






'''simple docstring'''

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       (  a_				:			List[str]     , a_				:			Dict     , a_				:			List[Any]     , a_				:			str     , a_				:			List[Any]						)							->					List[str]:





      """simple docstring"""

      with open(a_						) as metadata_file:
            UpperCamelCase_					: Union[str, Any]            =				json.load(a_						)
      UpperCamelCase_					: int            =				LukeConfig(use_entity_aware_attention=a_     , **metadata["""model_config"""]						)

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

      # Load the entity vocab file
      UpperCamelCase_					: Optional[Any]            =				load_entity_vocab(a_						)

      UpperCamelCase_					: Union[str, Any]            =				RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]						)

      # Add special tokens to the token vocabulary for downstream tasks
      UpperCamelCase_					: Optional[int]            =				AddedToken("""<ent>"""     , lstrip=a_     , rstrip=a_						)
      UpperCamelCase_					: int            =				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_     , LukeTokenizer.vocab_files_names["""entity_vocab_file"""]						)     , """w"""						) as f:
            json.dump(a_     , a_						)

      UpperCamelCase_					: Optional[int]            =				LukeTokenizer.from_pretrained(a_						)

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

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
      UpperCamelCase_					: List[Any]            =				state_dict["""entity_embeddings.entity_embeddings.weight"""]
      UpperCamelCase_					: str            =				entity_emb[entity_vocab["""[MASK]"""]]

      UpperCamelCase_					: Optional[Any]            =				LukeModel(config=a_						).eval()

      UpperCamelCase_     ,      UpperCamelCase_					: Union[str, Any]            =				model.load_state_dict(a_     , strict=a_						)
      if not (len(a_						) == 1 and missing_keys[0] == "embeddings.position_ids"):
            raise ValueError(f"Missing keys {', '.join(a_						)}. 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
      UpperCamelCase_					: Dict            =				LukeTokenizer.from_pretrained(a_     , task="""entity_classification"""						)

      UpperCamelCase_					: Optional[int]            =				(
          """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 ."""
      )
      UpperCamelCase_					: str            =				(39, 42)
      UpperCamelCase_					: Dict            =				tokenizer(a_     , entity_spans=[span]     , add_prefix_space=a_     , return_tensors="""pt"""						)

      UpperCamelCase_					: List[Any]            =				model(**a_						)

      # Verify word hidden states
      if model_size == "large":
            UpperCamelCase_					: Optional[Any]            =				torch.Size((1, 42, 1_024)						)
            UpperCamelCase_					: str            =				torch.tensor(
                [[0.0_133, 0.0_865, 0.0_095], [0.3_093, -0.2_576, -0.7_418], [-0.1_720, -0.2_117, -0.2_869]]						)
      else:  # base
            UpperCamelCase_					: List[Any]            =				torch.Size((1, 42, 768)						)
            UpperCamelCase_					: Optional[Any]            =				torch.tensor([[0.0_037, 0.1_368, -0.0_091], [0.1_099, 0.3_329, -0.1_095], [0.0_765, 0.5_335, 0.1_179]]						)

      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":
            UpperCamelCase_					: Union[str, Any]            =				torch.Size((1, 1, 1_024)						)
            UpperCamelCase_					: int            =				torch.tensor([[0.0_466, -0.0_106, -0.0_179]]						)
      else:  # base
            UpperCamelCase_					: Union[str, Any]            =				torch.Size((1, 1, 768)						)
            UpperCamelCase_					: int            =				torch.tensor([[0.1_457, 0.1_044, 0.0_174]]						)

      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

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



def 				snake_case       (  a_				:			Optional[Any]						)							->					Any:





      """simple docstring"""

      UpperCamelCase_					: Optional[Any]            =				{}
      with open(a_     , """r"""     , encoding="""utf-8"""						) as f:
            for index, line in enumerate(a_						):
                  UpperCamelCase_     ,      UpperCamelCase_					: Optional[Any]            =				line.rstrip().split("""\t"""						)
                  UpperCamelCase_					: str            =				index

      return entity_vocab


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














'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class      A     (   unittest.TestCase    ):






 """simple docstring"""





 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)
       UpperCamelCase_					: int            =				UNetaDModel(
           block_out_channels=(32, 64)      ,  layers_per_block=2      ,  sample_size=32      ,  in_channels=3      ,  out_channels=3      ,  down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")      ,  up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")      ,  )
       return model





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Union[str, Any]            =				self.dummy_uncond_unet
       UpperCamelCase_					: List[str]            =				PNDMScheduler()

       UpperCamelCase_					: Dict            =				PNDMPipeline(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)
       pndm.to(__lowerCAmelCase			)
       pndm.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: Union[str, Any]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Optional[int]            =				pndm(generator=__lowerCAmelCase      ,  num_inference_steps=20      ,  output_type="""numpy"""			).images

       UpperCamelCase_					: Optional[int]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Tuple            =				pndm(generator=__lowerCAmelCase      ,  num_inference_steps=20      ,  output_type="""numpy"""      ,  return_dict=__lowerCAmelCase			)[0]

       UpperCamelCase_					: Union[str, Any]            =				image[0, -3:, -3:, -1]
       UpperCamelCase_					: int            =				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase_					: Optional[int]            =				np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]			)

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






@slow
@require_torch
class      A     (   unittest.TestCase    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				"""google/ddpm-cifar10-32"""

       UpperCamelCase_					: List[str]            =				UNetaDModel.from_pretrained(__lowerCAmelCase			)
       UpperCamelCase_					: int            =				PNDMScheduler()

       UpperCamelCase_					: Any            =				PNDMPipeline(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)
       pndm.to(__lowerCAmelCase			)
       pndm.set_progress_bar_config(disable=__lowerCAmelCase			)
       UpperCamelCase_					: Optional[int]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Optional[int]            =				pndm(generator=__lowerCAmelCase      ,  output_type="""numpy"""			).images

       UpperCamelCase_					: Tuple            =				image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase_					: Union[str, Any]            =				np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]			)

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






