


'''simple docstring'''







import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoFeatureExtractor,
    AutoModelForAudioClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


lowerCAmelCase    :     Dict					=						logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.14.0""", """To fix: pip install -r examples/pytorch/audio-classification/requirements.txt""")






def 			lowercase				(_A   ,						_A   ,						_A = 1_6_0_0_0					):







       """simple docstring"""







       _lowerCAmelCase    :							Optional[int]   			=	int(round(sample_rate * max_length					)					)
       if len(_A					) <= sample_length:
              return wav
       _lowerCAmelCase    :							Any   			=	randint(0   ,						len(_A					) - sample_length - 1					)
       return wav[random_offset : random_offset + sample_length]
@dataclass
class 	UpperCamelCase__					:



     """simple docstring"""






     __magic_name__    			=    field(default=snake_case__    ,					metadata={"help": "Name of a dataset from the datasets package"}	)
     __magic_name__    			=    field(
         default=snake_case__    ,					metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}	)
     __magic_name__    			=    field(
         default=snake_case__    ,					metadata={"help": "A file containing the training audio paths and labels."}	)
     __magic_name__    			=    field(
         default=snake_case__    ,					metadata={"help": "A file containing the validation audio paths and labels."}	)
     __magic_name__    			=    field(
         default="train"    ,					metadata={
             "help": "The name of the training data set split to use (via the datasets library). Defaults to \'train\'"
         }    ,					)
     __magic_name__    			=    field(
         default="validation"    ,					metadata={
             "help": (
                 "The name of the training data set split to use (via the datasets library). Defaults to \'validation\'"
             )
         }    ,					)
     __magic_name__    			=    field(
         default="audio"    ,					metadata={"help": "The name of the dataset column containing the audio data. Defaults to \'audio\'"}    ,					)
     __magic_name__    			=    field(
         default="label"    ,					metadata={"help": "The name of the dataset column containing the labels. Defaults to \'label\'"}	)
     __magic_name__    			=    field(
         default=snake_case__    ,					metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of training examples to this "
                 "value if set."
             )
         }    ,					)
     __magic_name__    			=    field(
         default=snake_case__    ,					metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                 "value if set."
             )
         }    ,					)
     __magic_name__    			=    field(
         default=2_0    ,					metadata={"help": "Audio clips will be randomly cut to this length during training if the value is set."}    ,					)
@dataclass
class 	UpperCamelCase__					:



     """simple docstring"""






     __magic_name__    			=    field(
         default="facebook/wav2vec2-base"    ,					metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    ,					)
     __magic_name__    			=    field(
         default=snake_case__    ,					metadata={"help": "Pretrained config name or path if not the same as model_name"}	)
     __magic_name__    			=    field(
         default=snake_case__    ,					metadata={"help": "Where do you want to store the pretrained models downloaded from the Hub"}	)
     __magic_name__    			=    field(
         default="main"    ,					metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}    ,					)
     __magic_name__    			=    field(
         default=snake_case__    ,					metadata={"help": "Name or path of preprocessor config."}	)
     __magic_name__    			=    field(
         default=snake_case__    ,					metadata={"help": "Whether to freeze the feature encoder layers of the model."}	)
     __magic_name__    			=    field(
         default=snake_case__    ,					metadata={"help": "Whether to generate an attention mask in the feature extractor."}	)
     __magic_name__    			=    field(
         default=snake_case__    ,					metadata={
             "help": (
                 "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                 "with private models)."
             )
         }    ,					)
     __magic_name__    			=    field(
         default=snake_case__    ,					metadata={"help": "Whether to freeze the feature extractor layers of the model."}	)
     __magic_name__    			=    field(
         default=snake_case__    ,					metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}    ,					)







     def a	( self   ):

            '''simple docstring'''

            if not self.freeze_feature_extractor and self.freeze_feature_encoder:
                   warnings.warn(
                       'The argument `--freeze_feature_extractor` is deprecated and '
                       'will be removed in a future version. Use `--freeze_feature_encoder`'
                       'instead. Setting `freeze_feature_encoder==True`.'      ,	_A      ,	)
            if self.freeze_feature_extractor and not self.freeze_feature_encoder:
                   raise ValueError(
                       'The argument `--freeze_feature_extractor` is deprecated and '
                       'should not be used in combination with `--freeze_feature_encoder`.'
                       'Only make use of `--freeze_feature_encoder`.'   )






def 			lowercase				():







       """simple docstring"""







