






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


UpperCamelCase        =				logging.getLogger(__name__)

@dataclass
class        lowerCAmelCase_				:







				"""simple docstring"""




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

@dataclass
class        lowerCAmelCase_				:







				"""simple docstring"""




				_snake_case				:   str        =			field(
				    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}	)
				_snake_case				:   Optional[str]        =			field(
				    default=lowercase						,      metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}						,      )
				_snake_case				:   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."""
				        )
				    }						,      )
				_snake_case				:   bool        =			field(
				    default=lowercase						,      metadata={"""help""": """Overwrite the cached training and evaluation sets"""}	)




def 				A		(					)		-> str:
	# 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__ :str						=      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.
		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :Dict						=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
	else:
		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :List[Any]						=      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__ :Optional[Any]						=      import_module("""tasks"""  )
	try:
		UpperCamelCase__ :Tuple						=      getattr(lowercase__ ,		model_args.task_type  )
		UpperCamelCase__ :TokenClassificationTask						=      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
	UpperCamelCase__ :Dict						=      token_classification_task.get_labels(data_args.labels  )
	UpperCamelCase__ :Dict[int, str]						=      dict(enumerate(lowercase__  )  )
	UpperCamelCase__ :Any						=      len(lowercase__  )

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

	UpperCamelCase__ :Tuple						=      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 ,		)
	UpperCamelCase__ :Tuple						=      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 ,		)
	UpperCamelCase__ :List[str]						=      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
	UpperCamelCase__ :List[str]						=      (
	    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
	)
	UpperCamelCase__ :Tuple						=      (
	    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(lowercase__  :  np.ndarray ,		lowercase__  :  np.ndarray  ) -> Tuple[List[int], List[int]]:
		UpperCamelCase__ :List[Any]						=      np.argmax(lowercase__ ,		axis=2  )

		UpperCamelCase__				,		UpperCamelCase__ :int						=      preds.shape

		UpperCamelCase__ :Optional[Any]						=      [[] for _ in range(lowercase__  )]
		UpperCamelCase__ :Optional[int]						=      [[] 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(lowercase__  :  EvalPrediction  ) -> Dict:
		UpperCamelCase__				,		UpperCamelCase__ :str						=      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
	UpperCamelCase__ :int						=      DataCollatorWithPadding(lowercase__ ,		pad_to_multiple_of=8  ) if training_args.fpaa else None

	# Initialize our Trainer
	UpperCamelCase__ :Dict						=      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
	UpperCamelCase__ :Dict						=      {}
	if training_args.do_eval:
		logger.info("""*** Evaluate ***"""  )

		UpperCamelCase__ :List[str]						=      trainer.evaluate()

		UpperCamelCase__ :str						=      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:
		UpperCamelCase__ :Dict						=      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 ,		)

		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :Any						=      trainer.predict(lowercase__  )
		UpperCamelCase__				,		UpperCamelCase__ :Optional[int]						=      align_predictions(lowercase__ ,		lowercase__  )

		UpperCamelCase__ :Dict						=      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
		UpperCamelCase__ :int						=      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 				A		(					lowercase__  :  int  )		-> Union[str, Any]:
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
							main()






from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional

from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


try:
							from fastapi import Body, FastAPI, HTTPException
							from fastapi.routing import APIRoute
							from pydantic import BaseModel
							from starlette.responses import JSONResponse
							from uvicorn import run

							UpperCAmelCase_							=				True
except (ImportError, AttributeError):
							UpperCAmelCase_							=				object





							def  __magic_name__  (				*lowercase				,       **lowercase				)				-> Optional[Any]:

														"""simple docstring"""



														pass




							UpperCAmelCase_							=				False


UpperCAmelCase_							=				logging.get_logger("""transformers-cli/serving""")





def  __magic_name__  (				lowercase				)				-> int:

							"""simple docstring"""



							lowercase_	:   Any  				=     pipeline(
							    task=args.task				,       model=args.model if args.model else None				,       config=args.config				,       tokenizer=args.tokenizer				,       device=args.device				,       )
							return ServeCommand(lowercase				,       args.host				,       args.port				,       args.workers				)
class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       dict




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       List[str]
		__a       :       Optional[List[int]]




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       str




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		__a       :       Any




class     UpperCamelCase__					(     lowerCamelCase__				):






		'''simple docstring'''






		@staticmethod
		def 						snake_case__				(							snake_case__  )       -> Optional[int]:






									"""simple docstring"""
									lowercase_	:   List[str]  				=     parser.add_parser(
									    """serve""",   help="""CLI tool to run inference requests through REST and GraphQL endpoints."""  )
									serve_parser.add_argument(
									    """--task""",   type=snake_case__,   choices=get_supported_tasks(),   help="""The task to run the pipeline on""",   )
									serve_parser.add_argument("""--host""",   type=snake_case__,   default="""localhost""",   help="""Interface the server will listen on."""  )
									serve_parser.add_argument("""--port""",   type=snake_case__,   default=88_88,   help="""Port the serving will listen to."""  )
									serve_parser.add_argument("""--workers""",   type=snake_case__,   default=1,   help="""Number of http workers"""  )
									serve_parser.add_argument("""--model""",   type=snake_case__,   help="""Model's name or path to stored model."""  )
									serve_parser.add_argument("""--config""",   type=snake_case__,   help="""Model's config name or path to stored model."""  )
									serve_parser.add_argument("""--tokenizer""",   type=snake_case__,   help="""Tokenizer name to use."""  )
									serve_parser.add_argument(
									    """--device""",   type=snake_case__,   default=-1,   help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)""",   )
									serve_parser.set_defaults(func=snake_case__  )






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






									"""simple docstring"""
									lowercase_	:   str  				=     pipeline

									lowercase_	:   List[str]  				=     host
									lowercase_	:   int  				=     port
									lowercase_	:   Optional[Any]  				=     workers

									if not _serve_dependencies_installed:
																raise RuntimeError(
																    """Using serve command requires FastAPI and uvicorn. """
																    """Please install transformers with [serving]: pip install \"transformers[serving]\"."""
																    """Or install FastAPI and uvicorn separately."""  )
									else:
																logger.info(f"""Serving model over {host}:{port}"""  )
																lowercase_	:   Union[str, Any]  				=     FastAPI(
																    routes=[
																        APIRoute(
																            """/""",   self.model_info,   response_model=snake_case__,   response_class=snake_case__,   methods=["""GET"""],   ),
																        APIRoute(
																            """/tokenize""",   self.tokenize,   response_model=snake_case__,   response_class=snake_case__,   methods=["""POST"""],   ),
																        APIRoute(
																            """/detokenize""",   self.detokenize,   response_model=snake_case__,   response_class=snake_case__,   methods=["""POST"""],   ),
																        APIRoute(
																            """/forward""",   self.forward,   response_model=snake_case__,   response_class=snake_case__,   methods=["""POST"""],   ),
																    ],   timeout=6_00,   )






		def 						snake_case__				(							self  )       -> Union[str, Any]:






									"""simple docstring"""
									run(self._app,   host=self.host,   port=self.port,   workers=self.workers  )






		def 						snake_case__				(							self  )       -> Dict:






									"""simple docstring"""
									return ServeModelInfoResult(infos=vars(self._pipeline.model.config  )  )






		def 						snake_case__				(							self,   snake_case__ = Body(snake_case__,   embed=snake_case__  ),   snake_case__ = Body(snake_case__,   embed=snake_case__  )  )       -> Optional[int]:






									"""simple docstring"""
									try:
																lowercase_	:   Tuple  				=     self._pipeline.tokenizer.tokenize(snake_case__  )

																if return_ids:
																							lowercase_	:   Union[str, Any]  				=     self._pipeline.tokenizer.convert_tokens_to_ids(snake_case__  )
																							return ServeTokenizeResult(tokens=snake_case__,   tokens_ids=snake_case__  )
																else:
																							return ServeTokenizeResult(tokens=snake_case__  )

									except Exception as e:
																raise HTTPException(status_code=5_00,   detail={"""model""": """""", """error""": str(snake_case__  )}  )






		def 						snake_case__				(							self,   snake_case__ = Body(snake_case__,   embed=snake_case__  ),   snake_case__ = Body(snake_case__,   embed=snake_case__  ),   snake_case__ = Body(snake_case__,   embed=snake_case__  ),   )       -> Dict:






									"""simple docstring"""
									try:
																lowercase_	:   List[Any]  				=     self._pipeline.tokenizer.decode(snake_case__,   snake_case__,   snake_case__  )
																return ServeDeTokenizeResult(model="""""",   text=snake_case__  )
									except Exception as e:
																raise HTTPException(status_code=5_00,   detail={"""model""": """""", """error""": str(snake_case__  )}  )






		async def 						snake_case__				(							self,   snake_case__=Body(snake_case__,   embed=snake_case__  )  )       -> Tuple:






									"""simple docstring"""

									# Check we don't have empty string
									if len(snake_case__  ) == 0:
																return ServeForwardResult(output=[],   attention=[]  )

									try:
																# Forward through the model
																lowercase_	:   Optional[int]  				=     self._pipeline(snake_case__  )
																return ServeForwardResult(output=snake_case__  )
									except Exception as e:
																raise HTTPException(5_00,   {"""error""": str(snake_case__  )}  )