





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       SCREAMING_SNAKE_CASE__  (       _lowercase     : Union[str, Any]   ,    _lowercase     : Dict   ,    _lowercase     : Any   ,    _lowercase     : Dict   ,    _lowercase     : int			)   ->							List[str]:




		'''simple docstring'''
		# Load configuration defined in the metadata file
		with open(_lowercase			) as metadata_file:
				lowercase__					:      Dict      =		json.load(_lowercase			)
		lowercase__					:      Tuple      =		LukeConfig(use_entity_aware_attention=_lowercase   ,    **metadata['model_config']			)

		# Load in the weights from the checkpoint_path
		lowercase__					:      str      =		torch.load(_lowercase   ,    map_location='cpu'			)

		# Load the entity vocab file
		lowercase__					:      Dict      =		load_entity_vocab(_lowercase			)

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

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

		lowercase__					:      Optional[int]      =		LukeTokenizer.from_pretrained(_lowercase			)

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

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

		lowercase__					:      str      =		LukeModel(config=_lowercase			).eval()

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

		lowercase__					:      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 .'
		)
		lowercase__					:      Tuple      =		(39, 42)
		lowercase__					:      int      =		tokenizer(_lowercase   ,    entity_spans=[span]   ,    add_prefix_space=_lowercase   ,    return_tensors='pt'			)

		lowercase__					:      List[Any]      =		model(**_lowercase			)

		# Verify word hidden states
		if model_size == "large":
				lowercase__					:      Optional[int]      =		torch.Size((1, 42, 1_024)			)
				lowercase__					:      Union[str, Any]      =		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
				lowercase__					:      Optional[Any]      =		torch.Size((1, 42, 768)			)
				lowercase__					:      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]   ,    _lowercase   ,    atol=1e-4			):
				raise ValueError

		# Verify entity hidden states
		if model_size == "large":
				lowercase__					:      Dict      =		torch.Size((1, 1, 1_024)			)
				lowercase__					:      Optional[Any]      =		torch.tensor([[0.0_466, -0.0_106, -0.0_179]]			)
		else:  # base
				lowercase__					:      Optional[Any]      =		torch.Size((1, 1, 768)			)
				lowercase__					:      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]   ,    _lowercase   ,    atol=1e-4			):
				raise ValueError

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


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Union[str, Any]			)   ->							List[str]:




		'''simple docstring'''
		lowercase__					:      List[str]      =		{}
		with open(_lowercase   ,    'r'   ,    encoding='utf-8'			) as f:
				for index, line in enumerate(_lowercase			):
						lowercase__   ,				lowercase__					:      Optional[Any]      =		line.rstrip().split('\t'			)
						lowercase__					:      Tuple      =		index

		return entity_vocab


if __name__ == "__main__":
			__UpperCamelCase:	Tuple = 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:	Optional[int] = 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 argparse
import os
import re


__UpperCamelCase:	Any = """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__UpperCamelCase:	Dict = re.compile(r"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
__UpperCamelCase:	List[str] = re.compile(r"""\s*\(\s*\"(\S[^\"]+)\"""")


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : bool = False			)   ->							List[str]:




		'''simple docstring'''
		with open(_lowercase   ,    'r'   ,    encoding='utf-8'			) as f:
				lowercase__					:      Union[str, Any]      =		f.read()

		lowercase__					:      Optional[Any]      =		content.split('\n'			)
		lowercase__					:      Optional[int]      =		[]
		lowercase__					:      int      =		0
		while line_idx < len(_lowercase			):
				if _re_intro_mapping.search(lines[line_idx]			) is not None:
						lowercase__					:      Tuple      =		len(re.search(r'^(\s*)\S'   ,    lines[line_idx]			).groups()[0]			) + 8
						# Start of a new mapping!
						while not lines[line_idx].startswith(' ' * indent + '('			):
								new_lines.append(lines[line_idx]			)
								line_idx += 1

						lowercase__					:      Tuple      =		[]
						while lines[line_idx].strip() != "]":
								# Blocks either fit in one line or not
								if lines[line_idx].strip() == "(":
										lowercase__					:      Any      =		line_idx
										while not lines[line_idx].startswith(' ' * indent + ')'			):
												line_idx += 1
										blocks.append('\n'.join(lines[start_idx : line_idx + 1]			)			)
								else:
										blocks.append(lines[line_idx]			)
								line_idx += 1

						# Sort blocks by their identifiers
						lowercase__					:      List[str]      =		sorted(_lowercase   ,    key=lambda _lowercase			: _re_identifier.search(_lowercase			).groups()[0]			)
						new_lines += blocks
				else:
						new_lines.append(lines[line_idx]			)
						line_idx += 1

		if overwrite:
				with open(_lowercase   ,    'w'   ,    encoding='utf-8'			) as f:
						f.write('\n'.join(_lowercase			)			)
		elif "\n".join(_lowercase			) != content:
				return True


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : bool = False			)   ->							List[Any]:




		'''simple docstring'''
		lowercase__					:      List[Any]      =		[os.path.join(_lowercase   ,    _lowercase			) for f in os.listdir(_lowercase			) if f.endswith('.py'			)]
		lowercase__					:      str      =		[sort_auto_mapping(_lowercase   ,    overwrite=_lowercase			) for fname in fnames]

		if not overwrite and any(_lowercase			):
				lowercase__					:      List[Any]      =		[f for f, d in zip(_lowercase   ,    _lowercase			) if d]
				raise ValueError(
				    f"""The following files have auto mappings that need sorting: {", ".join(_lowercase			)}. Run `make style` to fix"""
				    ' this.'			)


if __name__ == "__main__":
			__UpperCamelCase:	Tuple = argparse.ArgumentParser()
			parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
			__UpperCamelCase:	List[Any] = parser.parse_args()

			sort_all_auto_mappings(not args.check_only)