       _lowerCAmelCase    :							Optional[Any]   			=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
       if len(sys.argv					) == 2 and sys.argv[1].endswith('.json'					):
              # If we pass only one argument to the script and it's the path to a json file,
              # let's parse it to get our arguments.
              _lowerCAmelCase    :							List[str]   			=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
       else:
              _lowerCAmelCase    :							int   			=	parser.parse_args_into_dataclasses()

       # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
       # information sent is the one passed as arguments along with your Python/PyTorch versions.
       send_example_telemetry('run_audio_classification'   ,						_A   ,						_A					)

       # Setup logging
       logging.basicConfig(
           format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'   ,						datefmt='%m/%d/%Y %H:%M:%S'   ,						handlers=[logging.StreamHandler(sys.stdout					)]   ,						)

       if training_args.should_log:
              # The default of training_args.log_level is passive, so we set log level at info here to have that default.
              transformers.utils.logging.set_verbosity_info()

       _lowerCAmelCase    :							List[Any]   			=	training_args.get_process_log_level()
       logger.setLevel(_A					)
       transformers.utils.logging.set_verbosity(_A					)
       transformers.utils.logging.enable_default_handler()
       transformers.utils.logging.enable_explicit_format()

       # Log on each process the small summary:
       logger.warning(
           f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} '
           + f'distributed training: {bool(training_args.local_rank != -1					)}, 16-bits training: {training_args.fpaa}'					)
       logger.info(f'Training/evaluation parameters {training_args}'					)

       # Set seed before initializing model.
       set_seed(training_args.seed					)

       # Detecting last checkpoint.
       _lowerCAmelCase    :							Optional[Any]   			=	None
       if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
              _lowerCAmelCase    :							List[str]   			=	get_last_checkpoint(training_args.output_dir					)
              if last_checkpoint is None and len(os.listdir(training_args.output_dir					)					) > 0:
                     raise ValueError(
                         f'Output directory ({training_args.output_dir}) already exists and is not empty. '
                         'Use --overwrite_output_dir to train from scratch.'					)
              elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
                     logger.info(
                         f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                         'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'					)

    # Initialize our dataset and prepare it for the audio classification task.
       _lowerCAmelCase    :							int   			=	DatasetDict()
       _lowerCAmelCase    :							Optional[Any]   			=	load_dataset(
           data_args.dataset_name   ,						data_args.dataset_config_name   ,						split=data_args.train_split_name   ,						use_auth_token=True if model_args.use_auth_token else None   ,						)
       _lowerCAmelCase    :							Dict   			=	load_dataset(
           data_args.dataset_name   ,						data_args.dataset_config_name   ,						split=data_args.eval_split_name   ,						use_auth_token=True if model_args.use_auth_token else None   ,						)

       if data_args.audio_column_name not in raw_datasets["train"].column_names:
              raise ValueError(
                  f'--audio_column_name {data_args.audio_column_name} not found in dataset \'{data_args.dataset_name}\'. '
                  'Make sure to set `--audio_column_name` to the correct audio column - one of '
                  f'{", ".join(raw_datasets["train"].column_names					)}.'					)

       if data_args.label_column_name not in raw_datasets["train"].column_names:
              raise ValueError(
                  f'--label_column_name {data_args.label_column_name} not found in dataset \'{data_args.dataset_name}\'. '
                  'Make sure to set `--label_column_name` to the correct text column - one of '
                  f'{", ".join(raw_datasets["train"].column_names					)}.'					)

       # Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
       # transformer outputs in the classifier, but it doesn't always lead to better accuracy
       _lowerCAmelCase    :							Optional[int]   			=	AutoFeatureExtractor.from_pretrained(
           model_args.feature_extractor_name or model_args.model_name_or_path   ,						return_attention_mask=model_args.attention_mask   ,						cache_dir=model_args.cache_dir   ,						revision=model_args.model_revision   ,						use_auth_token=True if model_args.use_auth_token else None   ,						)

       # `datasets` takes care of automatically loading and resampling the audio,
       # so we just need to set the correct target sampling rate.
       _lowerCAmelCase    :							Union[str, Any]   			=	raw_datasets.cast_column(
           data_args.audio_column_name   ,						datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate					)					)

