



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()


UpperCAmelCase__  =			logging.getLogger(__name__)







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







@dataclass
class      __lowerCAmelCase       :
			UpperCamelCase     =	field(
			    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}		)
			UpperCamelCase     =	field(
			    metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}		)
			UpperCamelCase     =	field(
			    default=1_2_8							,		metadata={
			        '''help''': (
			            '''The maximum total input sequence length after tokenization. Sequences longer '''
			            '''than this will be truncated, sequences shorter will be padded.'''
			        )
			    }							,		)
			UpperCamelCase     =	field(
			    default=A							,		metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}		)





def 				A      (	)							->					Tuple:







			'''simple docstring'''
			# 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.
			_UpperCAmelCase									=  HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)       )
			_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  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.'       )

			_UpperCAmelCase									=  import_module('tasks'       )

			try:
						_UpperCAmelCase									=  getattr(_UpperCAmelCase				,  model_args.task_type       )
						_UpperCAmelCase									=  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'				,  _UpperCAmelCase       )

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

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

			_UpperCAmelCase									=  AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path				,  num_labels=_UpperCAmelCase				,  idalabel=_UpperCAmelCase				,  labelaid={label: i for i, label in enumerate(_UpperCAmelCase       )}				,  cache_dir=model_args.cache_dir				,  )
			_UpperCAmelCase									=  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():
						_UpperCAmelCase									=  TFAutoModelForTokenClassification.from_pretrained(
						    model_args.model_name_or_path				,  from_pt=bool('.bin' in model_args.model_name_or_path       )				,  config=_UpperCAmelCase				,  cache_dir=model_args.cache_dir				,  )

			# Get datasets
			_UpperCAmelCase									=  (
			    TFTokenClassificationDataset(
			        token_classification_task=_UpperCAmelCase				,  data_dir=data_args.data_dir				,  tokenizer=_UpperCAmelCase				,  labels=_UpperCAmelCase				,  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
			)
			_UpperCAmelCase									=  (
			    TFTokenClassificationDataset(
			        token_classification_task=_UpperCAmelCase				,  data_dir=data_args.data_dir				,  tokenizer=_UpperCAmelCase				,  labels=_UpperCAmelCase				,  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							:  np.ndarray				,  _UpperCAmelCase							:  np.ndarray       ) -> Tuple[List[int], List[int]]:
						_UpperCAmelCase									=  np.argmax(_UpperCAmelCase				,  axis=2       )
						_UpperCAmelCase			,	_UpperCAmelCase									=  preds.shape
						_UpperCAmelCase									=  [[] for _ in range(_UpperCAmelCase       )]
						_UpperCAmelCase									=  [[] for _ in range(_UpperCAmelCase       )]

						for i in range(_UpperCAmelCase       ):
									for j in range(_UpperCAmelCase       ):
												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(_UpperCAmelCase							:  EvalPrediction       ) -> Dict:
						_UpperCAmelCase			,	_UpperCAmelCase									=  align_predictions(p.predictions				,  p.label_ids       )

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

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

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

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

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

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

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

									results.update(_UpperCAmelCase       )

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

						_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  trainer.predict(test_dataset.get_dataset()       )
						_UpperCAmelCase			,	_UpperCAmelCase									=  align_predictions(_UpperCAmelCase				,  _UpperCAmelCase       )
						_UpperCAmelCase									=  classification_report(_UpperCAmelCase				,  _UpperCAmelCase       )

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

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

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

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

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

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

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

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

			return results


if __name__ == "__main__":
							main()





import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 				A      (	_UpperCAmelCase							:  Tuple				,  _UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  str       )							->					Union[str, Any]:







			'''simple docstring'''
			# Construct configuration depending on size
			_UpperCAmelCase									=  {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  MODELS[model_size]  # set model hyperparameters
			_UpperCAmelCase									=  ImageGPTConfig(n_embd=_UpperCAmelCase				,  n_layer=_UpperCAmelCase				,  n_head=_UpperCAmelCase       )
			_UpperCAmelCase									=  ImageGPTForCausalLM(_UpperCAmelCase       )

			# Load weights from numpy
			load_tf_weights_in_imagegpt(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase       )

			# Save pytorch-model
			_UpperCAmelCase									=  pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			_UpperCAmelCase									=  pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(F"Save PyTorch model to {pytorch_weights_dump_path}"       )
			torch.save(model.state_dict()				,  _UpperCAmelCase       )
			print(F"Save configuration file to {pytorch_config_dump_path}"       )
			with open(_UpperCAmelCase				,  'w'				,  encoding='utf-8'       ) as f:
						f.write(config.to_json_string()       )


if __name__ == "__main__":
							UpperCAmelCase__  =			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--imagegpt_checkpoint_path",
							    default=None,
							    type=str,
							    required=True,
							    help="Path to the TensorFlow checkpoint path.",
							)
							parser.add_argument(
							    "--model_size",
							    default=None,
							    type=str,
							    required=True,
							    help="Size of the model (can be either 'small', 'medium' or 'large').",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							UpperCAmelCase__  =			parser.parse_args()
							convert_imagegpt_checkpoint_to_pytorch(
							    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
							)
