






'''simple docstring'''







import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
			from transformers import SpeechaTextFeatureExtractor

_A     :      int   					=random.Random()





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase=1.0    ,      UpperCamelCase=None    ,      UpperCamelCase=None				)				->	Optional[Any]:
							if rng is None:
														lowerCamelCase__ :     str	  =	global_rng

							lowerCamelCase__ :     Optional[int]	  =	[]
							for batch_idx in range(shape[0]				):
														values.append([]				)
														for _ in range(shape[1]				):
																					values[-1].append(rng.random() * scale				)

							return values

@require_torch
@require_torchaudio
class _lowercase     (    unittest.TestCase		):




							def __init__(							self:    List[str]      ,							UpperCamelCase__:    int      ,							UpperCamelCase__:    Optional[Any]=7      ,							UpperCamelCase__:    List[str]=400      ,							UpperCamelCase__:    Optional[Any]=2_000      ,							UpperCamelCase__:    int=24      ,							UpperCamelCase__:    List[str]=24      ,							UpperCamelCase__:    str=0.0      ,							UpperCamelCase__:    List[Any]=16_000      ,							UpperCamelCase__:    Any=True      ,							UpperCamelCase__:    str=True      ,							):
														lowerCamelCase__ :     str	  =	parent
														lowerCamelCase__ :     Optional[Any]	  =	batch_size
														lowerCamelCase__ :     Optional[int]	  =	min_seq_length
														lowerCamelCase__ :     Union[str, Any]	  =	max_seq_length
														lowerCamelCase__ :     Union[str, Any]	  =	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
														lowerCamelCase__ :     Any	  =	feature_size
														lowerCamelCase__ :     List[str]	  =	num_mel_bins
														lowerCamelCase__ :     Union[str, Any]	  =	padding_value
														lowerCamelCase__ :     List[str]	  =	sampling_rate
														lowerCamelCase__ :     Union[str, Any]	  =	return_attention_mask
														lowerCamelCase__ :     Any	  =	do_normalize




							def 		lowerCamelCase_    (							self:    Optional[int]					):
														return {
														    "feature_size": self.feature_size,
														    "num_mel_bins": self.num_mel_bins,
														    "padding_value": self.padding_value,
														    "sampling_rate": self.sampling_rate,
														    "return_attention_mask": self.return_attention_mask,
														    "do_normalize": self.do_normalize,
														}




							def 		lowerCamelCase_    (							self:    List[Any]      ,							UpperCamelCase__:    Dict=False      ,							UpperCamelCase__:    Optional[int]=False					):
														def _flatten(UpperCamelCase__:    Optional[int]					):
																					return list(itertools.chain(*UpperCamelCase__					)					)

														if equal_length:
																					lowerCamelCase__ :     Any	  =	[floats_list((self.max_seq_length, self.feature_size)					) for _ in range(self.batch_size					)]
														else:
																					# make sure that inputs increase in size
																					lowerCamelCase__ :     List[str]	  =	[
																					    floats_list((x, self.feature_size)					)
																					    for x in range(self.min_seq_length      ,							self.max_seq_length      ,							self.seq_length_diff					)
																					]
														if numpify:
																					lowerCamelCase__ :     List[str]	  =	[np.asarray(UpperCamelCase__					) for x in speech_inputs]
														return speech_inputs



@require_torch
@require_torchaudio
class _lowercase     (    _lowercase    ,				unittest.TestCase		):
							a    				=	SpeechaTextFeatureExtractor if is_speech_available() else None




							def 		lowerCamelCase_    (							self:    int					):
														lowerCamelCase__ :     Tuple	  =	SpeechaTextFeatureExtractionTester(self					)




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    List[str]					):
														self.assertTrue(np.all(np.mean(UpperCamelCase__      ,							axis=0					) < 1e-3					)					)
														self.assertTrue(np.all(np.abs(np.var(UpperCamelCase__      ,							axis=0					) - 1					) < 1e-3					)					)




							def 		lowerCamelCase_    (							self:    List[Any]					):
														# Tests that all call wrap to encode_plus and batch_encode_plus
														lowerCamelCase__ :     Any	  =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
														# create three inputs of length 800, 1000, and 1200
														lowerCamelCase__ :     int	  =	[floats_list((1, x)					)[0] for x in range(800      ,							1_400      ,							200					)]
														lowerCamelCase__ :     Union[str, Any]	  =	[np.asarray(UpperCamelCase__					) for speech_input in speech_inputs]

