



"""simple docstring"""


import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator

def  _lowerCAmelCase      (   lowercase_						):
						UpperCAmelCase             = fname.split(os.path.sep						)[-1]
						return re.search(R'^(.*)_\d+\.jpg$'		,	lowercase_						).groups()[0]







class       A_			(     SCREAMING_SNAKE_CASE_				):



						"""simple docstring"""






						def __init__(					self :List[str]   ,  lowercase_ :Dict   ,  lowercase_ :List[str]=None   ,  lowercase_ :Optional[Any]=None					)				->			Optional[int]:
												UpperCAmelCase             = file_names
												UpperCAmelCase             = image_transform
												UpperCAmelCase             = label_to_id






						def __len__(					self :Optional[int]					)				->			Optional[Any]:
												return len(self.file_names					)






						def __getitem__(					self :int   ,  lowercase_ :str					)				->			List[str]:
												UpperCAmelCase             = self.file_names[idx]
												UpperCAmelCase             = PIL.Image.open(lowercase_					)
												UpperCAmelCase             = raw_image.convert('RGB'					)
												if self.image_transform is not None:
																		UpperCAmelCase             = self.image_transform(lowercase_					)
												UpperCAmelCase             = extract_label(lowercase_					)
												if self.label_to_id is not None:
																		UpperCAmelCase             = self.label_to_id[label]
												return {"image": image, "label": label}


def  _lowerCAmelCase      (   lowercase_		,	lowercase_						):
						# Initialize accelerator
						if args.with_tracking:
												UpperCAmelCase             = Accelerator(
												    cpu=args.cpu		,	mixed_precision=args.mixed_precision		,	log_with='all'		,	project_dir=args.project_dir						)
						else:
												UpperCAmelCase             = Accelerator(cpu=args.cpu		,	mixed_precision=args.mixed_precision						)

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						UpperCAmelCase             = config['lr']
						UpperCAmelCase             = int(config['num_epochs']						)
						UpperCAmelCase             = int(config['seed']						)
						UpperCAmelCase             = int(config['batch_size']						)
						UpperCAmelCase             = config['image_size']
						if not isinstance(lowercase_		,	(list, tuple)						):
												UpperCAmelCase             = (image_size, image_size)

						# Parse out whether we are saving every epoch or after a certain number of batches
						if hasattr(args.checkpointing_steps		,	'isdigit'						):
												if args.checkpointing_steps == "epoch":
																		UpperCAmelCase             = args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		UpperCAmelCase             = int(args.checkpointing_steps						)
												else:
																		raise ValueError(
																		    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""						)
						else:
												UpperCAmelCase             = None

						# We need to initialize the trackers we use, and also store our configuration
						if args.with_tracking:
												UpperCAmelCase             = os.path.split(lowercase_						)[-1].split('.'						)[0]
												accelerator.init_trackers(lowercase_		,	lowercase_						)

						# Grab all the image filenames
						UpperCAmelCase             = [os.path.join(args.data_dir		,	lowercase_						) for fname in os.listdir(args.data_dir						) if fname.endswith('.jpg'						)]

						# Build the label correspondences
						UpperCAmelCase             = [extract_label(lowercase_						) for fname in file_names]
						UpperCAmelCase             = list(set(lowercase_						)						)
						id_to_label.sort()
						UpperCAmelCase             = {lbl: i for i, lbl in enumerate(lowercase_						)}

						# Set the seed before splitting the data.
						np.random.seed(lowercase_						)
						torch.manual_seed(lowercase_						)
						torch.cuda.manual_seed_all(lowercase_						)

						# Split our filenames between train and validation
						UpperCAmelCase             = np.random.permutation(len(lowercase_						)						)
						UpperCAmelCase             = int(0.8 * len(lowercase_						)						)
						UpperCAmelCase             = random_perm[:cut]
						UpperCAmelCase             = random_perm[cut:]

						# For training we use a simple RandomResizedCrop
						UpperCAmelCase             = Compose([RandomResizedCrop(lowercase_		,	scale=(0.5, 1.0)						), ToTensor()]						)
						UpperCAmelCase             = PetsDataset(
						    [file_names[i] for i in train_split]		,	image_transform=lowercase_		,	label_to_id=lowercase_						)

