




import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel






def 					snake_case					(							A						,   A			)							->							Union[str, Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Optional[Any]							   =							UNetaDModel(**A			)
	lowerCamelCase_							:		Tuple							   =							checkpoint['all_modules.0.W'].data
	lowerCamelCase_							:		Union[str, Any]							   =							checkpoint['all_modules.0.W'].data
	lowerCamelCase_							:		Tuple							   =							checkpoint['all_modules.1.weight'].data
	lowerCamelCase_							:		Dict							   =							checkpoint['all_modules.1.bias'].data

	lowerCamelCase_							:		List[Any]							   =							checkpoint['all_modules.2.weight'].data
	lowerCamelCase_							:		List[str]							   =							checkpoint['all_modules.2.bias'].data

	lowerCamelCase_							:		Any							   =							checkpoint['all_modules.3.weight'].data
	lowerCamelCase_							:		List[str]							   =							checkpoint['all_modules.3.bias'].data

	lowerCamelCase_							:		List[str]							   =							checkpoint[list(checkpoint.keys()			)[-4]].data
	lowerCamelCase_							:		int							   =							checkpoint[list(checkpoint.keys()			)[-3]].data
	lowerCamelCase_							:		Any							   =							checkpoint[list(checkpoint.keys()			)[-2]].data
	lowerCamelCase_							:		List[str]							   =							checkpoint[list(checkpoint.keys()			)[-1]].data

	lowerCamelCase_							:		Tuple							   =							4

	def set_attention_weights(A						,   A						,   A			):
		lowerCamelCase_							:		Union[str, Any]							   =							old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
		lowerCamelCase_							:		Dict							   =							old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
		lowerCamelCase_							:		Optional[Any]							   =							old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

		lowerCamelCase_							:		List[str]							   =							old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
		lowerCamelCase_							:		Optional[Any]							   =							old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
		lowerCamelCase_							:		List[Any]							   =							old_checkpoint[f"all_modules.{index}.NIN_2.b"].data

		lowerCamelCase_							:		List[str]							   =							old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
		lowerCamelCase_							:		Optional[int]							   =							old_checkpoint[f"all_modules.{index}.NIN_3.b"].data

		lowerCamelCase_							:		Dict							   =							old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
		lowerCamelCase_							:		str							   =							old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

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

		lowerCamelCase_							:		int							   =							old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
		lowerCamelCase_							:		int							   =							old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
		lowerCamelCase_							:		List[Any]							   =							old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
		lowerCamelCase_							:		Any							   =							old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data

		lowerCamelCase_							:		List[Any]							   =							old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
		lowerCamelCase_							:		List[Any]							   =							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:
			lowerCamelCase_							:		Optional[int]							   =							old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
			lowerCamelCase_							:		Tuple							   =							old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

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

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

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

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

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

	lowerCamelCase_							:		Tuple							   =							checkpoint[f"all_modules.{module_index}.weight"].data
	lowerCamelCase_							:		Union[str, Any]							   =							checkpoint[f"all_modules.{module_index}.bias"].data
	module_index += 1
	lowerCamelCase_							:		Tuple							   =							checkpoint[f"all_modules.{module_index}.weight"].data
	lowerCamelCase_							:		Optional[int]							   =							checkpoint[f"all_modules.{module_index}.bias"].data

	return new_model_architecture.state_dict()


if __name__ == "__main__":
							UpperCamelCase__              =						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.',
							)

							UpperCamelCase__              =						parser.parse_args()

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

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

							UpperCamelCase__              =						convert_ncsnpp_checkpoint(
							    checkpoint,
							    config,
							)

