

'''simple docstring'''


import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


lowerCAmelCase__						:       str										=						logging.getLogger(__name__)

lowerCAmelCase__						:       str										=						"""pytorch_model.bin"""




@dataclasses.dataclass
class     a			:






						"""simple docstring"""




						__UpperCAmelCase			     =    dataclasses.field(
						    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""}       )
						__UpperCAmelCase			     =    dataclasses.field(
						    default=SCREAMING_SNAKE_CASE			,			metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co."""}			,			)





@dataclasses.dataclass
class     a			:






						"""simple docstring"""




						__UpperCAmelCase			     =    dataclasses.field(metadata={"""help""": """A csv or a json file containing the training data."""}       )
						__UpperCAmelCase			     =    dataclasses.field(metadata={"""help""": """A csv or a json file containing the data to predict on."""}       )
						__UpperCAmelCase			     =    dataclasses.field(
						    default=SCREAMING_SNAKE_CASE			,			metadata={"""help""": """A csv or a json file containing the validation data."""}       )
						__UpperCAmelCase			     =    dataclasses.field(
						    default=SCREAMING_SNAKE_CASE			,			metadata={"""help""": """The name of the task to train on."""}			,			)
						__UpperCAmelCase			     =    dataclasses.field(
						    default=SCREAMING_SNAKE_CASE			,			metadata={"""help""": """The list of labels for the task."""}       )





@dataclasses.dataclass
class     a			:






						"""simple docstring"""




						__UpperCAmelCase			     =    dataclasses.field(
						    metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""}       )
						__UpperCAmelCase			     =    dataclasses.field(
						    default="""accuracy"""			,			metadata={"""help""": """The evaluation metric used for the task."""}       )
						__UpperCAmelCase			     =    dataclasses.field(
						    default="""no"""			,			metadata={
						        """help""": """The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"""
						    }			,			)
						__UpperCAmelCase			     =    dataclasses.field(
						    default=10			,			metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}			,			)
						__UpperCAmelCase			     =    dataclasses.field(
						    default=0.0			,			metadata={
						        """help""": """How much the specified evaluation metric must improve to satisfy early stopping conditions."""
						    }			,			)
						__UpperCAmelCase			     =    dataclasses.field(
						    default=SCREAMING_SNAKE_CASE			,			metadata={"""help""": """Whether to filter the pseudo-labeled data based on the confidence score."""}			,			)
						__UpperCAmelCase			     =    dataclasses.field(
						    default=SCREAMING_SNAKE_CASE			,			metadata={"""help""": """Whether to filter the pseudo-labeled data based on the validation performance."""}			,			)
						__UpperCAmelCase			     =    dataclasses.field(
						    default=SCREAMING_SNAKE_CASE			,			metadata={"""help""": """Whether to fine-tune on labeled data after pseudo training."""}			,			)
						__UpperCAmelCase			     =    dataclasses.field(
						    default=0.0			,			metadata={"""help""": """Confidence threshold for pseudo-labeled data filtering."""}			,			)
						__UpperCAmelCase			     =    dataclasses.field(
						    default=100			,			metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}			,			)
						__UpperCAmelCase			     =    dataclasses.field(
						    default=SCREAMING_SNAKE_CASE			,			metadata={"""help""": """Random seed for initialization."""}			,			)





def 	_a       (				__lowerCAmelCase		:      Union[str, Any]   ,							__lowerCAmelCase		:      List[str]   ,							__lowerCAmelCase		:      str   ,							__lowerCAmelCase		:      Any   ,							__lowerCAmelCase		:      Optional[Any]   ,							__lowerCAmelCase		:      List[str]     ):