														# Test feature size
														lowerCamelCase__ :     List[str]	  =	feature_extractor(UpperCamelCase__      ,							padding=UpperCamelCase__      ,							return_tensors="""np"""					).input_features
														self.assertTrue(input_features.ndim == 3					)
														self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size					)

														# Test not batched input
														lowerCamelCase__ :     List[str]	  =	feature_extractor(speech_inputs[0]      ,							return_tensors="""np"""					).input_features
														lowerCamelCase__ :     str	  =	feature_extractor(np_speech_inputs[0]      ,							return_tensors="""np"""					).input_features
														self.assertTrue(np.allclose(UpperCamelCase__      ,							UpperCamelCase__      ,							atol=1e-3					)					)

														# Test batched
														lowerCamelCase__ :     Any	  =	feature_extractor(UpperCamelCase__      ,							return_tensors="""np"""					).input_features
														lowerCamelCase__ :     str	  =	feature_extractor(UpperCamelCase__      ,							return_tensors="""np"""					).input_features
														for enc_seq_a, enc_seq_a in zip(UpperCamelCase__      ,							UpperCamelCase__					):
																					self.assertTrue(np.allclose(UpperCamelCase__      ,							UpperCamelCase__      ,							atol=1e-3					)					)

														# Test 2-D numpy arrays are batched.
														lowerCamelCase__ :     str	  =	[floats_list((1, x)					)[0] for x in (800, 800, 800)]
														lowerCamelCase__ :     Optional[Any]	  =	np.asarray(UpperCamelCase__					)
														lowerCamelCase__ :     int	  =	feature_extractor(UpperCamelCase__      ,							return_tensors="""np"""					).input_features
														lowerCamelCase__ :     Optional[Any]	  =	feature_extractor(UpperCamelCase__      ,							return_tensors="""np"""					).input_features
														for enc_seq_a, enc_seq_a in zip(UpperCamelCase__      ,							UpperCamelCase__					):
																					self.assertTrue(np.allclose(UpperCamelCase__      ,							UpperCamelCase__      ,							atol=1e-3					)					)




							def 		lowerCamelCase_    (							self:    List[str]					):
														lowerCamelCase__ :     Optional[Any]	  =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
														lowerCamelCase__ :     Optional[Any]	  =	[floats_list((1, x)					)[0] for x in range(800      ,							1_400      ,							200					)]

														lowerCamelCase__ :     int	  =	["""longest""", """max_length""", """do_not_pad"""]
														lowerCamelCase__ :     Dict	  =	[None, 16, None]
														for max_length, padding in zip(UpperCamelCase__      ,							UpperCamelCase__					):
																					lowerCamelCase__ :     List[Any]	  =	feature_extractor(
																					    UpperCamelCase__      ,							padding=UpperCamelCase__      ,							max_length=UpperCamelCase__      ,							return_attention_mask=UpperCamelCase__					)
																					lowerCamelCase__ :     Union[str, Any]	  =	inputs.input_features
																					lowerCamelCase__ :     List[Any]	  =	inputs.attention_mask
																					lowerCamelCase__ :     Dict	  =	[np.sum(UpperCamelCase__					) for x in attention_mask]

																					self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]					)
																					self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]					)
																					self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]					)




							def 		lowerCamelCase_    (							self:    str					):
														lowerCamelCase__ :     Optional[int]	  =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
														lowerCamelCase__ :     List[str]	  =	[floats_list((1, x)					)[0] for x in range(800      ,							1_400      ,							200					)]

