code: 
  
  """simple docstring"""
  import logging
  import os
  import time
  from argparse import Namespace
  from pathlib import Path
  
  import datasets
  import torch
  from accelerate import Accelerator, DistributedType
  from accelerate.utils import ProjectConfiguration
  from arguments import TrainingArguments
  from datasets import load_dataset
  from huggingface_hub import Repository
  from torch.optim import AdamW
  from torch.utils.data import IterableDataset
  from torch.utils.data.dataloader import DataLoader
  from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe
  
  import transformers
  from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed
  
  class 			lowerCamelCase  (					snake_case__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase=False       ,					_lowerCamelCase=1024       ,					_lowerCamelCase=1024       ,					_lowerCamelCase=3.6       ,					_lowerCamelCase=False       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						tokenizer
          UpperCAmelCase__ :       str						=						tokenizer.bos_token_id
          UpperCAmelCase__ :       Dict						=						dataset
          UpperCAmelCase__ :       Dict						=						seq_length
          UpperCAmelCase__ :       Optional[int]						=						0
          UpperCAmelCase__ :       Union[str, Any]						=						infinite
          UpperCAmelCase__ :       Optional[int]						=						0
          UpperCAmelCase__ :       str						=						tokenized
  
          if self.tokenized:
                UpperCAmelCase__ :       int						=						seq_length * num_of_sequences
                UpperCAmelCase__ :       List[str]						=						'input_ids'
          else:
                UpperCAmelCase__ :       str						=						seq_length * chars_per_token * num_of_sequences
                UpperCAmelCase__ :       int						=						'content'
  
  
  
    def __iter__(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						iter(self.dataset  )
          UpperCAmelCase__ :       Any						=						True
          while more_examples:
                UpperCAmelCase__ :       str						=						[], 0
                while True:
                      if buffer_len >= self.max_buffer_size:
                            break
                      try:
                            buffer.append(next(_lowerCamelCase  )[self.content_field]  )
                            buffer_len += len(buffer[-1]  )
                      except StopIteration:
                            if self.infinite:
                                  UpperCAmelCase__ :       Any						=						iter(self.dataset  )
                                  self.epoch += 1
                                  logger.info(F"""Dataset epoch: {self.epoch}"""  )
                            else:
                                  UpperCAmelCase__ :       List[str]						=						False
                                  break
                if self.tokenized:
                      UpperCAmelCase__ :       Optional[Any]						=						buffer
                else:
                      UpperCAmelCase__ :       Union[str, Any]						=						self.tokenizer(_lowerCamelCase       ,					truncation=_lowerCamelCase  )['input_ids']
                UpperCAmelCase__ :       Union[str, Any]						=						[]
                for tokenized_input in tokenized_inputs:
                      all_token_ids.extend(tokenized_input + [self.concat_token_id]  )
                for i in range(0       ,					len(_lowerCamelCase  )       ,					self.seq_length  ):
                      UpperCAmelCase__ :       Optional[int]						=						all_token_ids[i : i + self.seq_length]
                      if len(_lowerCamelCase  ) == self.seq_length:
                            self.current_size += 1
                            yield torch.tensor(_lowerCamelCase  )
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase=1000  ):
  
  
          """simple docstring"""
  
