import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


_lowercase:	Tuple       			=		logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

_lowercase:	Optional[int]       			=		{
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
_lowercase:	Optional[int]       			=		{
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
_lowercase:	Dict       			=		sorted(arg_to_scheduler.keys())
_lowercase:	List[Any]       			=		'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''



class        lowerCamelCase__				(   pl.LightningModule				):





			def __init__(       self	:	Dict  ,  lowercase__	:	argparse.Namespace  ,  lowercase__	:	List[str]=None  ,  lowercase__	:	Any="base"  ,  lowercase__	:	Optional[Any]=None  ,  lowercase__	:	int=None  ,  lowercase__	:	int=None  ,  **lowercase__	:	Optional[Any]  ,  ):
										super().__init__()
										# TODO: move to self.save_hyperparameters()
										# self.save_hyperparameters()
										# can also expand arguments into trainer signature for easier reading

										self.save_hyperparameters(lowercase__						)
										_lowerCAmelCase							=							0
										_lowerCAmelCase							=							Path(self.hparams.output_dir						)
										_lowerCAmelCase							=							self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
																	_lowerCAmelCase							=							AutoConfig.from_pretrained(
																	    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path  ,  **({'num_labels': num_labels} if num_labels is not None else {})  ,  cache_dir=lowercase__  ,  **lowercase__  ,  )
										else:
																	_lowerCAmelCase							=							config

										_lowerCAmelCase							=							('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
										for p in extra_model_params:
																	if getattr(self.hparams  ,  lowercase__  ,  lowercase__						):
																								assert hasattr(self.config  ,  lowercase__						), f'model config doesn\'t have a `{p}` attribute'
																								setattr(self.config  ,  lowercase__  ,  getattr(self.hparams  ,  lowercase__						)						)

										if tokenizer is None:
																	_lowerCAmelCase							=							AutoTokenizer.from_pretrained(
																	    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path  ,  cache_dir=lowercase__  ,  )
										else:
																	_lowerCAmelCase							=							tokenizer
										_lowerCAmelCase							=							MODEL_MODES[mode]
										if model is None:
																	_lowerCAmelCase							=							self.model_type.from_pretrained(
																	    self.hparams.model_name_or_path  ,  from_tf=bool('.ckpt' in self.hparams.model_name_or_path						)  ,  config=self.config  ,  cache_dir=lowercase__  ,  )
										else:
																	_lowerCAmelCase							=							model





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  *lowercase__	:	Dict  ,  **lowercase__	:	Tuple						):
										_lowerCAmelCase							=							self.model_type.from_pretrained(*lowercase__  ,  **lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										_lowerCAmelCase							=							arg_to_scheduler[self.hparams.lr_scheduler]
										_lowerCAmelCase							=							get_schedule_func(
										    self.opt  ,  num_warmup_steps=self.hparams.warmup_steps  ,  num_training_steps=self.total_steps()						)
										_lowerCAmelCase							=							{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
										return scheduler





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										_lowerCAmelCase							=							self.model
										_lowerCAmelCase							=							['bias', 'LayerNorm.weight']
										_lowerCAmelCase							=							[
										    {
										        'params': [
										            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay						)
										        ],  # check this named paramters
										        'weight_decay': self.hparams.weight_decay,
										    },
										    {
										        'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay						)],
										        'weight_decay': 0.0,
										    },
										]
										if self.hparams.adafactor:
																	_lowerCAmelCase							=							Adafactor(
																	    lowercase__  ,  lr=self.hparams.learning_rate  ,  scale_parameter=lowercase__  ,  relative_step=lowercase__						)

										else:
																	_lowerCAmelCase							=							AdamW(
																	    lowercase__  ,  lr=self.hparams.learning_rate  ,  eps=self.hparams.adam_epsilon						)
										_lowerCAmelCase							=							optimizer

										_lowerCAmelCase							=							self.get_lr_scheduler()

										return [optimizer], [scheduler]





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]  ,  lowercase__	:	str  ,  lowercase__	:	Union[str, Any]						):
										return self.validation_step(lowercase__  ,  lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	Any						):
										return self.validation_end(lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										_lowerCAmelCase							=							max(1  ,  self.hparams.gpus						)  # TODO: consider num_tpu_cores
										_lowerCAmelCase							=							self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
										return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict  ,  lowercase__	:	str						):
										if stage == "test":
																	_lowerCAmelCase							=							len(self.test_dataloader().dataset						)
										else:
																	_lowerCAmelCase							=							self.get_dataloader('train'  ,  self.hparams.train_batch_size  ,  shuffle=lowercase__						)
																	_lowerCAmelCase							=							len(self.train_dataloader().dataset						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]  ,  lowercase__	:	str  ,  lowercase__	:	int  ,  lowercase__	:	bool = False						):
										raise NotImplementedError('You must implement this for your task'						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										return self.train_loader