														lowerCamelCase__ :     Optional[int]	  =	["""longest""", """max_length""", """do_not_pad"""]
														lowerCamelCase__ :     Any	  =	[None, 16, None]
														for max_length, padding in zip(UpperCamelCase__      ,							UpperCamelCase__					):
																					lowerCamelCase__ :     List[str]	  =	feature_extractor(
																					    UpperCamelCase__      ,							max_length=UpperCamelCase__      ,							padding=UpperCamelCase__      ,							return_tensors="""np"""      ,							return_attention_mask=UpperCamelCase__					)
																					lowerCamelCase__ :     Any	  =	inputs.input_features
																					lowerCamelCase__ :     List[str]	  =	inputs.attention_mask
																					lowerCamelCase__ :     Union[str, Any]	  =	[np.sum(UpperCamelCase__					) for x in attention_mask]

																					self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]					)
																					self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6					)
																					self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]					)
																					self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6					)
																					self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]					)




							def 		lowerCamelCase_    (							self:    Union[str, Any]					):
														lowerCamelCase__ :     List[Any]	  =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
														lowerCamelCase__ :     Union[str, Any]	  =	[floats_list((1, x)					)[0] for x in range(800      ,							1_400      ,							200					)]
														lowerCamelCase__ :     int	  =	feature_extractor(
														    UpperCamelCase__      ,							padding="""max_length"""      ,							max_length=4      ,							truncation=UpperCamelCase__      ,							return_tensors="""np"""      ,							return_attention_mask=UpperCamelCase__      ,							)
														lowerCamelCase__ :     List[Any]	  =	inputs.input_features
														lowerCamelCase__ :     Optional[int]	  =	inputs.attention_mask
														lowerCamelCase__ :     int	  =	np.sum(attention_mask == 1      ,							axis=1					)

														self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]					)
														self._check_zero_mean_unit_variance(input_features[1]					)
														self._check_zero_mean_unit_variance(input_features[2]					)




							def 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     List[Any]	  =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
														lowerCamelCase__ :     Tuple	  =	[floats_list((1, x)					)[0] for x in range(800      ,							1_400      ,							200					)]
														lowerCamelCase__ :     Dict	  =	feature_extractor(
														    UpperCamelCase__      ,							padding="""longest"""      ,							max_length=4      ,							truncation=UpperCamelCase__      ,							return_tensors="""np"""      ,							return_attention_mask=UpperCamelCase__      ,							)
														lowerCamelCase__ :     Any	  =	inputs.input_features
														lowerCamelCase__ :     Optional[int]	  =	inputs.attention_mask
														lowerCamelCase__ :     Tuple	  =	np.sum(attention_mask == 1      ,							axis=1					)

														self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]					)
														self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]					)
														self._check_zero_mean_unit_variance(input_features[2]					)

														# make sure that if max_length < longest -> then pad to max_length
														self.assertEqual(input_features.shape      ,							(3, 4, 24)					)

														lowerCamelCase__ :     List[str]	  =	[floats_list((1, x)					)[0] for x in range(800      ,							1_400      ,							200					)]
														lowerCamelCase__ :     Optional[Any]	  =	feature_extractor(
														    UpperCamelCase__      ,							padding="""longest"""      ,							max_length=16      ,							truncation=UpperCamelCase__      ,							return_tensors="""np"""      ,							return_attention_mask=UpperCamelCase__      ,							)
														lowerCamelCase__ :     str	  =	inputs.input_features
														lowerCamelCase__ :     List[str]	  =	inputs.attention_mask
														lowerCamelCase__ :     Any	  =	np.sum(attention_mask == 1      ,							axis=1					)

														self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]					)
														self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]					)
														self._check_zero_mean_unit_variance(input_features[2]					)

														# make sure that if max_length < longest -> then pad to max_length
														self.assertEqual(input_features.shape      ,							(3, 6, 24)					)




							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														import torch

														lowerCamelCase__ :     Tuple	  =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
														lowerCamelCase__ :     str	  =	np.random.rand(100      ,							32					).astype(np.floataa					)
														lowerCamelCase__ :     List[str]	  =	np_speech_inputs.tolist()

														for inputs in [py_speech_inputs, np_speech_inputs]:
																					lowerCamelCase__ :     Tuple	  =	feature_extractor.pad([{"""input_features""": inputs}]      ,							return_tensors="""np"""					)
																					self.assertTrue(np_processed.input_features.dtype == np.floataa					)
																					lowerCamelCase__ :     Any	  =	feature_extractor.pad([{"""input_features""": inputs}]      ,							return_tensors="""pt"""					)
																					self.assertTrue(pt_processed.input_features.dtype == torch.floataa					)