		"""simple docstring"""

		snake_case__						:       List[Any]       						=  datasets.concatenate_datasets([infer_input, infer_output]   ,							axis=1     )

		if args.do_filter_by_confidence:
				snake_case__						:       List[str]       						=  dataset.filter(lambda __lowerCAmelCase     : example["probability"] > args.confidence_threshold     )

		if args.do_filter_by_val_performance:
				assert eval_result >= 0.0 and eval_result <= 1.0
				snake_case__						:       List[Any]       						=  int(eval_result * len(__lowerCAmelCase     )     )
				print(__lowerCAmelCase     )
				snake_case__						:       List[Any]       						=  dataset.sort('''probability'''   ,							reverse=__lowerCAmelCase     )
				snake_case__						:       Union[str, Any]       						=  dataset.select(range(__lowerCAmelCase     )     )

		snake_case__						:       List[str]       						=  dataset.remove_columns(['''label''', '''probability''']     )
		snake_case__						:       List[Any]       						=  dataset.rename_column('''prediction'''   ,							'''label'''     )
		snake_case__						:       Dict       						=  dataset.map(lambda __lowerCAmelCase     : {"label": idalabel[example["label"]]}     )
		snake_case__						:       int       						=  dataset.shuffle(seed=args.seed     )

		snake_case__						:       Dict       						=  os.path.join(__lowerCAmelCase   ,							F"""train_pseudo.{args.data_file_extension}"""     )
		if args.data_file_extension == "csv":
				dataset.to_csv(__lowerCAmelCase   ,							index=__lowerCAmelCase     )
		else:
				dataset.to_json(__lowerCAmelCase     )





def 	_a       (				__lowerCAmelCase		:      str   ,							__lowerCAmelCase		:      List[str]   ,							__lowerCAmelCase		:      str   ,							__lowerCAmelCase		:      str   ,							**__lowerCAmelCase		:      Optional[int]     ):

		"""simple docstring"""

		snake_case__						:       Dict       						=  Accelerator()
		# Make one log on every process with the configuration for debugging.
		logging.basicConfig(
		    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''   ,							datefmt='''%m/%d/%Y %H:%M:%S'''   ,							level=logging.INFO   ,							)
		logger.info(accelerator.state     )

		# Setup logging, we only want one process per machine to log things on the
		# screen. accelerator.is_local_main_process is only True for one process per
		# machine.
		logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR     )

		if accelerator.is_local_main_process:
				datasets.utils.logging.set_verbosity_warning()
				transformers.utils.logging.set_verbosity_info()
		else:
				datasets.utils.logging.set_verbosity_error()
				transformers.utils.logging.set_verbosity_error()

		snake_case__						:       Any       						=  STModelArguments(model_name_or_path=__lowerCAmelCase     )
		snake_case__						:       Optional[int]       						=  STDataArguments(train_file=__lowerCAmelCase   ,							infer_file=__lowerCAmelCase     )
		snake_case__						:       List[Any]       						=  STTrainingArguments(output_dir=__lowerCAmelCase     )
		snake_case__						:       Optional[Any]       						=  argparse.Namespace()

		for arg_class in (model_args, data_args, training_args):
				for key, value in vars(__lowerCAmelCase     ).items():
						setattr(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase     )

		for key, value in kwargs.items():
				if hasattr(__lowerCAmelCase   ,							__lowerCAmelCase     ):
						setattr(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase     )

    # Sanity checks
		snake_case__						:       int       						=  {}
		snake_case__						:       Optional[Any]       						=  None

		# You need to provide the training data and the data to predict on
		assert args.train_file is not None
		assert args.infer_file is not None
		snake_case__						:       List[Any]       						=  args.train_file
		snake_case__						:       Tuple       						=  args.infer_file

		if args.evaluation_strategy != IntervalStrategy.NO.value:
				assert args.eval_file is not None
				snake_case__						:       List[str]       						=  args.eval_file

		for key in data_files:
				snake_case__						:       Optional[int]       						=  data_files[key].split('''.'''     )[-1]
				assert extension in ["csv", "json"], F"""`{key}_file` should be a csv or a json file."""
				if args.data_file_extension is None:
						snake_case__						:       List[Any]       						=  extension
				else:
						assert extension == args.data_file_extension, F"""`{key}_file` should be a {args.data_file_extension} file`."""

		assert (
		    args.eval_metric in datasets.list_metrics()
		), F"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""

