





'''simple docstring'''


import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class     lowerCAmelCase__ (				UpperCAmelCase_      , unittest.TestCase    ):

       '''simple docstring'''
       _lowerCamelCase								=ConsistencyModelPipeline
       _lowerCamelCase								=UNCONDITIONAL_IMAGE_GENERATION_PARAMS
       _lowerCamelCase								=UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

       # Override required_optional_params to remove num_images_per_prompt
       _lowerCamelCase								=frozenset(
           [
               "num_inference_steps",
               "generator",
               "latents",
               "output_type",
               "return_dict",
               "callback",
               "callback_steps",
           ]    )

       @property
       def 			__snake_case      ( self					:				List[str]						):
              UpperCAmelCase  =						UNetaDModel.from_pretrained(
                  '''diffusers/consistency-models-test'''       ,     subfolder='''test_unet'''       ,     )
              return unet

       @property
       def 			__snake_case      ( self					:				int						):
              UpperCAmelCase  =						UNetaDModel.from_pretrained(
                  '''diffusers/consistency-models-test'''       ,     subfolder='''test_unet_class_cond'''       ,     )
              return unet

       def 			__snake_case      ( self					:				int       ,     a__					:				List[Any]=False						):
              if class_cond:
                     UpperCAmelCase  =						self.dummy_cond_unet
              else:
                     UpperCAmelCase  =						self.dummy_uncond_unet

              # Default to CM multistep sampler
              UpperCAmelCase  =						CMStochasticIterativeScheduler(
                  num_train_timesteps=40       ,     sigma_min=0.002       ,     sigma_max=80.0       ,     )

              UpperCAmelCase  =						{
                  '''unet''': unet,
                  '''scheduler''': scheduler,
              }

              return components

       def 			__snake_case      ( self					:				Any       ,     a__					:				Optional[Any]       ,     a__					:				List[str]=0						):
              if str(a__						).startswith('''mps'''						):
                     UpperCAmelCase  =						torch.manual_seed(a__						)
              else:
                     UpperCAmelCase  =						torch.Generator(device=a__						).manual_seed(a__						)

              UpperCAmelCase  =						{
                  '''batch_size''': 1,
                  '''num_inference_steps''': None,
                  '''timesteps''': [22, 0],
                  '''generator''': generator,
                  '''output_type''': '''np''',
              }

              return inputs

       def 			__snake_case      ( self					:				Optional[Any]						):
              UpperCAmelCase  =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
              UpperCAmelCase  =						self.get_dummy_components()
              UpperCAmelCase  =						ConsistencyModelPipeline(**a__						)
              UpperCAmelCase  =						pipe.to(a__						)
              pipe.set_progress_bar_config(disable=a__						)

              UpperCAmelCase  =						self.get_dummy_inputs(a__						)
              UpperCAmelCase  =						pipe(**a__						).images
              assert image.shape == (1, 32, 32, 3)

              UpperCAmelCase  =						image[0, -3:, -3:, -1]
              UpperCAmelCase  =						np.array([0.3_572, 0.6_273, 0.4_031, 0.3_961, 0.4_321, 0.5_730, 0.5_266, 0.4_780, 0.5_004]						)

              assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3

       def 			__snake_case      ( self					:				int						):
              UpperCAmelCase  =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
              UpperCAmelCase  =						self.get_dummy_components(class_cond=a__						)
              UpperCAmelCase  =						ConsistencyModelPipeline(**a__						)
              UpperCAmelCase  =						pipe.to(a__						)
              pipe.set_progress_bar_config(disable=a__						)

              UpperCAmelCase  =						self.get_dummy_inputs(a__						)
              UpperCAmelCase  =						0
              UpperCAmelCase  =						pipe(**a__						).images
              assert image.shape == (1, 32, 32, 3)

              UpperCAmelCase  =						image[0, -3:, -3:, -1]
              UpperCAmelCase  =						np.array([0.3_572, 0.6_273, 0.4_031, 0.3_961, 0.4_321, 0.5_730, 0.5_266, 0.4_780, 0.5_004]						)

              assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3

       def 			__snake_case      ( self					:				int						):
              UpperCAmelCase  =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
              UpperCAmelCase  =						self.get_dummy_components()
              UpperCAmelCase  =						ConsistencyModelPipeline(**a__						)
              UpperCAmelCase  =						pipe.to(a__						)
              pipe.set_progress_bar_config(disable=a__						)