							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    Optional[Any]					):
														from datasets import load_dataset

														lowerCamelCase__ :     List[Any]	  =	load_dataset("""hf-internal-testing/librispeech_asr_dummy"""      ,							"""clean"""      ,							split="""validation"""					)
														# automatic decoding with librispeech
														lowerCamelCase__ :     Any	  =	ds.sort("""id"""					).select(range(UpperCamelCase__					)					)[:num_samples]["""audio"""]

														return [x["array"] for x in speech_samples]




							def 		lowerCamelCase_    (							self:    Union[str, Any]					):
														# fmt: off
														lowerCamelCase__ :     Optional[Any]	  =	np.array([
														    -1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
														    -1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
														    -1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
														]					)
														# fmt: on

														lowerCamelCase__ :     List[Any]	  =	self._load_datasamples(1					)
														lowerCamelCase__ :     int	  =	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
														lowerCamelCase__ :     List[Any]	  =	feature_extractor(UpperCamelCase__      ,							return_tensors="""pt"""					).input_features
														self.assertEquals(input_features.shape      ,							(1, 584, 24)					)
														self.assertTrue(np.allclose(input_features[0, 0, :30]      ,							UpperCamelCase__      ,							atol=1e-4					)					)









'''simple docstring'''







import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_A     :      str   					={
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [32, 64],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_A     :      Union[str, Any]   					={
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [192, 192 * 2, 192 * 3, 192 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_A     :      Dict   					={
    '''sample_size''': 256,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

_A     :      Dict   					={
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

_A     :      str   					={
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}

_A     :      int   					={
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.002,
    '''sigma_max''': 80.0,
}





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Dict:
							if isinstance(UpperCamelCase    ,      UpperCamelCase				):
														return v
							if v.lower() in ("yes", "true", "t", "y", "1"):
														return True
							elif v.lower() in ("no", "false", "f", "n", "0"):
														return False
							else:
														raise argparse.ArgumentTypeError("""boolean value expected"""				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase=False				)				->	Any:
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.0.weight''']
							lowerCamelCase__ :     int	  =	checkpoint[f'''{old_prefix}.in_layers.0.bias''']
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.2.weight''']
							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.in_layers.2.bias''']
							lowerCamelCase__ :     Optional[Any]	  =	checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
							lowerCamelCase__ :     Optional[int]	  =	checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
							lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.out_layers.0.weight''']
							lowerCamelCase__ :     Tuple	  =	checkpoint[f'''{old_prefix}.out_layers.0.bias''']
							lowerCamelCase__ :     str	  =	checkpoint[f'''{old_prefix}.out_layers.3.weight''']
							lowerCamelCase__ :     int	  =	checkpoint[f'''{old_prefix}.out_layers.3.bias''']

							if has_skip:
														lowerCamelCase__ :     Tuple	  =	checkpoint[f'''{old_prefix}.skip_connection.weight''']
														lowerCamelCase__ :     List[Any]	  =	checkpoint[f'''{old_prefix}.skip_connection.bias''']

							return new_checkpoint





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase=None				)				->	str:
							lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3    ,      dim=0				)
							lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     str	  =	checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3    ,      dim=0				)

							lowerCamelCase__ :     Any	  =	checkpoint[f'''{old_prefix}.norm.weight''']
							lowerCamelCase__ :     Optional[int]	  =	checkpoint[f'''{old_prefix}.norm.bias''']

							lowerCamelCase__ :     List[Any]	  =	weight_q.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     List[Any]	  =	bias_q.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Any	  =	weight_k.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Optional[Any]	  =	bias_k.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Dict	  =	weight_v.squeeze(-1				).squeeze(-1				)
							lowerCamelCase__ :     Union[str, Any]	  =	bias_v.squeeze(-1				).squeeze(-1				)

