'''simple docstring'''


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional

import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
    utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


snake_case__     :			Any      			=  '''
Distills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class
names. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but
when only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled
dataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided
class names.
'''

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






@dataclass
class      __SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  lowerCamelCase_		:Optional[str]						    =		field(
      default='''roberta-large-mnli'''						,	metadata={'''help''': '''The NLI/zero-shot teacher model to be distilled.'''}       )
  lowerCamelCase_		:Optional[str]						    =		field(
      default='''This example is {}.'''						,	metadata={
          '''help''': (
              '''Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}'''
              '''where class name is inserted.'''
          )
      }						,	)
  lowerCamelCase_		:Optional[int]						    =		field(
      default=32						,	metadata={'''help''': '''Batch size for generating teacher predictions.'''}       )
  lowerCamelCase_		:Optional[bool]						    =		field(
      default=lowerCamelCase_						,	metadata={
          '''help''': (
              '''Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called'''
              '''multi-class multi-label classification).'''
          )
      }						,	)
  lowerCamelCase_		:Optional[float]						    =		field(
      default=1.0						,	metadata={'''help''': '''Temperature applied to teacher softmax for distillation.'''}       )







@dataclass
class      __SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  lowerCamelCase_		:Optional[str]						    =		field(
      default='''distilbert-base-uncased'''						,	metadata={'''help''': '''The NLI/zero-shot teacher model to be distilled.'''}       )







@dataclass
class      __SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  lowerCamelCase_		:str						    =		field(metadata={'''help''': '''Text file with one unlabeled instance per line.'''}       )
  lowerCamelCase_		:str						    =		field(metadata={'''help''': '''Text file with one class name per line.'''}       )
  lowerCamelCase_		:bool						    =		field(
      default=lowerCamelCase_						,	metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not.'''}						,	)







@dataclass
class      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_       ):







  '''simple docstring'''






  lowerCamelCase_		:Optional[str]						    =		field(
      default=lowerCamelCase_						,	metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}						,	)
  lowerCamelCase_		:int						    =		field(
      default=32						,	metadata={'''help''': '''Batch size per GPU/TPU core/CPU for training.'''}       )
  lowerCamelCase_		:int						    =		field(
      default=128						,	metadata={'''help''': '''Batch size per GPU/TPU core/CPU for evaluation.'''}       )
  lowerCamelCase_		:float						    =		field(default=1.0						,	metadata={'''help''': '''Total number of training epochs to perform.'''}       )
  lowerCamelCase_		:bool						    =		field(default=lowerCamelCase_						,	metadata={'''help''': '''Whether to run training of student model.'''}       )
  lowerCamelCase_		:bool						    =		field(
      default=lowerCamelCase_						,	metadata={
          '''help''': (
              '''Whether to evaluate the agreement of the final student predictions and the teacher predictions'''
              '''after training.'''
          )
      }						,	)
  lowerCamelCase_		:Optional[int]						    =		field(
      default=0						,	metadata={
          '''help''': (
              '''Limit the total amount of checkpoints.'''
              '''Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints).'''
          )
      }						,	)







class      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_       ):







  '''simple docstring'''






  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_=False						):




       '''simple docstring'''

       UpperCAmelCase_							:   Tuple   =      inputs['labels']
       UpperCAmelCase_							:   List[str]   =      model(inputs['input_ids']      ,		attention_mask=inputs['attention_mask']						)
       UpperCAmelCase_							:   List[str]   =      outputs[0]

       UpperCAmelCase_							:   Dict   =      -torch.sum(target_p * logits.log_softmax(dim=-1						)      ,		axis=-1						).mean()

       if return_outputs:
            return loss, outputs

       return loss







def 					_lowerCamelCase    (      lowerCamelCase_					:							Optional[int]   ):



     """simple docstring"""





     UpperCAmelCase_							:   Optional[Any]   =      []
     with open(lowerCamelCase_	,     'r'   ) as f:
          for line in f:
               UpperCAmelCase_							:   Optional[int]   =      line.strip()
               if len(lowerCamelCase_   ) > 0:
                    lines.append(lowerCamelCase_   )
     return lines


def 					_lowerCamelCase    (      lowerCamelCase_					:							Dict	,     lowerCamelCase_					:							str	,     lowerCamelCase_					:							Optional[Any]   ):