						# For evaluation, we use a deterministic Resize
						UpperCAmelCase             = Compose([Resize(lowercase_						), ToTensor()]						)
						UpperCAmelCase             = PetsDataset([file_names[i] for i in eval_split]		,	image_transform=lowercase_		,	label_to_id=lowercase_						)

						# Instantiate dataloaders.
						UpperCAmelCase             = DataLoader(lowercase_		,	shuffle=lowercase_		,	batch_size=lowercase_		,	num_workers=4						)
						UpperCAmelCase             = DataLoader(lowercase_		,	shuffle=lowercase_		,	batch_size=lowercase_		,	num_workers=4						)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						UpperCAmelCase             = create_model('resnet50d'		,	pretrained=lowercase_		,	num_classes=len(lowercase_						)						)

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						UpperCAmelCase             = model.to(accelerator.device						)

						# Freezing the base model
						for param in model.parameters():
												UpperCAmelCase             = False
						for param in model.get_classifier().parameters():
												UpperCAmelCase             = True

						# We normalize the batches of images to be a bit faster.
						UpperCAmelCase             = torch.tensor(model.default_cfg['mean']						)[None, :, None, None].to(accelerator.device						)
						UpperCAmelCase             = torch.tensor(model.default_cfg['std']						)[None, :, None, None].to(accelerator.device						)

						# Instantiate optimizer
						UpperCAmelCase             = torch.optim.Adam(params=model.parameters()		,	lr=lr / 25						)

						# Instantiate learning rate scheduler
						UpperCAmelCase             = OneCycleLR(optimizer=lowercase_		,	max_lr=lowercase_		,	epochs=lowercase_		,	steps_per_epoch=len(lowercase_						)						)

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						UpperCAmelCase      ,    UpperCAmelCase      ,    UpperCAmelCase      ,    UpperCAmelCase      ,    UpperCAmelCase             = accelerator.prepare(
						    lowercase_		,	lowercase_		,	lowercase_		,	lowercase_		,	lowercase_						)
						# We need to keep track of how many total steps we have iterated over
						UpperCAmelCase             = 0
						# We also need to keep track of the starting epoch so files are named properly
						UpperCAmelCase             = 0

						# Potentially load in the weights and states from a previous save
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""						)
																		accelerator.load_state(args.resume_from_checkpoint						)
																		UpperCAmelCase             = os.path.basename(args.resume_from_checkpoint						)
												else:
																		# Get the most recent checkpoint
																		UpperCAmelCase             = [f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
																		dirs.sort(key=os.path.getctime						)
																		UpperCAmelCase             = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												UpperCAmelCase             = os.path.splitext(lowercase_						)[0]

												if "epoch" in training_difference:
																		UpperCAmelCase             = int(training_difference.replace('epoch_'		,	''						)						) + 1
																		UpperCAmelCase             = None
												else:
																		UpperCAmelCase             = int(training_difference.replace('step_'		,	''						)						)
																		UpperCAmelCase             = resume_step // len(lowercase_						)
																		resume_step -= starting_epoch * len(lowercase_						)