		# If passed along, set the training seed now.
		if args.seed is not None:
				set_seed(args.seed     )

		logger.info('''Creating the initial data directory for self-training...'''     )
		snake_case__						:       Optional[int]       						=  F"""{args.output_dir}/self-train_iter-{{}}""".format
		snake_case__						:       str       						=  data_dir_format(0     )

		if accelerator.is_main_process:
				if args.output_dir is not None:
						os.makedirs(args.output_dir   ,							exist_ok=__lowerCAmelCase     )
						os.makedirs(__lowerCAmelCase   ,							exist_ok=__lowerCAmelCase     )
		accelerator.wait_for_everyone()

		snake_case__						:       Tuple       						=  None
		snake_case__						:       Union[str, Any]       						=  None
		snake_case__						:       str       						=  0
		snake_case__						:       Optional[Any]       						=  False
		# Show the progress bar
		snake_case__						:       Any       						=  tqdm(range(args.max_selftrain_iterations     )   ,							disable=not accelerator.is_local_main_process     )

		# Self-train
		for iteration in range(0   ,							int(args.max_selftrain_iterations     )     ):
				snake_case__						:       int       						=  data_dir_format(__lowerCAmelCase     )
				assert os.path.exists(__lowerCAmelCase     )

				# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
				# iteration > 0
				snake_case__						:       List[str]       						=  os.path.join(__lowerCAmelCase   ,							'''stage-1'''     )
				snake_case__						:       Dict       						=  {
				    '''accelerator''': accelerator,
				    '''model_name_or_path''': args.model_name_or_path,
				    '''cache_dir''': args.cache_dir,
				    '''do_train''': True,
				    '''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
				    '''do_eval''': True if args.eval_file is not None else False,
				    '''eval_file''': data_files['''eval'''],
				    '''do_predict''': True,
				    '''infer_file''': data_files['''infer'''],
				    '''task_name''': args.task_name,
				    '''label_list''': args.label_list,
				    '''output_dir''': current_output_dir,
				    '''eval_metric''': args.eval_metric,
				    '''evaluation_strategy''': args.evaluation_strategy,
				    '''early_stopping_patience''': args.early_stopping_patience,
				    '''early_stopping_threshold''': args.early_stopping_threshold,
				    '''seed''': args.seed,
				}
				# Add additional training arguments
				for key, value in kwargs.items():
						if key not in arguments_dict and not hasattr(__lowerCAmelCase   ,							__lowerCAmelCase     ):
								arguments_dict.update({key: value}     )

				snake_case__						:       Tuple       						=  os.path.join(__lowerCAmelCase   ,							'''best-checkpoint'''   ,							__lowerCAmelCase     )
				if os.path.exists(__lowerCAmelCase     ):
						logger.info(
						    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.'''   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							)
				else:
						logger.info('''***** Running self-training: iteration: %d, stage: 1 *****'''   ,							__lowerCAmelCase     )
						finetune(**__lowerCAmelCase     )
						accelerator.wait_for_everyone()
						assert os.path.exists(__lowerCAmelCase     )
						logger.info('''Self-training job completed: iteration: %d, stage: 1.'''   ,							__lowerCAmelCase     )

				if iteration > 0 and args.finetune_on_labeled_data:
						# Stage 2 (optional): fine-tuning on the original labeled data
						snake_case__						:       Union[str, Any]       						=  os.path.join(__lowerCAmelCase   ,							'''best-checkpoint'''     )
						snake_case__						:       int       						=  os.path.join(__lowerCAmelCase   ,							'''stage-2'''     )
						# Update arguments_dict
						snake_case__						:       List[str]       						=  model_path
						snake_case__						:       str       						=  data_files['''train''']
						snake_case__						:       str       						=  current_output_dir

						snake_case__						:       Tuple       						=  os.path.join(__lowerCAmelCase   ,							'''best-checkpoint'''   ,							__lowerCAmelCase     )
						if os.path.exists(__lowerCAmelCase     ):
								logger.info(
								    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'''   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							)
						else:
								logger.info('''***** Running self-training: iteration: %d, stage: 2 *****'''   ,							__lowerCAmelCase     )
								finetune(**__lowerCAmelCase     )
								accelerator.wait_for_everyone()
								assert os.path.exists(__lowerCAmelCase     )
								logger.info('''Self-training job completed: iteration: %d, stage: 2.'''   ,							__lowerCAmelCase     )