     """simple docstring"""





     UpperCAmelCase_							:   str   =      []
     UpperCAmelCase_							:   Any   =      []
     for example in examples:
          for name in class_names:
               premises.append(lowerCamelCase_   )
               hypotheses.append(hypothesis_template.format(lowerCamelCase_   )   )
     return premises, hypotheses


def 					_lowerCamelCase    (      lowerCamelCase_					:							Union[str, Any]   ):



     """simple docstring"""





     for label, ind in config.labelaid.items():
          if label.lower().startswith('entail'   ):
               return ind
     logger.warning('Could not identify entailment dimension from teacher config label2id. Setting to -1.'   )
     return -1


def 					_lowerCamelCase    (      lowerCamelCase_					:							str	,     lowerCamelCase_					:							List[str]	,     lowerCamelCase_					:							List[str]	,     lowerCamelCase_					:							str	,     lowerCamelCase_					:							int	,     lowerCamelCase_					:							float	,     lowerCamelCase_					:							bool	,     lowerCamelCase_					:							bool	,     lowerCamelCase_					:							bool	,     lowerCamelCase_					:							bool	,     ):



     """simple docstring"""





     UpperCAmelCase_							:   Optional[int]   =      AutoModelForSequenceClassification.from_pretrained(lowerCamelCase_   )
     UpperCAmelCase_							:   str   =      model.config
     if not no_cuda and torch.cuda.is_available():
          UpperCAmelCase_							:   Dict   =      nn.DataParallel(model.cuda()   )
          batch_size *= len(model.device_ids   )
     UpperCAmelCase_							:   str   =      AutoTokenizer.from_pretrained(lowerCamelCase_	,     use_fast=lowerCamelCase_   )

     UpperCAmelCase_   ,			UpperCAmelCase_							:   int   =      get_premise_hypothesis_pairs(lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_   )
     UpperCAmelCase_							:   Any   =      []

     for i in tqdm(range(0	,     len(lowerCamelCase_   )	,     lowerCamelCase_   )   ):
          UpperCAmelCase_							:   int   =      premises[i : i + batch_size]
          UpperCAmelCase_							:   Optional[int]   =      hypotheses[i : i + batch_size]

          UpperCAmelCase_							:   Optional[Any]   =      tokenizer(
              lowerCamelCase_	,     lowerCamelCase_	,     padding=lowerCamelCase_	,     truncation='only_first'	,     return_tensors='pt'	,     )

          with torch.cuda.amp.autocast(enabled=lowerCamelCase_   ):
               with torch.no_grad():
                    UpperCAmelCase_							:   str   =      model(**lowerCamelCase_   )
          logits.append(outputs.logits.detach().cpu().float()   )

     UpperCAmelCase_							:   str   =      get_entailment_id(lowerCamelCase_   )
     UpperCAmelCase_							:   Union[str, Any]   =      -1 if entail_id == 0 else 0
     UpperCAmelCase_							:   List[str]   =      torch.cat(lowerCamelCase_	,     dim=0   )  # N*K x 3
     UpperCAmelCase_							:   Optional[Any]   =      logits.reshape(len(lowerCamelCase_   )	,     len(lowerCamelCase_   )	,     -1   )[..., [contr_id, entail_id]]  # N x K x 2

     if multi_label:
          # softmax over (contr, entail) logits for each class independently
          UpperCAmelCase_							:   Optional[Any]   =      (nli_logits / temperature).softmax(-1   )
     else:
          # softmax over entail logits across classes s.t. class probabilities sum to 1.
          UpperCAmelCase_							:   int   =      (nli_logits / temperature).softmax(1   )

     return nli_prob[..., 1]  # N x K


def 					_lowerCamelCase    (      ):



     """simple docstring"""





     UpperCAmelCase_							:   Union[str, Any]   =      HfArgumentParser(
         (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)	,     description=lowerCamelCase_	,     )

