

"""simple docstring"""


import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser






def    a__   (   __SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None			)     ->     str:
		return field(default_factory=lambda: default ,			metadata=__SCREAMING_SNAKE_CASE			)





@dataclass
class    snake_case     :
				SCREAMING_SNAKE_CASE_       :				str       				=  field(
				    metadata={"""help""": """The csv file to plot."""},							)
				SCREAMING_SNAKE_CASE_       :				bool       				=  field(
				    default=__snake_case,							metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""},							)
				SCREAMING_SNAKE_CASE_       :				bool       				=  field(
				    default=__snake_case,							metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""},							)
				SCREAMING_SNAKE_CASE_       :				bool       				=  field(
				    default=__snake_case,							metadata={"""help""": """Disable logarithmic scale when plotting"""},							)
				SCREAMING_SNAKE_CASE_       :				bool       				=  field(
				    default=__snake_case,							metadata={
				        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
				    },							)
				SCREAMING_SNAKE_CASE_       :				Optional[str]       				=  field(
				    default=__snake_case,							metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""},							)
				SCREAMING_SNAKE_CASE_       :				Optional[List[str]]       				=  list_field(
				    default=__snake_case,							metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}  )






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Any:
		try:
				int(__SCREAMING_SNAKE_CASE			)
				return True
		except ValueError:
				return False






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     List[Any]:
		try:
				float(__SCREAMING_SNAKE_CASE			)
				return True
		except ValueError:
				return False





class    snake_case     :

				def __init__(     self							:    Optional[Any]					,  UpperCamelCase__							:    List[Any])->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      args
						__lowerCAmelCase:	int   				=      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}})

						with open(self.args.csv_file					,  newline="") as csv_file:
								__lowerCAmelCase:	Union[str, Any]   				=      csv.DictReader(UpperCamelCase__)
								for row in reader:
										__lowerCAmelCase:	List[str]   				=      row["model"]
										self.result_dict[model_name]["bsz"].append(int(row["batch_size"]))
										self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]))
										if can_convert_to_int(row["result"]):
												# value is not None
												__lowerCAmelCase:	List[str]   				=      int(row["result"])
										elif can_convert_to_float(row["result"]):
												# value is not None
												__lowerCAmelCase:	int   				=      float(row["result"])




				def 							lowercase_					(     self							:    int)->       Tuple:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      plt.subplots()
						__lowerCAmelCase:	List[Any]   				=      "Time usage" if self.args.is_time else "Memory usage"
						__lowerCAmelCase:	List[str]   				=      title_str + " for training" if self.args.is_train else title_str + " for inference"

						if not self.args.no_log_scale:
								# set logarithm scales
								ax.set_xscale("log")
								ax.set_yscale("log")

						for axis in [ax.xaxis, ax.yaxis]:
								axis.set_major_formatter(ScalarFormatter())

						for model_name_idx, model_name in enumerate(self.result_dict.keys()):
								__lowerCAmelCase:	Tuple   				=      sorted(set(self.result_dict[model_name]["bsz"]))
								__lowerCAmelCase:	List[Any]   				=      sorted(set(self.result_dict[model_name]["seq_len"]))
								__lowerCAmelCase:	Optional[int]   				=      self.result_dict[model_name]["result"]

								(__lowerCAmelCase):	str   				=      (
								    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
								)

								__lowerCAmelCase:	int   				=      (
								    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
								)

								for inner_loop_value in inner_loop_array:
										if self.args.plot_along_batch:
												__lowerCAmelCase:	Dict   				=      np.asarray(
												    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]					,  dtype=UpperCamelCase__					,  )
										else:
												__lowerCAmelCase:	List[Any]   				=      np.asarray(
												    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]					,  dtype=np.floataa					,  )

										(__lowerCAmelCase):	Union[str, Any]   				=      (
										    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
										)

										__lowerCAmelCase:	int   				=      np.asarray(UpperCamelCase__					,  UpperCamelCase__)[: len(UpperCamelCase__)]
										plt.scatter(
										    UpperCamelCase__					,  UpperCamelCase__					,  label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}")
										plt.plot(UpperCamelCase__					,  UpperCamelCase__					,  "--")

								title_str += f" {label_model_name} vs."

						__lowerCAmelCase:	int   				=      title_str[:-4]
						__lowerCAmelCase:	Dict   				=      "Time in s" if self.args.is_time else "Memory in MB"

						# plot
						plt.title(UpperCamelCase__)
						plt.xlabel(UpperCamelCase__)
						plt.ylabel(UpperCamelCase__)
						plt.legend()

						if self.args.figure_png_file is not None:
								plt.savefig(self.args.figure_png_file)
						else:
								plt.show()






def    a__   (   )     ->     Optional[int]:
		__lowerCAmelCase:	Union[str, Any]   				=      HfArgumentParser(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Union[str, Any]   				=      parser.parse_args_into_dataclasses()[0]
		__lowerCAmelCase:	Any   				=      Plot(args=__SCREAMING_SNAKE_CASE			)
		plot.plot()


if __name__ == "__main__":
			main()





"""simple docstring"""


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    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Any:
		# Load configuration defined in the metadata file
		with open(__SCREAMING_SNAKE_CASE			) as metadata_file:
				__lowerCAmelCase:	List[Any]   				=      json.load(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Dict   				=      LukeConfig(use_entity_aware_attention=__SCREAMING_SNAKE_CASE ,			**metadata["model_config"]			)