				snake_case__						:       int       						=  iteration
				snake_case__						:       Optional[int]       						=  data_dir_format(iteration + 1     )

				snake_case__						:       Tuple       						=  AutoConfig.from_pretrained(os.path.join(__lowerCAmelCase   ,							'''best-checkpoint'''     )     )
				snake_case__						:       Any       						=  config.idalabel
				snake_case__						:       int       						=  os.path.join(__lowerCAmelCase   ,							'''eval_results_best-checkpoint.json'''     )
				snake_case__						:       List[str]       						=  os.path.join(__lowerCAmelCase   ,							'''test_results_best-checkpoint.json'''     )
				assert os.path.exists(__lowerCAmelCase     )

				with open(__lowerCAmelCase   ,							'''r'''     ) as f:
						snake_case__						:       Dict       						=  float(json.load(__lowerCAmelCase     )[args.eval_metric]     )
				snake_case__						:       List[Any]       						=  os.path.join(__lowerCAmelCase   ,							'''infer_output_best-checkpoint.csv'''     )
				assert os.path.exists(__lowerCAmelCase     )
				# Loading the dataset from local csv or json files.
				snake_case__						:       List[str]       						=  load_dataset(args.data_file_extension   ,							data_files={'''data''': data_files['''infer''']}     )['''data''']
				snake_case__						:       Dict       						=  load_dataset('''csv'''   ,							data_files={'''data''': infer_output_file}     )['''data''']

				if accelerator.is_main_process:
						os.makedirs(__lowerCAmelCase   ,							exist_ok=__lowerCAmelCase     )
						shutil.copy(__lowerCAmelCase   ,							os.path.join(__lowerCAmelCase   ,							F"""eval_results_iter-{iteration}.json"""     )     )
						if os.path.exists(__lowerCAmelCase     ):
								shutil.copy(__lowerCAmelCase   ,							os.path.join(__lowerCAmelCase   ,							F"""test_results_iter-{iteration}.json"""     )     )
						create_pseudo_labeled_data(__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase     )
				accelerator.wait_for_everyone()

				snake_case__						:       int       						=  os.path.join(__lowerCAmelCase   ,							F"""train_pseudo.{args.data_file_extension}"""     )

				if args.evaluation_strategy != IntervalStrategy.NO.value:
						snake_case__						:       Tuple       						=  eval_result

						if best_iteration is None:
								snake_case__						:       Optional[Any]       						=  new_iteration
								snake_case__						:       Any       						=  new_eval_result
						else:
								if new_eval_result - best_eval_result > args.early_stopping_threshold:
										snake_case__						:       int       						=  new_iteration
										snake_case__						:       Optional[int]       						=  new_eval_result
										snake_case__						:       Optional[Any]       						=  0
								else:
										if new_eval_result == best_eval_result:
												snake_case__						:       Optional[Any]       						=  new_iteration
												snake_case__						:       Optional[int]       						=  new_eval_result
										early_stopping_patience_counter += 1

								if early_stopping_patience_counter >= args.early_stopping_patience:
										snake_case__						:       Optional[int]       						=  True

				progress_bar.update(1     )

				if should_training_stop:
						break

		if best_iteration is not None:
				# Save the best iteration
				logger.info('''Best iteration: %d'''   ,							__lowerCAmelCase     )
				logger.info('''Best evaluation result: %s = %f'''   ,							args.eval_metric   ,							__lowerCAmelCase     )
				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						shutil.copy(
						    os.path.join(__lowerCAmelCase   ,							F"""eval_results_iter-{iteration}.json"""     )   ,							os.path.join(__lowerCAmelCase   ,							'''eval_results_best-iteration.json'''     )   ,							)
		else:
				# Assume that the last iteration is the best
				logger.info('''Best iteration: %d'''   ,							args.max_selftrain_iterations - 1     )
				logger.info('''Best evaluation result: %s = %f'''   ,							args.eval_metric   ,							__lowerCAmelCase     )
				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						shutil.copy(
						    os.path.join(__lowerCAmelCase   ,							F"""eval_results_iter-{args.max_selftrain_iterations - 1}.json"""     )   ,							os.path.join(__lowerCAmelCase   ,							'''eval_results_best-iteration.json'''     )   ,							)