    # Now we train the model
						for epoch in range(lowercase_		,	lowercase_						):
												model.train()
												if args.with_tracking:
																		UpperCAmelCase             = 0
												if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																		# We need to skip steps until we reach the resumed step
																		UpperCAmelCase             = accelerator.skip_first_batches(lowercase_		,	lowercase_						)
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		UpperCAmelCase             = train_dataloader
												for batch in active_dataloader:
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		UpperCAmelCase             = {k: v.to(accelerator.device						) for k, v in batch.items()}
																		UpperCAmelCase             = (batch['image'] - mean) / std
																		UpperCAmelCase             = model(lowercase_						)
																		UpperCAmelCase             = torch.nn.functional.cross_entropy(lowercase_		,	batch['label']						)
																		# We keep track of the loss at each epoch
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		accelerator.backward(lowercase_						)
																		optimizer.step()
																		lr_scheduler.step()
																		optimizer.zero_grad()
																		overall_step += 1
																		if isinstance(lowercase_		,	lowercase_						):
																								UpperCAmelCase             = F"""step_{overall_step}"""
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				UpperCAmelCase             = os.path.join(args.output_dir		,	lowercase_						)
																														accelerator.save_state(lowercase_						)
												model.eval()
												UpperCAmelCase             = 0
												UpperCAmelCase             = 0
												for step, batch in enumerate(lowercase_						):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		UpperCAmelCase             = {k: v.to(accelerator.device						) for k, v in batch.items()}
																		UpperCAmelCase             = (batch['image'] - mean) / std
																		with torch.no_grad():
																								UpperCAmelCase             = model(lowercase_						)
																		UpperCAmelCase             = outputs.argmax(dim=-1						)
																		UpperCAmelCase      ,    UpperCAmelCase             = accelerator.gather_for_metrics((predictions, batch['label'])						)
																		UpperCAmelCase             = predictions == references
																		num_elems += accurate_preds.shape[0]
																		accurate += accurate_preds.long().sum()

												UpperCAmelCase             = accurate.item() / num_elems
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}"""						)
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        'accuracy': 100 * eval_metric,
																		        'train_loss': total_loss.item() / len(lowercase_						),
																		        'epoch': epoch,
																		    }		,	step=lowercase_		,	)
												if checkpointing_steps == "epoch":
																		UpperCAmelCase             = F"""epoch_{epoch}"""
																		if args.output_dir is not None:
																								UpperCAmelCase             = os.path.join(args.output_dir		,	lowercase_						)
																		accelerator.save_state(lowercase_						)

						if args.with_tracking:
												accelerator.end_training()

def  _lowerCAmelCase      (   ):
						UpperCAmelCase             = argparse.ArgumentParser(description='Simple example of training script.'						)
						parser.add_argument('--data_dir'		,	required=lowercase_		,	help='The data folder on disk.'						)
						parser.add_argument('--fp16'		,	action='store_true'		,	help='If passed, will use FP16 training.'						)
						parser.add_argument(
						    '--mixed_precision'		,	type=lowercase_		,	default=lowercase_		,	choices=['no', 'fp16', 'bf16', 'fp8']		,	help='Whether to use mixed precision. Choose'
						    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
						    'and an Nvidia Ampere GPU.'		,	)
						parser.add_argument('--cpu'		,	action='store_true'		,	help='If passed, will train on the CPU.'						)
						parser.add_argument(
						    '--checkpointing_steps'		,	type=lowercase_		,	default=lowercase_		,	help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'		,	)
						parser.add_argument(
						    '--output_dir'		,	type=lowercase_		,	default='.'		,	help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'		,	)
						parser.add_argument(
						    '--resume_from_checkpoint'		,	type=lowercase_		,	default=lowercase_		,	help='If the training should continue from a checkpoint folder.'		,	)
						parser.add_argument(
						    '--with_tracking'		,	action='store_true'		,	help='Whether to load in all available experiment trackers from the environment and use them for logging.'		,	)
						parser.add_argument(
						    '--project_dir'		,	type=lowercase_		,	default='logs'		,	help='Location on where to store experiment tracking logs` and relevent project information'		,	)
						UpperCAmelCase             = parser.parse_args()
						UpperCAmelCase             = {'lr': 3e-2, 'num_epochs': 3, 'seed': 42, 'batch_size': 64, 'image_size': 224}
						training_function(lowercase_		,	lowercase_						)


if __name__ == "__main__":
				main()













import os
import tempfile
import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           MODEL_FOR_PRETRAINING_MAPPING,
           NezhaForMaskedLM,
           NezhaForMultipleChoice,
           NezhaForNextSentencePrediction,
           NezhaForPreTraining,
           NezhaForQuestionAnswering,
           NezhaForSequenceClassification,
           NezhaForTokenClassification,
           NezhaModel,
       )
       from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST
class     a    :



