
from typing import Optional, Tuple

import flax.linen as nn
import jax
import jax.numpy as jnp
from configuration_hybrid_clip import HybridCLIPConfig
from flax.core.frozen_dict import FrozenDict

from transformers import FLAX_MODEL_MAPPING, FlaxCLIPVisionModel
from transformers.modeling_flax_utils import FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPOutput
from transformers.utils import logging


SCREAMING_SNAKE_CASE__				=							logging.get_logger(__name__)
class       A__  (		nn.Module							):
							lowerCAmelCase__						:		HybridCLIPConfig
							lowerCAmelCase__						:		jnp.dtype            =				jnp.floataa





							def      a__					(					self    :  str							)		->						List[str]:



											"""simple docstring"""


											__lowercase								=   self.config.text_config
											__lowercase								=   self.config.vision_config

											__lowercase								=   self.config.projection_dim
											__lowercase								=   text_config.hidden_size
											__lowercase								=   vision_config.hidden_size

											__lowercase								=   FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
											__lowercase								=   FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__	,					_UpperCAmelCase							).module_class

											__lowercase								=   text_module(_UpperCAmelCase	,					dtype=self.dtype							)
											__lowercase								=   vision_module(_UpperCAmelCase	,					dtype=self.dtype							)

											__lowercase								=   nn.Dense(
											    self.projection_dim	,					dtype=self.dtype	,					kernel_init=jax.nn.initializers.normal(0.02							)	,					use_bias=_UpperCAmelCase	,					)
											__lowercase								=   nn.Dense(
											    self.projection_dim	,					dtype=self.dtype	,					kernel_init=jax.nn.initializers.normal(0.02							)	,					use_bias=_UpperCAmelCase	,					)
											__lowercase								=   self.param('logit_scale'	,					jax.nn.initializers.ones	,					[]							)





							def __call__(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  Optional[Any]=None	,					_UpperCAmelCase    :  int=None	,					_UpperCAmelCase    :  str=None	,					_UpperCAmelCase    :  Optional[int]=None	,					_UpperCAmelCase    :  Union[str, Any]=None	,					_UpperCAmelCase    :  bool = True	,					_UpperCAmelCase    :  Tuple=None	,					_UpperCAmelCase    :  int=None	,					_UpperCAmelCase    :  str=None	,					)		->						Union[str, Any]:



											"""simple docstring"""


											__lowercase								=   return_dict if return_dict is not None else self.config.return_dict

											__lowercase								=   self.vision_model(
											    pixel_values=_UpperCAmelCase	,					deterministic=_UpperCAmelCase	,					output_attentions=_UpperCAmelCase	,					output_hidden_states=_UpperCAmelCase	,					return_dict=_UpperCAmelCase	,					)

											__lowercase								=   self.text_model(
											    input_ids=_UpperCAmelCase	,					attention_mask=_UpperCAmelCase	,					token_type_ids=_UpperCAmelCase	,					position_ids=_UpperCAmelCase	,					deterministic=_UpperCAmelCase	,					output_attentions=_UpperCAmelCase	,					output_hidden_states=_UpperCAmelCase	,					return_dict=_UpperCAmelCase	,					)

											__lowercase								=   vision_outputs[1]
											__lowercase								=   self.visual_projection(_UpperCAmelCase							)

											__lowercase								=   text_outputs[1]
											__lowercase								=   self.text_projection(_UpperCAmelCase							)

											# normalized features
											__lowercase								=   image_embeds / jnp.linalg.norm(_UpperCAmelCase	,					axis=-1	,					keepdims=_UpperCAmelCase							)
											__lowercase								=   text_embeds / jnp.linalg.norm(_UpperCAmelCase	,					axis=-1	,					keepdims=_UpperCAmelCase							)

											# cosine similarity as logits
											__lowercase								=   jnp.exp(self.logit_scale							)
											__lowercase								=   jnp.matmul(_UpperCAmelCase	,					image_embeds.T							) * logit_scale
											__lowercase								=   logits_per_text.T

											if not return_dict:
															return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)

											return FlaxCLIPOutput(
											    logits_per_image=_UpperCAmelCase	,					logits_per_text=_UpperCAmelCase	,					text_embeds=_UpperCAmelCase	,					image_embeds=_UpperCAmelCase	,					text_model_output=_UpperCAmelCase	,					vision_model_output=_UpperCAmelCase	,					)






class       A__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		Tuple            =				HybridCLIPConfig
							lowerCAmelCase__						:		int            =				FlaxHybridCLIPModule





							def __init__(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  HybridCLIPConfig	,					_UpperCAmelCase    :  Optional[Tuple] = None	,					_UpperCAmelCase    :  int = 0	,					_UpperCAmelCase    :  jnp.dtype = jnp.floataa	,					**_UpperCAmelCase    :  int	,					)		->						Tuple:



											"""simple docstring"""


											if input_shape is None:
															__lowercase								=   ((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))

