


"""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       lowerCamelCase					(a_						:List[str]				,				a_						:Any				,				a_						:Any				,				a_						:str				,				a_						:Optional[Any])     ->							Optional[Any]:
       # Load configuration defined in the metadata file
       with open(a_) as metadata_file:
              lowercase		:List[str]       							=				json.load(a_)
       lowercase		:Optional[Any]       							=				LukeConfig(use_entity_aware_attention=a_				,				**metadata['''model_config'''])

       # Load in the weights from the checkpoint_path
       lowercase		:Dict       							=				torch.load(a_				,				map_location='''cpu''')

       # Load the entity vocab file
       lowercase		:Dict       							=				load_entity_vocab(a_)

       lowercase		:int       							=				RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name'''])

       # Add special tokens to the token vocabulary for downstream tasks
       lowercase		:List[str]       							=				AddedToken('''<ent>'''				,				lstrip=a_				,				rstrip=a_)
       lowercase		:Optional[Any]       							=				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_)

       lowercase		:Optional[Any]       							=				LukeTokenizer.from_pretrained(a_)

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

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
       lowercase		:Any       							=				state_dict['''entity_embeddings.entity_embeddings.weight''']
       lowercase		:Tuple       							=				entity_emb[entity_vocab['''[MASK]''']]

       lowercase		:Union[str, Any]       							=				LukeModel(config=a_).eval()

       lowercase							,  lowercase		:List[str]       							=				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
       lowercase		:str       							=				LukeTokenizer.from_pretrained(a_				,				task='''entity_classification''')

       lowercase		:str       							=				(
           '''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 .'''
       )
       lowercase		:str       							=				(39, 42)
       lowercase		:Any       							=				tokenizer(a_				,				entity_spans=[span]				,				add_prefix_space=a_				,				return_tensors='''pt''')

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

       # Verify word hidden states
       if model_size == "large":
              lowercase		:str       							=				torch.Size((1, 42, 1024))
              lowercase		:Any       							=				torch.tensor(
                  [[0.01_33, 0.08_65, 0.00_95], [0.30_93, -0.25_76, -0.74_18], [-0.17_20, -0.21_17, -0.28_69]])
       else:  # base
              lowercase		:Union[str, Any]       							=				torch.Size((1, 42, 768))
              lowercase		:Tuple       							=				torch.tensor([[0.00_37, 0.13_68, -0.00_91], [0.10_99, 0.33_29, -0.10_95], [0.07_65, 0.53_35, 0.11_79]])

       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":
              lowercase		:Union[str, Any]       							=				torch.Size((1, 1, 1024))
              lowercase		:Any       							=				torch.tensor([[0.04_66, -0.01_06, -0.01_79]])
       else:  # base
              lowercase		:str       							=				torch.Size((1, 1, 768))
              lowercase		:int       							=				torch.tensor([[0.14_57, 0.10_44, 0.01_74]])

       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       lowerCamelCase					(a_						:Union[str, Any])     ->							Any:
       lowercase		:Union[str, Any]       							=				{}
       with open(a_				,				'''r'''				,				encoding='''utf-8''') as f:
              for index, line in enumerate(a_):
                     lowercase							,  lowercase		:str       							=				line.rstrip().split('''\t''')
                     lowercase		:Tuple       							=				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

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
 import jax
@require_flax
class 					__magic_name__		(			__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      str        =   FlaxAutoencoderKL






       @property
       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase		:Dict       							=				4
              lowercase		:Optional[int]       							=				3
              lowercase		:Any       							=				(3_2, 3_2)

              lowercase		:Optional[int]       							=				jax.random.PRNGKey(0				)
              lowercase		:Any       							=				jax.random.uniform(snake_case__  ,     ((batch_size, num_channels) + sizes)				)

              return {"sample": image, "prng_key": prng_key}







       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				{
                  '''block_out_channels''': [3_2, 6_4],
                  '''in_channels''': 3,
                  '''out_channels''': 3,
                  '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
                  '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
                  '''latent_channels''': 4,
              }
              lowercase		:Optional[int]       							=				self.dummy_input
              return init_dict, inputs_dict