      """simple docstring"""


      def __init__(      self					:			List[Any]		,      __lowercase					:			List[Any]		,      __lowercase					:			Any=13		,      __lowercase					:			str=7		,      __lowercase					:			Union[str, Any]=True		,      __lowercase					:			Any=True		,      __lowercase					:			int=True		,      __lowercase					:			Optional[int]=True		,      __lowercase					:			List[str]=99		,      __lowercase					:			str=32		,      __lowercase					:			Dict=5		,      __lowercase					:			List[str]=4		,      __lowercase					:			Dict=37		,      __lowercase					:			Optional[int]="gelu"		,      __lowercase					:			int=0.1		,      __lowercase					:			List[Any]=0.1		,      __lowercase					:			Tuple=128		,      __lowercase					:			Union[str, Any]=32		,      __lowercase					:			str=16		,      __lowercase					:			List[str]=2		,      __lowercase					:			Optional[int]=0.02		,      __lowercase					:			Any=3		,      __lowercase					:			Any=4		,      __lowercase					:			Optional[Any]=None		,      )							->      Any:
        __UpperCAmelCase							:	Optional[Any]           =  parent
        __UpperCAmelCase							:	Optional[int]           =  batch_size
        __UpperCAmelCase							:	int           =  seq_length
        __UpperCAmelCase							:	Optional[Any]           =  is_training
        __UpperCAmelCase							:	int           =  use_input_mask
        __UpperCAmelCase							:	Tuple           =  use_token_type_ids
        __UpperCAmelCase							:	List[str]           =  use_labels
        __UpperCAmelCase							:	List[str]           =  vocab_size
        __UpperCAmelCase							:	Any           =  hidden_size
        __UpperCAmelCase							:	Any           =  num_hidden_layers
        __UpperCAmelCase							:	List[Any]           =  num_attention_heads
        __UpperCAmelCase							:	List[str]           =  intermediate_size
        __UpperCAmelCase							:	Tuple           =  hidden_act
        __UpperCAmelCase							:	Tuple           =  hidden_dropout_prob
        __UpperCAmelCase							:	Tuple           =  attention_probs_dropout_prob
        __UpperCAmelCase							:	List[Any]           =  max_position_embeddings
        __UpperCAmelCase							:	Dict           =  type_vocab_size
        __UpperCAmelCase							:	int           =  type_sequence_label_size
        __UpperCAmelCase							:	Union[str, Any]           =  initializer_range
        __UpperCAmelCase							:	List[Any]           =  num_labels
        __UpperCAmelCase							:	Optional[int]           =  num_choices
        __UpperCAmelCase							:	Any           =  scope
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      List[Any]:
        __UpperCAmelCase							:	Optional[Any]           =  ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size					)

        __UpperCAmelCase							:	Any           =  None
        if self.use_input_mask:
          __UpperCAmelCase							:	Tuple           =  random_attention_mask([self.batch_size, self.seq_length]					)

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

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

        __UpperCAmelCase							:	str           =  self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      def 	UpperCAmelCase      (      self					:			str					)							->      str:
        return NezhaConfig(
            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		,      is_decoder=__lowercase		,      initializer_range=self.initializer_range		,      )
      def 	UpperCAmelCase      (      self					:			int					)							->      Optional[Any]:
        (
            (
            __UpperCAmelCase
        )      , (
            __UpperCAmelCase
        )      , (
            __UpperCAmelCase
        )      , (
            __UpperCAmelCase
        )      , (
            __UpperCAmelCase
        )      , (
            __UpperCAmelCase
        )      , (
            __UpperCAmelCase
        )      , 
        )							:	List[str]           =  self.prepare_config_and_inputs()

        __UpperCAmelCase							:	str           =  True
        __UpperCAmelCase							:	Union[str, Any]           =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
        __UpperCAmelCase							:	Tuple           =  ids_tensor([self.batch_size, self.seq_length]		,      vocab_size=2					)