     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_   ,			UpperCAmelCase_							:   Optional[Any]   =      parser.parse_json_file(
              json_file=os.path.abspath(sys.argv[1]   )   )
     else:
          UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_							:   Any   =      parser.parse_args_into_dataclasses()

     # Detecting last checkpoint.
     UpperCAmelCase_							:   Tuple   =      None
     if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
          UpperCAmelCase_							:   str   =      get_last_checkpoint(training_args.output_dir   )
          if last_checkpoint is None and len(os.listdir(training_args.output_dir   )   ) > 0:
               raise ValueError(
                   F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
                   'Use --overwrite_output_dir to overcome.'   )
          elif last_checkpoint is not None:
               logger.info(
                   F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
                   'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'   )

    # Setup logging
     logging.basicConfig(
         format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'	,     datefmt='%m/%d/%Y %H:%M:%S'	,     handlers=[logging.StreamHandler(sys.stdout   )]	,     )
     logger.setLevel(logging.INFO if is_main_process(training_args.local_rank   ) else logging.WARN   )

     # Log on each process the small summary:
     logger.warning(
         F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
         + F'''distributed training: {bool(training_args.local_rank != -1   )}, 16-bits training: {training_args.fpaa}'''   )
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank   ):
          utils.logging.set_verbosity_info()
          utils.logging.enable_default_handler()
          utils.logging.enable_explicit_format()

     if training_args.local_rank != -1:
          raise ValueError('Distributed training is not currently supported.'   )
     if training_args.tpu_num_cores is not None:
          raise ValueError('TPU acceleration is not currently supported.'   )

     logger.info(F'''Training/evaluation parameters {training_args}'''   )

     # Set seed before initializing model.
     set_seed(training_args.seed   )

     # 1. read in data
     UpperCAmelCase_							:   Optional[int]   =      read_lines(data_args.data_file   )
     UpperCAmelCase_							:   Any   =      read_lines(data_args.class_names_file   )

     # 2. get teacher predictions and load into dataset
     logger.info('Generating predictions from zero-shot teacher model'   )
     UpperCAmelCase_							:   int   =      get_teacher_predictions(
         teacher_args.teacher_name_or_path	,     lowerCamelCase_	,     lowerCamelCase_	,     teacher_args.hypothesis_template	,     teacher_args.teacher_batch_size	,     teacher_args.temperature	,     teacher_args.multi_label	,     data_args.use_fast_tokenizer	,     training_args.no_cuda	,     training_args.fpaa	,     )
     UpperCAmelCase_							:   Union[str, Any]   =      Dataset.from_dict(
         {
             'text': examples,
             'labels': teacher_soft_preds,
         }   )

     # 3. create student
     logger.info('Initializing student model'   )
     UpperCAmelCase_							:   Optional[Any]   =      AutoModelForSequenceClassification.from_pretrained(
         student_args.student_name_or_path	,     num_labels=len(lowerCamelCase_   )   )
     UpperCAmelCase_							:   int   =      AutoTokenizer.from_pretrained(student_args.student_name_or_path	,     use_fast=data_args.use_fast_tokenizer   )
     UpperCAmelCase_							:   int   =      dict(enumerate(lowerCamelCase_   )   )
     UpperCAmelCase_							:   Optional[Any]   =      {label: i for i, label in enumerate(lowerCamelCase_   )}

     # 4. train student on teacher predictions
     UpperCAmelCase_							:   Optional[int]   =      dataset.map(lowerCamelCase_	,     input_columns='text'   )
     dataset.set_format('torch'   )

     def compute_metrics(lowerCamelCase_					:							Any	,     lowerCamelCase_					:							Optional[Any]=False   ):
          UpperCAmelCase_							:   int   =      p.predictions.argmax(-1   )
          UpperCAmelCase_							:   List[str]   =      p.label_ids.argmax(-1   )  # "label_ids" are actually distributions
          return {"agreement": (preds == proxy_labels).mean().item()}