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										return self.get_dataloader('dev'  ,  self.hparams.eval_batch_size  ,  shuffle=lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										return self.get_dataloader('test'  ,  self.hparams.eval_batch_size  ,  shuffle=lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	int						):
										return os.path.join(
										    self.hparams.data_dir  ,  'cached_{}_{}_{}'.format(
										        lowercase__  ,  list(filter(lowercase__  ,  self.hparams.model_name_or_path.split('/'						)						)						).pop()  ,  str(self.hparams.max_seq_length						)  ,  )  ,  )





			@pl.utilities.rank_zero_only
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	Dict[str, Any]						):
										_lowerCAmelCase							=							self.output_dir.joinpath('best_tfmr'						)
										_lowerCAmelCase							=							self.step_count
										self.model.save_pretrained(lowercase__						)
										self.tokenizer.save_pretrained(lowercase__						)





			@staticmethod
			def   SCREAMING_SNAKE_CASE__     (       lowercase__	:	Dict  ,  lowercase__	:	Union[str, Any]						):
										parser.add_argument(
										    '--model_name_or_path'  ,  default=lowercase__  ,  type=lowercase__  ,  required=lowercase__  ,  help='Path to pretrained model or model identifier from huggingface.co/models'  ,  )
										parser.add_argument(
										    '--config_name'  ,  default=''  ,  type=lowercase__  ,  help='Pretrained config name or path if not the same as model_name'						)
										parser.add_argument(
										    '--tokenizer_name'  ,  default=lowercase__  ,  type=lowercase__  ,  help='Pretrained tokenizer name or path if not the same as model_name'  ,  )
										parser.add_argument(
										    '--cache_dir'  ,  default=str(Path(lowercase__						).parent / 'test_run' / 'cache'						)  ,  type=lowercase__  ,  help='Where do you want to store the pre-trained models downloaded from huggingface.co'  ,  )
										parser.add_argument(
										    '--encoder_layerdrop'  ,  type=lowercase__  ,  help='Encoder layer dropout probability (Optional). Goes into model.config'  ,  )
										parser.add_argument(
										    '--decoder_layerdrop'  ,  type=lowercase__  ,  help='Decoder layer dropout probability (Optional). Goes into model.config'  ,  )
										parser.add_argument(
										    '--dropout'  ,  type=lowercase__  ,  help='Dropout probability (Optional). Goes into model.config'  ,  )
										parser.add_argument(
										    '--attention_dropout'  ,  type=lowercase__  ,  help='Attention dropout probability (Optional). Goes into model.config'  ,  )
										parser.add_argument('--learning_rate'  ,  default=5e-5  ,  type=lowercase__  ,  help='The initial learning rate for Adam.'						)
										parser.add_argument(
										    '--lr_scheduler'  ,  default='linear'  ,  choices=lowercase__  ,  metavar=lowercase__  ,  type=lowercase__  ,  help='Learning rate scheduler'  ,  )
										parser.add_argument('--weight_decay'  ,  default=0.0  ,  type=lowercase__  ,  help='Weight decay if we apply some.'						)
										parser.add_argument('--adam_epsilon'  ,  default=1e-8  ,  type=lowercase__  ,  help='Epsilon for Adam optimizer.'						)
										parser.add_argument('--warmup_steps'  ,  default=0  ,  type=lowercase__  ,  help='Linear warmup over warmup_steps.'						)
										parser.add_argument('--num_workers'  ,  default=4  ,  type=lowercase__  ,  help='kwarg passed to DataLoader'						)
										parser.add_argument('--num_train_epochs'  ,  dest='max_epochs'  ,  default=3  ,  type=lowercase__						)
										parser.add_argument('--train_batch_size'  ,  default=32  ,  type=lowercase__						)
										parser.add_argument('--eval_batch_size'  ,  default=32  ,  type=lowercase__						)
										parser.add_argument('--adafactor'  ,  action='store_true'						)







class        lowerCamelCase__				(   pl.Callback				):





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  lowercase__	:	Any  ,  lowercase__	:	int						):
										if (
										    trainer.is_global_zero and trainer.global_rank == 0
										):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
																	pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.







class        lowerCamelCase__				(   pl.Callback				):





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any  ,  lowercase__	:	Dict  ,  lowercase__	:	int						):
										# print(pl_module.model.rag)
										for name, param in pl_module.model.rag.named_parameters():
																	if param.grad is None:
																								print(lowercase__						)







class        lowerCamelCase__				(   pl.Callback				):





