
'''simple docstring'''





import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor







def snake_case_     (					lowercase__		):
  if "cls_token" in name:
    UpperCAmelCase__      :					List[str]				       =							name.replace("cls_token"   ,       "vit.embeddings.cls_token"		)
  if "mask_token" in name:
    UpperCAmelCase__      :					Union[str, Any]				       =							name.replace("mask_token"   ,       "decoder.mask_token"		)
  if "decoder_pos_embed" in name:
    UpperCAmelCase__      :					List[Any]				       =							name.replace("decoder_pos_embed"   ,       "decoder.decoder_pos_embed"		)
  if "pos_embed" in name and "decoder" not in name:
    UpperCAmelCase__      :					int				       =							name.replace("pos_embed"   ,       "vit.embeddings.position_embeddings"		)
  if "patch_embed.proj" in name:
    UpperCAmelCase__      :					int				       =							name.replace("patch_embed.proj"   ,       "vit.embeddings.patch_embeddings.projection"		)
  if "patch_embed.norm" in name:
    UpperCAmelCase__      :					Any				       =							name.replace("patch_embed.norm"   ,       "vit.embeddings.norm"		)
  if "decoder_blocks" in name:
    UpperCAmelCase__      :					Tuple				       =							name.replace("decoder_blocks"   ,       "decoder.decoder_layers"		)
  if "blocks" in name:
    UpperCAmelCase__      :					Optional[Any]				       =							name.replace("blocks"   ,       "vit.encoder.layer"		)
  if "attn.proj" in name:
    UpperCAmelCase__      :					Optional[Any]				       =							name.replace("attn.proj"   ,       "attention.output.dense"		)
  if "attn" in name:
    UpperCAmelCase__      :					Any				       =							name.replace("attn"   ,       "attention.self"		)
  if "norm1" in name:
    UpperCAmelCase__      :					int				       =							name.replace("norm1"   ,       "layernorm_before"		)
  if "norm2" in name:
    UpperCAmelCase__      :					Any				       =							name.replace("norm2"   ,       "layernorm_after"		)
  if "mlp.fc1" in name:
    UpperCAmelCase__      :					Optional[int]				       =							name.replace("mlp.fc1"   ,       "intermediate.dense"		)
  if "mlp.fc2" in name:
    UpperCAmelCase__      :					Optional[Any]				       =							name.replace("mlp.fc2"   ,       "output.dense"		)
  if "decoder_embed" in name:
    UpperCAmelCase__      :					Optional[int]				       =							name.replace("decoder_embed"   ,       "decoder.decoder_embed"		)
  if "decoder_norm" in name:
    UpperCAmelCase__      :					Union[str, Any]				       =							name.replace("decoder_norm"   ,       "decoder.decoder_norm"		)
  if "decoder_pred" in name:
    UpperCAmelCase__      :					Union[str, Any]				       =							name.replace("decoder_pred"   ,       "decoder.decoder_pred"		)
  if "norm.weight" in name and "decoder" not in name:
    UpperCAmelCase__      :					Tuple				       =							name.replace("norm.weight"   ,       "vit.layernorm.weight"		)
  if "norm.bias" in name and "decoder" not in name:
    UpperCAmelCase__      :					Union[str, Any]				       =							name.replace("norm.bias"   ,       "vit.layernorm.bias"		)

  return name







def snake_case_     (					lowercase__   ,       lowercase__		):
  for key in orig_state_dict.copy().keys():
    UpperCAmelCase__      :					Optional[Any]				       =							orig_state_dict.pop(lowercase__		)

