

'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
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      lowercase_      (UpperCAmelCase_							,			unittest.TestCase ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Union[str, Any]							     =     KandinskyVaaControlnetImgaImgPipeline
  SCREAMING_SNAKE_CASE       :    Optional[int]							     =     ['image_embeds', 'negative_image_embeds', 'image', 'hint']
  SCREAMING_SNAKE_CASE       :    Optional[Any]							     =     ['image_embeds', 'negative_image_embeds', 'image', 'hint']
  SCREAMING_SNAKE_CASE       :    Tuple							     =     [
      'generator',
      'height',
      'width',
      'strength',
      'guidance_scale',
      'num_inference_steps',
      'return_dict',
      'guidance_scale',
      'num_images_per_prompt',
      'output_type',
      'return_dict',
  ]
  SCREAMING_SNAKE_CASE       :    Union[str, Any]							     =     False






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	Tuple     ):
   return 3_2






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   return 3_2






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   return self.time_input_dim






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   return self.time_input_dim * 4






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   return 1_0_0






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   torch.manual_seed(0     )

   __lowercase				    =					{
       '''in_channels''': 8,
       # Out channels is double in channels because predicts mean and variance
       '''out_channels''': 8,
       '''addition_embed_type''': '''image_hint''',
       '''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''': '''image_proj''',
       '''cross_attention_dim''': self.cross_attention_dim,
       '''attention_head_dim''': 4,
       '''resnet_time_scale_shift''': '''scale_shift''',
       '''class_embed_type''': None,
   }

   __lowercase				    =					UNetaDConditionModel(**__a     )
   return model






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   return {
       "block_out_channels": [3_2, 3_2, 6_4, 6_4],
       "down_block_types": [
           "DownEncoderBlock2D",
           "DownEncoderBlock2D",
           "DownEncoderBlock2D",
           "AttnDownEncoderBlock2D",
       ],
       "in_channels": 3,
       "latent_channels": 4,
       "layers_per_block": 1,
       "norm_num_groups": 8,
       "norm_type": "spatial",
       "num_vq_embeddings": 1_2,
       "out_channels": 3,
       "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
       "vq_embed_dim": 4,
   }






  @property
  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   torch.manual_seed(0     )
   __lowercase				    =					VQModel(**self.dummy_movq_kwargs     )
   return model






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   __lowercase				    =					self.dummy_unet
   __lowercase				    =					self.dummy_movq

   __lowercase				    =					{
       '''num_train_timesteps''': 1_0_0_0,
       '''beta_schedule''': '''linear''',
       '''beta_start''': 0.0_0_0_8_5,
       '''beta_end''': 0.0_1_2,
       '''clip_sample''': False,
       '''set_alpha_to_one''': False,
       '''steps_offset''': 0,
       '''prediction_type''': '''epsilon''',
       '''thresholding''': False,
   }

   __lowercase				    =					DDIMScheduler(**__a     )

   __lowercase				    =					{
       '''unet''': unet,
       '''scheduler''': scheduler,
       '''movq''': movq,
   }

   return components






  def    SCREAMING_SNAKE_CASE ( self  :	Any   ,lowercase__  :	Any   ,lowercase__  :	Tuple=0     ):
   __lowercase				    =					floats_tensor((1, self.text_embedder_hidden_size)   ,rng=random.Random(__a     )     ).to(__a     )
   __lowercase				    =					floats_tensor((1, self.text_embedder_hidden_size)   ,rng=random.Random(seed + 1     )     ).to(
       __a     )
   # create init_image
   __lowercase				    =					floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(__a     )     ).to(__a     )
   __lowercase				    =					image.cpu().permute(0   ,2   ,3   ,1     )[0]
   __lowercase				    =					Image.fromarray(np.uinta(__a     )     ).convert('''RGB'''     ).resize((2_5_6, 2_5_6)     )
   # create hint
   __lowercase				    =					floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(__a     )     ).to(__a     )

   if str(__a     ).startswith('''mps'''     ):
    __lowercase				    =					torch.manual_seed(__a     )
   else:
    __lowercase				    =					torch.Generator(device=__a     ).manual_seed(__a     )
   __lowercase				    =					{
       '''image''': init_image,
       '''image_embeds''': image_embeds,
       '''negative_image_embeds''': negative_image_embeds,
       '''hint''': hint,
       '''generator''': generator,
       '''height''': 6_4,
       '''width''': 6_4,
       '''num_inference_steps''': 1_0,
       '''guidance_scale''': 7.0,
       '''strength''': 0.2,
       '''output_type''': '''np''',
   }
   return inputs






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   __lowercase				    =					'''cpu'''

   __lowercase				    =					self.get_dummy_components()

   __lowercase				    =					self.pipeline_class(**__a     )
   __lowercase				    =					pipe.to(__a     )

   pipe.set_progress_bar_config(disable=__a     )