'''simple docstring'''


import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
					from transformers.models.mobilevitva.modeling_mobilevitva import (
					    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
					    make_divisible,
					)


if is_vision_available():
					from PIL import Image

					from transformers import MobileViTImageProcessor




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       str       						=  self.config_class(**self.inputs_dict  )
								self.parent.assertTrue(hasattr(snake_case_			,			'''width_multiplier'''  )  )





class     a			:






						"""simple docstring"""




						def __init__(							self					:      List[str]			,			snake_case_					:      Optional[int]			,			snake_case_					:      Dict=1_3			,			snake_case_					:      Any=6_4			,			snake_case_					:      Dict=2			,			snake_case_					:      Optional[int]=3			,			snake_case_					:      str="swish"			,			snake_case_					:      str=3			,			snake_case_					:      Union[str, Any]=3_2			,			snake_case_					:      Optional[Any]=0.1			,			snake_case_					:      Any=0.0_2			,			snake_case_					:      int=True			,			snake_case_					:      Tuple=True			,			snake_case_					:      Dict=1_0			,			snake_case_					:      Optional[int]=None			,			snake_case_					:      str=0.2_5			,			snake_case_					:      List[Any]=0.0			,			snake_case_					:      Optional[Any]=0.0			,			):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  parent
								snake_case__						:       Dict       						=  batch_size
								snake_case__						:       Dict       						=  image_size
								snake_case__						:       Tuple       						=  patch_size
								snake_case__						:       Tuple       						=  num_channels
								snake_case__						:       Tuple       						=  make_divisible(5_1_2 * width_multiplier			,			divisor=8  )
								snake_case__						:       Optional[int]       						=  hidden_act
								snake_case__						:       int       						=  conv_kernel_size
								snake_case__						:       Optional[int]       						=  output_stride
								snake_case__						:       List[Any]       						=  classifier_dropout_prob
								snake_case__						:       int       						=  use_labels
								snake_case__						:       Optional[Any]       						=  is_training
								snake_case__						:       int       						=  num_labels
								snake_case__						:       str       						=  initializer_range
								snake_case__						:       Dict       						=  scope
								snake_case__						:       Tuple       						=  width_multiplier
								snake_case__						:       Optional[Any]       						=  ffn_dropout
								snake_case__						:       Dict       						=  attn_dropout







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       int       						=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

								snake_case__						:       List[str]       						=  None
								snake_case__						:       Union[str, Any]       						=  None
								if self.use_labels:
										snake_case__						:       Tuple       						=  ids_tensor([self.batch_size]			,			self.num_labels  )
										snake_case__						:       Optional[Any]       						=  ids_tensor([self.batch_size, self.image_size, self.image_size]			,			self.num_labels  )

								snake_case__						:       Optional[int]       						=  self.get_config()

								return config, pixel_values, labels, pixel_labels







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								return MobileViTVaConfig(
								    image_size=self.image_size			,			patch_size=self.patch_size			,			num_channels=self.num_channels			,			hidden_act=self.hidden_act			,			conv_kernel_size=self.conv_kernel_size			,			output_stride=self.output_stride			,			classifier_dropout_prob=self.classifier_dropout_prob			,			initializer_range=self.initializer_range			,			width_multiplier=self.width_multiplier			,			ffn_dropout=self.ffn_dropout_prob			,			attn_dropout=self.attn_dropout_prob			,			)







						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      Optional[int]			,			snake_case_					:      Optional[int]			,			snake_case_					:      Optional[Any]			,			snake_case_					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  MobileViTVaModel(config=snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       Dict       						=  model(snake_case_  )
								self.parent.assertEqual(
								    result.last_hidden_state.shape			,			(
								        self.batch_size,
								        self.last_hidden_size,
								        self.image_size // self.output_stride,
								        self.image_size // self.output_stride,
								    )			,			)