          return ShufflerIterDataPipe(self       ,					buffer_size=_lowerCamelCase  )
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   Any:
        UpperCAmelCase__ :       Any						=						args.model_ckpt.split("""/"""						)[-1]
        UpperCAmelCase__ :       List[Any]						=						logging.getLogger(__name__						)
        UpperCAmelCase__ :       str						=						Path(args.save_dir						) / 'log/'
        log_dir.mkdir(exist_ok=lowerCAmelCase__						)
        UpperCAmelCase__ :       Dict						=						F"""debug_{accelerator.process_index}.log"""
        logging.basicConfig(
            format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" ,  datefmt="""%m/%d/%Y %H:%M:%S""" ,  level=logging.INFO ,  handlers=[logging.FileHandler(log_dir / filename						), logging.StreamHandler()] ,  )
        if accelerator.is_main_process:  # we only want to setup logging once
              accelerator.init_trackers(lowerCAmelCase__ ,  vars(lowerCAmelCase__						)						)
              UpperCAmelCase__ :       Optional[Any]						=						accelerator.trackers[0].run.name
              logger.setLevel(logging.INFO						)
              datasets.utils.logging.set_verbosity_info()
              transformers.utils.logging.set_verbosity_info()
        else:
              UpperCAmelCase__ :       Optional[Any]						=						''
              logger.setLevel(logging.ERROR						)
              datasets.utils.logging.set_verbosity_error()
              transformers.utils.logging.set_verbosity_error()
        return logger, run_name
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   Tuple:
        UpperCAmelCase__ :       Optional[Any]						=						{'streaming': True}
        UpperCAmelCase__ :       str						=						load_dataset(args.dataset_name_train ,  split="""train""" ,  **lowerCAmelCase__						)
        UpperCAmelCase__ :       Any						=						train_data.shuffle(buffer_size=args.shuffle_buffer ,  seed=args.seed						)
        UpperCAmelCase__ :       str						=						load_dataset(args.dataset_name_valid ,  split="""train""" ,  **lowerCAmelCase__						)
        UpperCAmelCase__ :       Tuple						=						ConstantLengthDataset(
            lowerCAmelCase__ ,  lowerCAmelCase__ ,  infinite=lowerCAmelCase__ ,  seq_length=args.seq_length ,  tokenized=args.tokenized						)
        UpperCAmelCase__ :       str						=						ConstantLengthDataset(
            lowerCAmelCase__ ,  lowerCAmelCase__ ,  infinite=lowerCAmelCase__ ,  seq_length=args.seq_length ,  tokenized=args.tokenized						)
        UpperCAmelCase__ :       str						=						train_dataset.shuffle(buffer_size=args.shuffle_buffer						)
        UpperCAmelCase__ :       List[Any]						=						DataLoader(lowerCAmelCase__ ,  batch_size=args.train_batch_size ,  shuffle=lowerCAmelCase__						)
        UpperCAmelCase__ :       List[Any]						=						DataLoader(lowerCAmelCase__ ,  batch_size=args.valid_batch_size						)
        return train_dataloader, eval_dataloader
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]						)  ->   Optional[int]:
        UpperCAmelCase__ :       Union[str, Any]						=						[], []
        for n, p in model.named_parameters():
              if any(nd in n for nd in no_decay						):
                    params_without_wd.append(lowerCAmelCase__						)
              else:
                    params_with_wd.append(lowerCAmelCase__						)
        return [
            {"params": params_with_wd, "weight_decay": args.weight_decay},
            {"params": params_without_wd, "weight_decay": 0.0},
        ]
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   List[str]:
        logger.info(F"""Step {step}: {metrics}"""						)
        if accelerator.is_main_process:
              accelerator.log(lowerCAmelCase__ ,  lowerCAmelCase__						)
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   List[Any]:
        # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
        UpperCAmelCase__ :       List[Any]						=						accelerator.unwrap_model(lowerCAmelCase__						).config
        UpperCAmelCase__ :       Dict						=						4 if args.gradient_checkpointing else 3
        UpperCAmelCase__ :       Any						=						args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
        UpperCAmelCase__ :       List[Any]						=						24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
        UpperCAmelCase__ :       Union[str, Any]						=						factor * (
            1.0
            + (args.seq_length / (6.0 * config_model.n_embd))
            + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
        )
        UpperCAmelCase__ :       int						=						flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
        return tflops
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   List[Any]:
        model.eval()
        UpperCAmelCase__ :       str						=						[]
        for step, batch in enumerate(lowerCAmelCase__						):
              with torch.no_grad():
                    UpperCAmelCase__ :       List[str]						=						model(lowerCAmelCase__ ,  labels=lowerCAmelCase__						)
              UpperCAmelCase__ :       int						=						outputs.loss.repeat(args.valid_batch_size						)
              losses.append(accelerator.gather(lowerCAmelCase__						)						)
              if args.max_eval_steps > 0 and step >= args.max_eval_steps:
                    break
        UpperCAmelCase__ :       Dict						=						torch.cat(lowerCAmelCase__						)
        UpperCAmelCase__ :       Dict						=						losses[: eval_dataloader.dataset.current_size].mean()
        try:
              UpperCAmelCase__ :       Optional[int]						=						torch.exp(lowerCAmelCase__						)
        except OverflowError:
              UpperCAmelCase__ :       Any						=						float("""inf"""						)
        return loss.item(), perplexity.item()
  