							lowerCamelCase__ :     Optional[Any]	  =	(
							    checkpoint[f'''{old_prefix}.proj_out.weight'''].squeeze(-1				).squeeze(-1				)
							)
							lowerCamelCase__ :     Dict	  =	checkpoint[f'''{old_prefix}.proj_out.bias'''].squeeze(-1				).squeeze(-1				)

							return new_checkpoint





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	List[Any]:
							lowerCamelCase__ :     str	  =	torch.load(UpperCamelCase    ,      map_location="""cpu"""				)
							lowerCamelCase__ :     Optional[int]	  =	{}

							lowerCamelCase__ :     Optional[int]	  =	checkpoint["""time_embed.0.weight"""]
							lowerCamelCase__ :     List[Any]	  =	checkpoint["""time_embed.0.bias"""]
							lowerCamelCase__ :     int	  =	checkpoint["""time_embed.2.weight"""]
							lowerCamelCase__ :     Optional[Any]	  =	checkpoint["""time_embed.2.bias"""]

							if unet_config["num_class_embeds"] is not None:
														lowerCamelCase__ :     Optional[Any]	  =	checkpoint["""label_emb.weight"""]

							lowerCamelCase__ :     Tuple	  =	checkpoint["""input_blocks.0.0.weight"""]
							lowerCamelCase__ :     List[str]	  =	checkpoint["""input_blocks.0.0.bias"""]

							lowerCamelCase__ :     Optional[Any]	  =	unet_config["""down_block_types"""]
							lowerCamelCase__ :     Any	  =	unet_config["""layers_per_block"""]
							lowerCamelCase__ :     Any	  =	unet_config["""attention_head_dim"""]
							lowerCamelCase__ :     List[Any]	  =	unet_config["""block_out_channels"""]
							lowerCamelCase__ :     str	  =	1
							lowerCamelCase__ :     str	  =	channels_list[0]

							for i, layer_type in enumerate(UpperCamelCase				):
														lowerCamelCase__ :     List[Any]	  =	channels_list[i]
														lowerCamelCase__ :     List[Any]	  =	current_channels != prev_channels
														if layer_type == "ResnetDownsampleBlock2D":
																					for j in range(UpperCamelCase				):
																												lowerCamelCase__ :     int	  =	f'''down_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     Dict	  =	f'''input_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     Tuple	  =	True if j == 0 and downsample_block_has_skip else False
																												lowerCamelCase__ :     List[Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												current_layer += 1

														elif layer_type == "AttnDownBlock2D":
																					for j in range(UpperCamelCase				):
																												lowerCamelCase__ :     Tuple	  =	f'''down_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     Optional[Any]	  =	f'''input_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     str	  =	True if j == 0 and downsample_block_has_skip else False
																												lowerCamelCase__ :     Union[str, Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												lowerCamelCase__ :     Any	  =	f'''down_blocks.{i}.attentions.{j}'''
																												lowerCamelCase__ :     Dict	  =	f'''input_blocks.{current_layer}.1'''
																												lowerCamelCase__ :     Tuple	  =	convert_attention(
																												    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																												current_layer += 1

														if i != len(UpperCamelCase				) - 1:
																					lowerCamelCase__ :     Tuple	  =	f'''down_blocks.{i}.downsamplers.0'''
																					lowerCamelCase__ :     str	  =	f'''input_blocks.{current_layer}.0'''
																					lowerCamelCase__ :     Union[str, Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																					current_layer += 1

														lowerCamelCase__ :     Union[str, Any]	  =	current_channels

							# hardcoded the mid-block for now
							lowerCamelCase__ :     Any	  =	"""mid_block.resnets.0"""
							lowerCamelCase__ :     Optional[Any]	  =	"""middle_block.0"""
							lowerCamelCase__ :     int	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
							lowerCamelCase__ :     List[Any]	  =	"""mid_block.attentions.0"""
							lowerCamelCase__ :     Dict	  =	"""middle_block.1"""
							lowerCamelCase__ :     int	  =	convert_attention(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
							lowerCamelCase__ :     Any	  =	"""mid_block.resnets.1"""
							lowerCamelCase__ :     Tuple	  =	"""middle_block.2"""
							lowerCamelCase__ :     int	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							lowerCamelCase__ :     Union[str, Any]	  =	0
							lowerCamelCase__ :     Any	  =	unet_config["""up_block_types"""]