     UpperCAmelCase_							:   Union[str, Any]   =      DistillationTrainer(
         model=lowerCamelCase_	,     tokenizer=lowerCamelCase_	,     args=lowerCamelCase_	,     train_dataset=lowerCamelCase_	,     compute_metrics=lowerCamelCase_	,     )

     if training_args.do_train:
          logger.info('Training student model on teacher predictions'   )
          trainer.train()

     if training_args.do_eval:
          UpperCAmelCase_							:   Any   =      trainer.evaluate(eval_dataset=lowerCamelCase_   )['eval_agreement']
          logger.info(F'''Agreement of student and teacher predictions: {agreement * 100:0.2f}%'''   )

     trainer.save_model()


if __name__ == "__main__":
 main()







'''simple docstring'''


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFXLMForMultipleChoice,
     TFXLMForQuestionAnsweringSimple,
     TFXLMForSequenceClassification,
     TFXLMForTokenClassification,
     TFXLMModel,
     TFXLMWithLMHeadModel,
     XLMConfig,
 )






class      __SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__( self      ,		snake_case_      ,		):




       '''simple docstring'''

       UpperCAmelCase_							:   str   =      parent
       UpperCAmelCase_							:   Tuple   =      1_3
       UpperCAmelCase_							:   Dict   =      7
       UpperCAmelCase_							:   Union[str, Any]   =      True
       UpperCAmelCase_							:   Optional[Any]   =      True
       UpperCAmelCase_							:   Tuple   =      True
       UpperCAmelCase_							:   Dict   =      True
       UpperCAmelCase_							:   List[Any]   =      True
       UpperCAmelCase_							:   Union[str, Any]   =      False
       UpperCAmelCase_							:   Any   =      False
       UpperCAmelCase_							:   List[str]   =      False
       UpperCAmelCase_							:   int   =      2
       UpperCAmelCase_							:   Union[str, Any]   =      9_9
       UpperCAmelCase_							:   Dict   =      0
       UpperCAmelCase_							:   List[str]   =      3_2
       UpperCAmelCase_							:   str   =      2
       UpperCAmelCase_							:   int   =      4
       UpperCAmelCase_							:   Dict   =      0.1
       UpperCAmelCase_							:   int   =      0.1
       UpperCAmelCase_							:   List[str]   =      5_1_2
       UpperCAmelCase_							:   Union[str, Any]   =      1_6
       UpperCAmelCase_							:   List[Any]   =      2
       UpperCAmelCase_							:   int   =      0.02
       UpperCAmelCase_							:   Union[str, Any]   =      3
       UpperCAmelCase_							:   Union[str, Any]   =      4
       UpperCAmelCase_							:   Optional[int]   =      'last'
       UpperCAmelCase_							:   List[Any]   =      True
       UpperCAmelCase_							:   Union[str, Any]   =      None
       UpperCAmelCase_							:   int   =      0


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Tuple   =      ids_tensor([self.batch_size, self.seq_length]      ,		self.vocab_size						)
       UpperCAmelCase_							:   Tuple   =      random_attention_mask([self.batch_size, self.seq_length]      ,		dtype=tf.floataa						)

       UpperCAmelCase_							:   Optional[Any]   =      None
       if self.use_input_lengths:
            UpperCAmelCase_							:   Any   =      (
                ids_tensor([self.batch_size]      ,		vocab_size=2						) + self.seq_length - 2
            )  # small variation of seq_length

       UpperCAmelCase_							:   Optional[Any]   =      None
       if self.use_token_type_ids:
            UpperCAmelCase_							:   Tuple   =      ids_tensor([self.batch_size, self.seq_length]      ,		self.n_langs						)

       UpperCAmelCase_							:   Any   =      None
       UpperCAmelCase_							:   Union[str, Any]   =      None
       UpperCAmelCase_							:   Optional[int]   =      None
       if self.use_labels:
            UpperCAmelCase_							:   Union[str, Any]   =      ids_tensor([self.batch_size]      ,		self.type_sequence_label_size						)
            UpperCAmelCase_							:   Any   =      ids_tensor([self.batch_size, self.seq_length]      ,		self.num_labels						)
            UpperCAmelCase_							:   Union[str, Any]   =      ids_tensor([self.batch_size]      ,		2      ,		dtype=tf.floataa						)
            UpperCAmelCase_							:   Any   =      ids_tensor([self.batch_size]      ,		self.num_choices						)

