import logging
import os
import sys
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 accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A_	     =   logging.getLogger(__name__)






@dataclass
class   __lowercase       :
						lowercase       			=  field(
						    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}     )
						lowercase       			=  field(
						    default=lowercase_							,  metadata={'help': 'Pretrained config name or path if not the same as model_name'}     )
						lowercase       			=  field(
						    default='NER'							,  metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'}     )
						lowercase       			=  field(
						    default=lowercase_							,  metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}     )
						lowercase       			=  field(default=lowercase_							,  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.
						lowercase       			=  field(
						    default=lowercase_							,  metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}							,  )






@dataclass
class   __lowercase       :
						lowercase       			=  field(
						    metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}     )
						lowercase       			=  field(
						    default=lowercase_							,  metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}							,  )
						lowercase       			=  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.'
						        )
						    }							,  )
						lowercase       			=  field(
						    default=lowercase_							,  metadata={'help': 'Overwrite the cached training and evaluation sets'}     )






def 							__UpperCAmelCase     (							)->							Tuple:



			"""simple docstring"""







			lowercase       		=		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
			if len(sys.argv							) == 2 and sys.argv[1].endswith('''.json'''							):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						lowercase		,lowercase		,lowercase       		=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
			else:
						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(__lowercase,		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 if training_args.local_rank in [-1, 0] else logging.WARN,		)
			logger.warning(
			    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''',		training_args.local_rank,		training_args.device,		training_args.n_gpu,		bool(training_args.local_rank != -1							),		training_args.fpaa,		)
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank							):
						transformers.utils.logging.set_verbosity_info()
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
			logger.info('''Training/evaluation parameters %s''',		__lowercase							)

			# Set seed
			set_seed(training_args.seed							)

			# Prepare CONLL-2003 task
			lowercase       		=		token_classification_task.get_labels(data_args.labels							)
			lowercase       		=		dict(enumerate(__lowercase							)							)
			lowercase       		=		len(__lowercase							)

			# 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=__lowercase,		idalabel=__lowercase,		labelaid={label: i for i, label in enumerate(__lowercase							)},		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,		)
			lowercase       		=		AutoModelForTokenClassification.from_pretrained(
			    model_args.model_name_or_path,		from_tf=bool('''.ckpt''' in model_args.model_name_or_path							),		config=__lowercase,		cache_dir=model_args.cache_dir,		)

			# Get datasets
			lowercase       		=		(
			    TokenClassificationDataset(
			        token_classification_task=__lowercase,		data_dir=data_args.data_dir,		tokenizer=__lowercase,		labels=__lowercase,		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       		=		(
			    TokenClassificationDataset(
			        token_classification_task=__lowercase,		data_dir=data_args.data_dir,		tokenizer=__lowercase,		labels=__lowercase,		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(UpperCAmelCase,		UpperCAmelCase							) -> Tuple[List[int], List[int]]:
						lowercase       		=		np.argmax(__lowercase,		axis=2							)

						lowercase		,lowercase       		=		preds.shape

						lowercase       		=		[[] for _ in range(__lowercase							)]
						lowercase       		=		[[] for _ in range(__lowercase							)]

						for i in range(__lowercase							):
									for j in range(__lowercase							):
												if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
															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(UpperCAmelCase							) -> Dict:
						lowercase		,lowercase       		=		align_predictions(p.predictions,		p.label_ids							)
						return {
						    "accuracy_score": accuracy_score(__lowercase,		__lowercase							),
						    "precision": precision_score(__lowercase,		__lowercase							),
						    "recall": recall_score(__lowercase,		__lowercase							),
						    "f1": fa_score(__lowercase,		__lowercase							),
						}

			# Data collator
			lowercase       		=		DataCollatorWithPadding(__lowercase,		pad_to_multiple_of=8							) if training_args.fpaa else None

			# Initialize our Trainer
			lowercase       		=		Trainer(
			    model=__lowercase,		args=__lowercase,		train_dataset=__lowercase,		eval_dataset=__lowercase,		compute_metrics=__lowercase,		data_collator=__lowercase,		)

