






import argparse
import json
import os
from collections import OrderedDict

import torch

from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken





@torch.no_grad()
def 					lowercase_ (    __snake_case     :							str	,	__snake_case     :							Optional[Any]	,	__snake_case     :							Union[str, Any]	,	__snake_case     :							int	,	__snake_case     :							str      )     -> List[Any]:





				'''simple docstring'''


				with open(__snake_case      ) as metadata_file:
								snake_case__      :str         =   json.load(__snake_case      )
				snake_case__      :Optional[Any]         =   LukeConfig(use_entity_aware_attention=__snake_case	,	**metadata["model_config"]      )

				# Load in the weights from the checkpoint_path
				snake_case__      :Any         =   torch.load(__snake_case	,	map_location="cpu"      )["module"]

				# Load the entity vocab file
				snake_case__      :Optional[int]         =   load_original_entity_vocab(__snake_case      )
				# add an entry for [MASK2]
				snake_case__      :Any         =   max(entity_vocab.values()      ) + 1
				config.entity_vocab_size += 1

				snake_case__      :Dict         =   XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]      )

				# Add special tokens to the token vocabulary for downstream tasks
				snake_case__      :List[Any]         =   AddedToken("<ent>"	,	lstrip=__snake_case	,	rstrip=__snake_case      )
				snake_case__      :List[Any]         =   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	,	"tokenizer_config.json"      )	,	"r"      ) as f:
								snake_case__      :str         =   json.load(__snake_case      )
				snake_case__      :Optional[Any]         =   "MLukeTokenizer"
				with open(os.path.join(__snake_case	,	"tokenizer_config.json"      )	,	"w"      ) as f:
								json.dump(__snake_case	,	__snake_case      )

				with open(os.path.join(__snake_case	,	MLukeTokenizer.vocab_files_names["entity_vocab_file"]      )	,	"w"      ) as f:
								json.dump(__snake_case	,	__snake_case      )

				snake_case__      :List[Any]         =   MLukeTokenizer.from_pretrained(__snake_case      )

				# Initialize the embeddings of the special tokens
				snake_case__      :List[str]         =   tokenizer.convert_tokens_to_ids(["@"]      )[0]
				snake_case__      :Optional[Any]         =   tokenizer.convert_tokens_to_ids(["#"]      )[0]

				snake_case__      :Tuple         =   state_dict["embeddings.word_embeddings.weight"]
				snake_case__      :Dict         =   word_emb[ent_init_index].unsqueeze(0      )
				snake_case__      :Union[str, Any]         =   word_emb[enta_init_index].unsqueeze(0      )
				snake_case__      :Any         =   torch.cat([word_emb, ent_emb, enta_emb]      )
				# add special tokens for 'entity_predictions.bias'
				for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
								snake_case__      :Dict         =   state_dict[bias_name]
								snake_case__      :Optional[int]         =   decoder_bias[ent_init_index].unsqueeze(0      )
								snake_case__      :str         =   decoder_bias[enta_init_index].unsqueeze(0      )
								snake_case__      :Dict         =   torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias]      )

				# 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"]:
												snake_case__      :int         =   F'encoder.layer.{layer_index}.attention.self.'
												snake_case__      :Dict         =   state_dict[prefix + matrix_name]
												snake_case__      :Optional[Any]         =   state_dict[prefix + matrix_name]
												snake_case__      :Tuple         =   state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
				snake_case__      :List[str]         =   state_dict["entity_embeddings.entity_embeddings.weight"]
				snake_case__      :Dict         =   entity_emb[entity_vocab["[MASK]"]].unsqueeze(0      )
				snake_case__      :List[Any]         =   torch.cat([entity_emb, entity_mask_emb]      )
				# add [MASK2] for 'entity_predictions.bias'
				snake_case__      :Union[str, Any]         =   state_dict["entity_predictions.bias"]
				snake_case__      :Tuple         =   entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0      )
				snake_case__      :Union[str, Any]         =   torch.cat([entity_prediction_bias, entity_mask_bias]      )

				snake_case__      :List[Any]         =   LukeForMaskedLM(config=__snake_case      ).eval()

				state_dict.pop("entity_predictions.decoder.weight"      )
				state_dict.pop("lm_head.decoder.weight"      )
				state_dict.pop("lm_head.decoder.bias"      )
				snake_case__      :str         =   OrderedDict()
				for key, value in state_dict.items():
								if not (key.startswith("lm_head"      ) or key.startswith("entity_predictions"      )):
												snake_case__      :Tuple         =   state_dict[key]
								else:
												snake_case__      :Optional[int]         =   state_dict[key]

				snake_case__    ,						snake_case__      :str         =   model.load_state_dict(__snake_case	,	strict=__snake_case      )

				if set(__snake_case      ) != {"luke.embeddings.position_ids"}:
								raise ValueError(F'Unexpected unexpected_keys: {unexpected_keys}'      )
				if set(__snake_case      ) != {
				    "lm_head.decoder.weight",
				    "lm_head.decoder.bias",
				    "entity_predictions.decoder.weight",
				}:
								raise ValueError(F'Unexpected missing_keys: {missing_keys}'      )

				model.tie_weights()
				assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
				assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()