  
  # Settings
  _A						=     HfArgumentParser(TrainingArguments)
  _A						=     parser.parse_args()
  
  # Accelerator
  _A						=     ProjectConfiguration(project_dir=args.save_dir, logging_dir="""log""")
  _A						=     Accelerator(log_with=["""wandb""", """tensorboard"""], project_config=config)
  _A						=     {str(k): str(v) for k, v in accelerator.state.__dict__.items()}
  
  _A						=     Namespace(**vars(args), **acc_state)
  _A						=     accelerator.state.num_processes * args.train_batch_size
  set_seed(args.seed)
  
  # Clone model repository
  if accelerator.is_main_process:
     _A						=     Repository(args.save_dir, clone_from=args.model_ckpt)
  
  # Logging
  _A						=     setup_logging(args)
  logger.info(accelerator.state)
  
  # Checkout new branch on repo
  if accelerator.is_main_process:
     hf_repo.git_checkout(run_name, create_branch_ok=True)
  
  # Load model and tokenizer
  _A						=     AutoModelForCausalLM.from_pretrained(args.save_dir)
  if args.gradient_checkpointing:
     model.gradient_checkpointing_enable()
  _A						=     AutoTokenizer.from_pretrained(args.save_dir)
  
  # Load dataset and dataloader
  _A						=     create_dataloaders(args)
  
  # Prepare the optimizer and learning rate scheduler
  _A						=     AdamW(get_grouped_params(model, args), lr=args.learning_rate)
  _A						=     get_scheduler(
      name=args.lr_scheduler_type,
      optimizer=optimizer,
      num_warmup_steps=args.num_warmup_steps,
      num_training_steps=args.max_train_steps,
  )
  accelerator.register_for_checkpointing(lr_scheduler)
  
  
  
  
  def 				a__	(	)  ->   Union[str, Any]:
        return optimizer.param_groups[0]["lr"]
  
  
  # Prepare everything with our `accelerator`.
  _A						=     accelerator.prepare(
      model, optimizer, train_dataloader, eval_dataloader
  )
  
  # 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)
        _A						=     os.path.basename(args.resume_from_checkpoint)
     else:
        # Get the most recent checkpoint
        _A						=     [f.name for f in os.scandir(args.save_dir) if f.is_dir() and """step""" in str(f)]
        dirs.sort(key=os.path.getctime)
        _A						=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
     # Extract the step of the checkpoint to continue from there
     _A						=     os.path.splitext(path)[0]
     _A						=     int(training_difference.replace("""step_""", """"""))
  