						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      Optional[int]			,			snake_case_					:      List[Any]			,			snake_case_					:      Tuple			,			snake_case_					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  self.num_labels
								snake_case__						:       Optional[Any]       						=  MobileViTVaForImageClassification(snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       int       						=  model(snake_case_			,			labels=snake_case_  )
								self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.num_labels)  )







						def       __magic_name__							(							self					:      int			,			snake_case_					:      List[Any]			,			snake_case_					:      Dict			,			snake_case_					:      Any			,			snake_case_					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  self.num_labels
								snake_case__						:       Any       						=  MobileViTVaForSemanticSegmentation(snake_case_  )
								model.to(snake_case_  )
								model.eval()
								snake_case__						:       str       						=  model(snake_case_  )
								self.parent.assertEqual(
								    result.logits.shape			,			(
								        self.batch_size,
								        self.num_labels,
								        self.image_size // self.output_stride,
								        self.image_size // self.output_stride,
								    )			,			)
								snake_case__						:       Optional[int]       						=  model(snake_case_			,			labels=snake_case_  )
								self.parent.assertEqual(
								    result.logits.shape			,			(
								        self.batch_size,
								        self.num_labels,
								        self.image_size // self.output_stride,
								        self.image_size // self.output_stride,
								    )			,			)







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  self.prepare_config_and_inputs()
								snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       List[str]       						=  config_and_inputs
								snake_case__						:       List[str]       						=  {'''pixel_values''': pixel_values}
								return config, inputs_dict





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






						"""simple docstring"""




						__UpperCAmelCase			     =    (
						    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)

						__UpperCAmelCase			     =    (
						    {
						        """feature-extraction""": MobileViTVaModel,
						        """image-classification""": MobileViTVaForImageClassification,
						        """image-segmentation""": MobileViTVaForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						__UpperCAmelCase			     =    False
						__UpperCAmelCase			     =    False
						__UpperCAmelCase			     =    False
						__UpperCAmelCase			     =    False







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       str       						=  MobileViTVaModelTester(self  )
								snake_case__						:       Union[str, Any]       						=  MobileViTVaConfigTester(self			,			config_class=snake_case_			,			has_text_modality=snake_case_  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								self.config_tester.run_common_tests()







						@unittest.skip(reason='''MobileViTV2 does not use inputs_embeds'''  )
						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								pass







						@unittest.skip(reason='''MobileViTV2 does not support input and output embeddings'''  )
						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								pass







						@unittest.skip(reason='''MobileViTV2 does not output attentions'''  )
						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								pass







						@require_torch_multi_gpu
						@unittest.skip(reason='''Got `CUDA error: misaligned address` for tests after this one being run.'''  )
						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								pass







						@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								pass







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						:       Union[str, Any]       						=  self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										snake_case__						:       Optional[int]       						=  model_class(snake_case_  )
										snake_case__						:       Tuple       						=  inspect.signature(model.forward  )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										snake_case__						:       Any       						=  [*signature.parameters.keys()]

										snake_case__						:       Optional[int]       						=  ['''pixel_values''']
										self.assertListEqual(arg_names[:1]			,			snake_case_  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								def check_hidden_states_output(snake_case_					:      Tuple			,			snake_case_					:      Optional[Any]			,			snake_case_					:      Tuple  ):
										snake_case__						:       Optional[Any]       						=  model_class(snake_case_  )
										model.to(snake_case_  )
										model.eval()

										with torch.no_grad():
												snake_case__						:       Tuple       						=  model(**self._prepare_for_class(snake_case_			,			snake_case_  )  )

										snake_case__						:       Union[str, Any]       						=  outputs.hidden_states

										snake_case__						:       Any       						=  5
										self.assertEqual(len(snake_case_  )			,			snake_case_  )

										# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
										# with the width and height being successively divided by 2.
										snake_case__						:       Dict       						=  2
										for i in range(len(snake_case_  )  ):
												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  )

								snake_case__						,       snake_case__						:       Union[str, Any]       						=  self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
										snake_case__						:       List[str]       						=  True
										check_hidden_states_output(snake_case_			,			snake_case_			,			snake_case_  )

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										snake_case__						:       int       						=  True

										check_hidden_states_output(snake_case_			,			snake_case_			,			snake_case_  )







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



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







						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*snake_case_  )







						@slow
						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										snake_case__						:       Any       						=  MobileViTVaModel.from_pretrained(snake_case_  )
										self.assertIsNotNone(snake_case_  )





def 	_a       (				):

