


import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[str]						):  # picklable for multiprocessing




      """simple docstring"""





      return x.sum()
def 				__lowerCamelCase						( UpperCAmelCase_						:		str						):  # picklable for multiprocessing




      """simple docstring"""





      return i + 1







@dataclass
class 		_snake_case  :
 SCREAMING_SNAKE_CASE__      =       42
 SCREAMING_SNAKE_CASE__      =       42







class 		_snake_case  (      _snake_case							):




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:str 							=       {}
       a						:int 							=       []
       a						:Dict 							=       1
       a						:Tuple 							=       [1, 2]
       a						:Dict 							=       {'''a''': 1, '''b''': 2}
       a						:Optional[int] 							=       {'''a''': [1, 2], '''b''': [3, 4]}
       a						:str 							=       {'''a''': {'''1''': 1}, '''b''': 2}
       a						:Optional[int] 							=       {'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
       a						:List[Any] 							=       {}
       a						:Union[str, Any] 							=       []
       a						:Union[str, Any] 							=       2
       a						:int 							=       [2, 3]
       a						:str 							=       {'''a''': 2, '''b''': 3}
       a						:Optional[Any] 							=       {'''a''': [2, 3], '''b''': [4, 5]}
       a						:Union[str, Any] 							=       {'''a''': {'''1''': 2}, '''b''': 3}
       a						:Dict 							=       {'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}
       self.assertEqual(map_nested(_lowerCamelCase					,  _lowerCamelCase    )					,  _lowerCamelCase    )
       self.assertEqual(map_nested(_lowerCamelCase					,  _lowerCamelCase    )					,  _lowerCamelCase    )
       self.assertEqual(map_nested(_lowerCamelCase					,  _lowerCamelCase    )					,  _lowerCamelCase    )
       self.assertEqual(map_nested(_lowerCamelCase					,  _lowerCamelCase    )					,  _lowerCamelCase    )
       self.assertEqual(map_nested(_lowerCamelCase					,  _lowerCamelCase    )					,  _lowerCamelCase    )
       self.assertEqual(map_nested(_lowerCamelCase					,  _lowerCamelCase    )					,  _lowerCamelCase    )
       self.assertEqual(map_nested(_lowerCamelCase					,  _lowerCamelCase    )					,  _lowerCamelCase    )
       self.assertEqual(map_nested(_lowerCamelCase					,  _lowerCamelCase    )					,  _lowerCamelCase    )

       a						:List[Any] 							=       2
       self.assertEqual(map_nested(_lowerCamelCase					,  _lowerCamelCase					,  num_proc=_lowerCamelCase    )					,  _lowerCamelCase    )
       self.assertEqual(map_nested(_lowerCamelCase					,  _lowerCamelCase					,  num_proc=_lowerCamelCase    )					,  _lowerCamelCase    )
       self.assertEqual(map_nested(_lowerCamelCase					,  _lowerCamelCase					,  num_proc=_lowerCamelCase    )					,  _lowerCamelCase    )
       self.assertEqual(map_nested(_lowerCamelCase					,  _lowerCamelCase					,  num_proc=_lowerCamelCase    )					,  _lowerCamelCase    )
       self.assertEqual(map_nested(_lowerCamelCase					,  _lowerCamelCase					,  num_proc=_lowerCamelCase    )					,  _lowerCamelCase    )
       self.assertEqual(map_nested(_lowerCamelCase					,  _lowerCamelCase					,  num_proc=_lowerCamelCase    )					,  _lowerCamelCase    )
       self.assertEqual(map_nested(_lowerCamelCase					,  _lowerCamelCase					,  num_proc=_lowerCamelCase    )					,  _lowerCamelCase    )
       self.assertEqual(map_nested(_lowerCamelCase					,  _lowerCamelCase					,  num_proc=_lowerCamelCase    )					,  _lowerCamelCase    )

       a						:Tuple 							=       {'''a''': np.eye(2    ), '''b''': np.zeros(3    ), '''c''': np.ones(2    )}
       a						:Dict 							=       {'''a''': 2, '''b''': 0, '''c''': 2}
       a						:str 							=       {
           '''a''': np.eye(2    ).astype(_lowerCamelCase    ),
           '''b''': np.zeros(3    ).astype(_lowerCamelCase    ),
           '''c''': np.ones(2    ).astype(_lowerCamelCase    ),
       }
       self.assertEqual(map_nested(_lowerCamelCase					,  _lowerCamelCase					,  map_numpy=_lowerCamelCase    )					,  _lowerCamelCase    )
       self.assertEqual(
           {k: v.tolist() for k, v in map_nested(_lowerCamelCase					,  _lowerCamelCase					,  map_numpy=_lowerCamelCase    ).items()}					,  {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}					,  )
       self.assertEqual(map_nested(_lowerCamelCase					,  _lowerCamelCase					,  map_numpy=_lowerCamelCase					,  num_proc=_lowerCamelCase    )					,  _lowerCamelCase    )
       self.assertEqual(
           {k: v.tolist() for k, v in map_nested(_lowerCamelCase					,  _lowerCamelCase					,  map_numpy=_lowerCamelCase					,  num_proc=_lowerCamelCase    ).items()}					,  {k: v.tolist() for k, v in expected_map_nested_sna_int.items()}					,  )
       with self.assertRaises(_lowerCamelCase    ):  # can't pickle a local lambda
             map_nested(lambda _lowerCamelCase    : x + 1					,  _lowerCamelCase					,  num_proc=_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[Any] 							=       {'''a''': 1, '''b''': 2}
       a						:Optional[Any] 							=       {'''a''': 3, '''b''': 4}
       a						:Any 							=       {'''a''': 5, '''b''': 6}
       a						:Dict 							=       sorted([('''a''', (1, 3, 5)), ('''b''', (2, 4, 6))]    )
       self.assertEqual(sorted(zip_dict(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    )    )					,  _lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):