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )
      def 	UpperCAmelCase      (      self					:			str		,      __lowercase					:			Union[str, Any]		,      __lowercase					:			str		,      __lowercase					:			Optional[int]		,      __lowercase					:			Dict		,      __lowercase					:			Union[str, Any]		,      __lowercase					:			str		,      __lowercase					:			int					)							->      Any:
        __UpperCAmelCase							:	Union[str, Any]           =  NezhaModel(config=__lowercase					)
        model.to(__lowercase					)
        model.eval()
        __UpperCAmelCase							:	int           =  model(__lowercase		,      attention_mask=__lowercase		,      token_type_ids=__lowercase					)
        __UpperCAmelCase							:	Optional[Any]           =  model(__lowercase		,      token_type_ids=__lowercase					)
        __UpperCAmelCase							:	List[Any]           =  model(__lowercase					)
        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 	UpperCAmelCase      (      self					:			Optional[int]		,      __lowercase					:			Union[str, Any]		,      __lowercase					:			Optional[int]		,      __lowercase					:			Dict		,      __lowercase					:			Optional[int]		,      __lowercase					:			Any		,      __lowercase					:			str		,      __lowercase					:			Tuple		,      __lowercase					:			Optional[Any]		,      __lowercase					:			Tuple		,      )							->      Optional[int]:
        __UpperCAmelCase							:	List[Any]           =  True
        __UpperCAmelCase							:	Optional[Any]           =  NezhaModel(__lowercase					)
        model.to(__lowercase					)
        model.eval()
        __UpperCAmelCase							:	List[Any]           =  model(
            __lowercase		,      attention_mask=__lowercase		,      token_type_ids=__lowercase		,      encoder_hidden_states=__lowercase		,      encoder_attention_mask=__lowercase		,      )
        __UpperCAmelCase							:	Optional[Any]           =  model(
            __lowercase		,      attention_mask=__lowercase		,      token_type_ids=__lowercase		,      encoder_hidden_states=__lowercase		,      )
        __UpperCAmelCase							:	List[str]           =  model(__lowercase		,      attention_mask=__lowercase		,      token_type_ids=__lowercase					)
        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 	UpperCAmelCase      (      self					:			Any		,      __lowercase					:			int		,      __lowercase					:			str		,      __lowercase					:			Dict		,      __lowercase					:			Union[str, Any]		,      __lowercase					:			Tuple		,      __lowercase					:			Optional[int]		,      __lowercase					:			Optional[int]					)							->      Optional[int]:
        __UpperCAmelCase							:	Optional[int]           =  NezhaForMaskedLM(config=__lowercase					)
        model.to(__lowercase					)
        model.eval()
        __UpperCAmelCase							:	Optional[int]           =  model(__lowercase		,      attention_mask=__lowercase		,      token_type_ids=__lowercase		,      labels=__lowercase					)
        self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.seq_length, self.vocab_size)					)
      def 	UpperCAmelCase      (      self					:			Dict		,      __lowercase					:			str		,      __lowercase					:			Optional[Any]		,      __lowercase					:			int		,      __lowercase					:			List[str]		,      __lowercase					:			Dict		,      __lowercase					:			str		,      __lowercase					:			str					)							->      Dict:
        __UpperCAmelCase							:	Optional[int]           =  NezhaForNextSentencePrediction(config=__lowercase					)
        model.to(__lowercase					)
        model.eval()
        __UpperCAmelCase							:	Union[str, Any]           =  model(
            __lowercase		,      attention_mask=__lowercase		,      token_type_ids=__lowercase		,      labels=__lowercase		,      )
        self.parent.assertEqual(result.logits.shape		,      (self.batch_size, 2)					)
      def 	UpperCAmelCase      (      self					:			Tuple		,      __lowercase					:			Any		,      __lowercase					:			Dict		,      __lowercase					:			Optional[int]		,      __lowercase					:			Tuple		,      __lowercase					:			Optional[int]		,      __lowercase					:			Optional[int]		,      __lowercase					:			List[str]					)							->      int:
        __UpperCAmelCase							:	Optional[Any]           =  NezhaForPreTraining(config=__lowercase					)