  # Train model
  model.train()
  _A						=     0
  _A						=     time.time()
  _A						=     0
  for step, batch in enumerate(train_dataloader, start=1):
     if args.resume_from_checkpoint and step < resume_step:
        continue  # we need to skip steps until we reach the resumed step
     _A						=     model(batch, labels=batch, use_cache=False).loss
     _A						=     accelerator.gather(loss.repeat(args.train_batch_size)).mean()
     loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
     log_metrics(step, {"""samples""": step * samples_per_step, """loss_per_step/train""": loss.item()})
     _A						=     loss / args.gradient_accumulation_steps
     if step % args.gradient_accumulation_steps != 0:
        # Prevent backward from doing gradient all_reduce in every step
        if accelerator.distributed_type == DistributedType.MULTI_GPU:
           with model.no_sync():
              accelerator.backward(loss)
        else:
           accelerator.backward(loss)
     else:
        _A						=     get_lr()
        accelerator.backward(loss)
        accelerator.clip_grad_norm_(model.parameters(), 1.0)
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()
        _A						=     time.time() - t_start
        _A						=     compute_tflops(elapsed_time, accelerator, args)
        log_metrics(
            step,
            {
                """steps""": completed_steps,
                """loss/train""": loss_tracking,
                """lr""": lr,
                """tflops""": tflops,
                """time_per_iteration""": elapsed_time,
            },
        )
        _A						=     time.time()
        _A						=     0
        completed_steps += 1
     if step % args.save_checkpoint_steps == 0:
        logger.info("""Evaluating and saving model checkpoint""")
        _A						=     evaluate(args)
        log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
        accelerator.wait_for_everyone()
        _A						=     os.path.join(args.save_dir, f'''step_{step}''')
        accelerator.save_state(save_dir)
        if accelerator.is_main_process:
           hf_repo.push_to_hub(commit_message=f'''step {step}''')
        model.train()
     if completed_steps >= args.max_train_steps:
        break
  
  # Evaluate and save the last checkpoint
  logger.info("""Evaluating and saving model after training""")
  _A						=     evaluate(args)
  log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
  accelerator.wait_for_everyone()
  _A						=     accelerator.unwrap_model(model)
  unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
  _A						=     os.path.join(args.save_dir, f'''step_{step}''')
  accelerator.save_state(save_dir)
  if accelerator.is_main_process:
     hf_repo.push_to_hub(commit_message="""final model""")
  
  
style_context: 
  
  """simple docstring"""
  from __future__ import annotations
  
  import inspect
  import unittest
  
  from transformers import MobileViTConfig
  from transformers.file_utils import is_tf_available, is_vision_available
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
      import numpy as np
      import tensorflow as tf
  
      from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
      from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  if is_vision_available():
      from PIL import Image
  
      from transformers import MobileViTImageProcessor
  
  class 			lowerCamelCase  (					lowerCAmelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						self.config_class(**self.inputs_dict  )
          self.parent.assertTrue(hasattr(_lowerCamelCase       ,					"""hidden_sizes"""  )  )
          self.parent.assertTrue(hasattr(_lowerCamelCase       ,					"""neck_hidden_sizes"""  )  )
          self.parent.assertTrue(hasattr(_lowerCamelCase       ,					"""num_attention_heads"""  )  )
  
  
  
  
  class 			lowerCamelCase  :
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase       ,					_lowerCamelCase=13       ,					_lowerCamelCase=32       ,					_lowerCamelCase=2       ,					_lowerCamelCase=3       ,					_lowerCamelCase=640       ,					_lowerCamelCase=4       ,					_lowerCamelCase="silu"       ,					_lowerCamelCase=3       ,					_lowerCamelCase=32       ,					_lowerCamelCase=0.1       ,					_lowerCamelCase=0.1       ,					_lowerCamelCase=0.1       ,					_lowerCamelCase=0.02       ,					_lowerCamelCase=True       ,					_lowerCamelCase=True       ,					_lowerCamelCase=10       ,					_lowerCamelCase=None       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						parent
          UpperCAmelCase__ :       str						=						batch_size
          UpperCAmelCase__ :       Dict						=						image_size
          UpperCAmelCase__ :       Dict						=						patch_size
          UpperCAmelCase__ :       str						=						num_channels
          UpperCAmelCase__ :       Optional[int]						=						last_hidden_size
          UpperCAmelCase__ :       Dict						=						num_attention_heads
          UpperCAmelCase__ :       List[str]						=						hidden_act
          UpperCAmelCase__ :       Union[str, Any]						=						conv_kernel_size
          UpperCAmelCase__ :       str						=						output_stride
          UpperCAmelCase__ :       List[Any]						=						hidden_dropout_prob
          UpperCAmelCase__ :       Dict						=						attention_probs_dropout_prob
          UpperCAmelCase__ :       str						=						classifier_dropout_prob
          UpperCAmelCase__ :       Dict						=						use_labels
          UpperCAmelCase__ :       Dict						=						is_training
          UpperCAmelCase__ :       List[str]						=						num_labels
          UpperCAmelCase__ :       Tuple						=						initializer_range
          UpperCAmelCase__ :       List[str]						=						scope
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )
  