       _lowerCAmelCase    :							Optional[Any]   			=	feature_extractor.model_input_names[0]

       def train_transforms(_A					):
              _lowerCAmelCase    :							Any   			=	[]
              for audio in batch[data_args.audio_column_name]:
                     _lowerCAmelCase    :							Optional[Any]   			=	random_subsample(
                         audio['array']   ,						max_length=data_args.max_length_seconds   ,						sample_rate=feature_extractor.sampling_rate					)
                     subsampled_wavs.append(_A					)
              _lowerCAmelCase    :							Any   			=	feature_extractor(_A   ,						sampling_rate=feature_extractor.sampling_rate					)
              _lowerCAmelCase    :							Union[str, Any]   			=	{model_input_name: inputs.get(_A					)}
              _lowerCAmelCase    :							List[Any]   			=	list(batch[data_args.label_column_name]					)

              return output_batch

       def val_transforms(_A					):
              _lowerCAmelCase    :							Dict   			=	[audio['array'] for audio in batch[data_args.audio_column_name]]
              _lowerCAmelCase    :							Dict   			=	feature_extractor(_A   ,						sampling_rate=feature_extractor.sampling_rate					)
              _lowerCAmelCase    :							List[Any]   			=	{model_input_name: inputs.get(_A					)}
              _lowerCAmelCase    :							Tuple   			=	list(batch[data_args.label_column_name]					)

              return output_batch

       # Prepare label mappings.
       # We'll include these in the model's config to get human readable labels in the Inference API.
       _lowerCAmelCase    :							str   			=	raw_datasets['train'].features[data_args.label_column_name].names
       _lowerCAmelCase    :							int   			=	{}, {}
       for i, label in enumerate(_A					):
              _lowerCAmelCase    :							str   			=	str(_A					)
              _lowerCAmelCase    :							Any   			=	label

       # Load the accuracy metric from the datasets package
       _lowerCAmelCase    :							str   			=	evaluate.load('accuracy'					)

       # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
       # `predictions` and `label_ids` fields) and has to return a dictionary string to float.
       def compute_metrics(_A					):
              _lowerCAmelCase    :							Optional[int]   			=	np.argmax(eval_pred.predictions   ,						axis=1					)
              return metric.compute(predictions=_A   ,						references=eval_pred.label_ids					)

       _lowerCAmelCase    :							Union[str, Any]   			=	AutoConfig.from_pretrained(
           model_args.config_name or model_args.model_name_or_path   ,						num_labels=len(_A					)   ,						labelaid=_A   ,						idalabel=_A   ,						finetuning_task='audio-classification'   ,						cache_dir=model_args.cache_dir   ,						revision=model_args.model_revision   ,						use_auth_token=True if model_args.use_auth_token else None   ,						)
       _lowerCAmelCase    :							Tuple   			=	AutoModelForAudioClassification.from_pretrained(
           model_args.model_name_or_path   ,						from_tf=bool('.ckpt' in model_args.model_name_or_path					)   ,						config=_A   ,						cache_dir=model_args.cache_dir   ,						revision=model_args.model_revision   ,						use_auth_token=True if model_args.use_auth_token else None   ,						ignore_mismatched_sizes=model_args.ignore_mismatched_sizes   ,						)

       # freeze the convolutional waveform encoder
       if model_args.freeze_feature_encoder:
              model.freeze_feature_encoder()

       if training_args.do_train:
              if data_args.max_train_samples is not None:
                     _lowerCAmelCase    :							int   			=	(
                         raw_datasets['train'].shuffle(seed=training_args.seed					).select(range(data_args.max_train_samples					)					)
                     )
              # Set the training transforms
              raw_datasets["train"].set_transform(_A   ,						output_all_columns=_A					)

       if training_args.do_eval:
              if data_args.max_eval_samples is not None:
                     _lowerCAmelCase    :							List[Any]   			=	(
                         raw_datasets['eval'].shuffle(seed=training_args.seed					).select(range(data_args.max_eval_samples					)					)
                     )
              # Set the validation transforms
              raw_datasets["eval"].set_transform(_A   ,						output_all_columns=_A					)

       # Initialize our trainer
       _lowerCAmelCase    :							str   			=	Trainer(
           model=_A   ,						args=_A   ,						train_dataset=raw_datasets['train'] if training_args.do_train else None   ,						eval_dataset=raw_datasets['eval'] if training_args.do_eval else None   ,						compute_metrics=_A   ,						tokenizer=_A   ,						)

       # Training
       if training_args.do_train:
              _lowerCAmelCase    :							Dict   			=	None
              if training_args.resume_from_checkpoint is not None:
                     _lowerCAmelCase    :							Union[str, Any]   			=	training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     _lowerCAmelCase    :							Any   			=	last_checkpoint
              _lowerCAmelCase    :							Tuple   			=	trainer.train(resume_from_checkpoint=_A					)
              trainer.save_model()
              trainer.log_metrics('train'   ,						train_result.metrics					)
              trainer.save_metrics('train'   ,						train_result.metrics					)
              trainer.save_state()