              UpperCAmelCase  =						self.get_dummy_inputs(a__						)
              UpperCAmelCase  =						1
              UpperCAmelCase  =						None
              UpperCAmelCase  =						pipe(**a__						).images
              assert image.shape == (1, 32, 32, 3)

              UpperCAmelCase  =						image[0, -3:, -3:, -1]
              UpperCAmelCase  =						np.array([0.5_004, 0.5_004, 0.4_994, 0.5_008, 0.4_976, 0.5_018, 0.4_990, 0.4_982, 0.4_987]						)

              assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3



       def 			__snake_case      ( self					:				str						):
              UpperCAmelCase  =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
              UpperCAmelCase  =						self.get_dummy_components(class_cond=a__						)
              UpperCAmelCase  =						ConsistencyModelPipeline(**a__						)
              UpperCAmelCase  =						pipe.to(a__						)
              pipe.set_progress_bar_config(disable=a__						)

              UpperCAmelCase  =						self.get_dummy_inputs(a__						)
              UpperCAmelCase  =						1
              UpperCAmelCase  =						None
              UpperCAmelCase  =						0
              UpperCAmelCase  =						pipe(**a__						).images
              assert image.shape == (1, 32, 32, 3)

              UpperCAmelCase  =						image[0, -3:, -3:, -1]
              UpperCAmelCase  =						np.array([0.5_004, 0.5_004, 0.4_994, 0.5_008, 0.4_976, 0.5_018, 0.4_990, 0.4_982, 0.4_987]						)

              assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3






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

       '''simple docstring'''

       def 			__snake_case      ( self					:				Tuple						):
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()

       def 			__snake_case      ( self					:				str       ,     a__					:				Optional[Any]=0       ,     a__					:				List[str]=False       ,     a__					:				Tuple="cpu"       ,     a__					:				Optional[Any]=torch.floataa       ,     a__					:				List[Any]=(1, 3, 64, 64)						):
              UpperCAmelCase  =						torch.manual_seed(a__						)

              UpperCAmelCase  =						{
                  '''num_inference_steps''': None,
                  '''timesteps''': [22, 0],
                  '''class_labels''': 0,
                  '''generator''': generator,
                  '''output_type''': '''np''',
              }

              if get_fixed_latents:
                     UpperCAmelCase  =						self.get_fixed_latents(seed=a__       ,     device=a__       ,     dtype=a__       ,     shape=a__						)
                     UpperCAmelCase  =						latents

              return inputs

       def 			__snake_case      ( self					:				List[Any]       ,     a__					:				Optional[Any]=0       ,     a__					:				int="cpu"       ,     a__					:				Any=torch.floataa       ,     a__					:				List[str]=(1, 3, 64, 64)						):
              if type(a__						) == str:
                     UpperCAmelCase  =						torch.device(a__						)
              UpperCAmelCase  =						torch.Generator(device=a__						).manual_seed(a__						)
              UpperCAmelCase  =						randn_tensor(a__       ,     generator=a__       ,     device=a__       ,     dtype=a__						)
              return latents

       def 			__snake_case      ( self					:				Tuple						):
              UpperCAmelCase  =						UNetaDModel.from_pretrained('''diffusers/consistency_models'''       ,     subfolder='''diffusers_cd_imagenet64_l2'''						)
              UpperCAmelCase  =						CMStochasticIterativeScheduler(
                  num_train_timesteps=40       ,     sigma_min=0.002       ,     sigma_max=80.0       ,     )
              UpperCAmelCase  =						ConsistencyModelPipeline(unet=a__       ,     scheduler=a__						)
              pipe.to(torch_device=a__						)
              pipe.set_progress_bar_config(disable=a__						)

              UpperCAmelCase  =						self.get_inputs()
              UpperCAmelCase  =						pipe(**a__						).images
              assert image.shape == (1, 64, 64, 3)

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

              UpperCAmelCase  =						np.array([0.0_888, 0.0_881, 0.0_666, 0.0_479, 0.0_292, 0.0_195, 0.0_201, 0.0_163, 0.0_254]						)

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

       def 			__snake_case      ( self					:				Optional[Any]						):
              UpperCAmelCase  =						UNetaDModel.from_pretrained('''diffusers/consistency_models'''       ,     subfolder='''diffusers_cd_imagenet64_l2'''						)
              UpperCAmelCase  =						CMStochasticIterativeScheduler(
                  num_train_timesteps=40       ,     sigma_min=0.002       ,     sigma_max=80.0       ,     )
              UpperCAmelCase  =						ConsistencyModelPipeline(unet=a__       ,     scheduler=a__						)
              pipe.to(torch_device=a__						)
              pipe.set_progress_bar_config(disable=a__						)

