


import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							__snake_case	:    int											=     checkpoint

							__snake_case	:    int											=     {}

							__snake_case	:    List[str]											=     vae_state_dict["encoder.conv_in.weight"]
							__snake_case	:    Tuple											=     vae_state_dict["encoder.conv_in.bias"]
							__snake_case	:    Optional[int]											=     vae_state_dict["encoder.conv_out.weight"]
							__snake_case	:    Union[str, Any]											=     vae_state_dict["encoder.conv_out.bias"]
							__snake_case	:    Tuple											=     vae_state_dict["encoder.norm_out.weight"]
							__snake_case	:    Optional[int]											=     vae_state_dict["encoder.norm_out.bias"]

							__snake_case	:    int											=     vae_state_dict["decoder.conv_in.weight"]
							__snake_case	:    Any											=     vae_state_dict["decoder.conv_in.bias"]
							__snake_case	:    Dict											=     vae_state_dict["decoder.conv_out.weight"]
							__snake_case	:    int											=     vae_state_dict["decoder.conv_out.bias"]
							__snake_case	:    int											=     vae_state_dict["decoder.norm_out.weight"]
							__snake_case	:    Optional[Any]											=     vae_state_dict["decoder.norm_out.bias"]

							__snake_case	:    Any											=     vae_state_dict["quant_conv.weight"]
							__snake_case	:    Dict											=     vae_state_dict["quant_conv.bias"]
							__snake_case	:    Any											=     vae_state_dict["post_quant_conv.weight"]
							__snake_case	:    Union[str, Any]											=     vae_state_dict["post_quant_conv.bias"]

							# Retrieves the keys for the encoder down blocks only
							__snake_case	:    Tuple											=     len({".".join(layer.split("."			)[:3]			) for layer in vae_state_dict if "encoder.down" in layer}			)
							__snake_case	:    Optional[int]											=     {
							    layer_id: [key for key in vae_state_dict if F'down.{layer_id}' in key] for layer_id in range(__lowerCamelCase			)
							}

							# Retrieves the keys for the decoder up blocks only
							__snake_case	:    Any											=     len({".".join(layer.split("."			)[:3]			) for layer in vae_state_dict if "decoder.up" in layer}			)
							__snake_case	:    str											=     {
							    layer_id: [key for key in vae_state_dict if F'up.{layer_id}' in key] for layer_id in range(__lowerCamelCase			)
							}

							for i in range(__lowerCamelCase			):
														__snake_case	:    List[str]											=     [key for key in down_blocks[i] if F'down.{i}' in key and F'down.{i}.downsample' not in key]

														if F'encoder.down.{i}.downsample.conv.weight' in vae_state_dict:
																					__snake_case	:    List[Any]											=     vae_state_dict.pop(
																					    F'encoder.down.{i}.downsample.conv.weight'			)
																					__snake_case	:    Dict											=     vae_state_dict.pop(
																					    F'encoder.down.{i}.downsample.conv.bias'			)

														__snake_case	:    str											=     renew_vae_resnet_paths(__lowerCamelCase			)
														__snake_case	:    Tuple											=     {"old": F'down.{i}.block', "new": F'down_blocks.{i}.resnets'}
														assign_to_checkpoint(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			additional_replacements=[meta_path]							,			config=__lowerCamelCase			)

							__snake_case	:    int											=     [key for key in vae_state_dict if "encoder.mid.block" in key]
							__snake_case	:    int											=     2
							for i in range(1							,			num_mid_res_blocks + 1			):
														__snake_case	:    int											=     [key for key in mid_resnets if F'encoder.mid.block_{i}' in key]

														__snake_case	:    List[Any]											=     renew_vae_resnet_paths(__lowerCamelCase			)
														__snake_case	:    Union[str, Any]											=     {"old": F'mid.block_{i}', "new": F'mid_block.resnets.{i - 1}'}
														assign_to_checkpoint(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			additional_replacements=[meta_path]							,			config=__lowerCamelCase			)