       # Evaluation
       if training_args.do_eval:
              _lowerCAmelCase    :							str   			=	trainer.evaluate()
              trainer.log_metrics('eval'   ,						_A					)
              trainer.save_metrics('eval'   ,						_A					)

       # Write model card and (optionally) push to hub
       _lowerCAmelCase    :							Optional[Any]   			=	{
           'finetuned_from': model_args.model_name_or_path,
           'tasks': 'audio-classification',
           'dataset': data_args.dataset_name,
           'tags': ['audio-classification'],
       }
       if training_args.push_to_hub:
              trainer.push_to_hub(**_A					)
       else:
              trainer.create_model_card(**_A					)


if __name__ == "__main__":
       main()





'''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 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.config_class(**self.inputs_dict   )
            self.parent.assertTrue(hasattr(snake_case__      ,	'width_multiplier'   )   )
class 	UpperCamelCase__					:



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__=13      ,	snake_case__=64      ,	snake_case__=2      ,	snake_case__=3      ,	snake_case__="swish"      ,	snake_case__=3      ,	snake_case__=32      ,	snake_case__=0.1      ,	snake_case__=0.02      ,	snake_case__=True      ,	snake_case__=True      ,	snake_case__=10      ,	snake_case__=None      ,	snake_case__=0.25      ,	snake_case__=0.0      ,	snake_case__=0.0      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       parent
            _lowerCAmelCase    :							Optional[int]   			=       batch_size
            _lowerCAmelCase    :							List[Any]   			=       image_size
            _lowerCAmelCase    :							List[Any]   			=       patch_size
            _lowerCAmelCase    :							List[str]   			=       num_channels
            _lowerCAmelCase    :							Optional[Any]   			=       make_divisible(512 * width_multiplier      ,	divisor=8   )
            _lowerCAmelCase    :							Optional[Any]   			=       hidden_act
            _lowerCAmelCase    :							List[Any]   			=       conv_kernel_size
            _lowerCAmelCase    :							Optional[Any]   			=       output_stride
            _lowerCAmelCase    :							List[Any]   			=       classifier_dropout_prob
            _lowerCAmelCase    :							str   			=       use_labels
            _lowerCAmelCase    :							List[str]   			=       is_training
            _lowerCAmelCase    :							Optional[int]   			=       num_labels
            _lowerCAmelCase    :							List[str]   			=       initializer_range
            _lowerCAmelCase    :							str   			=       scope
            _lowerCAmelCase    :							Any   			=       width_multiplier
            _lowerCAmelCase    :							Union[str, Any]   			=       ffn_dropout
            _lowerCAmelCase    :							Optional[int]   			=       attn_dropout







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

            _lowerCAmelCase    :							Optional[Any]   			=       None
            _lowerCAmelCase    :							Dict   			=       None
            if self.use_labels:
                   _lowerCAmelCase    :							Optional[Any]   			=       ids_tensor([self.batch_size]      ,	self.num_labels   )
                   _lowerCAmelCase    :							Optional[Any]   			=       ids_tensor([self.batch_size, self.image_size, self.image_size]      ,	self.num_labels   )

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

            return config, pixel_values, labels, pixel_labels







     def a	( self   ):

            '''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 a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       MobileViTVaModel(config=snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							str   			=       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 a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

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







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.num_labels
            _lowerCAmelCase    :							Optional[int]   			=       MobileViTVaForSemanticSegmentation(snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							Dict   			=       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,
                )      ,	)
            _lowerCAmelCase    :							Any   			=       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 a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.prepare_config_and_inputs()
            _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[int]   			=       config_and_inputs
            _lowerCAmelCase    :							Tuple   			=       {'pixel_values': pixel_values}
            return config, inputs_dict
@require_torch
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_    ,					SCREAMING_SNAKE_CASE_    ,					unittest.TestCase	):



     """simple docstring"""






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

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

     __magic_name__    			=    False
     __magic_name__    			=    False
     __magic_name__    			=    False
     __magic_name__    			=    False







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       MobileViTVaModelTester(self   )
            _lowerCAmelCase    :							Dict   			=       MobileViTVaConfigTester(self      ,	config_class=snake_case__      ,	has_text_modality=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            self.config_tester.run_common_tests()







     @unittest.skip(reason='MobileViTV2 does not use inputs_embeds'   )
     def a	( self   ):

            '''simple docstring'''

            pass







     @unittest.skip(reason='MobileViTV2 does not support input and output embeddings'   )
     def a	( self   ):

