

"""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       lowercase__						(       snake_case_   :Optional[int]			,		snake_case_   :str			,		snake_case_   :str			,		snake_case_   :Union[str, Any]			,		snake_case_   :Tuple    ):
	# Load configuration defined in the metadata file
	with open(snake_case_    ) as metadata_file:
		__UpperCAmelCase      		= json.load(snake_case_    )
	__UpperCAmelCase      		= LukeConfig(use_entity_aware_attention=snake_case_			,		**metadata['''model_config''']    )

	# Load in the weights from the checkpoint_path
	__UpperCAmelCase      		= torch.load(snake_case_			,		map_location='''cpu'''    )

	# Load the entity vocab file
	__UpperCAmelCase      		= load_entity_vocab(snake_case_    )

	__UpperCAmelCase      		= RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']    )

	# Add special tokens to the token vocabulary for downstream tasks
	__UpperCAmelCase      		= AddedToken('''<ent>'''			,		lstrip=snake_case_			,		rstrip=snake_case_    )
	__UpperCAmelCase      		= AddedToken('''<ent2>'''			,		lstrip=snake_case_			,		rstrip=snake_case_    )
	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(snake_case_    )
	with open(os.path.join(snake_case_			,		LukeTokenizer.vocab_files_names['''entity_vocab_file''']    )			,		'''w'''    ) as f:
		json.dump(snake_case_			,		snake_case_    )

	__UpperCAmelCase      		= LukeTokenizer.from_pretrained(snake_case_    )

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

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

	__UpperCAmelCase      		= LukeModel(config=snake_case_    ).eval()

	__UpperCAmelCase		,							__UpperCAmelCase      		= model.load_state_dict(snake_case_			,		strict=snake_case_    )
	if not (len(snake_case_    ) == 1 and missing_keys[0] == "embeddings.position_ids"):
		raise ValueError(F'''Missing keys {", ".join(snake_case_    )}. 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      		= LukeTokenizer.from_pretrained(snake_case_			,		task='''entity_classification'''    )

	__UpperCAmelCase      		= (
	    '''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      		= (39, 42)
	__UpperCAmelCase      		= tokenizer(snake_case_			,		entity_spans=[span]			,		add_prefix_space=snake_case_			,		return_tensors='''pt'''    )

	__UpperCAmelCase      		= model(**snake_case_    )

	# Verify word hidden states
	if model_size == "large":
		__UpperCAmelCase      		= torch.Size((1, 42, 1_024)    )
		__UpperCAmelCase      		= torch.tensor(
		    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]    )
	else:  # base
		__UpperCAmelCase      		= torch.Size((1, 42, 768)    )
		__UpperCAmelCase      		= 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]			,		snake_case_			,		atol=1E-4    ):
		raise ValueError

	# Verify entity hidden states
	if model_size == "large":
		__UpperCAmelCase      		= torch.Size((1, 1, 1_024)    )
		__UpperCAmelCase      		= torch.tensor([[0.0466, -0.0106, -0.0179]]    )
	else:  # base
		__UpperCAmelCase      		= torch.Size((1, 1, 768)    )
		__UpperCAmelCase      		= 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]			,		snake_case_			,		atol=1E-4    ):
		raise ValueError

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





def       lowercase__						(       snake_case_   :Optional[Any]    ):
	__UpperCAmelCase      		= {}
	with open(snake_case_			,		'''r'''			,		encoding='''utf-8'''    ) as f:
		for index, line in enumerate(snake_case_    ):
			__UpperCAmelCase		,							__UpperCAmelCase      		= line.rstrip().split('''\t'''    )
			__UpperCAmelCase      		= index

	return entity_vocab


if __name__ == "__main__":
				_lowercase   :	str												=						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   :	Any												=						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 os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


SCREAMING_SNAKE_CASE__     					=			datasets.logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__     					=			"\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n"

SCREAMING_SNAKE_CASE__     					=			"\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project's README at https://github.com/google-research/bleurt#readme for more information.\n"

SCREAMING_SNAKE_CASE__     					=			"\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    'scores': List of scores.\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bleurt = datasets.load_metric(\"bleurt\")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [1.03, 1.04]\n"

SCREAMING_SNAKE_CASE__     					=			{
    "bleurt-tiny-128": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip",
    "bleurt-tiny-512": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip",
    "bleurt-base-128": "https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip",
    "bleurt-base-512": "https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip",
    "bleurt-large-128": "https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip",
    "bleurt-large-512": "https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip",
    "BLEURT-20-D3": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip",
    "BLEURT-20-D6": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip",
    "BLEURT-20-D12": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip",
    "BLEURT-20": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip",
}



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,		_KWARGS_DESCRIPTION							)
class 						_UpperCAmelCase      (     datasets.Metric							):






		def _snake_case		(						self				:     List[str]):
				return datasets.MetricInfo(
				    description=_DESCRIPTION   ,  citation=_CITATION   ,  homepage="https://github.com/google-research/bleurt"   ,  inputs_description=_KWARGS_DESCRIPTION   ,  features=datasets.Features(
				        {
				            "predictions": datasets.Value("string"   ,  id="sequence"),
				            "references": datasets.Value("string"   ,  id="sequence"),
				        })   ,  codebase_urls=["https://github.com/google-research/bleurt"]   ,  reference_urls=["https://github.com/google-research/bleurt", "https://arxiv.org/abs/2004.04696"]   ,  )






		def _snake_case		(						self				:     Any   ,  UpperCAmelCase				:     Any):
				# check that config name specifies a valid BLEURT model
				if self.config_name == "default":
						logger.warning(
						    "Using default BLEURT-Base checkpoint for sequence maximum length 128. "
						    "You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').")
						SCREAMING_SNAKE_CASE_				:str				     =						"bleurt-base-128"

				if self.config_name.lower() in CHECKPOINT_URLS:
						SCREAMING_SNAKE_CASE_				:Dict				     =						self.config_name.lower()

				elif self.config_name.upper() in CHECKPOINT_URLS:
						SCREAMING_SNAKE_CASE_				:Dict				     =						self.config_name.upper()

				else:
						raise KeyError(
						    F"{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}")

				# download the model checkpoint specified by self.config_name and set up the scorer
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])
				SCREAMING_SNAKE_CASE_				:Dict				     =						score.BleurtScorer(os.path.join(UpperCAmelCase   ,  UpperCAmelCase))







		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     List[str]   ,  UpperCAmelCase				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.scorer.score(references=UpperCAmelCase   ,  candidates=UpperCAmelCase)
				return {"scores": scores}






