
import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.9.0"""):
							raise Exception("""requires fairseq >= 0.9.0""")


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__				=							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__				=							"""Hello world! cécé herlolip"""




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: str       ,				SCREAMING_SNAKE_CASE			: str       ,				SCREAMING_SNAKE_CASE			: bool     )	->		Union[str, Any]:
				__lowercase								=   FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE     )
				roberta.eval()  # disable dropout
				__lowercase								=   roberta.model.encoder.sentence_encoder
				__lowercase								=   RobertaConfig(
				    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings       ,				hidden_size=roberta.args.encoder_embed_dim       ,				num_hidden_layers=roberta.args.encoder_layers       ,				num_attention_heads=roberta.args.encoder_attention_heads       ,				intermediate_size=roberta.args.encoder_ffn_embed_dim       ,				max_position_embeddings=514       ,				type_vocab_size=1       ,				layer_norm_eps=1E-5       ,				)
				if classification_head:
								__lowercase								=   roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
				print('Our BERT config:'       ,				SCREAMING_SNAKE_CASE     )

				__lowercase								=   RobertaForSequenceClassification(SCREAMING_SNAKE_CASE     ) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE     )
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				__lowercase								=   roberta_sent_encoder.embed_tokens.weight
				__lowercase								=   roberta_sent_encoder.embed_positions.weight
				__lowercase								=   torch.zeros_like(
				    model.roberta.embeddings.token_type_embeddings.weight     )  # just zero them out b/c RoBERTa doesn't use them.
				__lowercase								=   roberta_sent_encoder.emb_layer_norm.weight
				__lowercase								=   roberta_sent_encoder.emb_layer_norm.bias

				for i in range(config.num_hidden_layers     ):
								# Encoder: start of layer
								__lowercase								=   model.roberta.encoder.layer[i]
								__lowercase								=   roberta_sent_encoder.layers[i]

								# self attention
								__lowercase								=   layer.attention.self
								assert (
								    roberta_layer.self_attn.k_proj.weight.data.shape
								    == roberta_layer.self_attn.q_proj.weight.data.shape
								    == roberta_layer.self_attn.v_proj.weight.data.shape
								    == torch.Size((config.hidden_size, config.hidden_size)     )
								)

								__lowercase								=   roberta_layer.self_attn.q_proj.weight
								__lowercase								=   roberta_layer.self_attn.q_proj.bias
								__lowercase								=   roberta_layer.self_attn.k_proj.weight
								__lowercase								=   roberta_layer.self_attn.k_proj.bias
								__lowercase								=   roberta_layer.self_attn.v_proj.weight
								__lowercase								=   roberta_layer.self_attn.v_proj.bias

								# self-attention output
								__lowercase								=   layer.attention.output
								assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
								__lowercase								=   roberta_layer.self_attn.out_proj.weight
								__lowercase								=   roberta_layer.self_attn.out_proj.bias
								__lowercase								=   roberta_layer.self_attn_layer_norm.weight
								__lowercase								=   roberta_layer.self_attn_layer_norm.bias

								# intermediate
								__lowercase								=   layer.intermediate
								assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
								__lowercase								=   roberta_layer.fca.weight
								__lowercase								=   roberta_layer.fca.bias

								# output
								__lowercase								=   layer.output
								assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
								__lowercase								=   roberta_layer.fca.weight
								__lowercase								=   roberta_layer.fca.bias
								__lowercase								=   roberta_layer.final_layer_norm.weight
								__lowercase								=   roberta_layer.final_layer_norm.bias
								# end of layer

				if classification_head:
								__lowercase								=   roberta.model.classification_heads['mnli'].dense.weight
								__lowercase								=   roberta.model.classification_heads['mnli'].dense.bias
								__lowercase								=   roberta.model.classification_heads['mnli'].out_proj.weight
								__lowercase								=   roberta.model.classification_heads['mnli'].out_proj.bias
				else:
								# LM Head
								__lowercase								=   roberta.model.encoder.lm_head.dense.weight
								__lowercase								=   roberta.model.encoder.lm_head.dense.bias
								__lowercase								=   roberta.model.encoder.lm_head.layer_norm.weight
								__lowercase								=   roberta.model.encoder.lm_head.layer_norm.bias
								__lowercase								=   roberta.model.encoder.lm_head.weight
								__lowercase								=   roberta.model.encoder.lm_head.bias

				# Let's check that we get the same results.
				__lowercase								=   roberta.encode(SCREAMING_SNAKE_CASE     ).unsqueeze(0     )  # batch of size 1

