




'''simple docstring'''



import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase_    	=				5_0_0_0_0
UpperCamelCase_    	=				5_0_0_0

UpperCamelCase_	,    UpperCamelCase_    	=				os.path.split(__file__)
UpperCamelCase_    	=				os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))




@get_duration
def   lowercase__(			__UpperCamelCase:					datasets.Dataset      ,__UpperCamelCase:					List[Any]    ):

      """simple docstring"""






      for i in range(__UpperCamelCase    ):
            SCREAMING_SNAKE_CASE   :      str    					=						dataset[i]




@get_duration
def   lowercase__(			__UpperCamelCase:					datasets.Dataset      ,__UpperCamelCase:					Tuple      ,__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      for i in range(0      ,len(__UpperCamelCase    )      ,__UpperCamelCase    ):
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						dataset[i : i + batch_size]




@get_duration
def   lowercase__(			__UpperCamelCase:					datasets.Dataset      ,__UpperCamelCase:					List[str]      ,__UpperCamelCase:					Dict    ):

      """simple docstring"""






      with dataset.formatted_as(type=__UpperCamelCase    ):
            for i in range(__UpperCamelCase    ):
                  SCREAMING_SNAKE_CASE   :      Tuple    					=						dataset[i]




@get_duration
def   lowercase__(			__UpperCamelCase:					datasets.Dataset      ,__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					Optional[int]      ,__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      with dataset.formatted_as(type=__UpperCamelCase    ):
            for i in range(0      ,__UpperCamelCase      ,__UpperCamelCase    ):
                  SCREAMING_SNAKE_CASE   :      str    					=						dataset[i : i + batch_size]




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      int    					=						{'num examples': SPEED_TEST_N_EXAMPLES}
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[
          (read, {'length': SMALL_TEST}),
          (read, {'length': SPEED_TEST_N_EXAMPLES}),
          (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
          (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
          (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
          (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
          (read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
          (read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
          (read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
          (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
          (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
      ]

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[
          (read, {'length': SMALL_TEST}),
          (read, {'length': SPEED_TEST_N_EXAMPLES}),
          (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
          (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1_00}),
          (read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10_00}),
          (read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
          (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
          (read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10_00}),
      ]
      with tempfile.TemporaryDirectory() as tmp_dir:
            print('generating dataset'    )
            SCREAMING_SNAKE_CASE   :      Any    					=						datasets.Features(
                {'list': datasets.Sequence(datasets.Value('float32'    )    ), 'numbers': datasets.Value('float32'    )}    )
            SCREAMING_SNAKE_CASE   :      int    					=						generate_example_dataset(
                os.path.join(__UpperCamelCase      ,'dataset.arrow'    )      ,__UpperCamelCase      ,num_examples=__UpperCamelCase      ,seq_shapes={'list': (1_00,)}      ,)
            print('first set of iterations'    )
            for func, kwargs in functions:
                  print(func.__name__      ,str(__UpperCamelCase    )    )
                  SCREAMING_SNAKE_CASE   :      List[Any]    					=						func(__UpperCamelCase      ,**__UpperCamelCase    )

            print('shuffling dataset'    )
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						dataset.shuffle()
            print('Second set of iterations (after shuffling'    )
            for func, kwargs in functions_shuffled:
                  print('shuffled '      ,func.__name__      ,str(__UpperCamelCase    )    )
                  SCREAMING_SNAKE_CASE   :      List[str]    					=						func(
                      __UpperCamelCase      ,**__UpperCamelCase    )

      with open(__UpperCamelCase      ,'wb'    ) as f:
            f.write(json.dumps(__UpperCamelCase    ).encode('utf-8'    )    )


if __name__ == "__main__":  # useful to run the profiler
      benchmark_iterating()






'''simple docstring'''






import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def    _snake_case  (	A					,       A					,       A			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      OmegaConf.load(A			)
							lowerCAmelCase__			  =      torch.load(A					,       map_location='''cpu'''			)['''model''']
							lowerCAmelCase__			  =      list(state_dict.keys()			)

							# extract state_dict for VQVAE
							lowerCAmelCase__			  =      {}
							lowerCAmelCase__			  =      '''first_stage_model.'''
							for key in keys:
														if key.startswith(A			):
																					lowerCAmelCase__			  =      state_dict[key]

    # extract state_dict for UNetLDM
							lowerCAmelCase__			  =      {}
							lowerCAmelCase__			  =      '''model.diffusion_model.'''
							for key in keys:
														if key.startswith(A			):
																					lowerCAmelCase__			  =      state_dict[key]

							lowerCAmelCase__			  =      config.model.params.first_stage_config.params
							lowerCAmelCase__			  =      config.model.params.unet_config.params

							lowerCAmelCase__			  =      VQModel(**A			).eval()
							vqvae.load_state_dict(A			)

							lowerCAmelCase__			  =      UNetLDMModel(**A			).eval()
							unet.load_state_dict(A			)

							lowerCAmelCase__			  =      DDIMScheduler(
							    timesteps=config.model.params.timesteps					,       beta_schedule='''scaled_linear'''					,       beta_start=config.model.params.linear_start					,       beta_end=config.model.params.linear_end					,       clip_sample=A					,       )

							lowerCAmelCase__			  =      LDMPipeline(A					,       A					,       A			)
							pipeline.save_pretrained(A			)


if __name__ == "__main__":
		__UpperCAmelCase       							=				argparse.ArgumentParser()
		parser.add_argument('''--checkpoint_path''', type=str, required=True)
		parser.add_argument('''--config_path''', type=str, required=True)
		parser.add_argument('''--output_path''', type=str, required=True)
		__UpperCAmelCase       							=				parser.parse_args()

		convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)