    if "qkv" in key:
      UpperCAmelCase__      :					List[Any]				       =							key.split("."		)
      UpperCAmelCase__      :					int				       =							int(key_split[1]		)
      if "decoder_blocks" in key:
        UpperCAmelCase__      :					Dict				       =							config.decoder_hidden_size
        UpperCAmelCase__      :					Union[str, Any]				       =							"decoder.decoder_layers."
        if "weight" in key:
          UpperCAmelCase__      :					List[str]				       =							val[:dim, :]
          UpperCAmelCase__      :					str				       =							val[dim : dim * 2, :]
          UpperCAmelCase__      :					Dict				       =							val[-dim:, :]
        elif "bias" in key:
          UpperCAmelCase__      :					List[Any]				       =							val[:dim]
          UpperCAmelCase__      :					List[str]				       =							val[dim : dim * 2]
          UpperCAmelCase__      :					List[str]				       =							val[-dim:]
      else:
        UpperCAmelCase__      :					int				       =							config.hidden_size
        UpperCAmelCase__      :					str				       =							"vit.encoder.layer."
        if "weight" in key:
          UpperCAmelCase__      :					Dict				       =							val[:dim, :]
          UpperCAmelCase__      :					Tuple				       =							val[dim : dim * 2, :]
          UpperCAmelCase__      :					int				       =							val[-dim:, :]
        elif "bias" in key:
          UpperCAmelCase__      :					Dict				       =							val[:dim]
          UpperCAmelCase__      :					Union[str, Any]				       =							val[dim : dim * 2]
          UpperCAmelCase__      :					str				       =							val[-dim:]

    else:
      UpperCAmelCase__      :					int				       =							val

  return orig_state_dict







def snake_case_     (					lowercase__   ,       lowercase__		):
  UpperCAmelCase__      :					List[Any]				       =							ViTMAEConfig()
  if "large" in checkpoint_url:
    UpperCAmelCase__      :					str				       =							1_0_2_4
    UpperCAmelCase__      :					Optional[int]				       =							4_0_9_6
    UpperCAmelCase__      :					Optional[Any]				       =							2_4
    UpperCAmelCase__      :					str				       =							1_6
  elif "huge" in checkpoint_url:
    UpperCAmelCase__      :					Optional[Any]				       =							1_4
    UpperCAmelCase__      :					Any				       =							1_2_8_0
    UpperCAmelCase__      :					Dict				       =							5_1_2_0
    UpperCAmelCase__      :					Optional[int]				       =							3_2
    UpperCAmelCase__      :					int				       =							1_6

  UpperCAmelCase__      :					Dict				       =							ViTMAEForPreTraining(lowercase__		)

  UpperCAmelCase__      :					Tuple				       =							torch.hub.load_state_dict_from_url(lowercase__   ,       map_location="cpu"		)["model"]

  UpperCAmelCase__      :					Tuple				       =							ViTMAEImageProcessor(size=config.image_size		)

  UpperCAmelCase__      :					Optional[Any]				       =							convert_state_dict(lowercase__   ,       lowercase__		)

  model.load_state_dict(lowercase__		)
  model.eval()

  UpperCAmelCase__      :					Optional[int]				       =							"https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"

  UpperCAmelCase__      :					Dict				       =							Image.open(requests.get(lowercase__   ,       stream=lowercase__		).raw		)
  UpperCAmelCase__      :					int				       =							ViTMAEImageProcessor(size=config.image_size		)
  UpperCAmelCase__      :					int				       =							image_processor(images=lowercase__   ,       return_tensors="pt"		)

  # forward pass
  torch.manual_seed(2		)
  UpperCAmelCase__      :					Tuple				       =							model(**lowercase__		)
  UpperCAmelCase__      :					List[str]				       =							outputs.logits

  if "large" in checkpoint_url:
    UpperCAmelCase__      :					Any				       =							torch.tensor(
        [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]		)
  elif "huge" in checkpoint_url:
    UpperCAmelCase__      :					Optional[int]				       =							torch.tensor(
        [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]		)
  else:
    UpperCAmelCase__      :					Dict				       =							torch.tensor(
        [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]		)

  # verify logits
  assert torch.allclose(logits[0, :3, :3]   ,       lowercase__   ,       atol=1E-4		)

  print(F"""Saving model to {pytorch_dump_folder_path}"""		)
  model.save_pretrained(lowercase__		)

  print(F"""Saving image processor to {pytorch_dump_folder_path}"""		)
  image_processor.save_pretrained(lowercase__		)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE     =       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--checkpoint_url""",
          default="""https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth""",
          type=str,
          help="""URL of the checkpoint you'd like to convert.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
      )

      SCREAMING_SNAKE_CASE     =       parser.parse_args()
      convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 							UpperCAmelCase_							(    A					,				unittest.TestCase				):