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

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

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

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






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 				__SCREAMING_SNAKE_CASE							(		unittest.TestCase  ):

					def __init__(				self ,       __lowercase ,       __lowercase=13 ,       __lowercase=7 ,       __lowercase=True ,       __lowercase=True ,       __lowercase=True ,       __lowercase=True ,       __lowercase=99 ,       __lowercase=24 ,       __lowercase=32 ,       __lowercase=5 ,       __lowercase=4 ,       __lowercase=37 ,       __lowercase="gelu" ,       __lowercase=0.1 ,       __lowercase=0.1 ,       __lowercase=512 ,       __lowercase=16 ,       __lowercase=2 ,       __lowercase=0.02 ,       __lowercase=4 ,       ):







						'''simple docstring'''




						lowerCamelCase_							:		str							   =							parent
						lowerCamelCase_							:		Any							   =							batch_size
						lowerCamelCase_							:		Union[str, Any]							   =							seq_length
						lowerCamelCase_							:		Dict							   =							is_training
						lowerCamelCase_							:		Any							   =							use_attention_mask
						lowerCamelCase_							:		Any							   =							use_token_type_ids
						lowerCamelCase_							:		Any							   =							use_labels
						lowerCamelCase_							:		Any							   =							vocab_size
						lowerCamelCase_							:		Any							   =							hidden_size
						lowerCamelCase_							:		Optional[Any]							   =							embedding_size
						lowerCamelCase_							:		Dict							   =							num_hidden_layers
						lowerCamelCase_							:		Optional[Any]							   =							num_attention_heads
						lowerCamelCase_							:		Optional[Any]							   =							intermediate_size
						lowerCamelCase_							:		List[str]							   =							hidden_act
						lowerCamelCase_							:		Optional[Any]							   =							hidden_dropout_prob
						lowerCamelCase_							:		int							   =							attention_probs_dropout_prob
						lowerCamelCase_							:		Optional[int]							   =							max_position_embeddings
						lowerCamelCase_							:		List[str]							   =							type_vocab_size
						lowerCamelCase_							:		int							   =							type_sequence_label_size
						lowerCamelCase_							:		Tuple							   =							initializer_range
						lowerCamelCase_							:		int							   =							num_choices

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		int							   =							ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size					)

						lowerCamelCase_							:		List[Any]							   =							None
						if self.use_attention_mask:
							lowerCamelCase_							:		Tuple							   =							random_attention_mask([self.batch_size, self.seq_length]					)

						lowerCamelCase_							:		int							   =							None
						if self.use_token_type_ids:
							lowerCamelCase_							:		List[Any]							   =							ids_tensor([self.batch_size, self.seq_length] ,       self.type_vocab_size					)

						lowerCamelCase_							:		List[str]							   =							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 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							   =							self.prepare_config_and_inputs()
						lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_   ,			lowerCamelCase_							:		Tuple							   =							config_and_inputs
						lowerCamelCase_							:		Union[str, Any]							   =							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
						return config, inputs_dict


@require_flax
class 				__SCREAMING_SNAKE_CASE							(		lowerCamelCase   ,     unittest.TestCase  ):
					_lowerCamelCase		  = True

					_lowerCamelCase		  = (
					    (
					        FlaxElectraModel,
					        FlaxElectraForCausalLM,
					        FlaxElectraForMaskedLM,
					        FlaxElectraForPreTraining,
					        FlaxElectraForTokenClassification,
					        FlaxElectraForQuestionAnswering,
					        FlaxElectraForMultipleChoice,
					        FlaxElectraForSequenceClassification,
					    )
					    if is_flax_available()
					    else ()
					)

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							   =							FlaxElectraModelTester(self					)





					@slow
					def 	lowercase_							(				self					):







						'''simple docstring'''




						for model_class_name in self.all_model_classes:
							if model_class_name == FlaxElectraForMaskedLM:
								lowerCamelCase_							:		Union[str, Any]							   =							model_class_name.from_pretrained('google/electra-small-generator'					)
							else:
								lowerCamelCase_							:		List[Any]							   =							model_class_name.from_pretrained('google/electra-small-discriminator'					)
							lowerCamelCase_							:		Dict							   =							model(np.ones((1, 1)					)					)
							self.assertIsNotNone(__lowercase					)