							__snake_case	:    List[Any]											=     [key for key in vae_state_dict if "encoder.mid.attn" in key]
							__snake_case	:    Optional[Any]											=     renew_vae_attention_paths(__lowerCamelCase			)
							__snake_case	:    Any											=     {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
							assign_to_checkpoint(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			additional_replacements=[meta_path]							,			config=__lowerCamelCase			)
							conv_attn_to_linear(__lowerCamelCase			)

							for i in range(__lowerCamelCase			):
														__snake_case	:    List[Any]											=     num_up_blocks - 1 - i
														__snake_case	:    Tuple											=     [
														    key for key in up_blocks[block_id] if F'up.{block_id}' in key and F'up.{block_id}.upsample' not in key
														]

														if F'decoder.up.{block_id}.upsample.conv.weight' in vae_state_dict:
																					__snake_case	:    List[Any]											=     vae_state_dict[
																					    F'decoder.up.{block_id}.upsample.conv.weight'
																					]
																					__snake_case	:    Any											=     vae_state_dict[
																					    F'decoder.up.{block_id}.upsample.conv.bias'
																					]

														__snake_case	:    Union[str, Any]											=     renew_vae_resnet_paths(__lowerCamelCase			)
														__snake_case	:    Optional[Any]											=     {"old": F'up.{block_id}.block', "new": F'up_blocks.{i}.resnets'}
														assign_to_checkpoint(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			additional_replacements=[meta_path]							,			config=__lowerCamelCase			)

							__snake_case	:    Tuple											=     [key for key in vae_state_dict if "decoder.mid.block" in key]
							__snake_case	:    Optional[Any]											=     2
							for i in range(1							,			num_mid_res_blocks + 1			):
														__snake_case	:    List[Any]											=     [key for key in mid_resnets if F'decoder.mid.block_{i}' in key]

														__snake_case	:    List[Any]											=     renew_vae_resnet_paths(__lowerCamelCase			)
														__snake_case	:    Any											=     {"old": F'mid.block_{i}', "new": F'mid_block.resnets.{i - 1}'}
														assign_to_checkpoint(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			additional_replacements=[meta_path]							,			config=__lowerCamelCase			)

							__snake_case	:    Union[str, Any]											=     [key for key in vae_state_dict if "decoder.mid.attn" in key]
							__snake_case	:    Any											=     renew_vae_attention_paths(__lowerCamelCase			)
							__snake_case	:    str											=     {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
							assign_to_checkpoint(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			additional_replacements=[meta_path]							,			config=__lowerCamelCase			)
							conv_attn_to_linear(__lowerCamelCase			)
							return new_checkpoint







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase							,			):
							# Only support V1
							__snake_case	:    str											=     requests.get(
							    " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"			)
							__snake_case	:    Optional[int]											=     io.BytesIO(r.content			)

							__snake_case	:    Any											=     OmegaConf.load(__lowerCamelCase			)
							__snake_case	:    Tuple											=     5_1_2
							__snake_case	:    Any											=     "cuda" if torch.cuda.is_available() else "cpu"
							if checkpoint_path.endswith("safetensors"			):
														from safetensors import safe_open

														__snake_case	:    Any											=     {}
														with safe_open(__lowerCamelCase							,			framework="pt"							,			device="cpu"			) as f:
																					for key in f.keys():
																												__snake_case	:    Optional[Any]											=     f.get_tensor(__lowerCamelCase			)
							else:
														__snake_case	:    Optional[int]											=     torch.load(__lowerCamelCase							,			map_location=__lowerCamelCase			)["state_dict"]

							# Convert the VAE model.
							__snake_case	:    Optional[Any]											=     create_vae_diffusers_config(__lowerCamelCase							,			image_size=__lowerCamelCase			)
							__snake_case	:    Dict											=     custom_convert_ldm_vae_checkpoint(__lowerCamelCase							,			__lowerCamelCase			)

							__snake_case	:    str											=     AutoencoderKL(**__lowerCamelCase			)
							vae.load_state_dict(__lowerCamelCase			)
							vae.save_pretrained(__lowerCamelCase			)


if __name__ == "__main__":
			_snake_case  :	List[str]			      =       argparse.ArgumentParser()

			parser.add_argument("--vae_pt_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")
			parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the VAE.pt to convert.")