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

		# Load the entity vocab file
		__lowerCAmelCase:	List[Any]   				=      load_original_entity_vocab(__SCREAMING_SNAKE_CASE			)
		# add an entry for [MASK2]
		__lowerCAmelCase:	Any   				=      max(entity_vocab.values()			) + 1
		config.entity_vocab_size += 1

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

		# Add special tokens to the token vocabulary for downstream tasks
		__lowerCAmelCase:	str   				=      AddedToken("<ent>" ,			lstrip=__SCREAMING_SNAKE_CASE ,			rstrip=__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Optional[int]   				=      AddedToken("<ent2>" ,			lstrip=__SCREAMING_SNAKE_CASE ,			rstrip=__SCREAMING_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(__SCREAMING_SNAKE_CASE			)
		with open(os.path.join(__SCREAMING_SNAKE_CASE ,			"tokenizer_config.json"			) ,			"r"			) as f:
				__lowerCAmelCase:	Optional[int]   				=      json.load(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Any   				=      "MLukeTokenizer"
		with open(os.path.join(__SCREAMING_SNAKE_CASE ,			"tokenizer_config.json"			) ,			"w"			) as f:
				json.dump(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)

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

		__lowerCAmelCase:	Tuple   				=      MLukeTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			)

		# Initialize the embeddings of the special tokens
		__lowerCAmelCase:	Union[str, Any]   				=      tokenizer.convert_tokens_to_ids(["@"]			)[0]
		__lowerCAmelCase:	Optional[int]   				=      tokenizer.convert_tokens_to_ids(["#"]			)[0]

		__lowerCAmelCase:	Dict   				=      state_dict["embeddings.word_embeddings.weight"]
		__lowerCAmelCase:	Optional[int]   				=      word_emb[ent_init_index].unsqueeze(0			)
		__lowerCAmelCase:	int   				=      word_emb[enta_init_index].unsqueeze(0			)
		__lowerCAmelCase:	str   				=      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"]:
				__lowerCAmelCase:	Dict   				=      state_dict[bias_name]
				__lowerCAmelCase:	Union[str, Any]   				=      decoder_bias[ent_init_index].unsqueeze(0			)
				__lowerCAmelCase:	Optional[int]   				=      decoder_bias[enta_init_index].unsqueeze(0			)
				__lowerCAmelCase:	Optional[int]   				=      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"]:
						__lowerCAmelCase:	Optional[int]   				=      F"encoder.layer.{layer_index}.attention.self."
						__lowerCAmelCase:	Tuple   				=      state_dict[prefix + matrix_name]
						__lowerCAmelCase:	Dict   				=      state_dict[prefix + matrix_name]
						__lowerCAmelCase:	Optional[Any]   				=      state_dict[prefix + matrix_name]

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

		__lowerCAmelCase:	Optional[int]   				=      LukeForMaskedLM(config=__SCREAMING_SNAKE_CASE			).eval()

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

		__lowerCAmelCase				,			__lowerCAmelCase:	Tuple   				=      model.load_state_dict(__SCREAMING_SNAKE_CASE ,			strict=__SCREAMING_SNAKE_CASE			)

		if set(__SCREAMING_SNAKE_CASE			) != {"luke.embeddings.position_ids"}:
				raise ValueError(F"Unexpected unexpected_keys: {unexpected_keys}"			)
		if set(__SCREAMING_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
		__lowerCAmelCase:	Tuple   				=      MLukeTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE ,			task="entity_classification"			)

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

		__lowerCAmelCase:	int   				=      model(**__SCREAMING_SNAKE_CASE			)

		# Verify word hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				__lowerCAmelCase:	Dict   				=      torch.Size((1, 3_3, 7_6_8)			)
				__lowerCAmelCase:	Optional[int]   				=      torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]]			)

		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] ,			__SCREAMING_SNAKE_CASE ,			atol=1E-4			):
				raise ValueError

		# Verify entity hidden states
		if model_size == "large":
				raise NotImplementedError
		else:  # base
				__lowerCAmelCase:	Union[str, Any]   				=      torch.Size((1, 1, 7_6_8)			)
				__lowerCAmelCase:	Any   				=      torch.tensor([[-0.1482, 0.0609, 0.0322]]			)

		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] ,			__SCREAMING_SNAKE_CASE ,			atol=1E-4			):
				raise ValueError

		# Verify masked word/entity prediction
		__lowerCAmelCase:	Tuple   				=      MLukeTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	List[Any]   				=      "Tokyo is the capital of <mask>."
		__lowerCAmelCase:	List[str]   				=      (2_4, 3_0)
		__lowerCAmelCase:	int   				=      tokenizer(__SCREAMING_SNAKE_CASE ,			entity_spans=[span] ,			return_tensors="pt"			)

		__lowerCAmelCase:	Union[str, Any]   				=      model(**__SCREAMING_SNAKE_CASE			)

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

		__lowerCAmelCase:	Any   				=      outputs.entity_logits[0][0].argmax().item()
		__lowerCAmelCase:	Union[str, 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(__SCREAMING_SNAKE_CASE			)			)
		model.save_pretrained(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Any:
		__lowerCAmelCase:	Tuple   				=      ["[MASK]", "[PAD]", "[UNK]"]

		__lowerCAmelCase:	Optional[Any]   				=      [json.loads(__SCREAMING_SNAKE_CASE			) for line in open(__SCREAMING_SNAKE_CASE			)]

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


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