				__lowercase								=   model(SCREAMING_SNAKE_CASE     )[0]
				if classification_head:
								__lowercase								=   roberta.model.classification_heads['mnli'](roberta.extract_features(SCREAMING_SNAKE_CASE     )     )
				else:
								__lowercase								=   roberta.model(SCREAMING_SNAKE_CASE     )[0]
				print(our_output.shape       ,				their_output.shape     )
				__lowercase								=   torch.max(torch.abs(our_output - their_output     )     ).item()
				print(F"""max_absolute_diff = {max_absolute_diff}"""     )  # ~ 1e-7
				__lowercase								=   torch.allclose(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE       ,				atol=1E-3     )
				print('Do both models output the same tensors?'       ,				'🔥' if success else '💩'     )
				if not success:
								raise Exception('Something went wRoNg'     )

				pathlib.Path(SCREAMING_SNAKE_CASE     ).mkdir(parents=SCREAMING_SNAKE_CASE       ,				exist_ok=SCREAMING_SNAKE_CASE     )
				print(F"""Saving model to {pytorch_dump_folder_path}"""     )
				model.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__				=							argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--roberta_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							parser.add_argument(
							    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
							)
							SCREAMING_SNAKE_CASE__				=							parser.parse_args()
							convert_roberta_checkpoint_to_pytorch(
							    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)







import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


SCREAMING_SNAKE_CASE__				=							logging.getLogger(__name__)
@dataclass
class       A__  :
							lowerCAmelCase__						:		str            =				field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}							)
							lowerCAmelCase__						:		Optional[str]            =				field(
							    default=lowerCAmelCase__       ,		metadata={"help": "Pretrained config name or path if not the same as model_name"}							)
							lowerCAmelCase__						:		Optional[str]            =				field(
							    default="NER"       ,		metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}							)
							lowerCAmelCase__						:		Optional[str]            =				field(
							    default=lowerCAmelCase__       ,		metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}							)
							lowerCAmelCase__						:		bool            =				field(default=lowerCAmelCase__       ,		metadata={"help": "Set this flag to use fast tokenization."}							)
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							lowerCAmelCase__						:		Optional[str]            =				field(
							    default=lowerCAmelCase__       ,		metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,		)






@dataclass
class       A__  :
							lowerCAmelCase__						:		str            =				field(
							    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}							)
							lowerCAmelCase__						:		Optional[str]            =				field(
							    metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}							)
							lowerCAmelCase__						:		int            =				field(
							    default=128       ,		metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    }       ,		)
							lowerCAmelCase__						:		bool            =				field(
							    default=lowerCAmelCase__       ,		metadata={"help": "Overwrite the cached training and evaluation sets"}							)






def 				__SCREAMING_SNAKE_CASE  (						)	->		int:
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.
				__lowercase								=   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)     )
				__lowercase						,   __lowercase						,   __lowercase								=   parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir     )
				    and os.listdir(training_args.output_dir     )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
								    ' --overwrite_output_dir to overcome.'     )

				__lowercase								=   import_module('tasks'     )

				try:
								__lowercase								=   getattr(SCREAMING_SNAKE_CASE       ,				model_args.task_type     )
								__lowercase								=   token_classification_task_clazz()
				except AttributeError:
								raise ValueError(
								    F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
								    F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""     )

				# Setup logging
				logging.basicConfig(
				    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'       ,				datefmt='%m/%d/%Y %H:%M:%S'       ,				level=logging.INFO       ,				)
				logger.info(
				    'n_replicas: %s, distributed training: %s, 16-bits training: %s'       ,				training_args.n_replicas       ,				bool(training_args.n_replicas > 1     )       ,				training_args.fpaa       ,				)
				logger.info('Training/evaluation parameters %s'       ,				SCREAMING_SNAKE_CASE     )

				# Prepare Token Classification task
				__lowercase								=   token_classification_task.get_labels(data_args.labels     )
				__lowercase								=   dict(enumerate(SCREAMING_SNAKE_CASE     )     )
				__lowercase								=   len(SCREAMING_SNAKE_CASE     )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				__lowercase								=   AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,				num_labels=SCREAMING_SNAKE_CASE       ,				idalabel=SCREAMING_SNAKE_CASE       ,				labelaid={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE     )}       ,				cache_dir=model_args.cache_dir       ,				)
				__lowercase								=   AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,				cache_dir=model_args.cache_dir       ,				use_fast=model_args.use_fast       ,				)

				with training_args.strategy.scope():
								__lowercase								=   TFAutoModelForTokenClassification.from_pretrained(
								    model_args.model_name_or_path       ,				from_pt=bool('.bin' in model_args.model_name_or_path     )       ,				config=SCREAMING_SNAKE_CASE       ,				cache_dir=model_args.cache_dir       ,				)