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	Optional[Any]  ,  lowercase__	:	Optional[int]						):
										_lowerCAmelCase							=							trainer.lr_schedulers[0]['scheduler']
										_lowerCAmelCase							=							{f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()						)}
										pl_module.logger.log_metrics(lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict  ,  lowercase__	:	pl.Trainer  ,  lowercase__	:	pl.LightningModule						):
										rank_zero_info('***** Validation results *****'						)
										_lowerCAmelCase							=							trainer.callback_metrics
										# Log results
										for key in sorted(lowercase__						):
																	if key not in ["log", "progress_bar"]:
																								rank_zero_info('{} = {}\n'.format(lowercase__  ,  str(metrics[key]						)						)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]  ,  lowercase__	:	pl.Trainer  ,  lowercase__	:	pl.LightningModule						):
										rank_zero_info('***** Test results *****'						)
										_lowerCAmelCase							=							trainer.callback_metrics
										# Log and save results to file
										_lowerCAmelCase							=							os.path.join(pl_module.hparams.output_dir  ,  'test_results.txt'						)
										with open(lowercase__  ,  'w'						) as writer:
																	for key in sorted(lowercase__						):
																								if key not in ["log", "progress_bar"]:
																															rank_zero_info('{} = {}\n'.format(lowercase__  ,  str(metrics[key]						)						)						)
																															writer.write('{} = {}\n'.format(lowercase__  ,  str(metrics[key]						)						)						)







def _lowerCamelCase  (		snake_case     , snake_case  ):
							#  To allow all pl args uncomment the following line
							#  parser = pl.Trainer.add_argparse_args(parser)
							parser.add_argument(
							    '--output_dir'     , default=str(Path(snake_case  ).parent / 'test_run' / 'model_checkpoints'  )     , type=snake_case     , help='The output directory where the model predictions and checkpoints will be written.'     , )
							parser.add_argument(
							    '--fp16'     , action='store_true'     , help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'     , )

							parser.add_argument(
							    '--fp16_opt_level'     , type=snake_case     , default='O2'     , help=(
							        'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
							        'See details at https://nvidia.github.io/apex/amp.html'
							    )     , )
							parser.add_argument('--n_tpu_cores'     , dest='tpu_cores'     , type=snake_case  )
							parser.add_argument('--max_grad_norm'     , dest='gradient_clip_val'     , default=1.0     , type=snake_case     , help='Max gradient norm'  )
							parser.add_argument('--do_train'     , action='store_true'     , help='Whether to run training.'  )
							parser.add_argument('--do_predict'     , action='store_true'     , help='Whether to run predictions on the test set.'  )
							parser.add_argument(
							    '--gradient_accumulation_steps'     , dest='accumulate_grad_batches'     , type=snake_case     , default=1     , help='Number of updates steps to accumulate before performing a backward/update pass.'     , )
							parser.add_argument('--seed'     , type=snake_case     , default=42     , help='random seed for initialization'  )
							parser.add_argument(
							    '--data_dir'     , default=str(Path(snake_case  ).parent / 'test_run' / 'dummy-train-data'  )     , type=snake_case     , help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'     , )






def _lowerCamelCase  (		snake_case     , snake_case     , snake_case=None     , snake_case=True     , snake_case=[]     , snake_case=None     , snake_case=None     , **snake_case     , ):
							pl.seed_everything(args.seed  )

							# init model
							_lowerCAmelCase							=							Path(model.hparams.output_dir  )
							odir.mkdir(exist_ok=snake_case  )

							# add custom checkpoints
							if checkpoint_callback is None:
														_lowerCAmelCase							=							pl.callbacks.ModelCheckpoint(
														    filepath=args.output_dir     , prefix='checkpoint'     , monitor='val_loss'     , mode='min'     , save_top_k=1  )
							if early_stopping_callback:
														extra_callbacks.append(snake_case  )
							if logging_callback is None:
														_lowerCAmelCase							=							LoggingCallback()

							_lowerCAmelCase							=							{}

							if args.fpaa:
														_lowerCAmelCase							=							16

							if args.gpus > 1:
														_lowerCAmelCase							=							'auto'
														_lowerCAmelCase							=							'ddp'

							_lowerCAmelCase							=							args.accumulate_grad_batches
							_lowerCAmelCase							=							None
							_lowerCAmelCase							=							'auto'

