
'''simple docstring'''





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




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




def   A			(							):
						# 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.
						snake_case							:  int			=     HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)							)
						snake_case,       snake_case,       snake_case							:  int			=     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.'''							)

						snake_case							:  Tuple			=     import_module('''tasks'''							)

						try:
												snake_case							:  Optional[Any]			=     getattr(SCREAMING_SNAKE_CASE_     ,				model_args.task_type							)
												snake_case							:  List[Any]			=     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
						snake_case							:  Tuple			=     token_classification_task.get_labels(data_args.labels							)
						snake_case							:  int			=     dict(enumerate(SCREAMING_SNAKE_CASE_							)							)
						snake_case							:  Any			=     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.

						snake_case							:  Optional[int]			=     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     ,				)
						snake_case							:  List[str]			=     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():
												snake_case							:  List[str]			=     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
						snake_case							:  Optional[Any]			=     (
						    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
						)
						snake_case							:  str			=     (
						    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(A_				:       int     ,				A_				:       Dict							) -> Tuple[List[int], List[int]]:
												snake_case							:  Any			=     np.argmax(SCREAMING_SNAKE_CASE_     ,				axis=2							)
												snake_case,       snake_case							:  Tuple			=     preds.shape
												snake_case							:  Tuple			=     [[] for _ in range(SCREAMING_SNAKE_CASE_							)]
												snake_case							:  Optional[Any]			=     [[] 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(A_				:       Optional[Any]							) -> Dict:
												snake_case,       snake_case							:  int			=     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
						snake_case							:  List[Any]			=     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
						snake_case							:  List[str]			=     {}
						if training_args.do_eval:
												logger.info('''*** Evaluate ***'''							)

												snake_case							:  int			=     trainer.evaluate()
												snake_case							:  Union[str, Any]			=     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:
												snake_case							:  Any			=     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     ,				)

												snake_case,       snake_case,       snake_case							:  List[str]			=     trainer.predict(test_dataset.get_dataset()							)
												snake_case,       snake_case							:  Union[str, Any]			=     align_predictions(SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_							)
												snake_case							:  List[str]			=     classification_report(SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_							)

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

												snake_case							:  List[Any]			=     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
												snake_case							:  Optional[int]			=     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:
																								snake_case							:  str			=     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]:
																																				snake_case							:  Any			=     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()



"""simple docstring"""




import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


snake_case					  =						[nn.LayerNorm]

snake_case					  =						logging.get_logger(__name__)

snake_case					  =						version.parse(version.parse(torch.__version__).base_version)

snake_case					  =						parsed_torch_version_base >= version.parse('2.0')
snake_case					  =						parsed_torch_version_base >= version.parse('1.12')
snake_case					  =						parsed_torch_version_base >= version.parse('1.11')
snake_case					  =						parsed_torch_version_base < version.parse('1.11')

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	self     ):
					from torch import _softmax_backward_data

					if is_torch_less_than_1_11:
										return _softmax_backward_data(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	parent.dim,	self     )
					else:
										return _softmax_backward_data(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	parent.dim,	self.dtype     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = 0     ):
					SCREAMING_SNAKE_CASE           =      index.to(layer.weight.device     )
					SCREAMING_SNAKE_CASE           =      layer.weight.index_select(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ).clone().detach()
					if layer.bias is not None:
										if dim == 1:
															SCREAMING_SNAKE_CASE           =      layer.bias.clone().detach()
										else:
															SCREAMING_SNAKE_CASE           =      layer.bias[index].clone().detach()
					SCREAMING_SNAKE_CASE           =      list(layer.weight.size()     )
					SCREAMING_SNAKE_CASE           =      len(SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      nn.Linear(new_size[1],	new_size[0],	bias=layer.bias is not None     ).to(layer.weight.device     )
					SCREAMING_SNAKE_CASE           =      False
					new_layer.weight.copy_(W.contiguous()     )
					SCREAMING_SNAKE_CASE           =      True
					if layer.bias is not None:
										SCREAMING_SNAKE_CASE           =      False
										new_layer.bias.copy_(b.contiguous()     )
										SCREAMING_SNAKE_CASE           =      True
					return new_layer




class 	UpperCamelCase      (			nn.Module ):

