



"""simple docstring"""







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


lowercase__		:    Union[str, Any]				   =     logging.getLogger(__name__)







@dataclass
class 				UpperCamelCase__       :




						"""simple docstring"""




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







@dataclass
class 				UpperCamelCase__       :




						"""simple docstring"""




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




def     UpperCamelCase_   (   )						->       Any:

		"""simple docstring"""




		lowerCAmelCase_       :    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.
				lowerCAmelCase_       :    List[Any]								=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
		else:
				lowerCAmelCase_       :    Optional[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.'						)

		lowerCAmelCase_       :    Any								=			import_module('tasks'						)
		try:
				lowerCAmelCase_       :    Union[str, Any]								=			getattr(lowerCAmelCase__   ,       model_args.task_type						)
				lowerCAmelCase_       :    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'   ,       lowerCAmelCase__						)

		# Set seed
		set_seed(training_args.seed						)

		# Prepare CONLL-2003 task
		lowerCAmelCase_       :    List[Any]								=			token_classification_task.get_labels(data_args.labels						)
		lowerCAmelCase_       :    Dict[int, str]								=			dict(enumerate(lowerCAmelCase__						)						)
		lowerCAmelCase_       :    Tuple								=			len(lowerCAmelCase__						)

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

		lowerCAmelCase_       :    int								=			AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,       num_labels=lowerCAmelCase__   ,       idalabel=lowerCAmelCase__   ,       labelaid={label: i for i, label in enumerate(lowerCAmelCase__						)}   ,       cache_dir=model_args.cache_dir   ,       )
		lowerCAmelCase_       :    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   ,       )
		lowerCAmelCase_       :    Any								=			AutoModelForTokenClassification.from_pretrained(
		    model_args.model_name_or_path   ,       from_tf=bool('.ckpt' in model_args.model_name_or_path						)   ,       config=lowerCAmelCase__   ,       cache_dir=model_args.cache_dir   ,       )

		# Get datasets
		lowerCAmelCase_       :    Optional[int]								=			(
		    TokenClassificationDataset(
		        token_classification_task=lowerCAmelCase__   ,       data_dir=data_args.data_dir   ,       tokenizer=lowerCAmelCase__   ,       labels=lowerCAmelCase__   ,       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
		)
		lowerCAmelCase_       :    List[Any]								=			(
		    TokenClassificationDataset(
		        token_classification_task=lowerCAmelCase__   ,       data_dir=data_args.data_dir   ,       tokenizer=lowerCAmelCase__   ,       labels=lowerCAmelCase__   ,       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(lowerCAmelCase__				:    np.ndarray   ,       lowerCAmelCase__				:    np.ndarray						) -> Tuple[List[int], List[int]]:
				lowerCAmelCase_       :    List[str]								=			np.argmax(lowerCAmelCase__   ,       axis=2						)

				lowerCAmelCase_       :    Union[str, Any]								=			preds.shape

				lowerCAmelCase_       :    Optional[Any]								=			[[] for _ in range(lowerCAmelCase__						)]
				lowerCAmelCase_       :    List[str]								=			[[] for _ in range(lowerCAmelCase__						)]

				for i in range(lowerCAmelCase__						):
						for j in range(lowerCAmelCase__						):
								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(lowerCAmelCase__				:    EvalPrediction						) -> Dict:
				lowerCAmelCase_       :    Optional[Any]								=			align_predictions(p.predictions   ,       p.label_ids						)
				return {
				    "accuracy_score": accuracy_score(lowerCAmelCase__   ,       lowerCAmelCase__						),
				    "precision": precision_score(lowerCAmelCase__   ,       lowerCAmelCase__						),
				    "recall": recall_score(lowerCAmelCase__   ,       lowerCAmelCase__						),
				    "f1": fa_score(lowerCAmelCase__   ,       lowerCAmelCase__						),
				}

		# Data collator
		lowerCAmelCase_       :    str								=			DataCollatorWithPadding(lowerCAmelCase__   ,       pad_to_multiple_of=8						) if training_args.fpaa else None

		# Initialize our Trainer
		lowerCAmelCase_       :    Dict								=			Trainer(
		    model=lowerCAmelCase__   ,       args=lowerCAmelCase__   ,       train_dataset=lowerCAmelCase__   ,       eval_dataset=lowerCAmelCase__   ,       compute_metrics=lowerCAmelCase__   ,       data_collator=lowerCAmelCase__   ,       )