											__lowercase								=   self.module_class(config=_UpperCAmelCase	,					dtype=_UpperCAmelCase	,					**_UpperCAmelCase							)
											super().__init__(_UpperCAmelCase	,					_UpperCAmelCase	,					input_shape=_UpperCAmelCase	,					seed=_UpperCAmelCase	,					dtype=_UpperCAmelCase							)





							def      a__					(					self    :  Tuple	,					_UpperCAmelCase    :  jax.random.PRNGKey	,					_UpperCAmelCase    :  Tuple	,					_UpperCAmelCase    :  FrozenDict = None							)		->						FrozenDict:



											"""simple docstring"""


											__lowercase								=   jnp.zeros(input_shape[0]	,					dtype='i4'							)
											__lowercase								=   jnp.broadcast_to(jnp.arange(jnp.atleast_ad(_UpperCAmelCase							).shape[-1]							)	,					input_shape[0]							)
											__lowercase								=   jnp.ones_like(_UpperCAmelCase							)
											__lowercase								=   jnp.ones_like(_UpperCAmelCase							)

											__lowercase								=   jax.random.normal(_UpperCAmelCase	,					input_shape[1]							)

											__lowercase						,   __lowercase								=   jax.random.split(_UpperCAmelCase							)
											__lowercase								=   {'params': params_rng, 'dropout': dropout_rng}

											return self.module.init(_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase							)["params"]





							def __call__(					self    :  str	,					_UpperCAmelCase    :  str	,					_UpperCAmelCase    :  List[Any]	,					_UpperCAmelCase    :  Optional[Any]=None	,					_UpperCAmelCase    :  Optional[Any]=None	,					_UpperCAmelCase    :  str=None	,					_UpperCAmelCase    :  dict = None	,					_UpperCAmelCase    :  jax.random.PRNGKey = None	,					_UpperCAmelCase    :  bool = False	,					_UpperCAmelCase    :  Optional[bool] = None	,					_UpperCAmelCase    :  Optional[bool] = None	,					_UpperCAmelCase    :  Optional[bool] = None	,					)		->						Any:



											"""simple docstring"""


											__lowercase								=   output_attentions if output_attentions is not None else self.config.output_attentions
											__lowercase								=   (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											__lowercase								=   return_dict if return_dict is not None else self.config.return_dict

											if position_ids is None:
															__lowercase								=   jnp.broadcast_to(jnp.arange(jnp.atleast_ad(_UpperCAmelCase							).shape[-1]							)	,					input_ids.shape							)

											if token_type_ids is None:
															__lowercase								=   jnp.zeros_like(_UpperCAmelCase							)

											if attention_mask is None:
															__lowercase								=   jnp.ones_like(_UpperCAmelCase							)

											# Handle any PRNG if needed
											__lowercase								=   {}
											if dropout_rng is not None:
															__lowercase								=   dropout_rng

											return self.module.apply(
											    {'params': params or self.params}	,					jnp.array(_UpperCAmelCase	,					dtype='i4'							)	,					jnp.array(_UpperCAmelCase	,					dtype=jnp.floataa							)	,					jnp.array(_UpperCAmelCase	,					dtype='i4'							)	,					jnp.array(_UpperCAmelCase	,					dtype='i4'							)	,					jnp.array(_UpperCAmelCase	,					dtype='i4'							)	,					not train	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					rngs=_UpperCAmelCase	,					)





							def      a__					(					self    :  str	,					_UpperCAmelCase    :  int	,					_UpperCAmelCase    :  List[Any]=None	,					_UpperCAmelCase    :  Optional[Any]=None	,					_UpperCAmelCase    :  List[Any]=None	,					_UpperCAmelCase    :  dict = None	,					_UpperCAmelCase    :  jax.random.PRNGKey = None	,					_UpperCAmelCase    :  List[str]=False	,					)		->						Any:



											"""simple docstring"""


											if position_ids is None:
															__lowercase								=   jnp.broadcast_to(jnp.arange(jnp.atleast_ad(_UpperCAmelCase							).shape[-1]							)	,					input_ids.shape							)

											if token_type_ids is None:
															__lowercase								=   jnp.zeros_like(_UpperCAmelCase							)

											if attention_mask is None:
															__lowercase								=   jnp.ones_like(_UpperCAmelCase							)

