
import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  (PNDMScheduler,)
     SCREAMING_SNAKE_CASE_		   =  (('''num_inference_steps''', 50),)






     def    __SCREAMING_SNAKE_CASE			(							self						,						**__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				{
               '''num_train_timesteps''': 1_0_0_0,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
           }

           config.update(**__SCREAMING_SNAKE_CASE  )
           return config






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE=0						,						**__SCREAMING_SNAKE_CASE  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				dict(self.forward_default_kwargs  )
           UpperCamelCase__    :   str					=				kwargs.pop('''num_inference_steps'''						,						__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				self.dummy_sample
           UpperCamelCase__    :   Optional[Any]					=				0.1 * sample
           UpperCamelCase__    :   Union[str, Any]					=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase__    :   int					=				self.get_scheduler_config(**__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   List[str]					=				scheduler_class(**__SCREAMING_SNAKE_CASE  )
                 scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  )
                 # copy over dummy past residuals
                 UpperCamelCase__    :   str					=				dummy_past_residuals[:]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__SCREAMING_SNAKE_CASE  )
                       UpperCamelCase__    :   List[Any]					=				scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE  )
                       new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  )
                       # copy over dummy past residuals
                       UpperCamelCase__    :   Union[str, Any]					=				dummy_past_residuals[:]

                 UpperCamelCase__    :   Union[str, Any]					=				scheduler.step_prk(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample
                 UpperCamelCase__    :   Tuple					=				new_scheduler.step_prk(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample

                 assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

                 UpperCamelCase__    :   int					=				scheduler.step_plms(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample
                 UpperCamelCase__    :   str					=				new_scheduler.step_plms(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample

                 assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           pass






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE=0						,						**__SCREAMING_SNAKE_CASE  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				dict(self.forward_default_kwargs  )
           UpperCamelCase__    :   Tuple					=				kwargs.pop('''num_inference_steps'''						,						__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				self.dummy_sample
           UpperCamelCase__    :   Optional[Any]					=				0.1 * sample
           UpperCamelCase__    :   Dict					=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase__    :   List[Any]					=				self.get_scheduler_config()
                 UpperCamelCase__    :   Optional[Any]					=				scheduler_class(**__SCREAMING_SNAKE_CASE  )
                 scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  )

                 # copy over dummy past residuals (must be after setting timesteps)
                 UpperCamelCase__    :   Union[str, Any]					=				dummy_past_residuals[:]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__SCREAMING_SNAKE_CASE  )
                       UpperCamelCase__    :   Union[str, Any]					=				scheduler_class.from_pretrained(__SCREAMING_SNAKE_CASE  )
                       # copy over dummy past residuals
                       new_scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  )

                       # copy over dummy past residual (must be after setting timesteps)
                       UpperCamelCase__    :   str					=				dummy_past_residuals[:]

                 UpperCamelCase__    :   Optional[Any]					=				scheduler.step_prk(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample
                 UpperCamelCase__    :   Optional[int]					=				new_scheduler.step_prk(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample

                 assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

                 UpperCamelCase__    :   Optional[int]					=				scheduler.step_plms(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample
                 UpperCamelCase__    :   Optional[Any]					=				new_scheduler.step_plms(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample

                 assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"






     def    __SCREAMING_SNAKE_CASE			(							self						,						**__SCREAMING_SNAKE_CASE  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   Any					=				self.scheduler_classes[0]
           UpperCamelCase__    :   List[str]					=				self.get_scheduler_config(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[int]					=				scheduler_class(**__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Dict					=				1_0
           UpperCamelCase__    :   List[str]					=				self.dummy_model()
           UpperCamelCase__    :   Dict					=				self.dummy_sample_deter
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  )

           for i, t in enumerate(scheduler.prk_timesteps  ):
                 UpperCamelCase__    :   Any					=				model(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   List[Any]					=				scheduler.step_prk(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ).prev_sample

           for i, t in enumerate(scheduler.plms_timesteps  ):
                 UpperCamelCase__    :   Union[str, Any]					=				model(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   Dict					=				scheduler.step_plms(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ).prev_sample

           return sample






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				dict(self.forward_default_kwargs  )

