'''simple docstring'''




import inspect
import unittest

import numpy as np

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
						import jax

						from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel





class     __magic_name__						(    unittest.TestCase):







			def __init__(	self				:  str				,			lowercase_				:  Optional[Any]				,			lowercase_				:  Optional[Any]=13				,			lowercase_				:  int=30				,			lowercase_				:  Union[str, Any]=2				,			lowercase_				:  Dict=3				,			lowercase_				:  List[Any]=True				,			lowercase_				:  Optional[Any]=True				,			lowercase_				:  List[Any]=32				,			lowercase_				:  Any=5				,			lowercase_				:  str=4				,			lowercase_				:  Optional[int]=37				,			lowercase_				:  Optional[int]="gelu"				,			lowercase_				:  List[str]=0.1				,			lowercase_				:  Tuple=0.1				,			lowercase_				:  List[str]=10				,			lowercase_				:  Optional[int]=0.02				,			):
										lowercase_					:			Any										=       parent
										lowercase_					:			Optional[Any]										=       batch_size
										lowercase_					:			Any										=       image_size
										lowercase_					:			Any										=       patch_size
										lowercase_					:			Any										=       num_channels
										lowercase_					:			Dict										=       is_training
										lowercase_					:			int										=       use_labels
										lowercase_					:			List[str]										=       hidden_size
										lowercase_					:			int										=       num_hidden_layers
										lowercase_					:			Dict										=       num_attention_heads
										lowercase_					:			Dict										=       intermediate_size
										lowercase_					:			List[Any]										=       hidden_act
										lowercase_					:			List[Any]										=       hidden_dropout_prob
										lowercase_					:			Tuple										=       attention_probs_dropout_prob
										lowercase_					:			List[str]										=       type_sequence_label_size
										lowercase_					:			Tuple										=       initializer_range

										# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
										lowercase_					:			List[Any]										=       (image_size // patch_size) ** 2
										lowercase_					:			str										=       num_patches + 1







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										lowercase_					:			str										=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

										lowercase_					:			List[Any]										=       ViTConfig(
										    image_size=self.image_size				,			patch_size=self.patch_size				,			num_channels=self.num_channels				,			hidden_size=self.hidden_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				,			is_decoder=__a				,			initializer_range=self.initializer_range				,			)

										return config, pixel_values







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict				,			lowercase_				:  Any				,			lowercase_				:  List[Any]    ):
										lowercase_					:			int										=       FlaxViTModel(config=__a    )
										lowercase_					:			List[str]										=       model(__a    )
										# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
										lowercase_					:			Tuple										=       (self.image_size, self.image_size)
										lowercase_					:			Any										=       (self.patch_size, self.patch_size)
										lowercase_					:			List[str]										=       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
										self.parent.assertEqual(result.last_hidden_state.shape				,			(self.batch_size, num_patches + 1, self.hidden_size)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  str				,			lowercase_				:  Any    ):
										lowercase_					:			Dict										=       self.type_sequence_label_size
										lowercase_					:			List[str]										=       FlaxViTForImageClassification(config=__a    )
										lowercase_					:			Tuple										=       model(__a    )
										self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.type_sequence_label_size)    )

										# test greyscale images
										lowercase_					:			Optional[Any]										=       1
										lowercase_					:			Union[str, Any]										=       FlaxViTForImageClassification(__a    )

										lowercase_					:			Optional[Any]										=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
										lowercase_					:			Any										=       model(__a    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_					:			Dict										=       self.prepare_config_and_inputs()
										(
										    (
										    lowercase_ 
										)						,						(
										    lowercase_ 
										)						,						 
										)					:			List[Any]										=       config_and_inputs
										lowercase_					:			int										=       {"""pixel_values""": pixel_values}
										return config, inputs_dict





