

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 	a (     A__      :  Optional[Any] ,  A__      :  str ,  A__      :  Optional[Any] ,  A__      :  Union[str, Any] ,  A__      :  List[Any]	)       -> Optional[Any]:





      """simple docstring"""



      with open(A__	) as metadata_file:
            _lowercase      			=json.load(A__	)
      _lowercase      			=LukeConfig(use_entity_aware_attention=A__ ,  **metadata['model_config']	)

      # Load in the weights from the checkpoint_path
      _lowercase      			=torch.load(A__ ,  map_location='cpu'	)['module']

      # Load the entity vocab file
      _lowercase      			=load_original_entity_vocab(A__	)
      # add an entry for [MASK2]
      _lowercase      			=max(entity_vocab.values()	) + 1
      config.entity_vocab_size += 1

      _lowercase      			=XLMRobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']	)

      # Add special tokens to the token vocabulary for downstream tasks
      _lowercase      			=AddedToken('<ent>' ,  lstrip=A__ ,  rstrip=A__	)
      _lowercase      			=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:
            _lowercase      			=json.load(A__	)
      _lowercase      			='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__	)

      _lowercase      			=MLukeTokenizer.from_pretrained(A__	)

      # Initialize the embeddings of the special tokens
      _lowercase      			=tokenizer.convert_tokens_to_ids(['@']	)[0]
      _lowercase      			=tokenizer.convert_tokens_to_ids(['#']	)[0]

      _lowercase      			=state_dict['embeddings.word_embeddings.weight']
      _lowercase      			=word_emb[ent_init_index].unsqueeze(0	)
      _lowercase      			=word_emb[enta_init_index].unsqueeze(0	)
      _lowercase      			=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"]:
            _lowercase      			=state_dict[bias_name]
            _lowercase      			=decoder_bias[ent_init_index].unsqueeze(0	)
            _lowercase      			=decoder_bias[enta_init_index].unsqueeze(0	)
            _lowercase      			=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"]:
                  _lowercase      			=F'''encoder.layer.{layer_index}.attention.self.'''
                  _lowercase      			=state_dict[prefix + matrix_name]
                  _lowercase      			=state_dict[prefix + matrix_name]
                  _lowercase      			=state_dict[prefix + matrix_name]

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

      _lowercase      			=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'	)
      _lowercase      			=OrderedDict()
      for key, value in state_dict.items():
            if not (key.startswith('lm_head'	) or key.startswith('entity_predictions'	)):
                  _lowercase      			=state_dict[key]
            else:
                  _lowercase      			=state_dict[key]

      _lowercase			,			_lowercase      			=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
      _lowercase      			=MLukeTokenizer.from_pretrained(A__ ,  task='entity_classification'	)

      _lowercase      			='ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).'
      _lowercase      			=(0, 9)
      _lowercase      			=tokenizer(A__ ,  entity_spans=[span] ,  return_tensors='pt'	)

      _lowercase      			=model(**A__	)

      # Verify word hidden states
      if model_size == "large":
            raise NotImplementedError
      else:  # base
            _lowercase      			=torch.Size((1, 33, 768)	)
            _lowercase      			=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
            _lowercase      			=torch.Size((1, 1, 768)	)
            _lowercase      			=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
      _lowercase      			=MLukeTokenizer.from_pretrained(A__	)
      _lowercase      			='Tokyo is the capital of <mask>.'
      _lowercase      			=(24, 30)
      _lowercase      			=tokenizer(A__ ,  entity_spans=[span] ,  return_tensors='pt'	)

      _lowercase      			=model(**A__	)

      _lowercase      			=encoding['input_ids'][0].tolist()
      _lowercase      			=input_ids.index(tokenizer.convert_tokens_to_ids('<mask>'	)	)
      _lowercase      			=outputs.logits[0][mask_position_id].argmax(dim=-1	)
      assert "Japan" == tokenizer.decode(A__	)

      _lowercase      			=outputs.entity_logits[0][0].argmax().item()
      _lowercase      			=[
          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 	a (     A__      :  Any	)       -> Tuple:





      """simple docstring"""



