


import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken



@torch.no_grad()
def UpperCamelCase_				(				__a			,				__a			,				__a			,				__a			,				__a      )	->   Tuple:
      # Load configuration defined in the metadata file
      with open(__a      ) as metadata_file:
            a__	:  Optional[int]     =   json.load(__a      )
      a__	:  str     =   LukeConfig(use_entity_aware_attention=__a			,				**metadata["model_config"]      )

      # Load in the weights from the checkpoint_path
      a__	:  List[Any]     =   torch.load(__a			,				map_location="cpu"      )["module"]

      # Load the entity vocab file
      a__	:  Union[str, Any]     =   load_original_entity_vocab(__a      )
      # add an entry for [MASK2]
      a__	:  List[Any]     =   max(entity_vocab.values()      ) + 1
      config.entity_vocab_size += 1

      a__	:  Optional[int]     =   XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]      )

      # Add special tokens to the token vocabulary for downstream tasks
      a__	:  Union[str, Any]     =   AddedToken("<ent>"			,				lstrip=__a			,				rstrip=__a      )
      a__	:  Dict     =   AddedToken("<ent2>"			,				lstrip=__a			,				rstrip=__a      )
      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(__a      )
      with open(os.path.join(__a			,				"tokenizer_config.json"      )			,				"r"      ) as f:
            a__	:  int     =   json.load(__a      )
      a__	:  List[str]     =   "MLukeTokenizer"
      with open(os.path.join(__a			,				"tokenizer_config.json"      )			,				"w"      ) as f:
            json.dump(__a			,				__a      )

      with open(os.path.join(__a			,				MLukeTokenizer.vocab_files_names["entity_vocab_file"]      )			,				"w"      ) as f:
            json.dump(__a			,				__a      )

      a__	:  Dict     =   MLukeTokenizer.from_pretrained(__a      )

      # Initialize the embeddings of the special tokens
      a__	:  Dict     =   tokenizer.convert_tokens_to_ids(["@"]      )[0]
      a__	:  Union[str, Any]     =   tokenizer.convert_tokens_to_ids(["#"]      )[0]

      a__	:  List[str]     =   state_dict["embeddings.word_embeddings.weight"]
      a__	:  Optional[int]     =   word_emb[ent_init_index].unsqueeze(0      )
      a__	:  Union[str, Any]     =   word_emb[enta_init_index].unsqueeze(0      )
      a__	:  List[Any]     =   torch.cat([word_emb, ent_emb, enta_emb]      )
      # add special tokens for 'entity_predictions.bias'
      for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
            a__	:  str     =   state_dict[bias_name]
            a__	:  Optional[int]     =   decoder_bias[ent_init_index].unsqueeze(0      )
            a__	:  str     =   decoder_bias[enta_init_index].unsqueeze(0      )
            a__	:  Tuple     =   torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]      )

      # 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"]:
                  a__	:  Optional[Any]     =   f'''encoder.layer.{layer_index}.attention.self.'''
                  a__	:  List[str]     =   state_dict[prefix + matrix_name]
                  a__	:  str     =   state_dict[prefix + matrix_name]
                  a__	:  Any     =   state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
      a__	:  List[Any]     =   state_dict["entity_embeddings.entity_embeddings.weight"]
      a__	:  Union[str, Any]     =   entity_emb[entity_vocab["[MASK]"]].unsqueeze(0      )
      a__	:  List[Any]     =   torch.cat([entity_emb, entity_mask_emb]      )
      # add [MASK2] for 'entity_predictions.bias'
      a__	:  List[str]     =   state_dict["entity_predictions.bias"]
      a__	:  Dict     =   entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0      )
      a__	:  Any     =   torch.cat([entity_prediction_bias, entity_mask_bias]      )

      a__	:  int     =   LukeForMaskedLM(config=__a      ).eval()

      state_dict.pop("entity_predictions.decoder.weight"      )
      state_dict.pop("lm_head.decoder.weight"      )
      state_dict.pop("lm_head.decoder.bias"      )
      a__	:  Dict     =   OrderedDict()
      for key, value in state_dict.items():
            if not (key.startswith("lm_head"      ) or key.startswith("entity_predictions"      )):
                  a__	:  Union[str, Any]     =   state_dict[key]
            else:
                  a__	:  Dict     =   state_dict[key]

      a__,	a__	:  List[str]     =   model.load_state_dict(__a			,				strict=__a      )

      if set(__a      ) != {"luke.embeddings.position_ids"}:
            raise ValueError(f'''Unexpected unexpected_keys: {unexpected_keys}'''      )
      if set(__a      ) != {
          "lm_head.decoder.weight",
          "lm_head.decoder.bias",
          "entity_predictions.decoder.weight",
      }:
            raise ValueError(f'''Unexpected missing_keys: {missing_keys}'''      )

      model.tie_weights()
      assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
      assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