			_snake_case  :	Optional[Any]			      =       parser.parse_args()

			vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)










import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import CvtForImageClassification, CvtModel
			from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						str       )			->					Dict:
														__snake_case	:    Any											=     self.config_class(**self.inputs_dict       )
														self.parent.assertTrue(hasattr(lowerCamelCase					,			"embed_dim"       )       )
														self.parent.assertTrue(hasattr(lowerCamelCase					,			"num_heads"       )       )

class        a	:




							"""simple docstring"""


							def __init__(							self :						str					,			lowerCamelCase :						Union[str, Any]					,			lowerCamelCase :						Any=13					,			lowerCamelCase :						Any=64					,			lowerCamelCase :						int=3					,			lowerCamelCase :						Tuple=[16, 48, 96]					,			lowerCamelCase :						Optional[int]=[1, 3, 6]					,			lowerCamelCase :						List[str]=[1, 2, 10]					,			lowerCamelCase :						Any=[7, 3, 3]					,			lowerCamelCase :						Any=[4, 2, 2]					,			lowerCamelCase :						Optional[Any]=[2, 1, 1]					,			lowerCamelCase :						str=[2, 2, 2]					,			lowerCamelCase :						Dict=[False, False, True]					,			lowerCamelCase :						Dict=[0.0, 0.0, 0.0]					,			lowerCamelCase :						Optional[Any]=0.02					,			lowerCamelCase :						Tuple=1E-12					,			lowerCamelCase :						Union[str, Any]=True					,			lowerCamelCase :						Union[str, Any]=True					,			lowerCamelCase :						List[str]=2					,			)			->					Optional[int]:
														__snake_case	:    Tuple											=     parent
														__snake_case	:    List[str]											=     batch_size
														__snake_case	:    Optional[int]											=     image_size
														__snake_case	:    Optional[int]											=     patch_sizes
														__snake_case	:    Union[str, Any]											=     patch_stride
														__snake_case	:    int											=     patch_padding
														__snake_case	:    Optional[Any]											=     is_training
														__snake_case	:    Optional[Any]											=     use_labels
														__snake_case	:    Tuple											=     num_labels
														__snake_case	:    Union[str, Any]											=     num_channels
														__snake_case	:    Tuple											=     embed_dim
														__snake_case	:    List[str]											=     num_heads
														__snake_case	:    Dict											=     stride_kv
														__snake_case	:    Optional[int]											=     depth
														__snake_case	:    List[Any]											=     cls_token
														__snake_case	:    List[Any]											=     attention_drop_rate
														__snake_case	:    Any											=     initializer_range
														__snake_case	:    str											=     layer_norm_eps

							def 					__snake_case      (							self :						List[str]       )			->					str:
														__snake_case	:    str											=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

														__snake_case	:    Tuple											=     None
														if self.use_labels:
																					__snake_case	:    Tuple											=     ids_tensor([self.batch_size]					,			self.num_labels       )

