






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


__a   :							List[str]						= logging.getLogger(__name__)


@dataclass
class    __lowercase   :







     '''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    __lowercase   :







     '''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=1_28      ,   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       _SCREAMING_SNAKE_CASE    (			)    ->   Tuple:





    """simple docstring"""

    __A       =						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.
        __A     ,						__A     ,						__A       =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
    else:
        __A     ,						__A     ,						__A       =						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."""     )

    __A       =						import_module("""tasks"""     )
    try:
        __A       =						getattr(__lowercase      ,					model_args.task_type     )
        __A       =						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
    __A       =						token_classification_task.get_labels(data_args.labels     )
    __A       =						dict(enumerate(__lowercase     )     )
    __A       =						len(__lowercase     )

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

    __A       =						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      ,					)
    __A       =						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      ,					)
    __A       =						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
    __A       =						(
        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
    )
    __A       =						(
        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]]:
        __A       =						np.argmax(__lowercase      ,					axis=2     )

        __A     ,						__A       =						preds.shape

        __A       =						[[] for _ in range(__lowercase     )]
        __A       =						[[] 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:
        __A     ,						__A       =						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
    __A       =						DataCollatorWithPadding(__lowercase      ,					pad_to_multiple_of=8     ) if training_args.fpaa else None

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

        __A       =						trainer.evaluate()

        __A       =						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:
        __A       =						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      ,					)

        __A     ,						__A     ,						__A       =						trainer.predict(__lowercase     )
        __A     ,						__A       =						align_predictions(__lowercase      ,					__lowercase     )

        __A       =						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
        __A       =						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       _SCREAMING_SNAKE_CASE    (			__lowercase :      Any     )    ->   Optional[int]:





    """simple docstring"""

    main()


if __name__ == "__main__":
  main()












import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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 (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
  )


class    __lowercase   (						lowercase_	):







     '''simple docstring'''




     def __init__(					self							:      Optional[int]			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      List[str]=13			,						UpperCamelCase_							:      int=7			,						UpperCamelCase_							:      Dict=True			,						UpperCamelCase_							:      int=True			,						UpperCamelCase_							:      str=False			,						UpperCamelCase_							:      Dict=True			,						UpperCamelCase_							:      Union[str, Any]=99			,						UpperCamelCase_							:      Any=32			,						UpperCamelCase_							:      Union[str, Any]=5			,						UpperCamelCase_							:      List[str]=4			,						UpperCamelCase_							:      Union[str, Any]=64			,						UpperCamelCase_							:      Any="gelu"			,						UpperCamelCase_							:      str=0.1			,						UpperCamelCase_							:      List[Any]=0.1			,						UpperCamelCase_							:      int=512			,						UpperCamelCase_							:      Dict=16			,						UpperCamelCase_							:      List[str]=2			,						UpperCamelCase_							:      int=0.02			,						UpperCamelCase_							:      Dict=3			,						UpperCamelCase_							:      List[Any]=4			,						UpperCamelCase_							:      int=None			,						UpperCamelCase_							:      Any=2			,						UpperCamelCase_							:      Any=2			,						UpperCamelCase_							:      int=2			,						UpperCamelCase_							:      Optional[Any]=2			,						UpperCamelCase_							:      str=4			,						UpperCamelCase_							:      List[str]=1			,						):


         """simple docstring"""



         __A       =						parent
         __A       =						batch_size
         __A       =						seq_length
         __A       =						is_training
         __A       =						use_input_mask
         __A       =						use_token_type_ids
         __A       =						use_labels
         __A       =						vocab_size
         __A       =						hidden_size
         __A       =						num_hidden_layers
         __A       =						num_attention_heads
         __A       =						intermediate_size
         __A       =						hidden_act
         __A       =						hidden_dropout_prob
         __A       =						attention_probs_dropout_prob
         __A       =						max_position_embeddings
         __A       =						type_vocab_size
         __A       =						type_sequence_label_size
         __A       =						initializer_range
         __A       =						num_labels
         __A       =						num_choices
         __A       =						scope
         __A       =						q_groups
         __A       =						k_groups
         __A       =						v_groups
         __A       =						post_attention_groups
         __A       =						intermediate_groups
         __A       =						output_groups




     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __A       =						ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size					)

         __A       =						None
         if self.use_input_mask:
             __A       =						random_attention_mask([self.batch_size, self.seq_length]					)

         __A       =						None
         __A       =						None
         __A       =						None
         if self.use_labels:
             __A       =						ids_tensor([self.batch_size]			,						self.type_sequence_label_size					)
             __A       =						ids_tensor([self.batch_size, self.seq_length]			,						self.num_labels					)
             __A       =						ids_tensor([self.batch_size]			,						self.num_choices					)