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

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

				lowerCAmelCase_       :    Tuple								=			os.path.join(training_args.output_dir   ,       'eval_results.txt'						)
				if trainer.is_world_process_zero():
						with open(lowerCAmelCase__   ,       'w'						) as writer:
								logger.info('***** Eval results *****'						)
								for key, value in result.items():
										logger.info('  %s = %s'   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)
										writer.write('%s = %s\n' % (key, value)						)

						results.update(lowerCAmelCase__						)

				# Predict
		if training_args.do_predict:
				lowerCAmelCase_       :    List[str]								=			TokenClassificationDataset(
				    token_classification_task=lowerCAmelCase__   ,       data_dir=data_args.data_dir   ,       tokenizer=lowerCAmelCase__   ,       labels=lowerCAmelCase__   ,       model_type=config.model_type   ,       max_seq_length=data_args.max_seq_length   ,       overwrite_cache=data_args.overwrite_cache   ,       mode=Split.test   ,       )

				lowerCAmelCase_       :    Optional[int]								=			trainer.predict(lowerCAmelCase__						)
				lowerCAmelCase_       :    Tuple								=			align_predictions(lowerCAmelCase__   ,       lowerCAmelCase__						)

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

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

		return results

def     UpperCamelCase_   (   lowerCAmelCase__				:    Any						)						->       Optional[int]:

		"""simple docstring"""




		main()


if __name__ == "__main__":
					main()










"""simple docstring"""







import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         MODEL_FOR_PRETRAINING_MAPPING,
         AlbertForMaskedLM,
         AlbertForMultipleChoice,
         AlbertForPreTraining,
         AlbertForQuestionAnswering,
         AlbertForSequenceClassification,
         AlbertForTokenClassification,
         AlbertModel,
     )
     from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class 				UpperCamelCase__       :




      """simple docstring"""




      def __init__(				self    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Dict=1_3   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=7   ,      SCREAMING_SNAKE_CASE_    :				Dict=True   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=True   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=True   ,      SCREAMING_SNAKE_CASE_    :				str=True   ,      SCREAMING_SNAKE_CASE_    :				List[str]=9_9   ,      SCREAMING_SNAKE_CASE_    :				int=1_6   ,      SCREAMING_SNAKE_CASE_    :				List[str]=3_6   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=6   ,      SCREAMING_SNAKE_CASE_    :				Tuple=6   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=6   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=3_7   ,      SCREAMING_SNAKE_CASE_    :				Tuple="gelu"   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.1   ,      SCREAMING_SNAKE_CASE_    :				int=0.1   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=5_1_2   ,      SCREAMING_SNAKE_CASE_    :				List[str]=1_6   ,      SCREAMING_SNAKE_CASE_    :				List[str]=2   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=0.02   ,      SCREAMING_SNAKE_CASE_    :				Dict=3   ,      SCREAMING_SNAKE_CASE_    :				int=4   ,      SCREAMING_SNAKE_CASE_    :				Tuple=None   ,      ):
        lowerCAmelCase_       :    Any								=			parent
        lowerCAmelCase_       :    Optional[int]								=			batch_size
        lowerCAmelCase_       :    Dict								=			seq_length
        lowerCAmelCase_       :    Tuple								=			is_training
        lowerCAmelCase_       :    str								=			use_input_mask
        lowerCAmelCase_       :    Union[str, Any]								=			use_token_type_ids
        lowerCAmelCase_       :    Tuple								=			use_labels
        lowerCAmelCase_       :    Optional[int]								=			vocab_size
        lowerCAmelCase_       :    Any								=			embedding_size
        lowerCAmelCase_       :    Optional[Any]								=			hidden_size
        lowerCAmelCase_       :    str								=			num_hidden_layers
        lowerCAmelCase_       :    Optional[Any]								=			num_hidden_groups
        lowerCAmelCase_       :    Dict								=			num_attention_heads
        lowerCAmelCase_       :    Optional[Any]								=			intermediate_size
        lowerCAmelCase_       :    Any								=			hidden_act
        lowerCAmelCase_       :    Union[str, Any]								=			hidden_dropout_prob
        lowerCAmelCase_       :    int								=			attention_probs_dropout_prob
        lowerCAmelCase_       :    int								=			max_position_embeddings
        lowerCAmelCase_       :    List[Any]								=			type_vocab_size
        lowerCAmelCase_       :    Any								=			type_sequence_label_size
        lowerCAmelCase_       :    Optional[int]								=			initializer_range
        lowerCAmelCase_       :    Tuple								=			num_labels
        lowerCAmelCase_       :    Dict								=			num_choices
        lowerCAmelCase_       :    Tuple								=			scope