      # Check outputs
      a__	:  int     =   MLukeTokenizer.from_pretrained(__a			,				task="entity_classification"      )

      a__	:  int     =   "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
      a__	:  Optional[Any]     =   (0, 9)
      a__	:  int     =   tokenizer(__a			,				entity_spans=[span]			,				return_tensors="pt"      )

      a__	:  Optional[int]     =   model(**__a      )

      # Verify word hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            a__	:  List[Any]     =   torch.Size((1, 33, 768)      )
            a__	:  int     =   torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]      )

      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]			,				__a			,				atol=1e-4      ):
            raise ValueError

      # Verify entity hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            a__	:  str     =   torch.Size((1, 1, 768)      )
            a__	:  Any     =   torch.tensor([[-0.1482, 0.0609, 0.0322]]      )

      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]			,				__a			,				atol=1e-4      ):
            raise ValueError

      # Verify masked word/entity prediction
      a__	:  int     =   MLukeTokenizer.from_pretrained(__a      )
      a__	:  int     =   "Tokyo is the capital of <mask>."
      a__	:  Any     =   (24, 30)
      a__	:  Optional[int]     =   tokenizer(__a			,				entity_spans=[span]			,				return_tensors="pt"      )

      a__	:  Union[str, Any]     =   model(**__a      )

      a__	:  Any     =   encoding["input_ids"][0].tolist()
      a__	:  int     =   input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"      )      )
      a__	:  Any     =   outputs.logits[0][mask_position_id].argmax(dim=-1      )
      assert "Japan" == tokenizer.decode(__a      )

      a__	:  List[Any]     =   outputs.entity_logits[0][0].argmax().item()
      a__	:  Tuple     =   [
          entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
      ]
      assert [e for e in multilingual_predicted_entities if e.startswith("en:"      )][0] == "en:Japan"

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



def UpperCamelCase_				(				__a      )	->   int:
      a__	:  str     =   ["[MASK]", "[PAD]", "[UNK]"]

      a__	:  List[Any]     =   [json.loads(__a      ) for line in open(__a      )]

      a__	:  Optional[Any]     =   {}
      for entry in data:
            a__	:  Optional[int]     =   entry["id"]
            for entity_name, language in entry["entities"]:
                  if entity_name in SPECIAL_TOKENS:
                        a__	:  Dict     =   entity_id
                        break
                  a__	:  Union[str, Any]     =   f'''{language}:{entity_name}'''
                  a__	:  Union[str, Any]     =   entity_id
      return new_mapping


if __name__ == "__main__":
    UpperCamelCase					:       Dict			    =     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."""
    )
    UpperCamelCase					:       Tuple			    =     parser.parse_args()
    convert_luke_checkpoint(
        args.checkpoint_path,
        args.metadata_path,
        args.entity_vocab_path,
        args.pytorch_dump_folder_path,
        args.model_size,
    )






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
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


enable_full_determinism()





class  lowerCamelCase__	(    unittest.TestCase):
     """simple docstring"""



     def     _a   (self     ):






           '''simple docstring'''



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







     @property
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		1
           lowerCamelCase             =		3
           lowerCamelCase             =		(32, 32)

           lowerCamelCase             =		floats_tensor((batch_size, num_channels) + sizes					,      rng=random.Random(0     )     ).to(__a     )
           return image







     @property
     def     _a   (self     ):






           '''simple docstring'''



           torch.manual_seed(0     )
           lowerCamelCase             =		UNetaDConditionModel(
               block_out_channels=(32, 32, 64)					,      layers_per_block=2					,      sample_size=32					,      in_channels=7					,      out_channels=4					,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D")					,      up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D")					,      cross_attention_dim=32					,      attention_head_dim=8					,      use_linear_projection=__a					,      only_cross_attention=(True, True, False)					,      num_class_embeds=1_00					,      )
           return model







     @property
     def     _a   (self     ):






           '''simple docstring'''



           torch.manual_seed(0     )
           lowerCamelCase             =		AutoencoderKL(
               block_out_channels=[32, 32, 64]					,      in_channels=3					,      out_channels=3					,      down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"]					,      up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]					,      latent_channels=4					,      )
           return model







     @property
     def     _a   (self     ):