        model.to(__lowercase					)
        model.eval()
        __UpperCAmelCase							:	int           =  model(
            __lowercase		,      attention_mask=__lowercase		,      token_type_ids=__lowercase		,      labels=__lowercase		,      next_sentence_label=__lowercase		,      )
        self.parent.assertEqual(result.prediction_logits.shape		,      (self.batch_size, self.seq_length, self.vocab_size)					)
        self.parent.assertEqual(result.seq_relationship_logits.shape		,      (self.batch_size, 2)					)
      def 	UpperCAmelCase      (      self					:			Tuple		,      __lowercase					:			List[str]		,      __lowercase					:			Any		,      __lowercase					:			Tuple		,      __lowercase					:			Optional[Any]		,      __lowercase					:			Dict		,      __lowercase					:			List[Any]		,      __lowercase					:			Dict					)							->      List[Any]:
        __UpperCAmelCase							:	Optional[Any]           =  NezhaForQuestionAnswering(config=__lowercase					)
        model.to(__lowercase					)
        model.eval()
        __UpperCAmelCase							:	Any           =  model(
            __lowercase		,      attention_mask=__lowercase		,      token_type_ids=__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 	UpperCAmelCase      (      self					:			Tuple		,      __lowercase					:			Dict		,      __lowercase					:			Any		,      __lowercase					:			Any		,      __lowercase					:			Union[str, Any]		,      __lowercase					:			int		,      __lowercase					:			Dict		,      __lowercase					:			List[Any]					)							->      Optional[Any]:
        __UpperCAmelCase							:	Optional[int]           =  self.num_labels
        __UpperCAmelCase							:	Union[str, Any]           =  NezhaForSequenceClassification(__lowercase					)
        model.to(__lowercase					)
        model.eval()
        __UpperCAmelCase							:	Optional[Any]           =  model(__lowercase		,      attention_mask=__lowercase		,      token_type_ids=__lowercase		,      labels=__lowercase					)
        self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_labels)					)
      def 	UpperCAmelCase      (      self					:			Tuple		,      __lowercase					:			List[Any]		,      __lowercase					:			Union[str, Any]		,      __lowercase					:			str		,      __lowercase					:			List[Any]		,      __lowercase					:			List[str]		,      __lowercase					:			Dict		,      __lowercase					:			Dict					)							->      str:
        __UpperCAmelCase							:	Union[str, Any]           =  self.num_labels
        __UpperCAmelCase							:	Dict           =  NezhaForTokenClassification(config=__lowercase					)
        model.to(__lowercase					)
        model.eval()
        __UpperCAmelCase							:	Dict           =  model(__lowercase		,      attention_mask=__lowercase		,      token_type_ids=__lowercase		,      labels=__lowercase					)
        self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.seq_length, self.num_labels)					)
      def 	UpperCAmelCase      (      self					:			Tuple		,      __lowercase					:			Any		,      __lowercase					:			List[Any]		,      __lowercase					:			Dict		,      __lowercase					:			Tuple		,      __lowercase					:			List[Any]		,      __lowercase					:			Any		,      __lowercase					:			int					)							->      Optional[int]:
        __UpperCAmelCase							:	List[str]           =  self.num_choices
        __UpperCAmelCase							:	int           =  NezhaForMultipleChoice(config=__lowercase					)
        model.to(__lowercase					)
        model.eval()
        __UpperCAmelCase							:	List[str]           =  input_ids.unsqueeze(1					).expand(-1		,      self.num_choices		,      -1					).contiguous()
        __UpperCAmelCase							:	Tuple           =  token_type_ids.unsqueeze(1					).expand(-1		,      self.num_choices		,      -1					).contiguous()
        __UpperCAmelCase							:	int           =  input_mask.unsqueeze(1					).expand(-1		,      self.num_choices		,      -1					).contiguous()
        __UpperCAmelCase							:	Tuple           =  model(
            __lowercase		,      attention_mask=__lowercase		,      token_type_ids=__lowercase		,      labels=__lowercase		,      )
        self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_choices)					)




