

"""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""")




"""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  )