       UpperCAmelCase_							:   int   =      XLMConfig(
           vocab_size=self.vocab_size      ,		n_special=self.n_special      ,		emb_dim=self.hidden_size      ,		n_layers=self.num_hidden_layers      ,		n_heads=self.num_attention_heads      ,		dropout=self.hidden_dropout_prob      ,		attention_dropout=self.attention_probs_dropout_prob      ,		gelu_activation=self.gelu_activation      ,		sinusoidal_embeddings=self.sinusoidal_embeddings      ,		asm=self.asm      ,		causal=self.causal      ,		n_langs=self.n_langs      ,		max_position_embeddings=self.max_position_embeddings      ,		initializer_range=self.initializer_range      ,		summary_type=self.summary_type      ,		use_proj=self.use_proj      ,		bos_token_id=self.bos_token_id      ,		)

       return (
           config,
           input_ids,
           token_type_ids,
           input_lengths,
           sequence_labels,
           token_labels,
           is_impossible_labels,
           choice_labels,
           input_mask,
       )


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		):




       '''simple docstring'''

       UpperCAmelCase_							:   Tuple   =      TFXLMModel(config=snake_case_						)
       UpperCAmelCase_							:   int   =      {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
       UpperCAmelCase_							:   Tuple   =      model(snake_case_						)

       UpperCAmelCase_							:   Any   =      [input_ids, input_mask]
       UpperCAmelCase_							:   Optional[Any]   =      model(snake_case_						)
       self.parent.assertEqual(result.last_hidden_state.shape      ,		(self.batch_size, self.seq_length, self.hidden_size)						)


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		):




       '''simple docstring'''

       UpperCAmelCase_							:   List[str]   =      TFXLMWithLMHeadModel(snake_case_						)

       UpperCAmelCase_							:   Union[str, Any]   =      {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
       UpperCAmelCase_							:   Optional[Any]   =      model(snake_case_						)

       UpperCAmelCase_							:   Tuple   =      outputs

       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.seq_length, self.vocab_size)						)


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		):




       '''simple docstring'''

       UpperCAmelCase_							:   List[str]   =      TFXLMForQuestionAnsweringSimple(snake_case_						)

       UpperCAmelCase_							:   Optional[int]   =      {'input_ids': input_ids, 'lengths': input_lengths}

       UpperCAmelCase_							:   Optional[int]   =      model(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 _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		):




       '''simple docstring'''

       UpperCAmelCase_							:   List[str]   =      TFXLMForSequenceClassification(snake_case_						)

       UpperCAmelCase_							:   Dict   =      {'input_ids': input_ids, 'lengths': input_lengths}

       UpperCAmelCase_							:   Tuple   =      model(snake_case_						)

       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.type_sequence_label_size)						)


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		):




       '''simple docstring'''

       UpperCAmelCase_							:   int   =      self.num_labels
       UpperCAmelCase_							:   List[str]   =      TFXLMForTokenClassification(config=snake_case_						)
       UpperCAmelCase_							:   str   =      {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
       UpperCAmelCase_							:   Optional[int]   =      model(snake_case_						)
       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.seq_length, self.num_labels)						)


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		):




       '''simple docstring'''

       UpperCAmelCase_							:   List[Any]   =      self.num_choices
       UpperCAmelCase_							:   Optional[Any]   =      TFXLMForMultipleChoice(config=snake_case_						)
       UpperCAmelCase_							:   Tuple   =      tf.tile(tf.expand_dims(snake_case_      ,		1						)      ,		(1, self.num_choices, 1)						)
       UpperCAmelCase_							:   Dict   =      tf.tile(tf.expand_dims(snake_case_      ,		1						)      ,		(1, self.num_choices, 1)						)
       UpperCAmelCase_							:   str   =      tf.tile(tf.expand_dims(snake_case_      ,		1						)      ,		(1, self.num_choices, 1)						)
       UpperCAmelCase_							:   Tuple   =      {
           'input_ids': multiple_choice_inputs_ids,
           'attention_mask': multiple_choice_input_mask,
           'token_type_ids': multiple_choice_token_type_ids,
       }
       UpperCAmelCase_							:   int   =      model(snake_case_						)
       self.parent.assertEqual(result.logits.shape      ,		(self.batch_size, self.num_choices)						)