      def 	UpperCAmelCase      (      self					:			Dict					)							->      List[Any]:
        __UpperCAmelCase							:	Optional[int]           =  self.prepare_config_and_inputs()
        (
            (
            __UpperCAmelCase
        )      , (
            __UpperCAmelCase
        )      , (
            __UpperCAmelCase
        )      , (
            __UpperCAmelCase
        )      , (
            __UpperCAmelCase
        )      , (
            __UpperCAmelCase
        )      , (
            __UpperCAmelCase
        )      , 
        )							:	Tuple           =  config_and_inputs
        __UpperCAmelCase							:	Optional[Any]           =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
        return config, inputs_dict


@require_torch
class     a    ( lowercase__							,       lowercase__							,       lowercase__							,       unittest.TestCase   ):



      """simple docstring"""


      a  : Union[str, Any]							       =	(
          (
              NezhaModel,
              NezhaForMaskedLM,
              NezhaForMultipleChoice,
              NezhaForNextSentencePrediction,
              NezhaForPreTraining,
              NezhaForQuestionAnswering,
              NezhaForSequenceClassification,
              NezhaForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      a  : Tuple							       =	(
          {
              'feature-extraction': NezhaModel,
              'fill-mask': NezhaForMaskedLM,
              'question-answering': NezhaForQuestionAnswering,
              'text-classification': NezhaForSequenceClassification,
              'token-classification': NezhaForTokenClassification,
              'zero-shot': NezhaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      a  : Dict							       =	True
      def 	UpperCAmelCase      (      self					:			Optional[int]		,      __lowercase					:			str		,      __lowercase					:			Dict		,      __lowercase					:			int=False					)							->      Dict:
        __UpperCAmelCase							:	Optional[Any]           =  super()._prepare_for_class(__lowercase		,      __lowercase		,      return_labels=__lowercase					)

        if return_labels:
          if model_class in get_values(__lowercase					):
            __UpperCAmelCase							:	int           =  torch.zeros(
                (self.model_tester.batch_size, self.model_tester.seq_length)		,      dtype=torch.long		,      device=__lowercase					)
            __UpperCAmelCase							:	int           =  torch.zeros(
                self.model_tester.batch_size		,      dtype=torch.long		,      device=__lowercase					)
        return inputs_dict
      def 	UpperCAmelCase      (      self					:			Any					)							->      int:
        __UpperCAmelCase							:	Tuple           =  NezhaModelTester(self					)
        __UpperCAmelCase							:	List[Any]           =  ConfigTester(self		,      config_class=__lowercase		,      hidden_size=37					)
      def 	UpperCAmelCase      (      self					:			Dict					)							->      List[str]:
        self.config_tester.run_common_tests()
      def 	UpperCAmelCase      (      self					:			str					)							->      List[Any]:
        __UpperCAmelCase							:	Optional[int]           =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowercase					)
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      Tuple:
        __UpperCAmelCase							:	Optional[Any]           =  self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*__lowercase					)
      def 	UpperCAmelCase      (      self					:			int					)							->      str:
        # This regression test was failing with PyTorch < 1.3
        (
            (
            __UpperCAmelCase
        )      , (
            __UpperCAmelCase
        )      , (
            __UpperCAmelCase
        )      , (
            __UpperCAmelCase
        )      , (
            __UpperCAmelCase
        )      , (
            __UpperCAmelCase
        )      , (
            __UpperCAmelCase
        )      , (
            __UpperCAmelCase
        )      , (
            __UpperCAmelCase
        )      , 
        )							:	Tuple           =  self.model_tester.prepare_config_and_inputs_for_decoder()

        __UpperCAmelCase							:	int           =  None

        self.model_tester.create_and_check_model_as_decoder(
            __lowercase		,      __lowercase		,      __lowercase		,      __lowercase		,      __lowercase		,      __lowercase		,      __lowercase		,      __lowercase		,      __lowercase		,      )
      def 	UpperCAmelCase      (      self					:			Dict					)							->      Union[str, Any]:
        __UpperCAmelCase							:	List[Any]           =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__lowercase					)
      def 	UpperCAmelCase      (      self					:			int					)							->      Dict:
        __UpperCAmelCase							:	Optional[Any]           =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*__lowercase					)
      def 	UpperCAmelCase      (      self					:			List[str]					)							->      List[Any]:
        __UpperCAmelCase							:	int           =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_next_sequence_prediction(*__lowercase					)
      def 	UpperCAmelCase      (      self					:			Optional[int]					)							->      Optional[int]:
        __UpperCAmelCase							:	Optional[int]           =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*__lowercase					)
      def 	UpperCAmelCase      (      self					:			int					)							->      Dict:
        __UpperCAmelCase							:	Dict           =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__lowercase					)
      def 	UpperCAmelCase      (      self					:			List[str]					)							->      str:
        __UpperCAmelCase							:	Union[str, Any]           =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__lowercase					)
      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      str:
        __UpperCAmelCase							:	int           =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__lowercase					)
      @slow
      def 	UpperCAmelCase      (      self					:			str					)							->      Any:
        for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          __UpperCAmelCase							:	int           =  NezhaModel.from_pretrained(__lowercase					)
          self.assertIsNotNone(__lowercase					)