							_lowerCAmelCase							=							pl.Trainer.from_argparse_args(
							    snake_case     , weights_summary=snake_case     , callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]     , logger=snake_case     , val_check_interval=1     , num_sanity_val_steps=2     , **snake_case     , )

							if args.do_train:
														trainer.fit(snake_case  )

							else:
														print('RAG modeling tests with new set functions successfuly executed!'  )
							return trainer







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        lowerCamelCase__				(   UpperCAmelCase				):





			def __init__(       self	:	Dict  ,  lowercase__	:	Dict  ,  lowercase__	:	Optional[Any]=13  ,  lowercase__	:	Dict=7  ,  lowercase__	:	Dict=True  ,  lowercase__	:	Optional[Any]=True  ,  lowercase__	:	Optional[int]=False  ,  lowercase__	:	Any=True  ,  lowercase__	:	Union[str, Any]=99  ,  lowercase__	:	Optional[int]=32  ,  lowercase__	:	Any=5  ,  lowercase__	:	Any=4  ,  lowercase__	:	List[str]=64  ,  lowercase__	:	Any="gelu"  ,  lowercase__	:	Optional[Any]=0.1  ,  lowercase__	:	List[str]=0.1  ,  lowercase__	:	Dict=5_12  ,  lowercase__	:	List[str]=16  ,  lowercase__	:	Union[str, Any]=2  ,  lowercase__	:	str=0.0_2  ,  lowercase__	:	Optional[int]=3  ,  lowercase__	:	Union[str, Any]=4  ,  lowercase__	:	Union[str, Any]=None  ,  lowercase__	:	Optional[int]=2  ,  lowercase__	:	Optional[int]=2  ,  lowercase__	:	List[Any]=2  ,  lowercase__	:	Optional[int]=2  ,  lowercase__	:	Union[str, Any]=4  ,  lowercase__	:	Tuple=1  ,  ):
										_lowerCAmelCase							=							parent
										_lowerCAmelCase							=							batch_size
										_lowerCAmelCase							=							seq_length
										_lowerCAmelCase							=							is_training
										_lowerCAmelCase							=							use_input_mask
										_lowerCAmelCase							=							use_token_type_ids
										_lowerCAmelCase							=							use_labels
										_lowerCAmelCase							=							vocab_size
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							num_hidden_layers
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							intermediate_size
										_lowerCAmelCase							=							hidden_act
										_lowerCAmelCase							=							hidden_dropout_prob
										_lowerCAmelCase							=							attention_probs_dropout_prob
										_lowerCAmelCase							=							max_position_embeddings
										_lowerCAmelCase							=							type_vocab_size
										_lowerCAmelCase							=							type_sequence_label_size
										_lowerCAmelCase							=							initializer_range
										_lowerCAmelCase							=							num_labels
										_lowerCAmelCase							=							num_choices
										_lowerCAmelCase							=							scope
										_lowerCAmelCase							=							q_groups
										_lowerCAmelCase							=							k_groups
										_lowerCAmelCase							=							v_groups
										_lowerCAmelCase							=							post_attention_groups
										_lowerCAmelCase							=							intermediate_groups
										_lowerCAmelCase							=							output_groups





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										_lowerCAmelCase							=							ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size						)

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

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

										_lowerCAmelCase							=							self.get_config()

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





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										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   SCREAMING_SNAKE_CASE__     (       self	:	int  ,  lowercase__	:	Dict  ,  lowercase__	:	Any  ,  lowercase__	:	str  ,  lowercase__	:	Optional[Any]  ,  lowercase__	:	Any  ,  lowercase__	:	Tuple						):
										_lowerCAmelCase							=							SqueezeBertModel(config=lowercase__						)
										model.to(lowercase__						)
										model.eval()
										_lowerCAmelCase							=							model(lowercase__  ,  lowercase__						)
										_lowerCAmelCase							=							model(lowercase__						)
										self.parent.assertEqual(result.last_hidden_state.shape  ,  (self.batch_size, self.seq_length, self.hidden_size)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]  ,  lowercase__	:	Union[str, Any]  ,  lowercase__	:	Union[str, Any]  ,  lowercase__	:	int  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Dict  ,  lowercase__	:	Union[str, Any]						):
										_lowerCAmelCase							=							SqueezeBertForMaskedLM(config=lowercase__						)
										model.to(lowercase__						)
										model.eval()
										_lowerCAmelCase							=							model(lowercase__  ,  attention_mask=lowercase__  ,  labels=lowercase__						)
										self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.vocab_size)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int  ,  lowercase__	:	Tuple  ,  lowercase__	:	Dict  ,  lowercase__	:	str  ,  lowercase__	:	Dict  ,  lowercase__	:	Tuple  ,  lowercase__	:	int						):
										_lowerCAmelCase							=							SqueezeBertForQuestionAnswering(config=lowercase__						)
										model.to(lowercase__						)
										model.eval()
										_lowerCAmelCase							=							model(
										    lowercase__  ,  attention_mask=lowercase__  ,  start_positions=lowercase__  ,  end_positions=lowercase__						)
										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  ,  lowercase__	:	Dict  ,  lowercase__	:	Tuple  ,  lowercase__	:	Any  ,  lowercase__	:	List[str]  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Tuple						):
										_lowerCAmelCase							=							self.num_labels
										_lowerCAmelCase							=							SqueezeBertForSequenceClassification(lowercase__						)
										model.to(lowercase__						)
										model.eval()
										_lowerCAmelCase							=							model(lowercase__  ,  attention_mask=lowercase__  ,  labels=lowercase__						)
										self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_labels)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]  ,  lowercase__	:	Union[str, Any]  ,  lowercase__	:	str  ,  lowercase__	:	Dict  ,  lowercase__	:	Tuple  ,  lowercase__	:	Any  ,  lowercase__	:	Optional[int]						):
										_lowerCAmelCase							=							self.num_labels
										_lowerCAmelCase							=							SqueezeBertForTokenClassification(config=lowercase__						)
										model.to(lowercase__						)
										model.eval()