							for i, layer_type in enumerate(UpperCamelCase				):
														if layer_type == "ResnetUpsampleBlock2D":
																					for j in range(layers_per_block + 1				):
																												lowerCamelCase__ :     int	  =	f'''up_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     Optional[Any]	  =	f'''output_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     Any	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												current_layer += 1

																					if i != len(UpperCamelCase				) - 1:
																												lowerCamelCase__ :     Dict	  =	f'''up_blocks.{i}.upsamplers.0'''
																												lowerCamelCase__ :     List[str]	  =	f'''output_blocks.{current_layer-1}.1'''
																												lowerCamelCase__ :     Optional[Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
														elif layer_type == "AttnUpBlock2D":
																					for j in range(layers_per_block + 1				):
																												lowerCamelCase__ :     str	  =	f'''up_blocks.{i}.resnets.{j}'''
																												lowerCamelCase__ :     List[Any]	  =	f'''output_blocks.{current_layer}.0'''
																												lowerCamelCase__ :     Optional[Any]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      has_skip=UpperCamelCase				)
																												lowerCamelCase__ :     Optional[Any]	  =	f'''up_blocks.{i}.attentions.{j}'''
																												lowerCamelCase__ :     Any	  =	f'''output_blocks.{current_layer}.1'''
																												lowerCamelCase__ :     Optional[int]	  =	convert_attention(
																												    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																												current_layer += 1

																					if i != len(UpperCamelCase				) - 1:
																												lowerCamelCase__ :     Tuple	  =	f'''up_blocks.{i}.upsamplers.0'''
																												lowerCamelCase__ :     Tuple	  =	f'''output_blocks.{current_layer-1}.2'''
																												lowerCamelCase__ :     List[str]	  =	convert_resnet(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)

							lowerCamelCase__ :     Dict	  =	checkpoint["""out.0.weight"""]
							lowerCamelCase__ :     Dict	  =	checkpoint["""out.0.bias"""]
							lowerCamelCase__ :     Dict	  =	checkpoint["""out.2.weight"""]
							lowerCamelCase__ :     Tuple	  =	checkpoint["""out.2.bias"""]

							return new_checkpoint


if __name__ == "__main__":
			_A     :      Tuple   					=argparse.ArgumentParser()

			parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
			parser.add_argument(
			    '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
			)
			parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

			_A     :      Tuple   					=parser.parse_args()
			_A     :      Optional[int]   					=strabool(args.class_cond)

			_A     :      List[str]   					=os.path.basename(args.unet_path)
			print(F'Checkpoint: {ckpt_name}')

			# Get U-Net config
			if "imagenet64" in ckpt_name:
						_A     :      int   					=IMAGENET_64_UNET_CONFIG
			elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
						_A     :      Tuple   					=LSUN_256_UNET_CONFIG
			elif "test" in ckpt_name:
						_A     :      Any   					=TEST_UNET_CONFIG
			else:
						raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

			if not args.class_cond:
						_A     :      str   					=None

			_A     :      Optional[int]   					=con_pt_to_diffuser(args.unet_path, unet_config)

			_A     :      Optional[int]   					=UNetaDModel(**unet_config)
			image_unet.load_state_dict(converted_unet_ckpt)

			# Get scheduler config
			if "cd" in ckpt_name or "test" in ckpt_name:
						_A     :      Tuple   					=CD_SCHEDULER_CONFIG
			elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
						_A     :      int   					=CT_IMAGENET_64_SCHEDULER_CONFIG
			elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
						_A     :      Union[str, Any]   					=CT_LSUN_256_SCHEDULER_CONFIG
			else:
						raise ValueError(F'Checkpoint type {ckpt_name} is not currently supported.')

			_A     :      str   					=CMStochasticIterativeScheduler(**scheduler_config)

			_A     :      Optional[Any]   					=ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
			consistency_model.save_pretrained(args.dump_path)

