuple							,  a							:						Union[str, Any]=0      )      ->       List[str]:
										SCREAMING_SNAKE_CASE     =       torch.manual_seed(a      )
										SCREAMING_SNAKE_CASE     =       {
										    """prompt""": """A field of roses""",
										    """generator""": generator,
										    """num_inference_steps""": 3,
										    """guidance_scale""": 7.5,
										    """output_type""": """numpy""",
										}
										return inputs





							def  _UpperCAmelCase  (       self							:						Optional[int]      )      ->       Optional[int]:
										SCREAMING_SNAKE_CASE     =       """CompVis/stable-diffusion-v1-4"""
										SCREAMING_SNAKE_CASE     =       StableDiffusionModelEditingPipeline.from_pretrained(a							,  safety_checker=a      )
										pipe.to(a      )
										pipe.set_progress_bar_config(disable=a      )
										pipe.enable_attention_slicing()

										SCREAMING_SNAKE_CASE     =       self.get_inputs()
										SCREAMING_SNAKE_CASE     =       pipe(**a      ).images
										SCREAMING_SNAKE_CASE     =       image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)

										SCREAMING_SNAKE_CASE     =       np.array(
										    [0.674_9496, 0.638_6453, 0.5144_3267, 0.6609_4905, 0.6192_1215, 0.549_1332, 0.574_4417, 0.5807_5106, 0.517_4658]      )

										assert np.abs(expected_slice - image_slice      ).max() < 1E-2

										# make sure image changes after editing
										pipe.edit_model("""A pack of roses"""							,  """A pack of blue roses"""      )

										SCREAMING_SNAKE_CASE     =       pipe(**a      ).images
										SCREAMING_SNAKE_CASE     =       image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)

										assert np.abs(expected_slice - image_slice      ).max() > 1E-1







							def  _UpperCAmelCase  (       self							:						int      )      ->       int:
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										SCREAMING_SNAKE_CASE     =       """CompVis/stable-diffusion-v1-4"""
										SCREAMING_SNAKE_CASE     =       DDIMScheduler.from_pretrained(a							,  subfolder="""scheduler"""      )
										SCREAMING_SNAKE_CASE     =       StableDiffusionModelEditingPipeline.from_pretrained(
										    a							,  scheduler=a							,  safety_checker=a      )
										SCREAMING_SNAKE_CASE     =       pipe.to(a      )
										pipe.set_progress_bar_config(disable=a      )
										pipe.enable_attention_slicing(1      )
										pipe.enable_sequential_cpu_offload()

										SCREAMING_SNAKE_CASE     =       self.get_inputs()
										SCREAMING_SNAKE_CASE     =       pipe(**a      )

										SCREAMING_SNAKE_CASE     =       torch.cuda.max_memory_allocated()
										# make sure that less than 4.4 GB is allocated
										assert mem_bytes < 4.4 * 10**9












import argparse

import torch

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

			return orig_key
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			for key in orig_state_dict.copy().keys():
						SCREAMING_SNAKE_CASE     =       orig_state_dict.pop(SCREAMING_SNAKE_CASE )

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

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

			return orig_state_dict
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       torch.load(SCREAMING_SNAKE_CASE       ,						map_location="""cpu""" )["""model_state_dict"""]
			SCREAMING_SNAKE_CASE     =       NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       NystromformerForMaskedLM(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       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__":
		__A    :				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."""
		)
		__A    :				Union[str, Any]    						=	parser.parse_args()
		convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