           '''simple docstring'''



           torch.manual_seed(0     )
           lowerCamelCase             =		CLIPTextConfig(
               bos_token_id=0					,      eos_token_id=2					,      hidden_size=32					,      intermediate_size=37					,      layer_norm_eps=1E-05					,      num_attention_heads=4					,      num_hidden_layers=5					,      pad_token_id=1					,      vocab_size=10_00					,      hidden_act="gelu"					,      projection_dim=5_12					,      )
           return CLIPTextModel(__a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		"cpu"  # ensure determinism for the device-dependent torch.Generator
           lowerCamelCase             =		self.dummy_cond_unet_upscale
           lowerCamelCase             =		DDPMScheduler()
           lowerCamelCase             =		DDIMScheduler(prediction_type="v_prediction"     )
           lowerCamelCase             =		self.dummy_vae
           lowerCamelCase             =		self.dummy_text_encoder
           lowerCamelCase             =		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

           lowerCamelCase             =		self.dummy_image.cpu().permute(0					,      2					,      3					,      1     )[0]
           lowerCamelCase             =		Image.fromarray(np.uinta(__a     )     ).convert("RGB"     ).resize((64, 64)     )

           # make sure here that pndm scheduler skips prk
           lowerCamelCase             =		StableDiffusionUpscalePipeline(
               unet=__a					,      low_res_scheduler=__a					,      scheduler=__a					,      vae=__a					,      text_encoder=__a					,      tokenizer=__a					,      max_noise_level=3_50					,      )
           lowerCamelCase             =		sd_pipe.to(__a     )
           sd_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		"A painting of a squirrel eating a burger"
           lowerCamelCase             =		torch.Generator(device=__a     ).manual_seed(0     )
           lowerCamelCase             =		sd_pipe(
               [prompt]					,      image=__a					,      generator=__a					,      guidance_scale=6.0					,      noise_level=20					,      num_inference_steps=2					,      output_type="np"					,      )

           lowerCamelCase             =		output.images

           lowerCamelCase             =		torch.Generator(device=__a     ).manual_seed(0     )
           lowerCamelCase             =		sd_pipe(
               [prompt]					,      image=__a					,      generator=__a					,      guidance_scale=6.0					,      noise_level=20					,      num_inference_steps=2					,      output_type="np"					,      return_dict=__a					,      )[0]

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

           lowerCamelCase             =		low_res_image.size[0] * 4
           assert image.shape == (1, expected_height_width, expected_height_width, 3)
           lowerCamelCase             =		np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661]     )

           assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		"cpu"  # ensure determinism for the device-dependent torch.Generator
           lowerCamelCase             =		self.dummy_cond_unet_upscale
           lowerCamelCase             =		DDPMScheduler()
           lowerCamelCase             =		DDIMScheduler(prediction_type="v_prediction"     )
           lowerCamelCase             =		self.dummy_vae
           lowerCamelCase             =		self.dummy_text_encoder
           lowerCamelCase             =		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

           lowerCamelCase             =		self.dummy_image.cpu().permute(0					,      2					,      3					,      1     )[0]
           lowerCamelCase             =		Image.fromarray(np.uinta(__a     )     ).convert("RGB"     ).resize((64, 64)     )

           # make sure here that pndm scheduler skips prk
           lowerCamelCase             =		StableDiffusionUpscalePipeline(
               unet=__a					,      low_res_scheduler=__a					,      scheduler=__a					,      vae=__a					,      text_encoder=__a					,      tokenizer=__a					,      max_noise_level=3_50					,      )
           lowerCamelCase             =		sd_pipe.to(__a     )
           sd_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		"A painting of a squirrel eating a burger"
           lowerCamelCase             =		sd_pipe(
               2 * [prompt]					,      image=2 * [low_res_image]					,      guidance_scale=6.0					,      noise_level=20					,      num_inference_steps=2					,      output_type="np"					,      )
           lowerCamelCase             =		output.images
           assert image.shape[0] == 2

           lowerCamelCase             =		torch.Generator(device=__a     ).manual_seed(0     )
           lowerCamelCase             =		sd_pipe(
               [prompt]					,      image=__a					,      generator=__a					,      num_images_per_prompt=2					,      guidance_scale=6.0					,      noise_level=20					,      num_inference_steps=2					,      output_type="np"					,      )
           lowerCamelCase             =		output.images
           assert image.shape[0] == 2