          UpperCAmelCase__ :       Any						=						None
          UpperCAmelCase__ :       List[Any]						=						None
          if self.use_labels:
                UpperCAmelCase__ :       Tuple						=						ids_tensor([self.batch_size]       ,					self.num_labels  )
                UpperCAmelCase__ :       Optional[int]						=						ids_tensor([self.batch_size, self.image_size, self.image_size]       ,					self.num_labels  )
  
          UpperCAmelCase__ :       List[Any]						=						self.get_config()
  
          return config, pixel_values, labels, pixel_labels
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          return MobileViTConfig(
              image_size=self.image_size       ,					patch_size=self.patch_size       ,					num_channels=self.num_channels       ,					num_attention_heads=self.num_attention_heads       ,					hidden_act=self.hidden_act       ,					conv_kernel_size=self.conv_kernel_size       ,					output_stride=self.output_stride       ,					hidden_dropout_prob=self.hidden_dropout_prob       ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,					classifier_dropout_prob=self.classifier_dropout_prob       ,					initializer_range=self.initializer_range       ,					)
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						TFMobileViTModel(config=_lowerCamelCase  )
          UpperCAmelCase__ :       List[str]						=						model(_lowerCamelCase       ,					training=_lowerCamelCase  )
          UpperCAmelCase__ :       int						=						self.image_size // self.output_stride
          self.parent.assertEqual(
              result.last_hidden_state.shape       ,					(self.batch_size, self.last_hidden_size, expected_height, expected_width)  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						self.num_labels
          UpperCAmelCase__ :       Union[str, Any]						=						TFMobileViTForImageClassification(_lowerCamelCase  )
          UpperCAmelCase__ :       Optional[int]						=						model(_lowerCamelCase       ,					labels=_lowerCamelCase       ,					training=_lowerCamelCase  )
          self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels)  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						self.num_labels
          UpperCAmelCase__ :       Dict						=						TFMobileViTForSemanticSegmentation(_lowerCamelCase  )
          UpperCAmelCase__ :       Tuple						=						self.image_size // self.output_stride
  
          UpperCAmelCase__ :       List[Any]						=						model(_lowerCamelCase       ,					training=_lowerCamelCase  )
          self.parent.assertEqual(
              result.logits.shape       ,					(self.batch_size, self.num_labels, expected_height, expected_width)  )
  
          UpperCAmelCase__ :       Any						=						model(_lowerCamelCase       ,					labels=_lowerCamelCase       ,					training=_lowerCamelCase  )
          self.parent.assertEqual(
              result.logits.shape       ,					(self.batch_size, self.num_labels, expected_height, expected_width)  )
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						self.prepare_config_and_inputs()
          UpperCAmelCase__				,		UpperCAmelCase__				,		UpperCAmelCase__				,		UpperCAmelCase__ :       Optional[int]						=						config_and_inputs
          UpperCAmelCase__ :       List[str]						=						{"""pixel_values""": pixel_values}
          return config, inputs_dict
  
  
  
  
  @require_tf
  class 			lowerCamelCase  (					lowerCAmelCase__				,				lowerCAmelCase__				,				unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     (
        (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
        if is_tf_available()
        else ()
    )
    SCREAMING_SNAKE_CASE							=     (
        {'feature-extraction': TFMobileViTModel, 'image-classification': TFMobileViTForImageClassification}
        if is_tf_available()
        else {}
    )
  
