

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       __snake_case						( __UpperCamelCase	: Tuple				,__UpperCamelCase	: Dict				,__UpperCamelCase	: Union[str, Any]				,__UpperCamelCase	: List[Any]				,__UpperCamelCase	: List[Any]    ):



      """simple docstring"""

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

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

      # Load the entity vocab file
      A_						  =				load_entity_vocab(__UpperCamelCase    )

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

      # Add special tokens to the token vocabulary for downstream tasks
      A_						  =				AddedToken("<ent>"				,lstrip=__UpperCamelCase				,rstrip=__UpperCamelCase    )
      A_						  =				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    )

      A_						  =				LukeTokenizer.from_pretrained(__UpperCamelCase    )

      # Initialize the embeddings of the special tokens
      A_						  =				state_dict["embeddings.word_embeddings.weight"]
      A_						  =				word_emb[tokenizer.convert_tokens_to_ids(["@"]    )[0]].unsqueeze(0    )
      A_						  =				word_emb[tokenizer.convert_tokens_to_ids(["#"]    )[0]].unsqueeze(0    )
      A_						  =				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"]:
                  A_						  =				f'''encoder.layer.{layer_index}.attention.self.'''
                  A_						  =				state_dict[prefix + matrix_name]
                  A_						  =				state_dict[prefix + matrix_name]
                  A_						  =				state_dict[prefix + matrix_name]

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

      A_						  =				LukeModel(config=__UpperCamelCase    ).eval()

      A_  ,     A_						  =				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
      A_						  =				LukeTokenizer.from_pretrained(__UpperCamelCase				,task="entity_classification"    )

      A_						  =				(
          "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 ."
      )
      A_						  =				(39, 42)
      A_						  =				tokenizer(__UpperCamelCase				,entity_spans=[span]				,add_prefix_space=__UpperCamelCase				,return_tensors="pt"    )

      A_						  =				model(**__UpperCamelCase    )

      # Verify word hidden states
      if model_size == "large":
            A_						  =				torch.Size((1, 42, 1024)    )
            A_						  =				torch.tensor(
                [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]    )
      else:  # base
            A_						  =				torch.Size((1, 42, 768)    )
            A_						  =				torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]    )

      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":
            A_						  =				torch.Size((1, 1, 1024)    )
            A_						  =				torch.tensor([[0.0466, -0.0106, -0.0179]]    )
      else:  # base
            A_						  =				torch.Size((1, 1, 768)    )
            A_						  =				torch.tensor([[0.1457, 0.1044, 0.0174]]    )

      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       __snake_case						( __UpperCamelCase	: str    ):



      """simple docstring"""

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

      return entity_vocab


if __name__ == "__main__":
      __a    :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.'
      )
      __a    :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 random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class    UpperCAmelCase__(				lowerCamelCase  ,				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	str												=     IFImgaImgSuperResolutionPipeline
   A     :	Dict												=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
   A     :	Any												=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"original_image"}						)
   A     :	str												=     PipelineTesterMixin.required_optional_params - {"latents"}


   def       UpperCAmelCase		(    self	:      Dict)						->					Dict:




         """simple docstring"""

         return self._get_superresolution_dummy_components()


   def       UpperCAmelCase		(    self	:      Optional[Any]      ,					lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      Union[str, Any]=0)						->					str:




         """simple docstring"""

         if str(lowerCAmelCase).startswith('mps'):
               lowercase__  =	torch.manual_seed(lowerCAmelCase)
         else:
               lowercase__  =	torch.Generator(device=lowerCAmelCase).manual_seed(lowerCAmelCase)

         lowercase__  =	floats_tensor((1, 3, 32, 32)      ,					rng=random.Random(lowerCAmelCase)).to(lowerCAmelCase)
         lowercase__  =	floats_tensor((1, 3, 16, 16)      ,					rng=random.Random(lowerCAmelCase)).to(lowerCAmelCase)

         lowercase__  =	{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'original_image': original_image,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs


   @unittest.skipIf(
       torch_device != 'cuda' or not is_xformers_available()      ,					reason='XFormers attention is only available with CUDA and `xformers` installed'      ,					)
   def       UpperCAmelCase		(    self	:      str)						->					Dict:




         """simple docstring"""

         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)


   def       UpperCAmelCase		(    self	:      List[Any])						->					Dict:




         """simple docstring"""

         self._test_save_load_optional_components()


   @unittest.skipIf(torch_device != 'cuda'      ,					reason='float16 requires CUDA')
   def       UpperCAmelCase		(    self	:      Optional[Any])						->					List[Any]:




         """simple docstring"""

         super().test_save_load_floataa(expected_max_diff=1E-1)


   def       UpperCAmelCase		(    self	:      str)						->					Union[str, Any]:




         """simple docstring"""

         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)


   def       UpperCAmelCase		(    self	:      Tuple)						->					Tuple:




         """simple docstring"""

         self._test_save_load_local()



   def       UpperCAmelCase		(    self	:      List[str])						->					Tuple:




         """simple docstring"""

         self._test_inference_batch_single_identical(
             expected_max_diff=1E-2      ,					)