      _lowercase      			=['[MASK]', '[PAD]', '[UNK]']

      _lowercase      			=[json.loads(A__	) for line in open(A__	)]

      _lowercase      			={}
      for entry in data:
            _lowercase      			=entry['id']
            for entity_name, language in entry["entities"]:
                  if entity_name in SPECIAL_TOKENS:
                        _lowercase      			=entity_id
                        break
                  _lowercase      			=F'''{language}:{entity_name}'''
                  _lowercase      			=entity_id
      return new_mapping


if __name__ == "__main__":
  lowercase_   							=  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.'
  )
  lowercase_   							=  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 transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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				):




  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






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




  @property
  def     A__	(							self   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=1
        _lowercase      			=3
        _lowercase      			=(32, 32)

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




  @property
  def     A__	(							self   )    ->					Union[str, Any]:
        '''simple docstring'''






        torch.manual_seed(0   )
        _lowercase      			=UNetaDConditionModel(
            block_out_channels=(32, 64)				,  layers_per_block=2				,  sample_size=32				,  in_channels=4				,  out_channels=4				,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,  up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				,  cross_attention_dim=32				,  )
        return model




  @property
  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






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




  @property
  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        torch.manual_seed(0   )
        _lowercase      			=RobertaSeriesConfig(
            hidden_size=32				,  project_dim=32				,  intermediate_size=37				,  layer_norm_eps=1e-05				,  num_attention_heads=4				,  num_hidden_layers=5				,  pad_token_id=1				,  vocab_size=5_006				,  )
        return RobertaSeriesModelWithTransformation(lowerCAmelCase   )




  @property
  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        def extract(*lowerCAmelCase				,  **lowerCAmelCase   ):





              class 					__lowerCAmelCase	:




                def __init__(							self   )    ->					Union[str, Any]:
                      '''simple docstring'''






                      _lowercase      			=torch.ones([0]   )




                def     A__	(							self				,  lowerCAmelCase   )    ->					Dict:
                      '''simple docstring'''






                      self.pixel_values.to(lowerCAmelCase   )
                      return self
              return Out()

        return extract




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        _lowercase      			='cpu'  # ensure determinism for the device-dependent torch.Generator
        _lowercase      			=self.dummy_cond_unet
        _lowercase      			=PNDMScheduler(skip_prk_steps=lowerCAmelCase   )
        _lowercase      			=self.dummy_vae
        _lowercase      			=self.dummy_text_encoder
        _lowercase      			=XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'   )
        _lowercase      			=77

        _lowercase      			=self.dummy_image.to(lowerCAmelCase   )
        _lowercase      			=init_image / 2 + 0.5

        # make sure here that pndm scheduler skips prk
        _lowercase      			=AltDiffusionImgaImgPipeline(
            unet=lowerCAmelCase				,  scheduler=lowerCAmelCase				,  vae=lowerCAmelCase				,  text_encoder=lowerCAmelCase				,  tokenizer=lowerCAmelCase				,  safety_checker=lowerCAmelCase				,  feature_extractor=self.dummy_extractor				,  )
        _lowercase      			=VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor				,  do_normalize=lowerCAmelCase   )
        _lowercase      			=alt_pipe.to(lowerCAmelCase   )
        alt_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

        _lowercase      			='A painting of a squirrel eating a burger'
        _lowercase      			=torch.Generator(device=lowerCAmelCase   ).manual_seed(0   )
        _lowercase      			=alt_pipe(
            [prompt]				,  generator=lowerCAmelCase				,  guidance_scale=6.0				,  num_inference_steps=2				,  output_type='np'				,  image=lowerCAmelCase				,  )

        _lowercase      			=output.images

        _lowercase      			=torch.Generator(device=lowerCAmelCase   ).manual_seed(0   )
        _lowercase      			=alt_pipe(
            [prompt]				,  generator=lowerCAmelCase				,  guidance_scale=6.0				,  num_inference_steps=2				,  output_type='np'				,  image=lowerCAmelCase				,  return_dict=lowerCAmelCase				,  )[0]

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

        assert image.shape == (1, 32, 32, 3)
        _lowercase      			=np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]   )

        assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5e-3
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 5e-3