    SCREAMING_SNAKE_CASE							=     False
    SCREAMING_SNAKE_CASE							=     False
    SCREAMING_SNAKE_CASE							=     False
    SCREAMING_SNAKE_CASE							=     False
    SCREAMING_SNAKE_CASE							=     False
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						TFMobileViTModelTester(self  )
          UpperCAmelCase__ :       Dict						=						TFMobileViTConfigTester(self       ,					config_class=_lowerCamelCase       ,					has_text_modality=_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          self.config_tester.run_common_tests()
  
  
  
    @unittest.skip(reason="""MobileViT does not use inputs_embeds"""  )
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          pass
  
  
  
    @unittest.skip(reason="""MobileViT does not support input and output embeddings"""  )
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          pass
  
  
  
    @unittest.skip(reason="""MobileViT does not output attentions"""  )
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          pass
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__				,		UpperCAmelCase__ :       List[Any]						=						self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
                UpperCAmelCase__ :       Tuple						=						model_class(_lowerCamelCase  )
                UpperCAmelCase__ :       Optional[int]						=						inspect.signature(model.call  )
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                UpperCAmelCase__ :       Optional[int]						=						[*signature.parameters.keys()]
  
                UpperCAmelCase__ :       Any						=						["""pixel_values"""]
                self.assertListEqual(arg_names[:1]       ,					_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          def check_hidden_states_output(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
                UpperCAmelCase__ :       str						=						model_class(_lowerCamelCase  )
  
                UpperCAmelCase__ :       Any						=						model(**self._prepare_for_class(_lowerCamelCase       ,					_lowerCamelCase  )  )
  
                UpperCAmelCase__ :       Tuple						=						outputs.hidden_states
  
                UpperCAmelCase__ :       Union[str, Any]						=						5
                self.assertEqual(len(_lowerCamelCase  )       ,					_lowerCamelCase  )
  
                # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
                # with the width and height being successively divided by 2.
                UpperCAmelCase__ :       str						=						2
                for i in range(len(_lowerCamelCase  )  ):
                      self.assertListEqual(
                          list(hidden_states[i].shape[-2:]  )       ,					[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]       ,					)
                      divisor *= 2
  
                self.assertEqual(self.model_tester.output_stride       ,					divisor // 2  )
  
          UpperCAmelCase__				,		UpperCAmelCase__ :       List[Any]						=						self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
                UpperCAmelCase__ :       Optional[Any]						=						True
                check_hidden_states_output(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
  
                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                UpperCAmelCase__ :       str						=						True
  
                check_hidden_states_output(_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCamelCase  )
  
  
  
    @unittest.skipIf(
        not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""  )  ) == 0       ,					reason="""TF does not support backprop for grouped convolutions on CPU."""       ,					)
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          super().test_dataset_conversion()
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase=2e-1       ,					_lowerCamelCase=2e-1  ):
  
  
          """simple docstring"""
  
          self.assertTrue(np.allclose(_lowerCamelCase       ,					_lowerCamelCase       ,					atol=_lowerCamelCase       ,					rtol=_lowerCamelCase  )  )
  
  
  
    @unittest.skipIf(
        not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""  )  ) == 0       ,					reason="""TF does not support backprop for grouped convolutions on CPU."""       ,					)
    @slow
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__				,		UpperCAmelCase__ :       List[Any]						=						self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
                # Since `TFMobileViTModel` cannot operate with the default `fit()` method.
                if model_class.__name__ != "TFMobileViTModel":
                      UpperCAmelCase__ :       Optional[int]						=						model_class(_lowerCamelCase  )
                      if getattr(_lowerCamelCase       ,					"""hf_compute_loss"""       ,					_lowerCamelCase  ):
                            super().test_keras_fit()
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__				,		UpperCAmelCase__ :       str						=						self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
                # set an ignore index to correctly test the masked loss used in
                # `TFMobileViTForSemanticSegmentation`.
                if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
                      UpperCAmelCase__ :       List[Any]						=						5
  