				# Get datasets
				__lowercase								=   (
				    TFTokenClassificationDataset(
				        token_classification_task=SCREAMING_SNAKE_CASE       ,				data_dir=data_args.data_dir       ,				tokenizer=SCREAMING_SNAKE_CASE       ,				labels=SCREAMING_SNAKE_CASE       ,				model_type=config.model_type       ,				max_seq_length=data_args.max_seq_length       ,				overwrite_cache=data_args.overwrite_cache       ,				mode=Split.train       ,				)
				    if training_args.do_train
				    else None
				)
				__lowercase								=   (
				    TFTokenClassificationDataset(
				        token_classification_task=SCREAMING_SNAKE_CASE       ,				data_dir=data_args.data_dir       ,				tokenizer=SCREAMING_SNAKE_CASE       ,				labels=SCREAMING_SNAKE_CASE       ,				model_type=config.model_type       ,				max_seq_length=data_args.max_seq_length       ,				overwrite_cache=data_args.overwrite_cache       ,				mode=Split.dev       ,				)
				    if training_args.do_eval
				    else None
				)

				def align_predictions(SCREAMING_SNAKE_CASE			: np.ndarray       ,				SCREAMING_SNAKE_CASE			: np.ndarray     ) -> Tuple[List[int], List[int]]:
								__lowercase								=   np.argmax(SCREAMING_SNAKE_CASE       ,				axis=2     )
								__lowercase						,   __lowercase								=   preds.shape
								__lowercase								=   [[] for _ in range(SCREAMING_SNAKE_CASE     )]
								__lowercase								=   [[] for _ in range(SCREAMING_SNAKE_CASE     )]

								for i in range(SCREAMING_SNAKE_CASE     ):
												for j in range(SCREAMING_SNAKE_CASE     ):
																if label_ids[i, j] != -100:
																				out_label_list[i].append(label_map[label_ids[i][j]]     )
																				preds_list[i].append(label_map[preds[i][j]]     )

								return preds_list, out_label_list

				def compute_metrics(SCREAMING_SNAKE_CASE			: EvalPrediction     ) -> Dict:
								__lowercase						,   __lowercase								=   align_predictions(p.predictions       ,				p.label_ids     )

								return {
								    "precision": precision_score(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     ),
								    "recall": recall_score(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     ),
								    "f1": fa_score(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     ),
								}

				# Initialize our Trainer
				__lowercase								=   TFTrainer(
				    model=SCREAMING_SNAKE_CASE       ,				args=SCREAMING_SNAKE_CASE       ,				train_dataset=train_dataset.get_dataset() if train_dataset else None       ,				eval_dataset=eval_dataset.get_dataset() if eval_dataset else None       ,				compute_metrics=SCREAMING_SNAKE_CASE       ,				)

				# Training
				if training_args.do_train:
								trainer.train()
								trainer.save_model()
								tokenizer.save_pretrained(training_args.output_dir     )

				# Evaluation
				__lowercase								=   {}
				if training_args.do_eval:
								logger.info('*** Evaluate ***'     )

								__lowercase								=   trainer.evaluate()
								__lowercase								=   os.path.join(training_args.output_dir       ,				'eval_results.txt'     )

								with open(SCREAMING_SNAKE_CASE       ,				'w'     ) as writer:
												logger.info('***** Eval results *****'     )

												for key, value in result.items():
																logger.info('  %s = %s'       ,				SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
																writer.write('%s = %s\n' % (key, value)     )

												results.update(SCREAMING_SNAKE_CASE     )

    # Predict
				if training_args.do_predict:
								__lowercase								=   TFTokenClassificationDataset(
								    token_classification_task=SCREAMING_SNAKE_CASE       ,				data_dir=data_args.data_dir       ,				tokenizer=SCREAMING_SNAKE_CASE       ,				labels=SCREAMING_SNAKE_CASE       ,				model_type=config.model_type       ,				max_seq_length=data_args.max_seq_length       ,				overwrite_cache=data_args.overwrite_cache       ,				mode=Split.test       ,				)

								__lowercase						,   __lowercase						,   __lowercase								=   trainer.predict(test_dataset.get_dataset()     )
								__lowercase						,   __lowercase								=   align_predictions(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
								__lowercase								=   classification_report(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )

								logger.info('\n%s'       ,				SCREAMING_SNAKE_CASE     )

								__lowercase								=   os.path.join(training_args.output_dir       ,				'test_results.txt'     )

								with open(SCREAMING_SNAKE_CASE       ,				'w'     ) as writer:
												writer.write('%s\n' % report     )

								# Save predictions
								__lowercase								=   os.path.join(training_args.output_dir       ,				'test_predictions.txt'     )

								with open(SCREAMING_SNAKE_CASE       ,				'w'     ) as writer:
												with open(os.path.join(data_args.data_dir       ,				'test.txt'     )       ,				'r'     ) as f:
																__lowercase								=   0

																for line in f:
																				if line.startswith('-DOCSTART-'     ) or line == "" or line == "\n":
																								writer.write(SCREAMING_SNAKE_CASE     )

																								if not preds_list[example_id]:
																												example_id += 1
																				elif preds_list[example_id]:
																								__lowercase								=   line.split()[0] + ' ' + preds_list[example_id].pop(0     ) + '\n'

																								writer.write(SCREAMING_SNAKE_CASE     )
																				else:
																								logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.'       ,				line.split()[0]     )

				return results


if __name__ == "__main__":
							main()