       class 		_snake_case  :
        SCREAMING_SNAKE_CASE__      =       'bar'







       a						:Tuple 							=       Foo()
       self.assertEqual(foo.my_attr					,  '''bar'''    )
       with temporary_assignment(_lowerCamelCase					,  '''my_attr'''					,  '''BAR'''    ):
             self.assertEqual(foo.my_attr					,  '''BAR'''    )
       self.assertEqual(foo.my_attr					,  '''bar'''    )







@pytest.mark.parametrize(
    '''iterable_length, num_proc, expected_num_proc''' ,  [
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ] ,  )
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      with patch('''datasets.utils.py_utils._single_map_nested'''						) as mock_single_map_nested, patch(
          '''datasets.parallel.parallel.Pool'''						) as mock_multiprocessing_pool:
            a						:List[Any] 							=       {F'''{i}''': i for i in range(UpperCamelCase__						)}
            a						:Optional[Any] 							=       map_nested(lambda UpperCAmelCase_						: x + 10 ,  UpperCamelCase__ ,  num_proc=UpperCamelCase__ ,  parallel_min_length=16						)
            if expected_num_proc == 1:
                  assert mock_single_map_nested.called
                  assert not mock_multiprocessing_pool.called
            else:
                  assert not mock_single_map_nested.called
                  assert mock_multiprocessing_pool.called
                  assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc







class 		_snake_case  (      _snake_case							):




 @require_tf
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       import tensorflow as tf
       from tensorflow.keras import layers

       a						:Optional[int] 							=       layers.Dense(2    )

       def gen_random_output():
             a						:Tuple 							=       tf.random.uniform((1, 3)    )
             return model(_lowerCamelCase    ).numpy()

       with temp_seed(42					,  set_tensorflow=_lowerCamelCase    ):
             a						:List[str] 							=       gen_random_output()
       with temp_seed(42					,  set_tensorflow=_lowerCamelCase    ):
             a						:Optional[Any] 							=       gen_random_output()
       a						:str 							=       gen_random_output()

       np.testing.assert_equal(_lowerCamelCase					,  _lowerCamelCase    )
       self.assertGreater(np.abs(outa - outa    ).sum()					,  0    )




 @require_torch
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       import torch

       def gen_random_output():
             a						:Tuple 							=       torch.nn.Linear(3					,  2    )
             a						:Tuple 							=       torch.rand(1					,  3    )
             return model(_lowerCamelCase    ).detach().numpy()

       with temp_seed(42					,  set_pytorch=_lowerCamelCase    ):
             a						:Optional[Any] 							=       gen_random_output()
       with temp_seed(42					,  set_pytorch=_lowerCamelCase    ):
             a						:Optional[Any] 							=       gen_random_output()
       a						:str 							=       gen_random_output()

       np.testing.assert_equal(_lowerCamelCase					,  _lowerCamelCase    )
       self.assertGreater(np.abs(outa - outa    ).sum()					,  0    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       def gen_random_output():
             return np.random.rand(1					,  3    )

       with temp_seed(42    ):
             a						:List[str] 							=       gen_random_output()
       with temp_seed(42    ):
             a						:int 							=       gen_random_output()
       a						:Dict 							=       gen_random_output()

       np.testing.assert_equal(_lowerCamelCase					,  _lowerCamelCase    )
       self.assertGreater(np.abs(outa - outa    ).sum()					,  0    )







@pytest.mark.parametrize('''input_data''' ,  [{}]						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		str						):