                UpperCAmelCase__ :       Any						=						model_class(_lowerCamelCase  )
                if getattr(_lowerCamelCase       ,					"""hf_compute_loss"""       ,					_lowerCamelCase  ):
                      # The number of elements in the loss should be the same as the number of elements in the label
                      UpperCAmelCase__ :       Optional[int]						=						self._prepare_for_class(inputs_dict.copy()       ,					_lowerCamelCase       ,					return_labels=_lowerCamelCase  )
                      UpperCAmelCase__ :       Optional[Any]						=						prepared_for_class[
                          sorted(prepared_for_class.keys() - inputs_dict.keys()       ,					reverse=_lowerCamelCase  )[0]
                      ]
                      UpperCAmelCase__ :       Union[str, Any]						=						added_label.shape.as_list()[:1]
  
                      # Test that model correctly compute the loss with kwargs
                      UpperCAmelCase__ :       List[Any]						=						self._prepare_for_class(inputs_dict.copy()       ,					_lowerCamelCase       ,					return_labels=_lowerCamelCase  )
                      UpperCAmelCase__ :       Any						=						{"""input_ids""", """pixel_values""", """input_features"""}
                      UpperCAmelCase__ :       Optional[Any]						=						possible_input_names.intersection(set(_lowerCamelCase  )  ).pop()
                      UpperCAmelCase__ :       Optional[int]						=						prepared_for_class.pop(_lowerCamelCase  )
  
                      UpperCAmelCase__ :       Optional[int]						=						model(_lowerCamelCase       ,					**_lowerCamelCase  )[0]
                      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )
  
                      # Test that model correctly compute the loss when we mask some positions
                      UpperCAmelCase__ :       int						=						self._prepare_for_class(inputs_dict.copy()       ,					_lowerCamelCase       ,					return_labels=_lowerCamelCase  )
                      UpperCAmelCase__ :       List[str]						=						{"""input_ids""", """pixel_values""", """input_features"""}
                      UpperCAmelCase__ :       Optional[Any]						=						possible_input_names.intersection(set(_lowerCamelCase  )  ).pop()
                      UpperCAmelCase__ :       List[Any]						=						prepared_for_class.pop(_lowerCamelCase  )
                      if "labels" in prepared_for_class:
                            UpperCAmelCase__ :       int						=						prepared_for_class["""labels"""].numpy()
                            if len(labels.shape  ) > 1 and labels.shape[1] != 1:
                                  # labels[0] = -100
                                  UpperCAmelCase__ :       int						=						tf.convert_to_tensor(_lowerCamelCase  )
                                  UpperCAmelCase__ :       Dict						=						model(_lowerCamelCase       ,					**_lowerCamelCase  )[0]
                                  self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )
                                  self.assertTrue(not np.any(np.isnan(loss.numpy()  )  )  )
  
                  # Test that model correctly compute the loss with a dict
                      UpperCAmelCase__ :       List[Any]						=						self._prepare_for_class(inputs_dict.copy()       ,					_lowerCamelCase       ,					return_labels=_lowerCamelCase  )
                      UpperCAmelCase__ :       Union[str, Any]						=						model(_lowerCamelCase  )[0]
                      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )
  
                      # Test that model correctly compute the loss with a tuple
                      UpperCAmelCase__ :       List[Any]						=						self._prepare_for_class(inputs_dict.copy()       ,					_lowerCamelCase       ,					return_labels=_lowerCamelCase  )
  
                      # Get keys that were added with the _prepare_for_class function
                      UpperCAmelCase__ :       List[str]						=						prepared_for_class.keys() - inputs_dict.keys()
                      UpperCAmelCase__ :       List[str]						=						inspect.signature(model.call  ).parameters
                      UpperCAmelCase__ :       str						=						list(signature.keys()  )
  