														__snake_case	:    Optional[Any]											=     self.get_config()
														return config, pixel_values, labels

							def 					__snake_case      (							self :						Tuple       )			->					Optional[Any]:
														return CvtConfig(
														    image_size=self.image_size					,			num_labels=self.num_labels					,			num_channels=self.num_channels					,			embed_dim=self.embed_dim					,			num_heads=self.num_heads					,			patch_sizes=self.patch_sizes					,			patch_padding=self.patch_padding					,			patch_stride=self.patch_stride					,			stride_kv=self.stride_kv					,			depth=self.depth					,			cls_token=self.cls_token					,			attention_drop_rate=self.attention_drop_rate					,			initializer_range=self.initializer_range					,			)

							def 					__snake_case      (							self :						Optional[int]					,			lowerCamelCase :						Union[str, Any]					,			lowerCamelCase :						str					,			lowerCamelCase :						str       )			->					Tuple:
														__snake_case	:    int											=     CvtModel(config=lowerCamelCase       )
														model.to(lowerCamelCase       )
														model.eval()
														__snake_case	:    List[str]											=     model(lowerCamelCase       )
														__snake_case	:    List[Any]											=     (self.image_size, self.image_size)
														__snake_case				,    __snake_case	:    List[str]											=     image_size[0], image_size[1]
														for i in range(len(self.depth       )       ):
																					__snake_case	:    int											=     floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
																					__snake_case	:    Union[str, Any]											=     floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
														self.parent.assertEqual(result.last_hidden_state.shape					,			(self.batch_size, self.embed_dim[-1], height, width)       )

							def 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						Optional[int]					,			lowerCamelCase :						Dict					,			lowerCamelCase :						int       )			->					Optional[Any]:
														__snake_case	:    str											=     self.num_labels
														__snake_case	:    Any											=     CvtForImageClassification(lowerCamelCase       )
														model.to(lowerCamelCase       )
														model.eval()
														__snake_case	:    List[Any]											=     model(lowerCamelCase					,			labels=lowerCamelCase       )
														self.parent.assertEqual(result.logits.shape					,			(self.batch_size, self.num_labels)       )






							def 					__snake_case      (							self :						Union[str, Any]       )			->					List[Any]:
														__snake_case	:    List[Any]											=     self.prepare_config_and_inputs()
														__snake_case				,    __snake_case				,    __snake_case	:    str											=     config_and_inputs
														__snake_case	:    List[str]											=     {"pixel_values": pixel_values}
														return config, inputs_dict

@require_torch
class        a	(_lowerCAmelCase  ,       _lowerCAmelCase  ,       unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							List[str]  						=						(CvtModel, CvtForImageClassification) if is_torch_available() else ()
							__UpperCAmelCase					:							List[str]  						=						(
							    {"feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
							    if is_torch_available()
							    else {}
							)

							__UpperCAmelCase					:							List[str]  						=						False
							__UpperCAmelCase					:							Tuple  						=						False
							__UpperCAmelCase					:							List[Any]  						=						False
							__UpperCAmelCase					:							Optional[int]  						=						False
							__UpperCAmelCase					:							List[Any]  						=						False

							def 					__snake_case      (							self :						str       )			->					List[str]:
														__snake_case	:    Tuple											=     CvtModelTester(self       )
														__snake_case	:    Tuple											=     ConfigTester(self					,			config_class=lowerCamelCase					,			has_text_modality=lowerCamelCase					,			hidden_size=37       )

							def 					__snake_case      (							self :						Union[str, Any]       )			->					Union[str, Any]:
														self.create_and_test_config_common_properties()
														self.config_tester.create_and_test_config_to_json_string()
														self.config_tester.create_and_test_config_to_json_file()
														self.config_tester.create_and_test_config_from_and_save_pretrained()
														self.config_tester.create_and_test_config_with_num_labels()
														self.config_tester.check_config_can_be_init_without_params()
														self.config_tester.check_config_arguments_init()

							def 					__snake_case      (							self :						Tuple       )			->					Union[str, Any]:
														return

							@unittest.skip(reason="Cvt does not output attentions"       )
							def 					__snake_case      (							self :						Optional[int]       )			->					List[str]:
														pass

							@unittest.skip(reason="Cvt does not use inputs_embeds"       )
							def 					__snake_case      (							self :						List[str]       )			->					Any:
														pass