				# Check outputs
				snake_case__      :int         =   MLukeTokenizer.from_pretrained(__snake_case	,	task="entity_classification"      )

				snake_case__      :Any         =   "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
				snake_case__      :str         =   (0, 9)
				snake_case__      :Optional[Any]         =   tokenizer(__snake_case	,	entity_spans=[span]	,	return_tensors="pt"      )

				snake_case__      :List[Any]         =   model(**__snake_case      )

				# Verify word hidden states
				if model_size == "large":
								raise NotImplementedError
				else:  # base
								snake_case__      :int         =   torch.Size((1, 33, 7_68)      )
								snake_case__      :List[Any]         =   torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]]      )

				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":
								raise NotImplementedError
				else:  # base
								snake_case__      :int         =   torch.Size((1, 1, 7_68)      )
								snake_case__      :Optional[int]         =   torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]]      )

				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

				# Verify masked word/entity prediction
				snake_case__      :Union[str, Any]         =   MLukeTokenizer.from_pretrained(__snake_case      )
				snake_case__      :Optional[int]         =   "Tokyo is the capital of <mask>."
				snake_case__      :List[Any]         =   (24, 30)
				snake_case__      :Any         =   tokenizer(__snake_case	,	entity_spans=[span]	,	return_tensors="pt"      )

				snake_case__      :Optional[Any]         =   model(**__snake_case      )

				snake_case__      :int         =   encoding["input_ids"][0].tolist()
				snake_case__      :Optional[int]         =   input_ids.index(tokenizer.convert_tokens_to_ids("<mask>"      )      )
				snake_case__      :Tuple         =   outputs.logits[0][mask_position_id].argmax(dim=-1      )
				assert "Japan" == tokenizer.decode(__snake_case      )

				snake_case__      :Optional[Any]         =   outputs.entity_logits[0][0].argmax().item()
				snake_case__      :List[Any]         =   [
				    entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
				]
				assert [e for e in multilingual_predicted_entities if e.startswith("en:"      )][0] == "en:Japan"

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





def 					lowercase_ (    __snake_case     :							Any      )     -> Optional[Any]:





				'''simple docstring'''


				snake_case__      :List[Any]         =   ["[MASK]", "[PAD]", "[UNK]"]

				snake_case__      :List[Any]         =   [json.loads(__snake_case      ) for line in open(__snake_case      )]

				snake_case__      :Tuple         =   {}
				for entry in data:
								snake_case__      :str         =   entry["id"]
								for entity_name, language in entry["entities"]:
												if entity_name in SPECIAL_TOKENS:
																snake_case__      :List[str]         =   entity_id
																break
												snake_case__      :Optional[Any]         =   F'{language}:{entity_name}'
												snake_case__      :Dict         =   entity_id
				return new_mapping


if __name__ == "__main__":
	__UpperCAmelCase							:   Union[str, Any]							=  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							:   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 os
import sys
import unittest


__UpperCAmelCase							:   str							=  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase							:   Tuple							=  os.path.join(git_repo_path, "src", "diffusers")


class 					_snake_case						(			unittest.TestCase ):






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					snake_case__      :Tuple         =   find_backend("    if not is_torch_available():"       )
					self.assertEqual(UpperCamelCase					,"torch"       )

					# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
					# self.assertEqual(backend_with_underscore, "tensorflow_text")

					snake_case__      :Tuple         =   find_backend("    if not (is_torch_available() and is_transformers_available()):"       )
					self.assertEqual(UpperCamelCase					,"torch_and_transformers"       )

					# double_backend_with_underscore = find_backend(
					#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
					# )
					# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

					snake_case__      :str         =   find_backend(
					    "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"       )
					self.assertEqual(UpperCamelCase					,"torch_and_transformers_and_onnx"       )






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :int         =   read_init()
					# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
					self.assertIn("torch"					,UpperCamelCase       )
					self.assertIn("torch_and_transformers"					,UpperCamelCase       )
					self.assertIn("flax_and_transformers"					,UpperCamelCase       )
					self.assertIn("torch_and_transformers_and_onnx"					,UpperCamelCase       )

					# Likewise, we can't assert on the exact content of a key
					self.assertIn("UNet2DModel"					,objects["torch"]       )
					self.assertIn("FlaxUNet2DConditionModel"					,objects["flax"]       )
					self.assertIn("StableDiffusionPipeline"					,objects["torch_and_transformers"]       )
					self.assertIn("FlaxStableDiffusionPipeline"					,objects["flax_and_transformers"]       )
					self.assertIn("LMSDiscreteScheduler"					,objects["torch_and_scipy"]       )
					self.assertIn("OnnxStableDiffusionPipeline"					,objects["torch_and_transformers_and_onnx"]       )






	def    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :Union[str, Any]         =   create_dummy_object("CONSTANT"					,"'torch'"       )
					self.assertEqual(UpperCamelCase					,"\nCONSTANT = None\n"       )

					snake_case__      :Optional[Any]         =   create_dummy_object("function"					,"'torch'"       )
					self.assertEqual(
					    UpperCamelCase					,"\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"       )

					snake_case__      :str         =   "\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n"
					snake_case__      :List[str]         =   create_dummy_object("FakeClass"					,"'torch'"       )
					self.assertEqual(UpperCamelCase					,UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :Tuple         =   "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n"
					snake_case__      :int         =   create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}       )
					self.assertEqual(dummy_files["torch"]					,UpperCamelCase       )