		"""simple docstring"""

		snake_case__						:       Optional[Any]       						=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
		return image





@require_torch
@require_vision
class     a			(				unittest.TestCase       ):






						"""simple docstring"""




						@cached_property
						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								return (
								    MobileViTImageProcessor.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''  )
								    if is_vision_available()
								    else None
								)







						@slow
						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  MobileViTVaForImageClassification.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''  ).to(
								    snake_case_  )

								snake_case__						:       Any       						=  self.default_image_processor
								snake_case__						:       Tuple       						=  prepare_img()
								snake_case__						:       str       						=  image_processor(images=snake_case_			,			return_tensors='''pt'''  ).to(snake_case_  )

								# forward pass
								with torch.no_grad():
										snake_case__						:       Any       						=  model(**snake_case_  )

								# verify the logits
								snake_case__						:       Dict       						=  torch.Size((1, 1_0_0_0)  )
								self.assertEqual(outputs.logits.shape			,			snake_case_  )

								snake_case__						:       Tuple       						=  torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]  ).to(snake_case_  )

								self.assertTrue(torch.allclose(outputs.logits[0, :3]			,			snake_case_			,			atol=1e-4  )  )







						@slow
						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''  )
								snake_case__						:       Any       						=  model.to(snake_case_  )

								snake_case__						:       Tuple       						=  MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''  )

								snake_case__						:       str       						=  prepare_img()
								snake_case__						:       Union[str, Any]       						=  image_processor(images=snake_case_			,			return_tensors='''pt'''  ).to(snake_case_  )

								# forward pass
								with torch.no_grad():
										snake_case__						:       Union[str, Any]       						=  model(**snake_case_  )
								snake_case__						:       Tuple       						=  outputs.logits

								# verify the logits
								snake_case__						:       Optional[Any]       						=  torch.Size((1, 2_1, 3_2, 3_2)  )
								self.assertEqual(logits.shape			,			snake_case_  )

								snake_case__						:       List[Any]       						=  torch.tensor(
								    [
								        [[7.0_8_6_3, 7.1_5_2_5, 6.8_2_0_1], [6.6_9_3_1, 6.8_7_7_0, 6.8_9_3_3], [6.2_9_7_8, 7.0_3_6_6, 6.9_6_3_6]],
								        [[-3.7_1_3_4, -3.6_7_1_2, -3.6_6_7_5], [-3.5_8_2_5, -3.3_5_4_9, -3.4_7_7_7], [-3.3_4_3_5, -3.3_9_7_9, -3.2_8_5_7]],
								        [[-2.9_3_2_9, -2.8_0_0_3, -2.7_3_6_9], [-3.0_5_6_4, -2.4_7_8_0, -2.0_2_0_7], [-2.6_8_8_9, -1.9_2_9_8, -1.7_6_4_0]],
								    ]			,			device=snake_case_			,			)

								self.assertTrue(torch.allclose(logits[0, :3, :3, :3]			,			snake_case_			,			atol=1e-4  )  )







						@slow
						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''  )
								snake_case__						:       List[str]       						=  model.to(snake_case_  )

								snake_case__						:       Optional[int]       						=  MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''  )

								snake_case__						:       str       						=  prepare_img()
								snake_case__						:       str       						=  image_processor(images=snake_case_			,			return_tensors='''pt'''  ).to(snake_case_  )

								# forward pass
								with torch.no_grad():
										snake_case__						:       Any       						=  model(**snake_case_  )

								snake_case__						:       str       						=  outputs.logits.detach().cpu()

								snake_case__						:       int       						=  image_processor.post_process_semantic_segmentation(outputs=snake_case_			,			target_sizes=[(5_0, 6_0)]  )
								snake_case__						:       int       						=  torch.Size((5_0, 6_0)  )
								self.assertEqual(segmentation[0].shape			,			snake_case_  )

								snake_case__						:       str       						=  image_processor.post_process_semantic_segmentation(outputs=snake_case_  )
								snake_case__						:       Any       						=  torch.Size((3_2, 3_2)  )
								self.assertEqual(segmentation[0].shape			,			snake_case_  )