      """simple docstring"""





      a						:Optional[int] 							=       NestedDataStructure(UpperCamelCase__						).data
      assert output_data == input_data
@pytest.mark.parametrize(
    '''data, expected_output''' ,  [
        ({}, []),
        ([], []),
        ('''foo''', ['''foo''']),
        (['''foo''', '''bar'''], ['''foo''', '''bar''']),
        ([['''foo''', '''bar''']], ['''foo''', '''bar''']),
        ([[['''foo'''], ['''bar''']]], ['''foo''', '''bar''']),
        ([[['''foo'''], '''bar''']], ['''foo''', '''bar''']),
        ({'''a''': 1, '''b''': 2}, [1, 2]),
        ({'''a''': [1, 2], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[1, 2]], '''b''': [[3, 4]]}, [1, 2, 3, 4]),
        ({'''a''': [[1, 2]], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [[[3], [4]]]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [[3, 4]]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [3, 4]}, [1, 2, 3, 4]),
        ({'''a''': [[[1], [2]]], '''b''': [3, [4]]}, [1, 2, 3, 4]),
        ({'''a''': {'''1''': 1}, '''b''': 2}, [1, 2]),
        ({'''a''': {'''1''': [1]}, '''b''': 2}, [1, 2]),
        ({'''a''': {'''1''': [1]}, '''b''': [2]}, [1, 2]),
    ] ,  )
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any] ,  UpperCAmelCase_						:		Tuple						):




      """simple docstring"""





      a						:str 							=       NestedDataStructure(UpperCamelCase__						).flatten()
      assert output == expected_output
def 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:Dict 							=       A(x=1 ,  y='''foobar'''						)
      a						:str 							=       {'''x''': 1, '''y''': '''foobar'''}
      assert asdict(UpperCamelCase__						) == expected_output

      a						:int 							=       {'''a''': {'''b''': A(x=10 ,  y='''foo'''						)}, '''c''': [A(x=20 ,  y='''bar'''						)]}
      a						:Tuple 							=       {'''a''': {'''b''': {'''x''': 10, '''y''': '''foo'''}}, '''c''': [{'''x''': 20, '''y''': '''bar'''}]}
      assert asdict(UpperCamelCase__						) == expected_output

      with pytest.raises(UpperCamelCase__						):
            asdict([1, A(x=10 ,  y='''foo'''						)]						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		str						):




      """simple docstring"""





      return text.split()
def 				__lowerCamelCase						( UpperCAmelCase_						:		Tuple						):




      """simple docstring"""





      yield (time.time(), content)
      time.sleep(2						)
      yield (time.time(), content)






def 				__lowerCamelCase						( ):




      """simple docstring"""





      with Pool(2						) as pool:
            a						:Tuple 							=       list(iflatmap_unordered(UpperCamelCase__ ,  _split_text ,  kwargs_iterable=[{'''text''': '''hello there'''}] * 10						)						)
            assert out.count('''hello'''						) == 10
            assert out.count('''there'''						) == 10
            assert len(UpperCamelCase__						) == 20

      # check multiprocess from pathos (uses dill for pickling)
      with multiprocess.Pool(2						) as pool:
            a						:str 							=       list(iflatmap_unordered(UpperCamelCase__ ,  _split_text ,  kwargs_iterable=[{'''text''': '''hello there'''}] * 10						)						)
            assert out.count('''hello'''						) == 10
            assert out.count('''there'''						) == 10
            assert len(UpperCamelCase__						) == 20

      # check that we get items as fast as possible
      with Pool(2						) as pool:
            a						:Dict 							=       []
            for yield_time, content in iflatmap_unordered(
                UpperCamelCase__ ,  _aseconds_generator_of_aitems_with_timing ,  kwargs_iterable=[{'''content''': '''a'''}, {'''content''': '''b'''}]						):
                  assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
                  out.append(UpperCamelCase__						)
            assert out.count('''a'''						) == 2
            assert out.count('''b'''						) == 2
            assert len(UpperCamelCase__						) == 4










"""simple docstring"""
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Optional[Any]							):
    _UpperCAmelCase							:  int											=    OmegaConf.load(UpperCamelCase__							)
    _UpperCAmelCase							:  str											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)['''model''']
    _UpperCAmelCase							:  Optional[Any]											=    list(state_dict.keys()							)

    # extract state_dict for VQVAE
    _UpperCAmelCase							:  Any											=    {}
    _UpperCAmelCase							:  Any											=    '''first_stage_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    # extract state_dict for UNetLDM
    _UpperCAmelCase							:  Tuple											=    {}
    _UpperCAmelCase							:  int											=    '''model.diffusion_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    _UpperCAmelCase							:  List[str]											=    config.model.params.first_stage_config.params
    _UpperCAmelCase							:  Union[str, Any]											=    config.model.params.unet_config.params

    _UpperCAmelCase							:  Any											=    VQModel(**UpperCamelCase__							).eval()
    vqvae.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  Union[str, Any]											=    UNetLDMModel(**UpperCamelCase__							).eval()
    unet.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  int											=    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=UpperCamelCase__     ,       )

    _UpperCAmelCase							:  Optional[Any]											=    LDMPipeline(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
    pipeline.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  _lowerCAmelCase    :Union[str, Any]     =					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)
  _lowerCAmelCase    :List[Any]     =					parser.parse_args()

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


