
'''simple docstring'''
import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		UNetaDModel(**UpperCamelCase_   )
							A_ 						=		checkpoint["all_modules.0.W"].data
							A_ 						=		checkpoint["all_modules.0.W"].data
							A_ 						=		checkpoint["all_modules.1.weight"].data
							A_ 						=		checkpoint["all_modules.1.bias"].data

							A_ 						=		checkpoint["all_modules.2.weight"].data
							A_ 						=		checkpoint["all_modules.2.bias"].data

							A_ 						=		checkpoint["all_modules.3.weight"].data
							A_ 						=		checkpoint["all_modules.3.bias"].data

							A_ 						=		checkpoint[list(checkpoint.keys()   )[-4]].data
							A_ 						=		checkpoint[list(checkpoint.keys()   )[-3]].data
							A_ 						=		checkpoint[list(checkpoint.keys()   )[-2]].data
							A_ 						=		checkpoint[list(checkpoint.keys()   )[-1]].data

							A_ 						=		4

							def set_attention_weights(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
														A_ 						=		old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
														A_ 						=		old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
														A_ 						=		old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

														A_ 						=		old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
														A_ 						=		old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
														A_ 						=		old_checkpoint[f"all_modules.{index}.NIN_2.b"].data

														A_ 						=		old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
														A_ 						=		old_checkpoint[f"all_modules.{index}.NIN_3.b"].data

														A_ 						=		old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
														A_ 						=		old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

							def set_resnet_weights(UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_   ):
														A_ 						=		old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
														A_ 						=		old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
														A_ 						=		old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
														A_ 						=		old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

														A_ 						=		old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
														A_ 						=		old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
														A_ 						=		old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
														A_ 						=		old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data

														A_ 						=		old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
														A_ 						=		old_checkpoint[f"all_modules.{index}.Dense_0.bias"].data

														if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
																					A_ 						=		old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
																					A_ 						=		old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

							for i, block in enumerate(new_model_architecture.downsample_blocks   ):
														A_ 						=		hasattr(UpperCamelCase_	,     "attentions"   )
														for j in range(len(block.resnets   )   ):
																					set_resnet_weights(block.resnets[j]	,     UpperCamelCase_	,     UpperCamelCase_   )
																					module_index += 1
																					if has_attentions:
																												set_attention_weights(block.attentions[j]	,     UpperCamelCase_	,     UpperCamelCase_   )
																												module_index += 1

														if hasattr(UpperCamelCase_	,     "downsamplers"   ) and block.downsamplers is not None:
																					set_resnet_weights(block.resnet_down	,     UpperCamelCase_	,     UpperCamelCase_   )
																					module_index += 1
																					A_ 						=		checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
																					A_ 						=		checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
																					module_index += 1

							set_resnet_weights(new_model_architecture.mid_block.resnets[0]	,     UpperCamelCase_	,     UpperCamelCase_   )
							module_index += 1
							set_attention_weights(new_model_architecture.mid_block.attentions[0]	,     UpperCamelCase_	,     UpperCamelCase_   )
							module_index += 1
							set_resnet_weights(new_model_architecture.mid_block.resnets[1]	,     UpperCamelCase_	,     UpperCamelCase_   )
							module_index += 1

							for i, block in enumerate(new_model_architecture.up_blocks   ):
														A_ 						=		hasattr(UpperCamelCase_	,     "attentions"   )
														for j in range(len(block.resnets   )   ):
																					set_resnet_weights(block.resnets[j]	,     UpperCamelCase_	,     UpperCamelCase_   )
																					module_index += 1
														if has_attentions:
																					set_attention_weights(
																					    block.attentions[0]	,     UpperCamelCase_	,     UpperCamelCase_   )  # why can there only be a single attention layer for up?
																					module_index += 1

														if hasattr(UpperCamelCase_	,     "resnet_up"   ) and block.resnet_up is not None:
																					A_ 						=		checkpoint[f"all_modules.{module_index}.weight"].data
																					A_ 						=		checkpoint[f"all_modules.{module_index}.bias"].data
																					module_index += 1
																					A_ 						=		checkpoint[f"all_modules.{module_index}.weight"].data
																					A_ 						=		checkpoint[f"all_modules.{module_index}.bias"].data
																					module_index += 1
																					set_resnet_weights(block.resnet_up	,     UpperCamelCase_	,     UpperCamelCase_   )
																					module_index += 1