@require_flax
class     __magic_name__						(    UpperCamelCase__,     unittest.TestCase):
			UpperCamelCase__				=       (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										lowercase_					:			Optional[Any]										=       FlaxViTModelTester(self    )
										lowercase_					:			Optional[Any]										=       ConfigTester(self				,			config_class=__a				,			has_text_modality=__a				,			hidden_size=37    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										self.config_tester.run_common_tests()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										lowercase_					:			Dict										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__a    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										lowercase_					:			Dict										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__a    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_						,						lowercase_					:			List[str]										=       self.model_tester.prepare_config_and_inputs_for_common()

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

																	lowercase_					:			Union[str, Any]										=       ["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]				,			__a    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_						,						lowercase_					:			int										=       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__    ):
																								lowercase_					:			Any										=       self._prepare_for_class(__a				,			__a    )
																								lowercase_					:			str										=       model_class(__a    )

																								@jax.jit
																								def model_jitted(lowercase_				:  Tuple				,			**lowercase_				:  List[Any]    ):
																															return model(pixel_values=__a				,			**__a    )

																								with self.subTest("""JIT Enabled"""    ):
																															lowercase_					:			Optional[Any]										=       model_jitted(**__a    ).to_tuple()

																								with self.subTest("""JIT Disabled"""    ):
																															with jax.disable_jit():
																																						lowercase_					:			Optional[int]										=       model_jitted(**__a    ).to_tuple()

																								self.assertEqual(len(__a    )				,			len(__a    )    )
																								for jitted_output, output in zip(__a				,			__a    ):
																															self.assertEqual(jitted_output.shape				,			output.shape    )







			@slow
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										for model_class_name in self.all_model_classes:
																	lowercase_					:			Union[str, Any]										=       model_class_name.from_pretrained("""google/vit-base-patch16-224"""    )
																	lowercase_					:			Any										=       model(np.ones((1, 3, 224, 224)    )    )
																	self.assertIsNotNone(__a    )






'''simple docstring'''





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_				(					snake_case_       : List[Any]					,    snake_case_       : int			)    ->      Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_           =   checkpoint

     UpperCAmelCase_           =   {}

     UpperCAmelCase_           =   vae_state_dict["encoder.conv_in.weight"]
     UpperCAmelCase_           =   vae_state_dict["encoder.conv_in.bias"]
     UpperCAmelCase_           =   vae_state_dict["encoder.conv_out.weight"]
     UpperCAmelCase_           =   vae_state_dict["encoder.conv_out.bias"]
     UpperCAmelCase_           =   vae_state_dict["encoder.norm_out.weight"]
     UpperCAmelCase_           =   vae_state_dict["encoder.norm_out.bias"]

     UpperCAmelCase_           =   vae_state_dict["decoder.conv_in.weight"]
     UpperCAmelCase_           =   vae_state_dict["decoder.conv_in.bias"]
     UpperCAmelCase_           =   vae_state_dict["decoder.conv_out.weight"]
     UpperCAmelCase_           =   vae_state_dict["decoder.conv_out.bias"]
     UpperCAmelCase_           =   vae_state_dict["decoder.norm_out.weight"]
     UpperCAmelCase_           =   vae_state_dict["decoder.norm_out.bias"]

     UpperCAmelCase_           =   vae_state_dict["quant_conv.weight"]
     UpperCAmelCase_           =   vae_state_dict["quant_conv.bias"]
     UpperCAmelCase_           =   vae_state_dict["post_quant_conv.weight"]
     UpperCAmelCase_           =   vae_state_dict["post_quant_conv.bias"]

     # Retrieves the keys for the encoder down blocks only
     UpperCAmelCase_           =   len({".".join(layer.split("."			)[:3]			) for layer in vae_state_dict if "encoder.down" in layer}			)
     UpperCAmelCase_           =   {
         layer_id: [key for key in vae_state_dict if f"""down.{layer_id}""" in key] for layer_id in range(snake_case_			)
     }