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        lowerCAmelCase_       :    List[str]								=			ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size	)

        lowerCAmelCase_       :    str								=			None
        if self.use_input_mask:
          lowerCAmelCase_       :    int								=			random_attention_mask([self.batch_size, self.seq_length]	)

        lowerCAmelCase_       :    List[Any]								=			None
        if self.use_token_type_ids:
          lowerCAmelCase_       :    str								=			ids_tensor([self.batch_size, self.seq_length]   ,      self.type_vocab_size	)

        lowerCAmelCase_       :    List[str]								=			None
        lowerCAmelCase_       :    Union[str, Any]								=			None
        lowerCAmelCase_       :    str								=			None
        if self.use_labels:
          lowerCAmelCase_       :    Any								=			ids_tensor([self.batch_size]   ,      self.type_sequence_label_size	)
          lowerCAmelCase_       :    List[str]								=			ids_tensor([self.batch_size, self.seq_length]   ,      self.num_labels	)
          lowerCAmelCase_       :    Dict								=			ids_tensor([self.batch_size]   ,      self.num_choices	)

        lowerCAmelCase_       :    List[Any]								=			self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        return AlbertConfig(
            vocab_size=self.vocab_size   ,      hidden_size=self.hidden_size   ,      num_hidden_layers=self.num_hidden_layers   ,      num_attention_heads=self.num_attention_heads   ,      intermediate_size=self.intermediate_size   ,      hidden_act=self.hidden_act   ,      hidden_dropout_prob=self.hidden_dropout_prob   ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,      max_position_embeddings=self.max_position_embeddings   ,      type_vocab_size=self.type_vocab_size   ,      initializer_range=self.initializer_range   ,      num_hidden_groups=self.num_hidden_groups   ,      )



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				str	):
        lowerCAmelCase_       :    Union[str, Any]								=			AlbertModel(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    List[str]								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    List[Any]								=			model(SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    int								=			model(SCREAMING_SNAKE_CASE_	)
        self.parent.assertEqual(result.last_hidden_state.shape   ,      (self.batch_size, self.seq_length, self.hidden_size)	)
        self.parent.assertEqual(result.pooler_output.shape   ,      (self.batch_size, self.hidden_size)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Tuple	):
        lowerCAmelCase_       :    Optional[Any]								=			AlbertForPreTraining(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    Optional[Any]								=			model(
            SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_   ,      sentence_order_label=SCREAMING_SNAKE_CASE_   ,      )
        self.parent.assertEqual(result.prediction_logits.shape   ,      (self.batch_size, self.seq_length, self.vocab_size)	)
        self.parent.assertEqual(result.sop_logits.shape   ,      (self.batch_size, config.num_labels)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Dict	):
        lowerCAmelCase_       :    str								=			AlbertForMaskedLM(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    str								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_	)
        self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.seq_length, self.vocab_size)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				int   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]	):
        lowerCAmelCase_       :    List[str]								=			AlbertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    Any								=			model(
            SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_   ,      start_positions=SCREAMING_SNAKE_CASE_   ,      end_positions=SCREAMING_SNAKE_CASE_   ,      )
        self.parent.assertEqual(result.start_logits.shape   ,      (self.batch_size, self.seq_length)	)
        self.parent.assertEqual(result.end_logits.shape   ,      (self.batch_size, self.seq_length)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Dict	):
        lowerCAmelCase_       :    Union[str, Any]								=			self.num_labels
        lowerCAmelCase_       :    Union[str, Any]								=			AlbertForSequenceClassification(SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    Union[str, Any]								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_	)
        self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_labels)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				List[str]	):
        lowerCAmelCase_       :    List[str]								=			self.num_labels
        lowerCAmelCase_       :    List[Any]								=			AlbertForTokenClassification(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    str								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_	)
        self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.seq_length, self.num_labels)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				Any   ,      SCREAMING_SNAKE_CASE_    :				str   ,      SCREAMING_SNAKE_CASE_    :				List[str]	):
        lowerCAmelCase_       :    Optional[Any]								=			self.num_choices
        lowerCAmelCase_       :    int								=			AlbertForMultipleChoice(config=SCREAMING_SNAKE_CASE_	)
        model.to(SCREAMING_SNAKE_CASE_	)
        model.eval()
        lowerCAmelCase_       :    Optional[int]								=			input_ids.unsqueeze(1	).expand(-1   ,      self.num_choices   ,      -1	).contiguous()
        lowerCAmelCase_       :    List[Any]								=			token_type_ids.unsqueeze(1	).expand(-1   ,      self.num_choices   ,      -1	).contiguous()
        lowerCAmelCase_       :    List[Any]								=			input_mask.unsqueeze(1	).expand(-1   ,      self.num_choices   ,      -1	).contiguous()
        lowerCAmelCase_       :    List[Any]								=			model(
            SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_   ,      token_type_ids=SCREAMING_SNAKE_CASE_   ,      labels=SCREAMING_SNAKE_CASE_   ,      )
        self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_choices)	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        lowerCAmelCase_       :    Optional[int]								=			self.prepare_config_and_inputs()
        (
            (
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,
        )       :    Optional[int]								=			config_and_inputs
        lowerCAmelCase_       :    Dict								=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict







@require_torch
class 				UpperCamelCase__       (     lowercase_, lowercase_, unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					(
          (
              AlbertModel,
              AlbertForPreTraining,
              AlbertForMaskedLM,
              AlbertForMultipleChoice,
              AlbertForSequenceClassification,
              AlbertForTokenClassification,
              AlbertForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      _SCREAMING_SNAKE_CASE      =					(
          {
              """feature-extraction""": AlbertModel,
              """fill-mask""": AlbertForMaskedLM,
              """question-answering""": AlbertForQuestionAnswering,
              """text-classification""": AlbertForSequenceClassification,
              """token-classification""": AlbertForTokenClassification,
              """zero-shot""": AlbertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _SCREAMING_SNAKE_CASE      =					True



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Dict   ,      SCREAMING_SNAKE_CASE_    :				List[str]   ,      SCREAMING_SNAKE_CASE_    :				str=False	):
        lowerCAmelCase_       :    List[str]								=			super()._prepare_for_class(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_   ,      return_labels=SCREAMING_SNAKE_CASE_	)

        if return_labels:
          if model_class in get_values(SCREAMING_SNAKE_CASE_	):
            lowerCAmelCase_       :    List[str]								=			torch.zeros(
                (self.model_tester.batch_size, self.model_tester.seq_length)   ,      dtype=torch.long   ,      device=SCREAMING_SNAKE_CASE_	)
            lowerCAmelCase_       :    Any								=			torch.zeros(
                self.model_tester.batch_size   ,      dtype=torch.long   ,      device=SCREAMING_SNAKE_CASE_	)
        return inputs_dict



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    str								=			AlbertModelTester(self	)
        lowerCAmelCase_       :    Optional[int]								=			ConfigTester(self   ,      config_class=SCREAMING_SNAKE_CASE_   ,      hidden_size=3_7	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]	):
        self.config_tester.run_common_tests()



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        lowerCAmelCase_       :    List[Any]								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_       :    Any								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        lowerCAmelCase_       :    Tuple								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    List[Any]								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    str								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    Optional[int]								=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_       :    Tuple								=			self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
          lowerCAmelCase_       :    int								=			type
          self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)





      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_       :    Optional[Any]								=			AlbertModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)







@require_torch
class 				UpperCamelCase__       (     unittest.TestCase  ):




      """simple docstring"""




      @slow
      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        lowerCAmelCase_       :    Any								=			AlbertModel.from_pretrained('albert-base-v2'	)
        lowerCAmelCase_       :    Tuple								=			torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]	)
        lowerCAmelCase_       :    Tuple								=			torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
        with torch.no_grad():
          lowerCAmelCase_       :    List[Any]								=			model(SCREAMING_SNAKE_CASE_   ,      attention_mask=SCREAMING_SNAKE_CASE_	)[0]
        lowerCAmelCase_       :    str								=			torch.Size((1, 1_1, 7_6_8)	)
        self.assertEqual(output.shape   ,      SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    int								=			torch.tensor(
            [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]	)

        self.assertTrue(torch.allclose(output[:, 1:4, 1:4]   ,      SCREAMING_SNAKE_CASE_   ,      atol=1E-4	)	)