                      # Create a dictionary holding the location of the tensors in the tuple
                      UpperCAmelCase__ :       Any						=						{0: input_name}
                      for label_key in label_keys:
                            UpperCAmelCase__ :       Dict						=						signature_names.index(_lowerCamelCase  )
                            UpperCAmelCase__ :       Any						=						label_key
                      UpperCAmelCase__ :       Tuple						=						sorted(tuple_index_mapping.items()  )
                      # Initialize a list with their default values, update the values and convert to a tuple
                      UpperCAmelCase__ :       List[str]						=						[]
  
                      for name in signature_names:
                            if name != "kwargs":
                                  list_input.append(signature[name].default  )
  
                      for index, value in sorted_tuple_index_mapping:
                            UpperCAmelCase__ :       int						=						prepared_for_class[value]
  
                      UpperCAmelCase__ :       Union[str, Any]						=						tuple(_lowerCamelCase  )
  
                      # Send to model
                      UpperCAmelCase__ :       Union[str, Any]						=						model(tuple_input[:-1]  )[0]
  
                      self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]  )
  
  
  
  
    @slow
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase__ :       List[str]						=						TFMobileViTModel.from_pretrained(_lowerCamelCase  )
                self.assertIsNotNone(_lowerCamelCase  )
  
  
  
  
  def 				a__	(	)  ->   int:
        UpperCAmelCase__ :       Dict						=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
        return image
  
  
  
  
  
  @require_tf
  class 			lowerCamelCase  (					unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    @slow
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						TFMobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""  )
  
          UpperCAmelCase__ :       Optional[Any]						=						MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""  )
          UpperCAmelCase__ :       Dict						=						prepare_img()
          UpperCAmelCase__ :       List[str]						=						image_processor(images=_lowerCamelCase       ,					return_tensors="""tf"""  )
  
          # forward pass
          UpperCAmelCase__ :       str						=						model(**_lowerCamelCase       ,					training=_lowerCamelCase  )
  
          # verify the logits
          UpperCAmelCase__ :       Union[str, Any]						=						tf.TensorShape((1, 1000)  )
          self.assertEqual(outputs.logits.shape       ,					_lowerCamelCase  )
  
          UpperCAmelCase__ :       Union[str, Any]						=						tf.constant([-1.9_364, -1.2_327, -0.4_653]  )
  
          tf.debugging.assert_near(outputs.logits[0, :3]       ,					_lowerCamelCase       ,					atol=1e-4       ,					rtol=1e-04  )
  
  
  
  
    @slow
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						TFMobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""  )
  
          UpperCAmelCase__ :       Dict						=						MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""  )
  
          UpperCAmelCase__ :       List[str]						=						prepare_img()
          UpperCAmelCase__ :       List[str]						=						image_processor(images=_lowerCamelCase       ,					return_tensors="""tf"""  )
  
          # forward pass
          UpperCAmelCase__ :       Tuple						=						model(inputs.pixel_values       ,					training=_lowerCamelCase  )
          UpperCAmelCase__ :       Optional[int]						=						outputs.logits
  
          # verify the logits
          UpperCAmelCase__ :       Tuple						=						tf.TensorShape((1, 21, 32, 32)  )
          self.assertEqual(logits.shape       ,					_lowerCamelCase  )
  
          UpperCAmelCase__ :       Tuple						=						tf.constant(
              [
                  [[6.9_713, 6.9_786, 7.2_422], [7.2_893, 7.2_825, 7.4_446], [7.6_580, 7.8_797, 7.9_420]],
                  [[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9_868, -9.7_132], [-11.0_405, -11.0_221, -10.7_318]],
                  [[-3.3_089, -2.8_539, -2.6_740], [-3.2_706, -2.5_621, -2.5_108], [-3.2_534, -2.6_615, -2.6_651]],
              ]  )
  
          tf.debugging.assert_near(logits[0, :3, :3, :3]       ,					_lowerCamelCase       ,					rtol=1e-4       ,					atol=1e-4  )
  
  