      @slow
      @require_torch_gpu
      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      List[Any]:
        __UpperCAmelCase      , __UpperCAmelCase							:	Optional[Any]           =  self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
          # NezhaForMultipleChoice behaves incorrectly in JIT environments.
          if model_class == NezhaForMultipleChoice:
            return

          __UpperCAmelCase							:	Optional[Any]           =  True
          __UpperCAmelCase							:	Dict           =  model_class(config=__lowercase					)

          __UpperCAmelCase							:	Union[str, Any]           =  self._prepare_for_class(__lowercase		,      __lowercase					)
          __UpperCAmelCase							:	Union[str, Any]           =  torch.jit.trace(
              __lowercase		,      (inputs_dict["""input_ids"""].to("""cpu"""					), inputs_dict["""attention_mask"""].to("""cpu"""					))					)

          with tempfile.TemporaryDirectory() as tmp:
            torch.jit.save(__lowercase		,      os.path.join(__lowercase		,      """bert.pt"""					)					)
            __UpperCAmelCase							:	Optional[int]           =  torch.jit.load(os.path.join(__lowercase		,      """bert.pt"""					)		,      map_location=__lowercase					)
            loaded(inputs_dict["""input_ids"""].to(__lowercase					)		,      inputs_dict["""attention_mask"""].to(__lowercase					)					)


@require_torch
class     a    ( unittest.TestCase   ):



      """simple docstring"""


      @slow
      def 	UpperCAmelCase      (      self					:			Any					)							->      Optional[Any]:
        __UpperCAmelCase							:	Tuple           =  NezhaModel.from_pretrained("""sijunhe/nezha-cn-base"""					)
        __UpperCAmelCase							:	List[Any]           =  torch.tensor([[0, 1, 2, 3, 4, 5]]					)
        __UpperCAmelCase							:	Any           =  torch.tensor([[0, 1, 1, 1, 1, 1]]					)
        with torch.no_grad():
          __UpperCAmelCase							:	Optional[Any]           =  model(__lowercase		,      attention_mask=__lowercase					)[0]
        __UpperCAmelCase							:	Dict           =  torch.Size((1, 6, 768)					)
        self.assertEqual(output.shape		,      __lowercase					)
        __UpperCAmelCase							:	Any           =  torch.tensor([[[0.0_685, 0.2_441, 0.1_102], [0.0_600, 0.1_906, 0.1_349], [0.0_221, 0.0_819, 0.0_586]]]					)

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




      @slow
      def 	UpperCAmelCase      (      self					:			str					)							->      List[str]:
        __UpperCAmelCase							:	int           =  NezhaForMaskedLM.from_pretrained("""sijunhe/nezha-cn-base"""					)
        __UpperCAmelCase							:	Tuple           =  torch.tensor([[0, 1, 2, 3, 4, 5]]					)
        __UpperCAmelCase							:	Any           =  torch.tensor([[1, 1, 1, 1, 1, 1]]					)
        with torch.no_grad():
          __UpperCAmelCase							:	Dict           =  model(__lowercase		,      attention_mask=__lowercase					)[0]
        __UpperCAmelCase							:	Union[str, Any]           =  torch.Size((1, 6, 21128)					)
        self.assertEqual(output.shape		,      __lowercase					)
        __UpperCAmelCase							:	int           =  torch.tensor(
            [[-2.7_939, -1.7_902, -2.2_189], [-2.8_585, -1.8_908, -2.3_723], [-2.6_499, -1.7_750, -2.2_558]]					)

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