   __lowercase				    =					pipe(**self.get_dummy_inputs(__a     )     )
   __lowercase				    =					output.images

   __lowercase				    =					pipe(
       **self.get_dummy_inputs(__a     )   ,return_dict=__a   ,)[0]

   __lowercase				    =					image[0, -3:, -3:, -1]
   __lowercase				    =					image_from_tuple[0, -3:, -3:, -1]

   assert image.shape == (1, 6_4, 6_4, 3)

   __lowercase				    =					np.array(
       [0.5_4_9_8_5_0_3_4, 0.5_5_5_0_9_3_6_5, 0.5_2_5_6_1_5_0_4, 0.5_5_7_0_4_9_4, 0.5_5_9_3_8_1_8, 0.5_2_6_3_9_7_9, 0.5_0_2_8_5_6_4_3, 0.5_0_6_9_8_4_6, 0.5_1_1_9_6_7_3_6]     )
   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()}"






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




  """simple docstring"""




  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   __lowercase				    =					load_numpy(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
       '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''     )

   __lowercase				    =					load_image(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''     )
   __lowercase				    =					init_image.resize((5_1_2, 5_1_2)     )

   __lowercase				    =					load_image(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
       '''/kandinskyv22/hint_image_cat.png'''     )
   __lowercase				    =					torch.from_numpy(np.array(__a     )     ).float() / 2_5_5.0
   __lowercase				    =					hint.permute(2   ,0   ,1     ).unsqueeze(0     )

   __lowercase				    =					'''A robot, 4k photo'''

   __lowercase				    =					KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
       '''kandinsky-community/kandinsky-2-2-prior'''   ,torch_dtype=torch.floataa     )
   pipe_prior.to(__a     )

   __lowercase				    =					KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
       '''kandinsky-community/kandinsky-2-2-controlnet-depth'''   ,torch_dtype=torch.floataa     )
   __lowercase				    =					pipeline.to(__a     )

   pipeline.set_progress_bar_config(disable=__a     )

   __lowercase				    =					torch.Generator(device='''cpu'''     ).manual_seed(0     )

   __lowercase    ,      __lowercase				    =					pipe_prior(
       __a   ,image=__a   ,strength=0.8_5   ,generator=__a   ,negative_prompt=''''''   ,).to_tuple()

   __lowercase				    =					pipeline(
       image=__a   ,image_embeds=__a   ,negative_image_embeds=__a   ,hint=__a   ,generator=__a   ,num_inference_steps=1_0_0   ,height=5_1_2   ,width=5_1_2   ,strength=0.5   ,output_type='''np'''   ,)

   __lowercase				    =					output.images[0]

   assert image.shape == (5_1_2, 5_1_2, 3)

   assert_mean_pixel_difference(__a   ,__a     )






import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken

@torch.no_grad()
def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      with open(UpperCAmelCase__			) as metadata_file:
            lowerCamelCase             =		json.load(UpperCAmelCase__			)
      lowerCamelCase             =		LukeConfig(use_entity_aware_attention=UpperCAmelCase__ , **metadata["model_config"]			)

      # Load in the weights from the checkpoint_path
      lowerCamelCase             =		torch.load(UpperCAmelCase__ , map_location="cpu"			)

      # Load the entity vocab file
      lowerCamelCase             =		load_entity_vocab(UpperCAmelCase__			)

      lowerCamelCase             =		RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]			)

      # Add special tokens to the token vocabulary for downstream tasks
      lowerCamelCase             =		AddedToken("<ent>" , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__			)
      lowerCamelCase             =		AddedToken("<ent2>" , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__			)
      tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]}			)
      config.vocab_size += 2

      print(F"""Saving tokenizer to {pytorch_dump_folder_path}"""			)
      tokenizer.save_pretrained(UpperCAmelCase__			)
      with open(os.path.join(UpperCAmelCase__ , LukeTokenizer.vocab_files_names["entity_vocab_file"]			) , "w"			) as f:
            json.dump(UpperCAmelCase__ , UpperCAmelCase__			)

      lowerCamelCase             =		LukeTokenizer.from_pretrained(UpperCAmelCase__			)

      # Initialize the embeddings of the special tokens
      lowerCamelCase             =		state_dict["embeddings.word_embeddings.weight"]
      lowerCamelCase             =		word_emb[tokenizer.convert_tokens_to_ids(["@"]			)[0]].unsqueeze(0			)
      lowerCamelCase             =		word_emb[tokenizer.convert_tokens_to_ids(["#"]			)[0]].unsqueeze(0			)
      lowerCamelCase             =		torch.cat([word_emb, ent_emb, enta_emb]			)