											# Handle any PRNG if needed
											__lowercase								=   {}
											if dropout_rng is not None:
															__lowercase								=   dropout_rng

											def _get_features(_UpperCAmelCase    :  Any	,					_UpperCAmelCase    :  Optional[Any]	,					_UpperCAmelCase    :  Union[str, Any]	,					_UpperCAmelCase    :  Optional[Any]	,					_UpperCAmelCase    :  Union[str, Any]	,					_UpperCAmelCase    :  Optional[int]							):
															__lowercase								=   module.text_model(
															    input_ids=_UpperCAmelCase	,					attention_mask=_UpperCAmelCase	,					position_ids=_UpperCAmelCase	,					token_type_ids=_UpperCAmelCase	,					deterministic=_UpperCAmelCase	,					)
															__lowercase								=   text_outputs[1]
															__lowercase								=   module.text_projection(_UpperCAmelCase							)
															return text_features

											return self.module.apply(
											    {'params': params or self.params}	,					jnp.array(_UpperCAmelCase	,					dtype='i4'							)	,					jnp.array(_UpperCAmelCase	,					dtype='i4'							)	,					jnp.array(_UpperCAmelCase	,					dtype='i4'							)	,					jnp.array(_UpperCAmelCase	,					dtype='i4'							)	,					not train	,					method=_get_features	,					rngs=_UpperCAmelCase	,					)





							def      a__					(					self    :  Any	,					_UpperCAmelCase    :  Dict	,					_UpperCAmelCase    :  dict = None	,					_UpperCAmelCase    :  jax.random.PRNGKey = None	,					_UpperCAmelCase    :  str=False							)		->						int:



											"""simple docstring"""


											__lowercase								=   {}
											if dropout_rng is not None:
															__lowercase								=   dropout_rng

											def _get_features(_UpperCAmelCase    :  List[Any]	,					_UpperCAmelCase    :  List[str]	,					_UpperCAmelCase    :  List[Any]							):
															__lowercase								=   module.vision_model(pixel_values=_UpperCAmelCase	,					deterministic=_UpperCAmelCase							)
															__lowercase								=   vision_outputs[1]  # pooled_output
															__lowercase								=   module.visual_projection(_UpperCAmelCase							)
															return image_features

											return self.module.apply(
											    {'params': params or self.params}	,					jnp.array(_UpperCAmelCase	,					dtype=jnp.floataa							)	,					not train	,					method=_get_features	,					rngs=_UpperCAmelCase	,					)





							@classmethod
							def      a__					(					cls    :  Optional[Any]	,					_UpperCAmelCase    :  str = None	,					_UpperCAmelCase    :  str = None	,					*_UpperCAmelCase    :  Optional[int]	,					**_UpperCAmelCase    :  Union[str, Any]	,					)		->						FlaxPreTrainedModel:



											"""simple docstring"""


											__lowercase								=   {
											    argument[len('text_'							) :]: value for argument, value in kwargs.items() if argument.startswith('text_'							)
											}

											__lowercase								=   {
											    argument[len('vision_'							) :]: value for argument, value in kwargs.items() if argument.startswith('vision_'							)
											}

											# remove text, vision kwargs from kwargs
											for key in kwargs_text.keys():
															del kwargs["text_" + key]
											for key in kwargs_vision.keys():
															del kwargs["vision_" + key]

											# Load and initialize the text and vision model
											__lowercase								=   kwargs_text.pop('model'	,					_UpperCAmelCase							)
											if text_model is None:
															assert (
															    text_model_name_or_path is not None
															), "If `model` is not defined as an argument, a `text_model_name_or_path` has to be defined"
															from transformers import FlaxAutoModel

															if "config" not in kwargs_text:
																			from transformers import AutoConfig

																			__lowercase								=   AutoConfig.from_pretrained(_UpperCAmelCase							)
																			__lowercase								=   text_config

															__lowercase								=   FlaxAutoModel.from_pretrained(_UpperCAmelCase	,					*_UpperCAmelCase	,					**_UpperCAmelCase							)

											__lowercase								=   kwargs_vision.pop('model'	,					_UpperCAmelCase							)
											if vision_model is None:
															assert (
															    vision_model_name_or_path is not None
															), "If `model` is not defined as an argument, a `vision_model_name_or_path` has to be defined"
															from transformers import FlaxAutoModel

															if "config" not in kwargs_vision:
																			from transformers import AutoConfig

																			__lowercase								=   AutoConfig.from_pretrained(_UpperCAmelCase							)
																			__lowercase								=   vision_config

															__lowercase								=   FlaxAutoModel.from_pretrained(_UpperCAmelCase	,					*_UpperCAmelCase	,					**_UpperCAmelCase							)

											# instantiate config with corresponding kwargs
											__lowercase								=   kwargs.pop('dtype'	,					jnp.floataa							)
											__lowercase								=   HybridCLIPConfig.from_text_vision_configs(text_model.config	,					vision_model.config	,					**_UpperCAmelCase							)

											# init model
											__lowercase								=   cls(_UpperCAmelCase	,					*_UpperCAmelCase	,					dtype=_UpperCAmelCase	,					**_UpperCAmelCase							)

											if vision_config.model_type == "clip":
															__lowercase								=   vision_model.params['vision_model']
															__lowercase								=   vision_model.params['visual_projection']['kernel']
											else:
															__lowercase								=   vision_model.params

											__lowercase								=   text_model.params

											return model







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()





