





'''simple docstring'''



import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
	from transformers.models.electra.modeling_flax_electra import (
	    FlaxElectraForCausalLM,
	    FlaxElectraForMaskedLM,
	    FlaxElectraForMultipleChoice,
	    FlaxElectraForPreTraining,
	    FlaxElectraForQuestionAnswering,
	    FlaxElectraForSequenceClassification,
	    FlaxElectraForTokenClassification,
	    FlaxElectraModel,
	)






class 							__lowercase				(					unittest.TestCase  ):







	'''simple docstring'''

	def __init__(self   ,_lowerCamelCase   ,_lowerCamelCase=13   ,_lowerCamelCase=7   ,_lowerCamelCase=True   ,_lowerCamelCase=True   ,_lowerCamelCase=True   ,_lowerCamelCase=True   ,_lowerCamelCase=99   ,_lowerCamelCase=24   ,_lowerCamelCase=32   ,_lowerCamelCase=5   ,_lowerCamelCase=4   ,_lowerCamelCase=37   ,_lowerCamelCase="gelu"   ,_lowerCamelCase=0.1   ,_lowerCamelCase=0.1   ,_lowerCamelCase=512   ,_lowerCamelCase=16   ,_lowerCamelCase=2   ,_lowerCamelCase=0.0_2   ,_lowerCamelCase=4   ,)							->							List[str]:







				'''simple docstring'''





				__lowercase   =							parent
				__lowercase   =							batch_size
				__lowercase   =							seq_length
				__lowercase   =							is_training
				__lowercase   =							use_attention_mask
				__lowercase   =							use_token_type_ids
				__lowercase   =							use_labels
				__lowercase   =							vocab_size
				__lowercase   =							hidden_size
				__lowercase   =							embedding_size
				__lowercase   =							num_hidden_layers
				__lowercase   =							num_attention_heads
				__lowercase   =							intermediate_size
				__lowercase   =							hidden_act
				__lowercase   =							hidden_dropout_prob
				__lowercase   =							attention_probs_dropout_prob
				__lowercase   =							max_position_embeddings
				__lowercase   =							type_vocab_size
				__lowercase   =							type_sequence_label_size
				__lowercase   =							initializer_range
				__lowercase   =							num_choices


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







				'''simple docstring'''





				__lowercase   =							ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size       )

				__lowercase   =							None
				if self.use_attention_mask:
							__lowercase   =							random_attention_mask([self.batch_size, self.seq_length]       )

				__lowercase   =							None
				if self.use_token_type_ids:
							__lowercase   =							ids_tensor([self.batch_size, self.seq_length]   ,self.type_vocab_size       )

				__lowercase   =							ElectraConfig(
				    vocab_size=self.vocab_size   ,hidden_size=self.hidden_size   ,embedding_size=self.embedding_size   ,num_hidden_layers=self.num_hidden_layers   ,num_attention_heads=self.num_attention_heads   ,intermediate_size=self.intermediate_size   ,hidden_act=self.hidden_act   ,hidden_dropout_prob=self.hidden_dropout_prob   ,attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,max_position_embeddings=self.max_position_embeddings   ,type_vocab_size=self.type_vocab_size   ,initializer_range=self.initializer_range   ,)

				return config, input_ids, token_type_ids, attention_mask


	def   _UpperCAmelCase   (self       )							->							List[Any]:







				'''simple docstring'''





				__lowercase   =							self.prepare_config_and_inputs()
				__lowercase  ,						__lowercase  ,						__lowercase  ,						__lowercase   =							config_and_inputs
				__lowercase   =							{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
				return config, inputs_dict






@require_flax
class 							__lowercase				(					lowerCAmelCase__       ,					unittest.TestCase  ):







	'''simple docstring'''

	a			: List[str]      =				True

	a			: Any      =				(
	    (
	        FlaxElectraModel,
	        FlaxElectraForCausalLM,
	        FlaxElectraForMaskedLM,
	        FlaxElectraForPreTraining,
	        FlaxElectraForTokenClassification,
	        FlaxElectraForQuestionAnswering,
	        FlaxElectraForMultipleChoice,
	        FlaxElectraForSequenceClassification,
	    )
	    if is_flax_available()
	    else ()
	)


	def   _UpperCAmelCase   (self       )							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase   =							FlaxElectraModelTester(self       )


	@slow
	def   _UpperCAmelCase   (self       )							->							Optional[Any]:







				'''simple docstring'''





				for model_class_name in self.all_model_classes:
							if model_class_name == FlaxElectraForMaskedLM:
										__lowercase   =							model_class_name.from_pretrained('''google/electra-small-generator'''       )
							else:
										__lowercase   =							model_class_name.from_pretrained('''google/electra-small-discriminator'''       )
							__lowercase   =							model(np.ones((1, 1)       )       )
							self.assertIsNotNone(_lowerCamelCase       )












'''simple docstring'''



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def       _lowerCAmelCase  ( lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     List[Any] ):
			__lowercase   =							UNetaDModel(**lowerCamelCase_ )
			__lowercase   =							checkpoint['''all_modules.0.W'''].data
			__lowercase   =							checkpoint['''all_modules.0.W'''].data
			__lowercase   =							checkpoint['''all_modules.1.weight'''].data
			__lowercase   =							checkpoint['''all_modules.1.bias'''].data

			__lowercase   =							checkpoint['''all_modules.2.weight'''].data
			__lowercase   =							checkpoint['''all_modules.2.bias'''].data

			__lowercase   =							checkpoint['''all_modules.3.weight'''].data
			__lowercase   =							checkpoint['''all_modules.3.bias'''].data

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

			__lowercase   =							4

			def set_attention_weights(lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     int						,				lowerCamelCase_	:     str ):
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

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

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

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

			def set_resnet_weights(lowerCamelCase_	:     str						,				lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     Any ):
						__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

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

						__lowercase   =							old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
						__lowercase   =							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:
									__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
									__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

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

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

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

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

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

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

			return new_model_architecture.state_dict()


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE    =			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    =			parser.parse_args()

	_SCREAMING_SNAKE_CASE    =			torch.load(args.checkpoint_path, map_location='''cpu''')

	with open(args.config_file) as f:
		_SCREAMING_SNAKE_CASE    =			json.loads(f.read())

	_SCREAMING_SNAKE_CASE    =			convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

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

	_SCREAMING_SNAKE_CASE    =			UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

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

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