           UpperCamelCase__    :   List[Any]					=				kwargs.pop('''num_inference_steps'''						,						__SCREAMING_SNAKE_CASE  )

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase__    :   Optional[int]					=				self.get_scheduler_config()
                 UpperCamelCase__    :   List[str]					=				scheduler_class(**__SCREAMING_SNAKE_CASE  )

                 UpperCamelCase__    :   Tuple					=				self.dummy_sample
                 UpperCamelCase__    :   Optional[int]					=				0.1 * sample

                 if num_inference_steps is not None and hasattr(__SCREAMING_SNAKE_CASE						,						'''set_timesteps'''  ):
                       scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  )
                 elif num_inference_steps is not None and not hasattr(__SCREAMING_SNAKE_CASE						,						'''set_timesteps'''  ):
                       UpperCamelCase__    :   List[Any]					=				num_inference_steps

                 # copy over dummy past residuals (must be done after set_timesteps)
                 UpperCamelCase__    :   Optional[Any]					=				[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                 UpperCamelCase__    :   Optional[Any]					=				dummy_past_residuals[:]

                 UpperCamelCase__    :   List[str]					=				scheduler.step_prk(__SCREAMING_SNAKE_CASE						,						0						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample
                 UpperCamelCase__    :   int					=				scheduler.step_prk(__SCREAMING_SNAKE_CASE						,						1						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample

                 self.assertEqual(output_a.shape						,						sample.shape  )
                 self.assertEqual(output_a.shape						,						output_a.shape  )

                 UpperCamelCase__    :   Optional[Any]					=				scheduler.step_plms(__SCREAMING_SNAKE_CASE						,						0						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample
                 UpperCamelCase__    :   int					=				scheduler.step_plms(__SCREAMING_SNAKE_CASE						,						1						,						__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  ).prev_sample

                 self.assertEqual(output_a.shape						,						sample.shape  )
                 self.assertEqual(output_a.shape						,						output_a.shape  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           for timesteps in [1_0_0, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Dict					=				self.scheduler_classes[0]
           UpperCamelCase__    :   List[Any]					=				self.get_scheduler_config(steps_offset=1  )
           UpperCamelCase__    :   Any					=				scheduler_class(**__SCREAMING_SNAKE_CASE  )
           scheduler.set_timesteps(1_0  )
           assert torch.equal(
               scheduler.timesteps						,						torch.LongTensor(
                   [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]  )						,						)






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           for beta_start, beta_end in zip([0.0001, 0.001]						,						[0.002, 0.02]  ):
                 self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE						,						beta_end=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[str]:




           """simple docstring"""







           for t in [1, 5, 1_0]:
                 self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           for t, num_inference_steps in zip([1, 5, 1_0]						,						[1_0, 5_0, 1_0_0]  ):
                 self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Any					=				2_7

           for scheduler_class in self.scheduler_classes:
                 UpperCamelCase__    :   List[Any]					=				self.dummy_sample
                 UpperCamelCase__    :   str					=				0.1 * sample

                 UpperCamelCase__    :   Union[str, Any]					=				self.get_scheduler_config()
                 UpperCamelCase__    :   str					=				scheduler_class(**__SCREAMING_SNAKE_CASE  )

                 scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  )

                 # before power of 3 fix, would error on first step, so we only need to do two
                 for i, t in enumerate(scheduler.prk_timesteps[:2]  ):
                       UpperCamelCase__    :   Tuple					=				scheduler.step_prk(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ).prev_sample






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           with self.assertRaises(__SCREAMING_SNAKE_CASE  ):
                 UpperCamelCase__    :   Tuple					=				self.scheduler_classes[0]
                 UpperCamelCase__    :   Optional[Any]					=				self.get_scheduler_config()
                 UpperCamelCase__    :   Optional[Any]					=				scheduler_class(**__SCREAMING_SNAKE_CASE  )

                 scheduler.step_plms(self.dummy_sample						,						1						,						self.dummy_sample  ).prev_sample