     @unittest.skipIf(torch_device != "cuda"					,      "This test requires a GPU"     )
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.dummy_cond_unet_upscale
           lowerCamelCase             =		DDPMScheduler()
           lowerCamelCase             =		DDIMScheduler(prediction_type="v_prediction"     )
           lowerCamelCase             =		self.dummy_vae
           lowerCamelCase             =		self.dummy_text_encoder
           lowerCamelCase             =		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

           lowerCamelCase             =		self.dummy_image.cpu().permute(0					,      2					,      3					,      1     )[0]
           lowerCamelCase             =		Image.fromarray(np.uinta(__a     )     ).convert("RGB"     ).resize((64, 64)     )

           # put models in fp16, except vae as it overflows in fp16
           lowerCamelCase             =		unet.half()
           lowerCamelCase             =		text_encoder.half()

           # make sure here that pndm scheduler skips prk
           lowerCamelCase             =		StableDiffusionUpscalePipeline(
               unet=__a					,      low_res_scheduler=__a					,      scheduler=__a					,      vae=__a					,      text_encoder=__a					,      tokenizer=__a					,      max_noise_level=3_50					,      )
           lowerCamelCase             =		sd_pipe.to(__a     )
           sd_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		"A painting of a squirrel eating a burger"
           lowerCamelCase             =		torch.manual_seed(0     )
           lowerCamelCase             =		sd_pipe(
               [prompt]					,      image=__a					,      generator=__a					,      num_inference_steps=2					,      output_type="np"					,      ).images

           lowerCamelCase             =		low_res_image.size[0] * 4
           assert image.shape == (1, expected_height_width, expected_height_width, 3)





@slow
@require_torch_gpu
class  lowerCamelCase__	(    unittest.TestCase):
     """simple docstring"""



     def     _a   (self     ):






           '''simple docstring'''



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







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/sd2-upscale/low_res_cat.png"     )
           lowerCamelCase             =		load_numpy(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
               "/upsampled_cat.npy"     )

           lowerCamelCase             =		"stabilityai/stable-diffusion-x4-upscaler"
           lowerCamelCase             =		StableDiffusionUpscalePipeline.from_pretrained(__a     )
           pipe.to(__a     )
           pipe.set_progress_bar_config(disable=__a     )
           pipe.enable_attention_slicing()

           lowerCamelCase             =		"a cat sitting on a park bench"

           lowerCamelCase             =		torch.manual_seed(0     )
           lowerCamelCase             =		pipe(
               prompt=__a					,      image=__a					,      generator=__a					,      output_type="np"					,      )
           lowerCamelCase             =		output.images[0]

           assert image.shape == (5_12, 5_12, 3)
           assert np.abs(expected_image - image     ).max() < 1E-3







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/sd2-upscale/low_res_cat.png"     )
           lowerCamelCase             =		load_numpy(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
               "/upsampled_cat_fp16.npy"     )

           lowerCamelCase             =		"stabilityai/stable-diffusion-x4-upscaler"
           lowerCamelCase             =		StableDiffusionUpscalePipeline.from_pretrained(
               __a					,      torch_dtype=torch.floataa					,      )
           pipe.to(__a     )
           pipe.set_progress_bar_config(disable=__a     )
           pipe.enable_attention_slicing()

           lowerCamelCase             =		"a cat sitting on a park bench"

           lowerCamelCase             =		torch.manual_seed(0     )
           lowerCamelCase             =		pipe(
               prompt=__a					,      image=__a					,      generator=__a					,      output_type="np"					,      )
           lowerCamelCase             =		output.images[0]

           assert image.shape == (5_12, 5_12, 3)
           assert np.abs(expected_image - image     ).max() < 5E-1







     def     _a   (self     ):






           '''simple docstring'''



           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           lowerCamelCase             =		load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/sd2-upscale/low_res_cat.png"     )

           lowerCamelCase             =		"stabilityai/stable-diffusion-x4-upscaler"
           lowerCamelCase             =		StableDiffusionUpscalePipeline.from_pretrained(
               __a					,      torch_dtype=torch.floataa					,      )
           pipe.to(__a     )
           pipe.set_progress_bar_config(disable=__a     )
           pipe.enable_attention_slicing(1     )
           pipe.enable_sequential_cpu_offload()

           lowerCamelCase             =		"a cat sitting on a park bench"

           lowerCamelCase             =		torch.manual_seed(0     )
           lowerCamelCase             =		pipe(
               prompt=__a					,      image=__a					,      generator=__a					,      num_inference_steps=5					,      output_type="np"					,      )

           lowerCamelCase             =		torch.cuda.max_memory_allocated()
           # make sure that less than 2.9 GB is allocated
           assert mem_bytes < 2.9 * 10**9