      # Initialize the query layers of the entity-aware self-attention mechanism
      for layer_index in range(config.num_hidden_layers			):
            for matrix_name in ["query.weight", "query.bias"]:
                  lowerCamelCase             =		F"""encoder.layer.{layer_index}.attention.self."""
                  lowerCamelCase             =		state_dict[prefix + matrix_name]
                  lowerCamelCase             =		state_dict[prefix + matrix_name]
                  lowerCamelCase             =		state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
      lowerCamelCase             =		state_dict["entity_embeddings.entity_embeddings.weight"]
      lowerCamelCase             =		entity_emb[entity_vocab["[MASK]"]]

      lowerCamelCase             =		LukeModel(config=UpperCAmelCase__			).eval()

      lowerCamelCase      ,				lowerCamelCase             =		model.load_state_dict(UpperCAmelCase__ , strict=UpperCAmelCase__			)
      if not (len(UpperCAmelCase__			) == 1 and missing_keys[0] == "embeddings.position_ids"):
            raise ValueError(F"""Missing keys {", ".join(UpperCAmelCase__			)}. Expected only missing embeddings.position_ids"""			)
      if not (all(key.startswith("entity_predictions"			) or key.startswith("lm_head"			) for key in unexpected_keys			)):
            raise ValueError(
                "Unexpected keys"
                F""" {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"			) or key.startswith("lm_head"			))]			)}"""			)

      # Check outputs
      lowerCamelCase             =		LukeTokenizer.from_pretrained(UpperCAmelCase__ , task="entity_classification"			)

      lowerCamelCase             =		(
          "Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"
          " new world number one avoid a humiliating second- round exit at Wimbledon ."
      )
      lowerCamelCase             =		(39, 42)
      lowerCamelCase             =		tokenizer(UpperCAmelCase__ , entity_spans=[span] , add_prefix_space=UpperCAmelCase__ , return_tensors="pt"			)

      lowerCamelCase             =		model(**UpperCAmelCase__			)

      # Verify word hidden states
      if model_size == "large":
            lowerCamelCase             =		torch.Size((1, 42, 1024)			)
            lowerCamelCase             =		torch.tensor(
                [[0.0_1_3_3, 0.0_8_6_5, 0.0_0_9_5], [0.3_0_9_3, -0.2_5_7_6, -0.7_4_1_8], [-0.1_7_2_0, -0.2_1_1_7, -0.2_8_6_9]]			)
      else:  # base
            lowerCamelCase             =		torch.Size((1, 42, 768)			)
            lowerCamelCase             =		torch.tensor([[0.0_0_3_7, 0.1_3_6_8, -0.0_0_9_1], [0.1_0_9_9, 0.3_3_2_9, -0.1_0_9_5], [0.0_7_6_5, 0.5_3_3_5, 0.1_1_7_9]]			)

      if not (outputs.last_hidden_state.shape == expected_shape):
            raise ValueError(
                F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"""			)
      if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , UpperCAmelCase__ , atol=1E-4			):
            raise ValueError

      # Verify entity hidden states
      if model_size == "large":
            lowerCamelCase             =		torch.Size((1, 1, 1024)			)
            lowerCamelCase             =		torch.tensor([[0.0_4_6_6, -0.0_1_0_6, -0.0_1_7_9]]			)
      else:  # base
            lowerCamelCase             =		torch.Size((1, 1, 768)			)
            lowerCamelCase             =		torch.tensor([[0.1_4_5_7, 0.1_0_4_4, 0.0_1_7_4]]			)

      if not (outputs.entity_last_hidden_state.shape != expected_shape):
            raise ValueError(
                F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
                F""" {expected_shape}"""			)
      if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , UpperCAmelCase__ , atol=1E-4			):
            raise ValueError

      # Finally, save our PyTorch model and tokenizer
      print("Saving PyTorch model to {}".format(UpperCAmelCase__			)			)
      model.save_pretrained(UpperCAmelCase__			)

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		{}
      with open(UpperCAmelCase__ , "r" , encoding="utf-8"			) as f:
            for index, line in enumerate(UpperCAmelCase__			):
                  lowerCamelCase      ,				lowerCamelCase             =		line.rstrip().split("\t"			)
                  lowerCamelCase             =		index

      return entity_vocab


if __name__ == "__main__":
     a_    :  Tuple						  =		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
     parser.add_argument(
         '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
     )
     parser.add_argument(
         '--entity_vocab_path',
         default=None,
         type=str,
         help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
     )
     parser.add_argument(
         '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
     )
     a_    :  Dict						  =		parser.parse_args()
     convert_luke_checkpoint(
         args.checkpoint_path,
         args.metadata_path,
         args.entity_vocab_path,
         args.pytorch_dump_folder_path,
         args.model_size,
     )