							A_ 						=		checkpoint[f"all_modules.{module_index}.weight"].data
							A_ 						=		checkpoint[f"all_modules.{module_index}.bias"].data
							module_index += 1
							A_ 						=		checkpoint[f"all_modules.{module_index}.weight"].data
							A_ 						=		checkpoint[f"all_modules.{module_index}.bias"].data

							return new_model_architecture.state_dict()


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE				:							Any		       =		argparse.ArgumentParser()

							parser.add_argument(
							    '''--checkpoint_path''',
							    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin''',
							    type=str,
							    required=False,
							    help='''Path to the checkpoint to convert.''',
							)

							parser.add_argument(
							    '''--config_file''',
							    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/config.json''',
							    type=str,
							    required=False,
							    help='''The config json file corresponding to the architecture.''',
							)

							parser.add_argument(
							    '''--dump_path''',
							    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt''',
							    type=str,
							    required=False,
							    help='''Path to the output model.''',
							)

							__SCREAMING_SNAKE_CASE				:							int		       =		parser.parse_args()

							__SCREAMING_SNAKE_CASE				:							str		       =		torch.load(args.checkpoint_path, map_location='''cpu''')

							with open(args.config_file) as f:
														__SCREAMING_SNAKE_CASE				:							List[str]		       =		json.loads(f.read())

							__SCREAMING_SNAKE_CASE				:							Any		       =		convert_ncsnpp_checkpoint(
							    checkpoint,
							    config,
							)

							if "sde" in config:
														del config["sde"]

							__SCREAMING_SNAKE_CASE				:							Optional[int]		       =		UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

							try:
														__SCREAMING_SNAKE_CASE				:							str		       =		ScoreSdeVeScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

														__SCREAMING_SNAKE_CASE				:							Any		       =		ScoreSdeVePipeline(unet=model, scheduler=scheduler)
														pipe.save_pretrained(args.dump_path)
							except:  # noqa: E722
														model.save_pretrained(args.dump_path)









'''simple docstring'''
import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf

							from transformers.activations_tf import get_tf_activation






@require_tf
class 				__lowerCAmelCase      (      unittest.TestCase	):

						"""simple docstring"""






						def 							_UpperCAmelCase					(     self       :			List[Any]  ):
													A_ 						=		tf.constant([-1_00, -1.0, -0.1, 0, 0.1, 1.0, 1_0_0.0]  )
													A_ 						=		get_tf_activation("gelu"  )
													A_ 						=		get_tf_activation("gelu_10"  )

													A_ 						=		gelu(lowerCAmelCase  )
													A_ 						=		geluaa(lowerCAmelCase  )

													A_ 						=		tf.where(y_gelu_aa < 1_0.0   ,     1.0   ,     0.0  )

													self.assertEqual(tf.math.reduce_max(lowerCAmelCase  ).numpy().item()   ,     1_0.0  )
													self.assertTrue(np.allclose(y_gelu * clipped_mask   ,     y_gelu_aa * clipped_mask  )  )






						def 							_UpperCAmelCase					(     self       :			Union[str, Any]  ):
													get_tf_activation("gelu"  )
													get_tf_activation("gelu_10"  )
													get_tf_activation("gelu_fast"  )
													get_tf_activation("gelu_new"  )
													get_tf_activation("glu"  )
													get_tf_activation("mish"  )
													get_tf_activation("quick_gelu"  )
													get_tf_activation("relu"  )
													get_tf_activation("sigmoid"  )
													get_tf_activation("silu"  )
													get_tf_activation("swish"  )
													get_tf_activation("tanh"  )
													with self.assertRaises(lowerCAmelCase  ):
																				get_tf_activation("bogus"  )
													with self.assertRaises(lowerCAmelCase  ):
																				get_tf_activation(lowerCAmelCase  )