              UpperCAmelCase  =						self.get_inputs()
              UpperCAmelCase  =						1
              UpperCAmelCase  =						None
              UpperCAmelCase  =						pipe(**a__						).images
              assert image.shape == (1, 64, 64, 3)

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

              UpperCAmelCase  =						np.array([0.0_340, 0.0_152, 0.0_063, 0.0_267, 0.0_221, 0.0_107, 0.0_416, 0.0_186, 0.0_217]						)

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

       @require_torch_a
       def 			__snake_case      ( self					:				Union[str, Any]						):
              UpperCAmelCase  =						UNetaDModel.from_pretrained('''diffusers/consistency_models'''       ,     subfolder='''diffusers_cd_imagenet64_l2'''						)
              UpperCAmelCase  =						CMStochasticIterativeScheduler(
                  num_train_timesteps=40       ,     sigma_min=0.002       ,     sigma_max=80.0       ,     )
              UpperCAmelCase  =						ConsistencyModelPipeline(unet=a__       ,     scheduler=a__						)
              pipe.to(torch_device=a__       ,     torch_dtype=torch.floataa						)
              pipe.set_progress_bar_config(disable=a__						)

              UpperCAmelCase  =						self.get_inputs(get_fixed_latents=a__       ,     device=a__						)
              # Ensure usage of flash attention in torch 2.0
              with sdp_kernel(enable_flash=a__       ,     enable_math=a__       ,     enable_mem_efficient=a__						):
                     UpperCAmelCase  =						pipe(**a__						).images
              assert image.shape == (1, 64, 64, 3)

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

              UpperCAmelCase  =						np.array([0.1_875, 0.1_428, 0.1_289, 0.2_151, 0.2_092, 0.1_477, 0.1_877, 0.1_641, 0.1_353]						)

              assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3



       @require_torch_a
       def 			__snake_case      ( self					:				Any						):
              UpperCAmelCase  =						UNetaDModel.from_pretrained('''diffusers/consistency_models'''       ,     subfolder='''diffusers_cd_imagenet64_l2'''						)
              UpperCAmelCase  =						CMStochasticIterativeScheduler(
                  num_train_timesteps=40       ,     sigma_min=0.002       ,     sigma_max=80.0       ,     )
              UpperCAmelCase  =						ConsistencyModelPipeline(unet=a__       ,     scheduler=a__						)
              pipe.to(torch_device=a__       ,     torch_dtype=torch.floataa						)
              pipe.set_progress_bar_config(disable=a__						)

              UpperCAmelCase  =						self.get_inputs(get_fixed_latents=a__       ,     device=a__						)
              UpperCAmelCase  =						1
              UpperCAmelCase  =						None
              # Ensure usage of flash attention in torch 2.0
              with sdp_kernel(enable_flash=a__       ,     enable_math=a__       ,     enable_mem_efficient=a__						):
                     UpperCAmelCase  =						pipe(**a__						).images
              assert image.shape == (1, 64, 64, 3)

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

              UpperCAmelCase  =						np.array([0.1_663, 0.1_948, 0.2_275, 0.1_680, 0.1_204, 0.1_245, 0.1_858, 0.1_338, 0.2_095]						)

              assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3







import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






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

 return orig_key






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 for key in orig_state_dict.copy().keys():
  a__												= orig_state_dict.pop(__UpperCAmelCase       )

  if ("pooler" in key) or ("sen_class" in key):
   continue
  else:
   a__												= val

 a__												= orig_state_dict['''cls.predictions.decoder.bias''']
 a__												= torch.arange(__UpperCAmelCase       ).expand((1, -1)       ) + 2

 return orig_state_dict






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= torch.load(__UpperCAmelCase							,  map_location='''cpu'''       )['''model_state_dict''']
 a__												= YosoConfig.from_json_file(__UpperCAmelCase       )
 a__												= YosoForMaskedLM(__UpperCAmelCase       )

 a__												= convert_checkpoint_helper(config.max_position_embeddings							,  __UpperCAmelCase       )

 print(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__":
     a_      :      int    =     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
     )
     parser.add_argument(
         '--config_file',
         default=None,
         type=str,
         required=True,
         help='The json file for YOSO model config.',
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     a_      :      Any    =     parser.parse_args()
     convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