     # Retrieves the keys for the decoder up blocks only
     UpperCAmelCase_           =   len({".".join(layer.split("."			)[:3]			) for layer in vae_state_dict if "decoder.up" in layer}			)
     UpperCAmelCase_           =   {
         layer_id: [key for key in vae_state_dict if f"""up.{layer_id}""" in key] for layer_id in range(snake_case_			)
     }

     for i in range(snake_case_			):
          UpperCAmelCase_           =   [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:
               UpperCAmelCase_           =   vae_state_dict.pop(
                   f"""encoder.down.{i}.downsample.conv.weight"""			)
               UpperCAmelCase_           =   vae_state_dict.pop(
                   f"""encoder.down.{i}.downsample.conv.bias"""			)

          UpperCAmelCase_           =   renew_vae_resnet_paths(snake_case_			)
          UpperCAmelCase_           =   {"old": f"""down.{i}.block""", "new": f"""down_blocks.{i}.resnets"""}
          assign_to_checkpoint(snake_case_					,    snake_case_					,    snake_case_					,    additional_replacements=[meta_path]					,    config=snake_case_			)

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

          UpperCAmelCase_           =   renew_vae_resnet_paths(snake_case_			)
          UpperCAmelCase_           =   {"old": f"""mid.block_{i}""", "new": f"""mid_block.resnets.{i - 1}"""}
          assign_to_checkpoint(snake_case_					,    snake_case_					,    snake_case_					,    additional_replacements=[meta_path]					,    config=snake_case_			)

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

     for i in range(snake_case_			):
          UpperCAmelCase_           =   num_up_blocks - 1 - i
          UpperCAmelCase_           =   [
              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:
               UpperCAmelCase_           =   vae_state_dict[
                   f"""decoder.up.{block_id}.upsample.conv.weight"""
               ]
               UpperCAmelCase_           =   vae_state_dict[
                   f"""decoder.up.{block_id}.upsample.conv.bias"""
               ]

          UpperCAmelCase_           =   renew_vae_resnet_paths(snake_case_			)
          UpperCAmelCase_           =   {"old": f"""up.{block_id}.block""", "new": f"""up_blocks.{i}.resnets"""}
          assign_to_checkpoint(snake_case_					,    snake_case_					,    snake_case_					,    additional_replacements=[meta_path]					,    config=snake_case_			)

     UpperCAmelCase_           =   [key for key in vae_state_dict if "decoder.mid.block" in key]
     UpperCAmelCase_           =   2
     for i in range(1					,    num_mid_res_blocks + 1			):
          UpperCAmelCase_           =   [key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key]

          UpperCAmelCase_           =   renew_vae_resnet_paths(snake_case_			)
          UpperCAmelCase_           =   {"old": f"""mid.block_{i}""", "new": f"""mid_block.resnets.{i - 1}"""}
          assign_to_checkpoint(snake_case_					,    snake_case_					,    snake_case_					,    additional_replacements=[meta_path]					,    config=snake_case_			)

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





def 	lowerCAmelCase_				(					snake_case_       : str					,    snake_case_       : str					,    )    ->      Dict:


     '''simple docstring'''




     UpperCAmelCase_           =   requests.get(
         " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"			)
     UpperCAmelCase_           =   io.BytesIO(r.content			)

     UpperCAmelCase_           =   OmegaConf.load(snake_case_			)
     UpperCAmelCase_           =   5_12
     UpperCAmelCase_           =   "cuda" if torch.cuda.is_available() else "cpu"
     if checkpoint_path.endswith("safetensors"			):
          from safetensors import safe_open

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

     # Convert the VAE model.
     UpperCAmelCase_           =   create_vae_diffusers_config(snake_case_					,    image_size=snake_case_			)
     UpperCAmelCase_           =   custom_convert_ldm_vae_checkpoint(snake_case_					,    snake_case_			)

     UpperCAmelCase_           =   AutoencoderKL(**snake_case_			)
     vae.load_state_dict(snake_case_			)
     vae.save_pretrained(snake_case_			)


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     Optional[int]  =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.')

    SCREAMING_SNAKE_CASE_:     str  =parser.parse_args()

    vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)