 '''simple docstring'''
 lowercase_					:    Union[str, Any]				=							KandinskyInpaintPipeline
 lowercase_					:    Tuple				=							["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
 lowercase_					:    Dict				=							[
     "prompt",
     "negative_prompt",
     "image_embeds",
     "negative_image_embeds",
     "image",
     "mask_image",
 ]
 lowercase_					:    Optional[Any]				=							[
     "generator",
     "height",
     "width",
     "latents",
     "guidance_scale",
     "negative_prompt",
     "num_inference_steps",
     "return_dict",
     "guidance_scale",
     "num_images_per_prompt",
     "output_type",
     "return_dict",
 ]
 lowercase_					:    Optional[Any]				=							False







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   return 32







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   return 32







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   return self.time_input_dim







 @property
 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   return self.time_input_dim * 4







 @property
 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   return 1_00







 @property
 def 							UpperCamelCase  (					self		:  List[Any] ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[str]				       =							XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base" )
   return tokenizer







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   torch.manual_seed(0 )
   UpperCAmelCase__      :					Dict				       =							MCLIPConfig(
       numDims=self.cross_attention_dim			,				transformerDimensions=self.text_embedder_hidden_size			,				hidden_size=self.text_embedder_hidden_size			,				intermediate_size=37			,				num_attention_heads=4			,				num_hidden_layers=5			,				vocab_size=10_05			,				)

   UpperCAmelCase__      :					Any				       =							MultilingualCLIP(snake_case__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							text_encoder.eval()

   return text_encoder







 @property
 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   torch.manual_seed(0 )

   UpperCAmelCase__      :					str				       =							{
       "in_channels": 9,
       # Out channels is double in channels because predicts mean and variance
       "out_channels": 8,
       "addition_embed_type": "text_image",
       "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
       "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
       "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
       "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
       "layers_per_block": 1,
       "encoder_hid_dim": self.text_embedder_hidden_size,
       "encoder_hid_dim_type": "text_image_proj",
       "cross_attention_dim": self.cross_attention_dim,
       "attention_head_dim": 4,
       "resnet_time_scale_shift": "scale_shift",
       "class_embed_type": None,
   }

   UpperCAmelCase__      :					List[str]				       =							UNetaDConditionModel(**snake_case__ )
   return model







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   return {
       "block_out_channels": [32, 64],
       "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
       "in_channels": 3,
       "latent_channels": 4,
       "layers_per_block": 1,
       "norm_num_groups": 8,
       "norm_type": "spatial",
       "num_vq_embeddings": 12,
       "out_channels": 3,
       "up_block_types": [
           "AttnUpDecoderBlock2D",
           "UpDecoderBlock2D",
       ],
       "vq_embed_dim": 4,
   }







 @property
 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   torch.manual_seed(0 )
   UpperCAmelCase__      :					str				       =							VQModel(**self.dummy_movq_kwargs )
   return model







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					int				       =							self.dummy_text_encoder
   UpperCAmelCase__      :					Any				       =							self.dummy_tokenizer
   UpperCAmelCase__      :					Tuple				       =							self.dummy_unet
   UpperCAmelCase__      :					str				       =							self.dummy_movq

   UpperCAmelCase__      :					str				       =							DDIMScheduler(
       num_train_timesteps=10_00			,				beta_schedule="linear"			,				beta_start=0.00085			,				beta_end=0.012			,				clip_sample=snake_case__			,				set_alpha_to_one=snake_case__			,				steps_offset=1			,				prediction_type="epsilon"			,				thresholding=snake_case__			,				)

   UpperCAmelCase__      :					List[Any]				       =							{
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "unet": unet,
       "scheduler": scheduler,
       "movq": movq,
   }

   return components







 def 							UpperCamelCase  (					self		:  Optional[Any]			,				snake_case__		:  Any			,				snake_case__		:  Tuple=0 ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[Any]				       =							floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(snake_case__ ) ).to(snake_case__ )
   UpperCAmelCase__      :					Optional[Any]				       =							floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(seed + 1 ) ).to(snake_case__ )
   # create init_image
   UpperCAmelCase__      :					Any				       =							floats_tensor((1, 3, 64, 64)			,				rng=random.Random(snake_case__ ) ).to(snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							image.cpu().permute(0			,				2			,				3			,				1 )[0]
   UpperCAmelCase__      :					Optional[int]				       =							Image.fromarray(np.uinta(snake_case__ ) ).convert("RGB" ).resize((2_56, 2_56) )
   # create mask
   UpperCAmelCase__      :					Any				       =							np.ones((64, 64)			,				dtype=np.floataa )
   UpperCAmelCase__      :					Any				       =							0