							@unittest.skip(reason="Cvt does not support input and output embeddings"       )
							def 					__snake_case      (							self :						int       )			->					List[Any]:
														pass

							def 					__snake_case      (							self :						int       )			->					int:
														__snake_case				,    __snake_case	:    Optional[int]											=     self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					__snake_case	:    str											=     model_class(lowerCamelCase       )
																					__snake_case	:    List[Any]											=     inspect.signature(model.forward       )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					__snake_case	:    List[str]											=     [*signature.parameters.keys()]

																					__snake_case	:    Dict											=     ["pixel_values"]
																					self.assertListEqual(arg_names[:1]					,			lowerCamelCase       )

							def 					__snake_case      (							self :						str       )			->					Dict:
														__snake_case	:    List[str]											=     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase       )

							def 					__snake_case      (							self :						int       )			->					Optional[Any]:
														def check_hidden_states_output(lowerCamelCase :						Dict					,			lowerCamelCase :						Dict					,			lowerCamelCase :						Optional[Any]       ):
																					__snake_case	:    Any											=     model_class(lowerCamelCase       )
																					model.to(lowerCamelCase       )
																					model.eval()

																					with torch.no_grad():
																												__snake_case	:    List[str]											=     model(**self._prepare_for_class(lowerCamelCase					,			lowerCamelCase       )       )

																					__snake_case	:    List[Any]											=     outputs.hidden_states

																					__snake_case	:    Optional[int]											=     len(self.model_tester.depth       )
																					self.assertEqual(len(lowerCamelCase       )					,			lowerCamelCase       )

																					# verify the first hidden states (first block)
																					self.assertListEqual(
																					    list(hidden_states[0].shape[-3:]       )					,			[
																					        self.model_tester.embed_dim[0],
																					        self.model_tester.image_size // 4,
																					        self.model_tester.image_size // 4,
																					    ]					,			)

														__snake_case				,    __snake_case	:    List[str]											=     self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					__snake_case	:    Union[str, Any]											=     True
																					check_hidden_states_output(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					__snake_case	:    List[str]											=     True

																					check_hidden_states_output(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

							def 					__snake_case      (							self :						Optional[int]       )			->					Optional[Any]:
														__snake_case	:    Any											=     self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*lowerCamelCase       )

							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
							def 					__snake_case      (							self :						List[str]       )			->					Optional[int]:
														pass






							@slow
							def 					__snake_case      (							self :						Tuple       )			->					List[str]:
														for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					__snake_case	:    int											=     CvtModel.from_pretrained(lowerCamelCase       )
																					self.assertIsNotNone(lowerCamelCase       )







def        lowerCAmelCase_  (      ):
							__snake_case	:    Any											=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
							return image

@require_torch
@require_vision
class        a	(unittest.TestCase   ):




							"""simple docstring"""


							@cached_property
							def 					__snake_case      (							self :						Optional[Any]       )			->					Union[str, Any]:
														return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )






							@slow
							def 					__snake_case      (							self :						int       )			->					Tuple:
														__snake_case	:    Optional[Any]											=     CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(lowerCamelCase       )

														__snake_case	:    Optional[int]											=     self.default_image_processor
														__snake_case	:    Union[str, Any]											=     prepare_img()
														__snake_case	:    Dict											=     image_processor(images=lowerCamelCase					,			return_tensors="pt"       ).to(lowerCamelCase       )

														# forward pass
														with torch.no_grad():
																					__snake_case	:    Any											=     model(**lowerCamelCase       )

														# verify the logits
														__snake_case	:    Any											=     torch.Size((1, 1000)       )
														self.assertEqual(outputs.logits.shape					,			lowerCamelCase       )

														__snake_case	:    Optional[Any]											=     torch.tensor([0.92_85, 0.90_15, -0.31_50]       ).to(lowerCamelCase       )

														self.assertTrue(torch.allclose(outputs.logits[0, :3]					,			lowerCamelCase					,			atol=1E-4       )       )