  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[int]   =      self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase_
       )   ,			(
           UpperCAmelCase_
       )   ,			(
           UpperCAmelCase_
       )   ,			(
           UpperCAmelCase_
       )   ,			(
           UpperCAmelCase_
       )   ,			(
           UpperCAmelCase_
       )   ,			(
           UpperCAmelCase_
       )   ,			(
           UpperCAmelCase_
       )   ,			(
           UpperCAmelCase_
       )   ,			
       )							:   Optional[int]   =      config_and_inputs
       UpperCAmelCase_							:   Any   =      {
           'input_ids': input_ids,
           'token_type_ids': token_type_ids,
           'langs': token_type_ids,
           'lengths': input_lengths,
       }
       return config, inputs_dict







@require_tf
class      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_						,	lowerCamelCase_						,	unittest.TestCase       ):







  '''simple docstring'''






  lowerCamelCase_		:List[Any]						    =		(
      (
          TFXLMModel,
          TFXLMWithLMHeadModel,
          TFXLMForSequenceClassification,
          TFXLMForQuestionAnsweringSimple,
          TFXLMForTokenClassification,
          TFXLMForMultipleChoice,
      )
      if is_tf_available()
      else ()
  )
  lowerCamelCase_		:str						    =		(
      (TFXLMWithLMHeadModel,) if is_tf_available() else ()
  )  # TODO (PVP): Check other models whether language generation is also applicable
  lowerCamelCase_		:int						    =		(
      {
          '''feature-extraction''': TFXLMModel,
          '''fill-mask''': TFXLMWithLMHeadModel,
          '''question-answering''': TFXLMForQuestionAnsweringSimple,
          '''text-classification''': TFXLMForSequenceClassification,
          '''text-generation''': TFXLMWithLMHeadModel,
          '''token-classification''': TFXLMForTokenClassification,
          '''zero-shot''': TFXLMForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  lowerCamelCase_		:List[Any]						    =		False
  lowerCamelCase_		:Dict						    =		False


  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

       if (
           pipeline_test_casse_name == "QAPipelineTests"
           and tokenizer_name is not None
           and not tokenizer_name.endswith('Fast'						)
       ):
            # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
            # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
            # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
            return True

       return False


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   str   =      TFXLMModelTester(self						)
       UpperCAmelCase_							:   int   =      ConfigTester(self      ,		config_class=snake_case_      ,		emb_dim=3_7						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       self.config_tester.run_common_tests()


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Any   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_model(*snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Optional[int]   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_lm_head(*snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   List[Any]   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_qa(*snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Tuple   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_sequence_classif(*snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Union[str, Any]   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_for_token_classification(*snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Union[str, Any]   =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_for_multiple_choice(*snake_case_						)






  @slow
  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase_							:   int   =      TFXLMModel.from_pretrained(snake_case_						)
            self.assertIsNotNone(snake_case_						)







@require_tf
class      __SCREAMING_SNAKE_CASE						(       unittest.TestCase       ):







  '''simple docstring'''






  @slow
  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       UpperCAmelCase_							:   Any   =      TFXLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'						)
       UpperCAmelCase_							:   List[str]   =      tf.convert_to_tensor([[1_4, 4_4_7]]      ,		dtype=tf.intaa						)  # the president
       UpperCAmelCase_							:   Optional[int]   =      [
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
           1_4,
           4_4_7,
       ]  # the president the president the president the president the president the president the president the president the president the president
       # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
       UpperCAmelCase_							:   Optional[int]   =      model.generate(snake_case_      ,		do_sample=snake_case_						)
       self.assertListEqual(output_ids[0].numpy().tolist()      ,		snake_case_						)