  @unittest.skipIf(torch_device != 'cuda'				,  'This test requires a GPU'   )
  def     A__	(							self   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=self.dummy_cond_unet
        _lowercase      			=PNDMScheduler(skip_prk_steps=lowerCAmelCase   )
        _lowercase      			=self.dummy_vae
        _lowercase      			=self.dummy_text_encoder
        _lowercase      			=XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'   )
        _lowercase      			=77

        _lowercase      			=self.dummy_image.to(lowerCAmelCase   )

        # put models in fp16
        _lowercase      			=unet.half()
        _lowercase      			=vae.half()
        _lowercase      			=bert.half()

        # make sure here that pndm scheduler skips prk
        _lowercase      			=AltDiffusionImgaImgPipeline(
            unet=lowerCAmelCase				,  scheduler=lowerCAmelCase				,  vae=lowerCAmelCase				,  text_encoder=lowerCAmelCase				,  tokenizer=lowerCAmelCase				,  safety_checker=lowerCAmelCase				,  feature_extractor=self.dummy_extractor				,  )
        _lowercase      			=VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor				,  do_normalize=lowerCAmelCase   )
        _lowercase      			=alt_pipe.to(lowerCAmelCase   )
        alt_pipe.set_progress_bar_config(disable=lowerCAmelCase   )

        _lowercase      			='A painting of a squirrel eating a burger'
        _lowercase      			=torch.manual_seed(0   )
        _lowercase      			=alt_pipe(
            [prompt]				,  generator=lowerCAmelCase				,  num_inference_steps=2				,  output_type='np'				,  image=lowerCAmelCase				,  ).images

        assert image.shape == (1, 32, 32, 3)




  @unittest.skipIf(torch_device != 'cuda'				,  'This test requires a GPU'   )
  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        _lowercase      			=load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/img2img/sketch-mountains-input.jpg'   )
        # resize to resolution that is divisible by 8 but not 16 or 32
        _lowercase      			=init_image.resize((760, 504)   )

        _lowercase      			='BAAI/AltDiffusion'
        _lowercase      			=AltDiffusionImgaImgPipeline.from_pretrained(
            lowerCAmelCase				,  safety_checker=lowerCAmelCase				,  )
        pipe.to(lowerCAmelCase   )
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )
        pipe.enable_attention_slicing()

        _lowercase      			='A fantasy landscape, trending on artstation'

        _lowercase      			=torch.manual_seed(0   )
        _lowercase      			=pipe(
            prompt=lowerCAmelCase				,  image=lowerCAmelCase				,  strength=0.75				,  guidance_scale=7.5				,  generator=lowerCAmelCase				,  output_type='np'				,  )
        _lowercase      			=output.images[0]

        _lowercase      			=image[255:258, 383:386, -1]

        assert image.shape == (504, 760, 3)
        _lowercase      			=np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]   )

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





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




  def     A__	(							self   )    ->					Union[str, Any]:
        '''simple docstring'''






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




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        _lowercase      			=load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/img2img/sketch-mountains-input.jpg'   )
        _lowercase      			=init_image.resize((768, 512)   )
        _lowercase      			=load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'   )

        _lowercase      			='BAAI/AltDiffusion'
        _lowercase      			=AltDiffusionImgaImgPipeline.from_pretrained(
            lowerCAmelCase				,  safety_checker=lowerCAmelCase				,  )
        pipe.to(lowerCAmelCase   )
        pipe.set_progress_bar_config(disable=lowerCAmelCase   )
        pipe.enable_attention_slicing()

        _lowercase      			='A fantasy landscape, trending on artstation'

        _lowercase      			=torch.manual_seed(0   )
        _lowercase      			=pipe(
            prompt=lowerCAmelCase				,  image=lowerCAmelCase				,  strength=0.75				,  guidance_scale=7.5				,  generator=lowerCAmelCase				,  output_type='np'				,  )
        _lowercase      			=output.images[0]

        assert image.shape == (512, 768, 3)
        # img2img is flaky across GPUs even in fp32, so using MAE here
        assert np.abs(expected_image - image   ).max() < 1e-2