				"""simple docstring"""



				def __init__(							self						,      lowercase__						,      lowercase__     )       ->       List[Any]:
									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE           =      nf
									SCREAMING_SNAKE_CASE           =      nn.Parameter(torch.empty(lowercase__						,      lowercase__     )     )
									SCREAMING_SNAKE_CASE           =      nn.Parameter(torch.zeros(lowercase__     )     )
									nn.init.normal_(self.weight						,      std=0.02     )




				def 				A   (							self						,      lowercase__     )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      x.size()[:-1] + (self.nf,)
									SCREAMING_SNAKE_CASE           =      torch.addmm(self.bias						,      x.view(-1						,      x.size(-1     )     )						,      self.weight     )
									SCREAMING_SNAKE_CASE           =      x.view(lowercase__     )
									return x






def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = 1     ):
					SCREAMING_SNAKE_CASE           =      index.to(layer.weight.device     )
					SCREAMING_SNAKE_CASE           =      layer.weight.index_select(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ).clone().detach()
					if dim == 0:
										SCREAMING_SNAKE_CASE           =      layer.bias.clone().detach()
					else:
										SCREAMING_SNAKE_CASE           =      layer.bias[index].clone().detach()
					SCREAMING_SNAKE_CASE           =      list(layer.weight.size()     )
					SCREAMING_SNAKE_CASE           =      len(SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      ConvaD(new_size[1],	new_size[0]     ).to(layer.weight.device     )
					SCREAMING_SNAKE_CASE           =      False
					new_layer.weight.copy_(W.contiguous()     )
					SCREAMING_SNAKE_CASE           =      True
					SCREAMING_SNAKE_CASE           =      False
					new_layer.bias.copy_(b.contiguous()     )
					SCREAMING_SNAKE_CASE           =      True
					return new_layer

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = None     ):
					if isinstance(SCREAMING_SNAKE_CASE_,	nn.Linear     ):
										return prune_linear_layer(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	dim=0 if dim is None else dim     )
					elif isinstance(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
										return prune_convad_layer(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	dim=1 if dim is None else dim     )
					else:
										raise ValueError(f'''Can\'t prune layer of class {layer.__class__}'''     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	*SCREAMING_SNAKE_CASE_     ):
					assert len(SCREAMING_SNAKE_CASE_     ) > 0, f'''{input_tensors} has to be a tuple/list of tensors'''

					# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
					SCREAMING_SNAKE_CASE           =      len(inspect.signature(SCREAMING_SNAKE_CASE_     ).parameters     )
					if num_args_in_forward_chunk_fn != len(SCREAMING_SNAKE_CASE_     ):
										raise ValueError(
										    f'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(SCREAMING_SNAKE_CASE_     )} input '''
										    'tensors are given'     )

					if chunk_size > 0:
										SCREAMING_SNAKE_CASE           =      input_tensors[0].shape[chunk_dim]
										for input_tensor in input_tensors:
															if input_tensor.shape[chunk_dim] != tensor_shape:
																				raise ValueError(
																				    f'''All input tenors have to be of the same shape: {tensor_shape}, '''
																				    f'''found shape {input_tensor.shape[chunk_dim]}'''     )

										if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
															raise ValueError(
															    f'''The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '''
															    f'''size {chunk_size}'''     )

										SCREAMING_SNAKE_CASE           =      input_tensors[0].shape[chunk_dim] // chunk_size

										# chunk input tensor into tuples
										SCREAMING_SNAKE_CASE           =      tuple(input_tensor.chunk(SCREAMING_SNAKE_CASE_,	dim=SCREAMING_SNAKE_CASE_     ) for input_tensor in input_tensors     )
										# apply forward fn to every tuple
										SCREAMING_SNAKE_CASE           =      tuple(forward_fn(*SCREAMING_SNAKE_CASE_     ) for input_tensors_chunk in zip(*SCREAMING_SNAKE_CASE_     )     )
										# concatenate output at same dimension
										return torch.cat(SCREAMING_SNAKE_CASE_,	dim=SCREAMING_SNAKE_CASE_     )

					return forward_fn(*SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      torch.ones(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      set(SCREAMING_SNAKE_CASE_     ) - already_pruned_heads  # Convert to set and remove already pruned heads
					for head in heads:
										# Compute how many pruned heads are before the head and move the index accordingly
										SCREAMING_SNAKE_CASE           =      head - sum(1 if h < head else 0 for h in already_pruned_heads     )
										SCREAMING_SNAKE_CASE           =      0
					SCREAMING_SNAKE_CASE           =      mask.view(-1     ).contiguous().eq(1     )
					SCREAMING_SNAKE_CASE           =      torch.arange(len(SCREAMING_SNAKE_CASE_     )     )[mask].long()
					return heads, index

def     UpperCamelCase_    (						*SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_ = None     ):
					return torch.meshgrid(*SCREAMING_SNAKE_CASE_,	indexing=SCREAMING_SNAKE_CASE_     )



def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					return tensor.device, storage_ptr(SCREAMING_SNAKE_CASE_     ), storage_size(SCREAMING_SNAKE_CASE_     )