			# Training
			if training_args.do_train:
						trainer.train(
						    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path							) else None							)
						trainer.save_model()
						# For convenience, we also re-save the tokenizer to the same directory,
						# so that you can share your model easily on huggingface.co/models =)
						if trainer.is_world_process_zero():
									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'''							)
						if trainer.is_world_process_zero():
									with open(__lowercase,		'''w'''							) as writer:
												logger.info('''***** Eval results *****'''							)
												for key, value in result.items():
															logger.info('''  %s = %s''',		__lowercase,		__lowercase							)
															writer.write('''%s = %s\n''' % (key, value)							)

									results.update(__lowercase							)

    # Predict
			if training_args.do_predict:
						lowercase       		=		TokenClassificationDataset(
						    token_classification_task=__lowercase,		data_dir=data_args.data_dir,		tokenizer=__lowercase,		labels=__lowercase,		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(__lowercase							)
						lowercase		,lowercase       		=		align_predictions(__lowercase,		__lowercase							)

						lowercase       		=		os.path.join(training_args.output_dir,		'''test_results.txt'''							)
						if trainer.is_world_process_zero():
									with open(__lowercase,		'''w'''							) as writer:
												for key, value in metrics.items():
															logger.info('''  %s = %s''',		__lowercase,		__lowercase							)
															writer.write('''%s = %s\n''' % (key, value)							)

        # Save predictions
						lowercase       		=		os.path.join(training_args.output_dir,		'''test_predictions.txt'''							)
						if trainer.is_world_process_zero():
									with open(__lowercase,		'''w'''							) as writer:
												with open(os.path.join(data_args.data_dir,		'''test.txt'''							),		'''r'''							) as f:
															token_classification_task.write_predictions_to_file(__lowercase,		__lowercase,		__lowercase							)

			return results






def 							__UpperCAmelCase     (							UpperCAmelCase							)->							Optional[int]:



			"""simple docstring"""







			main()


if __name__ == "__main__":
						main()













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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("1.0.0a"):
  raise Exception("requires fairseq >= 1.0.0a")

logging.set_verbosity_info()
__a   :							Union[str, Any]						= logging.get_logger(__name__)

__a   :							Tuple						= "Hello world! cécé herlolip"



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      str      ,					__lowercase :      bool     )    ->   List[Any]:





    """simple docstring"""

    __A       =						FairseqRobertaModel.from_pretrained(__lowercase     )
    roberta.eval()  # disable dropout
    __A       =						roberta.model.encoder.sentence_encoder
    __A       =						XLMRobertaConfig(
        vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings      ,					hidden_size=roberta.cfg.model.encoder_embed_dim      ,					num_hidden_layers=roberta.cfg.model.encoder_layers      ,					num_attention_heads=roberta.cfg.model.encoder_attention_heads      ,					intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim      ,					max_position_embeddings=5_1_4      ,					type_vocab_size=1      ,					layer_norm_eps=1E-5      ,					)
    if classification_head:
        __A       =						roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

    print("""Our RoBERTa config:"""      ,					__lowercase     )

    __A       =						XLMRobertaXLForSequenceClassification(__lowercase     ) if classification_head else XLMRobertaXLForMaskedLM(__lowercase     )
    model.eval()

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

    __A       =						roberta_sent_encoder.layer_norm.weight
    __A       =						roberta_sent_encoder.layer_norm.bias

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

        __A       =						layer.attention
        __A       =						roberta_layer.self_attn_layer_norm.weight
        __A       =						roberta_layer.self_attn_layer_norm.bias

        # self attention
        __A       =						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)     )
        )

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

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

        # this one is final layer norm
        __A       =						roberta_layer.final_layer_norm.weight
        __A       =						roberta_layer.final_layer_norm.bias

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

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

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

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

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

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


if __name__ == "__main__":
  __a   :							List[Any]						= 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."
  )
  __a   :							Dict						= parser.parse_args()
  convert_xlm_roberta_xl_checkpoint_to_pytorch(
      args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )