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





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]  ,  lowercase__	:	Optional[int]  ,  lowercase__	:	Tuple  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Optional[int]  ,  lowercase__	:	Tuple  ,  lowercase__	:	Any						):
										_lowerCAmelCase							=							self.num_choices
										_lowerCAmelCase							=							SqueezeBertForMultipleChoice(config=lowercase__						)
										model.to(lowercase__						)
										model.eval()
										_lowerCAmelCase							=							input_ids.unsqueeze(1						).expand(-1  ,  self.num_choices  ,  -1						).contiguous()
										_lowerCAmelCase							=							input_mask.unsqueeze(1						).expand(-1  ,  self.num_choices  ,  -1						).contiguous()
										_lowerCAmelCase							=							model(
										    lowercase__  ,  attention_mask=lowercase__  ,  labels=lowercase__  ,  )
										self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_choices)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										_lowerCAmelCase							=							self.prepare_config_and_inputs()
										((_lowerCAmelCase)			,		(_lowerCAmelCase)			,		(_lowerCAmelCase)			,		(_lowerCAmelCase)			,		(_lowerCAmelCase)			,		(_lowerCAmelCase))							=							config_and_inputs
										_lowerCAmelCase							=							{'input_ids': input_ids, 'attention_mask': input_mask}
										return config, inputs_dict







@require_torch
class        lowerCamelCase__				(   UpperCAmelCase       ,UpperCAmelCase       ,unittest.TestCase				):
			UpperCamelCase__    							=(
			    (
			        SqueezeBertModel,
			        SqueezeBertForMaskedLM,
			        SqueezeBertForMultipleChoice,
			        SqueezeBertForQuestionAnswering,
			        SqueezeBertForSequenceClassification,
			        SqueezeBertForTokenClassification,
			    )
			    if is_torch_available()
			    else None
			)
			UpperCamelCase__    							=(
			    {
			        "feature-extraction": SqueezeBertModel,
			        "fill-mask": SqueezeBertForMaskedLM,
			        "question-answering": SqueezeBertForQuestionAnswering,
			        "text-classification": SqueezeBertForSequenceClassification,
			        "token-classification": SqueezeBertForTokenClassification,
			        "zero-shot": SqueezeBertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			UpperCamelCase__    							=False
			UpperCamelCase__    							=True
			UpperCamelCase__    							=False





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										_lowerCAmelCase							=							SqueezeBertModelTester(self						)
										_lowerCAmelCase							=							ConfigTester(self  ,  config_class=lowercase__  ,  dim=37						)





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





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_model(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_question_answering(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_token_classification(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowercase__						)





			@slow
			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_lowerCAmelCase							=							SqueezeBertModel.from_pretrained(lowercase__						)
																	self.assertIsNotNone(lowercase__						)







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





			@slow
			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase							=							SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli'						)

										_lowerCAmelCase							=							torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]]						)
										_lowerCAmelCase							=							model(lowercase__						)[0]
										_lowerCAmelCase							=							torch.Size((1, 3)						)
										self.assertEqual(output.shape  ,  lowercase__						)
										_lowerCAmelCase							=							torch.tensor([[0.6_4_0_1, -0.0_3_4_9, -0.6_0_4_1]]						)
										self.assertTrue(torch.allclose(lowercase__  ,  lowercase__  ,  atol=1e-4						)						)






