import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
    from .testing_utils import (
        floats_tensor,
        load_hf_numpy,
        load_image,
        load_numpy,
        load_pt,
        nightly,
        parse_flag_from_env,
        print_tensor_test,
        require_torch_a,
        require_torch_gpu,
        skip_mps,
        slow,
        torch_all_close,
        torch_device,
    )
    from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


lowerCAmelCase__							= get_logger(__name__)






def       __lowerCamelCase		(						lowerCAmelCase__	):
       if version.parse(lowerCAmelCase__	) < version.parse(lowerCAmelCase__	):
              if "dev" in min_version:
                     lowerCAmelCase__     	=					(
                         'This example requires a source install from HuggingFace diffusers (see '
                         '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
                     )
              else:
                     lowerCAmelCase__     	=					F"""This example requires a minimum version of {min_version},"""
              error_message += F""" but the version found is {__version__}.\n"""
              raise ImportError(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)





