
"""simple docstring"""






import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel






def 		_snake_case			(      lowercase__						:    Union[str, Any] ,	lowercase__						:    Dict						)    ->							int:


			'''simple docstring'''





			lowerCAmelCase_       :Any     	=    UNetaDModel(**_UpperCAmelCase						)
			lowerCAmelCase_       :List[str]     	=    checkpoint["all_modules.0.W"].data
			lowerCAmelCase_       :Union[str, Any]     	=    checkpoint["all_modules.0.W"].data
			lowerCAmelCase_       :int     	=    checkpoint["all_modules.1.weight"].data
			lowerCAmelCase_       :int     	=    checkpoint["all_modules.1.bias"].data

			lowerCAmelCase_       :str     	=    checkpoint["all_modules.2.weight"].data
			lowerCAmelCase_       :int     	=    checkpoint["all_modules.2.bias"].data

			lowerCAmelCase_       :Tuple     	=    checkpoint["all_modules.3.weight"].data
			lowerCAmelCase_       :Any     	=    checkpoint["all_modules.3.bias"].data

			lowerCAmelCase_       :Any     	=    checkpoint[list(checkpoint.keys()						)[-4]].data
			lowerCAmelCase_       :Dict     	=    checkpoint[list(checkpoint.keys()						)[-3]].data
			lowerCAmelCase_       :List[str]     	=    checkpoint[list(checkpoint.keys()						)[-2]].data
			lowerCAmelCase_       :Union[str, Any]     	=    checkpoint[list(checkpoint.keys()						)[-1]].data

			lowerCAmelCase_       :Tuple     	=    4

			def set_attention_weights(lowercase__						:    List[Any] ,	lowercase__						:    int ,	lowercase__						:    Dict						):
						lowerCAmelCase_       :int     	=    old_checkpoint[f"""all_modules.{index}.NIN_0.W"""].data.T
						lowerCAmelCase_       :Any     	=    old_checkpoint[f"""all_modules.{index}.NIN_1.W"""].data.T
						lowerCAmelCase_       :List[Any]     	=    old_checkpoint[f"""all_modules.{index}.NIN_2.W"""].data.T

						lowerCAmelCase_       :List[Any]     	=    old_checkpoint[f"""all_modules.{index}.NIN_0.b"""].data
						lowerCAmelCase_       :List[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_       :Dict     	=    old_checkpoint[f"""all_modules.{index}.NIN_3.W"""].data.T
						lowerCAmelCase_       :Any     	=    old_checkpoint[f"""all_modules.{index}.NIN_3.b"""].data

						lowerCAmelCase_       :List[Any]     	=    old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
						lowerCAmelCase_       :Tuple     	=    old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

			def set_resnet_weights(lowercase__						:    Dict ,	lowercase__						:    Optional[Any] ,	lowercase__						:    int						):
						lowerCAmelCase_       :Optional[Any]     	=    old_checkpoint[f"""all_modules.{index}.Conv_0.weight"""].data
						lowerCAmelCase_       :int     	=    old_checkpoint[f"""all_modules.{index}.Conv_0.bias"""].data
						lowerCAmelCase_       :Optional[int]     	=    old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
						lowerCAmelCase_       :Optional[Any]     	=    old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

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

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

			for i, block in enumerate(new_model_architecture.downsample_blocks						):
						lowerCAmelCase_       :Optional[int]     	=    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
									lowerCAmelCase_       :Optional[Any]     	=    checkpoint[f"""all_modules.{module_index}.Conv_0.weight"""].data
									lowerCAmelCase_       :Union[str, Any]     	=    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						):
						lowerCAmelCase_       :str     	=    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:
									lowerCAmelCase_       :Optional[int]     	=    checkpoint[f"""all_modules.{module_index}.weight"""].data
									lowerCAmelCase_       :List[Any]     	=    checkpoint[f"""all_modules.{module_index}.bias"""].data
									module_index += 1
									lowerCAmelCase_       :str     	=    checkpoint[f"""all_modules.{module_index}.weight"""].data
									lowerCAmelCase_       :Tuple     	=    checkpoint[f"""all_modules.{module_index}.bias"""].data
									module_index += 1
									set_resnet_weights(block.resnet_up ,	_UpperCAmelCase ,	_UpperCAmelCase						)
									module_index += 1

			lowerCAmelCase_       :List[str]     	=    checkpoint[f"""all_modules.{module_index}.weight"""].data
			lowerCAmelCase_       :Any     	=    checkpoint[f"""all_modules.{module_index}.bias"""].data
			module_index += 1
			lowerCAmelCase_       :List[str]     	=    checkpoint[f"""all_modules.{module_index}.weight"""].data
			lowerCAmelCase_       :Union[str, Any]     	=    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)










"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase  		=			{
    """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
    """tokenization_flaubert""": ["""FlaubertTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FlaubertForMultipleChoice""",
					    """FlaubertForQuestionAnswering""",
					    """FlaubertForQuestionAnsweringSimple""",
					    """FlaubertForSequenceClassification""",
					    """FlaubertForTokenClassification""",
					    """FlaubertModel""",
					    """FlaubertWithLMHeadModel""",
					    """FlaubertPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  		=			[
					    """TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFFlaubertForMultipleChoice""",
					    """TFFlaubertForQuestionAnsweringSimple""",
					    """TFFlaubertForSequenceClassification""",
					    """TFFlaubertForTokenClassification""",
					    """TFFlaubertModel""",
					    """TFFlaubertPreTrainedModel""",
					    """TFFlaubertWithLMHeadModel""",
					]


if TYPE_CHECKING:
					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
					from .tokenization_flaubert import FlaubertTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flaubert import (
										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaubertForMultipleChoice,
										    FlaubertForQuestionAnswering,
										    FlaubertForQuestionAnsweringSimple,
										    FlaubertForSequenceClassification,
										    FlaubertForTokenClassification,
										    FlaubertModel,
										    FlaubertPreTrainedModel,
										    FlaubertWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_flaubert import (
										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFlaubertForMultipleChoice,
										    TFFlaubertForQuestionAnsweringSimple,
										    TFFlaubertForSequenceClassification,
										    TFFlaubertForTokenClassification,
										    TFFlaubertModel,
										    TFFlaubertPreTrainedModel,
										    TFFlaubertWithLMHeadModel,
										)

else:
					import sys

					lowercase  		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