         __A       =						self.get_config()

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




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         return SqueezeBertConfig(
             embedding_size=self.hidden_size			,						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			,						attention_probs_dropout_prob=self.hidden_dropout_prob			,						attention_dropout=self.attention_probs_dropout_prob			,						max_position_embeddings=self.max_position_embeddings			,						initializer_range=self.initializer_range			,						q_groups=self.q_groups			,						k_groups=self.k_groups			,						v_groups=self.v_groups			,						post_attention_groups=self.post_attention_groups			,						intermediate_groups=self.intermediate_groups			,						output_groups=self.output_groups			,						)




     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Dict					):


         """simple docstring"""



         __A       =						SqueezeBertModel(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(UpperCamelCase_			,						UpperCamelCase_					)
         __A       =						model(UpperCamelCase_					)
         self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)					)




     def 		lowerCAmelCase_  (					self							:      List[str]			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      List[str]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      Optional[int]					):


         """simple docstring"""



         __A       =						SqueezeBertForMaskedLM(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_					)
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.vocab_size)					)




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      Optional[Any]					):


         """simple docstring"""



         __A       =						SqueezeBertForQuestionAnswering(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(
             UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						start_positions=UpperCamelCase_			,						end_positions=UpperCamelCase_					)
         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 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      Any			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      Any					):


         """simple docstring"""



         __A       =						self.num_labels
         __A       =						SqueezeBertForSequenceClassification(UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_					)
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)					)




     def 		lowerCAmelCase_  (					self							:      Any			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      int					):


         """simple docstring"""



         __A       =						self.num_labels
         __A       =						SqueezeBertForTokenClassification(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()

         __A       =						model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_					)
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.num_labels)					)




     def 		lowerCAmelCase_  (					self							:      Tuple			,						UpperCamelCase_							:      List[str]			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Dict					):


         """simple docstring"""



         __A       =						self.num_choices
         __A       =						SqueezeBertForMultipleChoice(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						input_ids.unsqueeze(1					).expand(-1			,						self.num_choices			,						-1					).contiguous()
         __A       =						input_mask.unsqueeze(1					).expand(-1			,						self.num_choices			,						-1					).contiguous()
         __A       =						model(
             UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_			,						)
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_choices)					)





     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         __A       =						self.prepare_config_and_inputs()
         ((__A)     ,						(__A)     ,						(__A)     ,						(__A)     ,						(__A)     ,						(__A))       =						config_and_inputs
         __A       =						{"""input_ids""": input_ids, """attention_mask""": input_mask}
         return config, inputs_dict


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







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					(
         (
             SqueezeBertModel,
             SqueezeBertForMaskedLM,
             SqueezeBertForMultipleChoice,
             SqueezeBertForQuestionAnswering,
             SqueezeBertForSequenceClassification,
             SqueezeBertForTokenClassification,
         )
         if is_torch_available()
         else None
     )
     SCREAMING_SNAKE_CASE     =					(
         {
             "feature-extraction": SqueezeBertModel,
             "fill-mask": SqueezeBertForMaskedLM,
             "question-answering": SqueezeBertForQuestionAnswering,
             "text-classification": SqueezeBertForSequenceClassification,
             "token-classification": SqueezeBertForTokenClassification,
             "zero-shot": SqueezeBertForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     SCREAMING_SNAKE_CASE     =					False
     SCREAMING_SNAKE_CASE     =					True
     SCREAMING_SNAKE_CASE     =					False




     def 		lowerCAmelCase_  (					self							:      Tuple					):


         """simple docstring"""



         __A       =						SqueezeBertModelTester(self					)
         __A       =						ConfigTester(self			,						config_class=UpperCamelCase_			,						dim=37					)




     def 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



         self.config_tester.run_common_tests()




     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_model(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_masked_lm(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_question_answering(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_sequence_classification(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_token_classification(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_squeezebert_for_multiple_choice(*UpperCamelCase_					)





     @slow
     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __A       =						SqueezeBertModel.from_pretrained(UpperCamelCase_					)
             self.assertIsNotNone(UpperCamelCase_					)


@require_sentencepiece
@require_tokenizers
@require_torch
class    __lowercase   (						unittest.TestCase	):







     '''simple docstring'''




     @slow
     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         __A       =						SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli"""					)

         __A       =						torch.tensor([[1, 29_414, 232, 328, 740, 1_140, 12_695, 69, 13, 1_588, 2]]					)
         __A       =						model(UpperCamelCase_					)[0]
         __A       =						torch.Size((1, 3)					)
         self.assertEqual(output.shape			,						UpperCamelCase_					)
         __A       =						torch.tensor([[0.6401, -0.0349, -0.6041]]					)
         self.assertTrue(torch.allclose(UpperCamelCase_			,						UpperCamelCase_			,						atol=1e-4					)					)




