

import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM





def 			_lowercase    (      __UpperCamelCase     :		Tuple ):
      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    (      __UpperCamelCase     :		Dict   ,       __UpperCamelCase     :		Any ):
      for key in orig_state_dict.copy().keys():
            snake_case__					=   orig_state_dict.pop(__UpperCamelCase )

            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    (      __UpperCamelCase     :		str   ,       __UpperCamelCase     :		str   ,       __UpperCamelCase     :		str ):
      snake_case__					=   torch.load(__UpperCamelCase   ,       map_location="""cpu""" )["""model_state_dict"""]
      snake_case__					=   NystromformerConfig.from_json_file(__UpperCamelCase )
      snake_case__					=   NystromformerForMaskedLM(__UpperCamelCase )

      snake_case__					=   convert_checkpoint_helper(__UpperCamelCase   ,       __UpperCamelCase )

      model.load_state_dict(__UpperCamelCase )
      model.eval()
      model.save_pretrained(__UpperCamelCase )

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


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









import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()
class     SCREAMING_SNAKE_CASE__     (						unittest.TestCase		):





       '''simple docstring'''




       @property
       def UpperCAmelCase_						(						self       :						Any							)			->    List[str]:
             torch.manual_seed(0							)
             snake_case__					=   UNetaDModel(
                 block_out_channels=(32, 64)    ,		layers_per_block=2    ,		sample_size=32    ,		in_channels=3    ,		out_channels=3    ,		down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,		up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,		)
             return model

       def UpperCAmelCase_						(						self       :						Optional[int]							)			->    int:
             snake_case__					=   """cpu"""
             snake_case__					=   self.dummy_uncond_unet
             snake_case__					=   DDPMScheduler()

             snake_case__					=   DDPMPipeline(unet=lowerCAmelCase__    ,		scheduler=lowerCAmelCase__							)
             ddpm.to(lowerCAmelCase__							)
             ddpm.set_progress_bar_config(disable=lowerCAmelCase__							)

             snake_case__					=   torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
             snake_case__					=   ddpm(generator=lowerCAmelCase__    ,		num_inference_steps=2    ,		output_type="""numpy"""							).images

             snake_case__					=   torch.Generator(device=lowerCAmelCase__							).manual_seed(0							)
             snake_case__					=   ddpm(generator=lowerCAmelCase__    ,		num_inference_steps=2    ,		output_type="""numpy"""    ,		return_dict=lowerCAmelCase__							)[0]

             snake_case__					=   image[0, -3:, -3:, -1]
             snake_case__					=   image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             snake_case__					=   np.array(
                 [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-04]							)

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

       def UpperCAmelCase_						(						self       :						Tuple							)			->    str:
             snake_case__					=   self.dummy_uncond_unet
             snake_case__					=   DDPMScheduler(prediction_type="""sample"""							)

             snake_case__					=   DDPMPipeline(unet=lowerCAmelCase__    ,		scheduler=lowerCAmelCase__							)
             ddpm.to(lowerCAmelCase__							)
             ddpm.set_progress_bar_config(disable=lowerCAmelCase__							)

             snake_case__					=   torch.manual_seed(0							)
             snake_case__					=   ddpm(generator=lowerCAmelCase__    ,		num_inference_steps=2    ,		output_type="""numpy"""							).images

             snake_case__					=   torch.manual_seed(0							)
             snake_case__					=   ddpm(generator=lowerCAmelCase__    ,		num_inference_steps=2    ,		output_type="""numpy"""							)[0]

             snake_case__					=   image[0, -3:, -3:, -1]
             snake_case__					=   image_eps[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             snake_case__					=   1E-2 if torch_device != """mps""" else 3E-2
             assert np.abs(image_slice.flatten() - image_eps_slice.flatten()							).max() < tolerance



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





       '''simple docstring'''




       def UpperCAmelCase_						(						self       :						Tuple							)			->    Optional[int]:
             snake_case__					=   """google/ddpm-cifar10-32"""

             snake_case__					=   UNetaDModel.from_pretrained(lowerCAmelCase__							)
             snake_case__					=   DDPMScheduler.from_pretrained(lowerCAmelCase__							)

             snake_case__					=   DDPMPipeline(unet=lowerCAmelCase__    ,		scheduler=lowerCAmelCase__							)
             ddpm.to(lowerCAmelCase__							)
             ddpm.set_progress_bar_config(disable=lowerCAmelCase__							)

             snake_case__					=   torch.manual_seed(0							)
             snake_case__					=   ddpm(generator=lowerCAmelCase__    ,		output_type="""numpy"""							).images

             snake_case__					=   image[0, -3:, -3:, -1]

             assert image.shape == (1, 32, 32, 3)
             snake_case__					=   np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]							)
             assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2