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				self.full_loop()
           UpperCamelCase__    :   Tuple					=				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE  )  )
           UpperCamelCase__    :   Any					=				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE  )  )

           assert abs(result_sum.item() - 198.1318  ) < 1e-2
           assert abs(result_mean.item() - 0.2580  ) < 1e-3






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				self.full_loop(prediction_type='''v_prediction'''  )
           UpperCamelCase__    :   Any					=				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE  )  )
           UpperCamelCase__    :   Tuple					=				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE  )  )

           assert abs(result_sum.item() - 67.3986  ) < 1e-2
           assert abs(result_mean.item() - 0.0878  ) < 1e-3






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   int					=				self.full_loop(set_alpha_to_one=__SCREAMING_SNAKE_CASE						,						beta_start=0.01  )
           UpperCamelCase__    :   List[str]					=				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE  )  )
           UpperCamelCase__    :   Union[str, Any]					=				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE  )  )

           assert abs(result_sum.item() - 230.0399  ) < 1e-2
           assert abs(result_mean.item() - 0.2995  ) < 1e-3






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[int]:




           """simple docstring"""







           UpperCamelCase__    :   List[str]					=				self.full_loop(set_alpha_to_one=__SCREAMING_SNAKE_CASE						,						beta_start=0.01  )
           UpperCamelCase__    :   Any					=				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE  )  )
           UpperCamelCase__    :   Optional[int]					=				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE  )  )

           assert abs(result_sum.item() - 186.9482  ) < 1e-2
           assert abs(result_mean.item() - 0.2434  ) < 1e-3




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCamelCase      =False
lowerCamelCase      =True
lowerCamelCase      =False


if __name__ == "__main__":
    lowerCamelCase      =argparse.ArgumentParser()

    parser.add_argument(
        "--repo_path",
        default=None,
        type=str,
        required=True,
        help="The config json file corresponding to the architecture.",
    )

    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

    lowerCamelCase      =parser.parse_args()

    lowerCamelCase      ={
        "image_size": "sample_size",
        "num_res_blocks": "layers_per_block",
        "block_channels": "block_out_channels",
        "down_blocks": "down_block_types",
        "up_blocks": "up_block_types",
        "downscale_freq_shift": "freq_shift",
        "resnet_num_groups": "norm_num_groups",
        "resnet_act_fn": "act_fn",
        "resnet_eps": "norm_eps",
        "num_head_channels": "attention_head_dim",
    }

    lowerCamelCase      ={
        "time_steps": "time_proj",
        "mid": "mid_block",
        "downsample_blocks": "down_blocks",
        "upsample_blocks": "up_blocks",
    }

    lowerCamelCase      ="" if has_file(args.repo_path, "config.json") else "unet"

    with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
        lowerCamelCase      =reader.read()
        lowerCamelCase      =json.loads(text)

    if do_only_config:
        for key in config_parameters_to_change.keys():
            config.pop(key, None)

    if has_file(args.repo_path, "config.json"):
        lowerCamelCase      =UNetaDModel(**config)
    else:
        lowerCamelCase      =UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
        lowerCamelCase      =class_name(**config)

    if do_only_config:
        model.save_config(os.path.join(args.repo_path, subfolder))

    lowerCamelCase      =dict(model.config)

    if do_only_renaming:
        for key, value in config_parameters_to_change.items():
            if key in config:
                lowerCamelCase      =config[key]
                del config[key]

        lowerCamelCase      =[k.replace("UNetRes", "") for k in config["down_block_types"]]
        lowerCamelCase      =[k.replace("UNetRes", "") for k in config["up_block_types"]]

    if do_only_weights:
        lowerCamelCase      =torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

        lowerCamelCase      ={}
        for param_key, param_value in state_dict.items():
            if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
                continue
            lowerCamelCase      =False
            for key, new_key in key_parameters_to_change.items():
                if not has_changed and param_key.split(".")[0] == key:
                    lowerCamelCase      =param_value
                    lowerCamelCase      =True
            if not has_changed:
                lowerCamelCase      =param_value

        model.load_state_dict(new_state_dict)
        model.save_pretrained(os.path.join(args.repo_path, subfolder))