            '''simple docstring'''

            pass







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

            '''simple docstring'''

            pass







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

            '''simple docstring'''

            pass







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

            '''simple docstring'''

            pass







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[int]   			=       self.model_tester.prepare_config_and_inputs_for_common()

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

                   _lowerCAmelCase    :							Any   			=       ['pixel_values']
                   self.assertListEqual(arg_names[:1]      ,	snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            def check_hidden_states_output(snake_case__      ,	snake_case__      ,	snake_case__   ):
                   _lowerCAmelCase    :							Dict   			=       model_class(snake_case__   )
                   model.to(snake_case__   )
                   model.eval()

                   with torch.no_grad():
                          _lowerCAmelCase    :							Optional[Any]   			=       model(**self._prepare_for_class(snake_case__      ,	snake_case__   )   )

                   _lowerCAmelCase    :							List[str]   			=       outputs.hidden_states

                   _lowerCAmelCase    :							List[str]   			=       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.
                   _lowerCAmelCase    :							List[Any]   			=       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   )

            _lowerCAmelCase			,     _lowerCAmelCase    :							Optional[int]   			=       self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _lowerCAmelCase    :							Optional[int]   			=       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"]
                   _lowerCAmelCase    :							Any   			=       True

                   check_hidden_states_output(snake_case__      ,	snake_case__      ,	snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_semantic_segmentation(*snake_case__   )







     @slow
     def a	( self   ):

            '''simple docstring'''

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






def 			lowercase				():







       """simple docstring"""







       _lowerCAmelCase    :							Any   			=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
       return image
@require_torch
@require_vision
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     @cached_property
     def a	( self   ):

            '''simple docstring'''

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







     @slow
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       MobileViTVaForImageClassification.from_pretrained('apple/mobilevitv2-1.0-imagenet1k-256'   ).to(
                snake_case__   )

            _lowerCAmelCase    :							str   			=       self.default_image_processor
            _lowerCAmelCase    :							Any   			=       prepare_img()
            _lowerCAmelCase    :							Optional[int]   			=       image_processor(images=snake_case__      ,	return_tensors='pt'   ).to(snake_case__   )

            # forward pass
            with torch.no_grad():
                   _lowerCAmelCase    :							Tuple   			=       model(**snake_case__   )

            # verify the logits
            _lowerCAmelCase    :							Optional[Any]   			=       torch.Size((1, 1000)   )
            self.assertEqual(outputs.logits.shape      ,	snake_case__   )

            _lowerCAmelCase    :							Tuple   			=       torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01]   ).to(snake_case__   )

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







     @slow
     def a	( self   ):

            '''simple docstring'''

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

            _lowerCAmelCase    :							int   			=       MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )

            _lowerCAmelCase    :							Optional[int]   			=       prepare_img()
            _lowerCAmelCase    :							Dict   			=       image_processor(images=snake_case__      ,	return_tensors='pt'   ).to(snake_case__   )

            # forward pass
            with torch.no_grad():
                   _lowerCAmelCase    :							int   			=       model(**snake_case__   )
            _lowerCAmelCase    :							Dict   			=       outputs.logits

            # verify the logits
            _lowerCAmelCase    :							str   			=       torch.Size((1, 21, 32, 32)   )
            self.assertEqual(logits.shape      ,	snake_case__   )

            _lowerCAmelCase    :							Any   			=       torch.tensor(
                [
                    [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
                    [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
                    [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
                ]      ,	device=snake_case__      ,	)

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







     @slow
     def a	( self   ):

            '''simple docstring'''

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

            _lowerCAmelCase    :							str   			=       MobileViTImageProcessor.from_pretrained('shehan97/mobilevitv2-1.0-voc-deeplabv3'   )

            _lowerCAmelCase    :							Tuple   			=       prepare_img()
            _lowerCAmelCase    :							List[str]   			=       image_processor(images=snake_case__      ,	return_tensors='pt'   ).to(snake_case__   )

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

            _lowerCAmelCase    :							Optional[Any]   			=       outputs.logits.detach().cpu()

            _lowerCAmelCase    :							Any   			=       image_processor.post_process_semantic_segmentation(outputs=snake_case__      ,	target_sizes=[(50, 60)]   )
            _lowerCAmelCase    :							List[Any]   			=       torch.Size((50, 60)   )
            self.assertEqual(segmentation[0].shape      ,	snake_case__   )

            _lowerCAmelCase    :							List[str]   			=       image_processor.post_process_semantic_segmentation(outputs=snake_case__   )
            _lowerCAmelCase    :							Tuple   			=       torch.Size((32, 32)   )
            self.assertEqual(segmentation[0].shape      ,	snake_case__   )