   if str(snake_case__ ).startswith("mps" ):
     UpperCAmelCase__      :					Tuple				       =							torch.manual_seed(snake_case__ )
   else:
     UpperCAmelCase__      :					str				       =							torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							{
       "prompt": "horse",
       "image": init_image,
       "mask_image": mask,
       "image_embeds": image_embeds,
       "negative_image_embeds": negative_image_embeds,
       "generator": generator,
       "height": 64,
       "width": 64,
       "num_inference_steps": 2,
       "guidance_scale": 4.0,
       "output_type": "np",
   }
   return inputs







 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   UpperCAmelCase__      :					str				       =							"cpu"

   UpperCAmelCase__      :					Optional[Any]				       =							self.get_dummy_components()

   UpperCAmelCase__      :					int				       =							self.pipeline_class(**snake_case__ )
   UpperCAmelCase__      :					List[Any]				       =							pipe.to(snake_case__ )

   pipe.set_progress_bar_config(disable=snake_case__ )

   UpperCAmelCase__      :					List[str]				       =							pipe(**self.get_dummy_inputs(snake_case__ ) )
   UpperCAmelCase__      :					int				       =							output.images

   UpperCAmelCase__      :					Tuple				       =							pipe(
       **self.get_dummy_inputs(snake_case__ )			,				return_dict=snake_case__			,				)[0]

   UpperCAmelCase__      :					Any				       =							image[0, -3:, -3:, -1]
   UpperCAmelCase__      :					Optional[int]				       =							image_from_tuple[0, -3:, -3:, -1]

   print(F"""image.shape {image.shape}""" )

   assert image.shape == (1, 64, 64, 3)

   UpperCAmelCase__      :					Any				       =							np.array(
       [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786] )

   assert (
       np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
   ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
   assert (
       np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
   ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







 def 							UpperCamelCase  (					self		:  Dict ):



   '''simple docstring'''




   super().test_inference_batch_single_identical(expected_max_diff=3e-3 )






@slow
@require_torch_gpu
class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  Tuple ):



   '''simple docstring'''




   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy" )

   UpperCAmelCase__      :					List[str]				       =							load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
   UpperCAmelCase__      :					Union[str, Any]				       =							np.ones((7_68, 7_68)			,				dtype=np.floataa )
   UpperCAmelCase__      :					List[Any]				       =							0

   UpperCAmelCase__      :					Tuple				       =							"a hat"

   UpperCAmelCase__      :					Union[str, Any]				       =							KandinskyPriorPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-1-prior"			,				torch_dtype=torch.floataa )
   pipe_prior.to(snake_case__ )

   UpperCAmelCase__      :					int				       =							KandinskyInpaintPipeline.from_pretrained(
       "kandinsky-community/kandinsky-2-1-inpaint"			,				torch_dtype=torch.floataa )
   UpperCAmelCase__      :					Optional[int]				       =							pipeline.to(snake_case__ )
   pipeline.set_progress_bar_config(disable=snake_case__ )

   UpperCAmelCase__      :					List[str]				       =							torch.Generator(device="cpu" ).manual_seed(0 )
   UpperCAmelCase__       ,			UpperCAmelCase__      :					Any				       =							pipe_prior(
       snake_case__			,				generator=snake_case__			,				num_inference_steps=5			,				negative_prompt=""			,				).to_tuple()

   UpperCAmelCase__      :					Optional[int]				       =							pipeline(
       snake_case__			,				image=snake_case__			,				mask_image=snake_case__			,				image_embeds=snake_case__			,				negative_image_embeds=snake_case__			,				generator=snake_case__			,				num_inference_steps=1_00			,				height=7_68			,				width=7_68			,				output_type="np"			,				)

   UpperCAmelCase__      :					int				       =							output.images[0]

   assert image.shape == (7_68, 7_68, 3)

   assert_mean_pixel_difference(snake_case__			,				snake_case__ )



