






'''simple docstring'''







import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_A			:    int					  =logging.getLogger(__name__)

@dataclass
class _lowercase			:
							a          = field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}	)
							a          = field(
							    default=UpperCAmelCase__       ,   metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}	)
							a          = field(
							    default="""NER"""       ,   metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}	)
							a          = field(
							    default=UpperCAmelCase__       ,   metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}	)
							a          = field(default=UpperCAmelCase__       ,   metadata={"""help""": """Set this flag to use fast tokenization."""}	)
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							a          = field(
							    default=UpperCAmelCase__       ,   metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}       ,   )



@dataclass
class _lowercase			:
							a          = field(
							    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}	)
							a          = field(
							    default=UpperCAmelCase__       ,   metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}       ,   )
							a          = field(
							    default=128       ,   metadata={
							        """help""": (
							            """The maximum total input sequence length after tokenization. Sequences longer """
							            """than this will be truncated, sequences shorter will be padded."""
							        )
							    }       ,   )
							a          = field(
							    default=UpperCAmelCase__       ,   metadata={"""help""": """Overwrite the cached training and evaluation sets"""}	)





def 				SCREAMING_SNAKE_CASE_ ()   -> int:
							lowerCamelCase__      :			Tuple 						= 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__						,	lowerCamelCase__						,	lowerCamelCase__      :			Optional[int] 						= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
							else:
														lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			Union[str, Any] 						= parser.parse_args_into_dataclasses()

							if (
							    os.path.exists(training_args.output_dir   )
							    and os.listdir(training_args.output_dir   )
							    and training_args.do_train
							    and not training_args.overwrite_output_dir
							):
														raise ValueError(
														    f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
														    """ --overwrite_output_dir to overcome."""   )

							lowerCamelCase__      :			Any 						= import_module("""tasks"""   )
							try:
														lowerCamelCase__      :			Optional[int] 						= getattr(UpperCAmelCase__       ,    model_args.task_type   )
														lowerCamelCase__      :			str 						= token_classification_task_clazz()
							except AttributeError:
														raise ValueError(
														    f'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
														    f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''   )

							# Setup logging
							logging.basicConfig(
							    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""       ,    datefmt="""%m/%d/%Y %H:%M:%S"""       ,    level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN       ,    )
							logger.warning(
							    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""       ,    training_args.local_rank       ,    training_args.device       ,    training_args.n_gpu       ,    bool(training_args.local_rank != -1   )       ,    training_args.fpaa       ,    )
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank   ):
														transformers.utils.logging.set_verbosity_info()
														transformers.utils.logging.enable_default_handler()
														transformers.utils.logging.enable_explicit_format()
							logger.info("""Training/evaluation parameters %s"""       ,    UpperCAmelCase__   )

							# Set seed
							set_seed(training_args.seed   )

							# Prepare CONLL-2003 task
							lowerCamelCase__      :			List[str] 						= token_classification_task.get_labels(data_args.labels   )
							lowerCamelCase__      :			Optional[Any] 						= dict(enumerate(UpperCAmelCase__   )   )
							lowerCamelCase__      :			Tuple 						= len(UpperCAmelCase__   )

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.

							lowerCamelCase__      :			Union[str, Any] 						= AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,    num_labels=UpperCAmelCase__       ,    idalabel=UpperCAmelCase__       ,    labelaid={label: i for i, label in enumerate(UpperCAmelCase__   )}       ,    cache_dir=model_args.cache_dir       ,    )
							lowerCamelCase__      :			Tuple 						= AutoTokenizer.from_pretrained(
							    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,    cache_dir=model_args.cache_dir       ,    use_fast=model_args.use_fast       ,    )
							lowerCamelCase__      :			List[Any] 						= AutoModelForTokenClassification.from_pretrained(
							    model_args.model_name_or_path       ,    from_tf=bool(""".ckpt""" in model_args.model_name_or_path   )       ,    config=UpperCAmelCase__       ,    cache_dir=model_args.cache_dir       ,    )

							# Get datasets
							lowerCamelCase__      :			int 						= (
							    TokenClassificationDataset(
							        token_classification_task=UpperCAmelCase__       ,    data_dir=data_args.data_dir       ,    tokenizer=UpperCAmelCase__       ,    labels=UpperCAmelCase__       ,    model_type=config.model_type       ,    max_seq_length=data_args.max_seq_length       ,    overwrite_cache=data_args.overwrite_cache       ,    mode=Split.train       ,    )
							    if training_args.do_train
							    else None
							)
							lowerCamelCase__      :			Any 						= (
							    TokenClassificationDataset(
							        token_classification_task=UpperCAmelCase__       ,    data_dir=data_args.data_dir       ,    tokenizer=UpperCAmelCase__       ,    labels=UpperCAmelCase__       ,    model_type=config.model_type       ,    max_seq_length=data_args.max_seq_length       ,    overwrite_cache=data_args.overwrite_cache       ,    mode=Split.dev       ,    )
							    if training_args.do_eval
							    else None
							)

							def align_predictions(UpperCamelCase       ,    UpperCamelCase   ) -> Tuple[List[int], List[int]]:
														lowerCamelCase__      :			Union[str, Any] 						= np.argmax(UpperCAmelCase__       ,    axis=2   )

														lowerCamelCase__						,	lowerCamelCase__      :			Union[str, Any] 						= preds.shape

														lowerCamelCase__      :			int 						= [[] for _ in range(UpperCAmelCase__   )]
														lowerCamelCase__      :			Optional[int] 						= [[] for _ in range(UpperCAmelCase__   )]

														for i in range(UpperCAmelCase__   ):
																					for j in range(UpperCAmelCase__   ):
																												if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																																			out_label_list[i].append(label_map[label_ids[i][j]]   )
																																			preds_list[i].append(label_map[preds[i][j]]   )

														return preds_list, out_label_list

							def compute_metrics(UpperCamelCase   ) -> Dict:
														lowerCamelCase__						,	lowerCamelCase__      :			Any 						= align_predictions(p.predictions       ,    p.label_ids   )
														return {
														    "accuracy_score": accuracy_score(UpperCAmelCase__       ,    UpperCAmelCase__   ),
														    "precision": precision_score(UpperCAmelCase__       ,    UpperCAmelCase__   ),
														    "recall": recall_score(UpperCAmelCase__       ,    UpperCAmelCase__   ),
														    "f1": fa_score(UpperCAmelCase__       ,    UpperCAmelCase__   ),
														}

							# Data collator
							lowerCamelCase__      :			Tuple 						= DataCollatorWithPadding(UpperCAmelCase__       ,    pad_to_multiple_of=8   ) if training_args.fpaa else None

							# Initialize our Trainer
							lowerCamelCase__      :			Tuple 						= Trainer(
							    model=UpperCAmelCase__       ,    args=UpperCAmelCase__       ,    train_dataset=UpperCAmelCase__       ,    eval_dataset=UpperCAmelCase__       ,    compute_metrics=UpperCAmelCase__       ,    data_collator=UpperCAmelCase__       ,    )

							# Training
							if training_args.do_train:
														trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
														trainer.save_model()
														# For convenience, we also re-save the tokenizer to the same directory,
														# so that you can share your model easily on huggingface.co/models =)
														if trainer.is_world_process_zero():
																					tokenizer.save_pretrained(training_args.output_dir   )

																					 # Evaluation
							lowerCamelCase__      :			List[str] 						= {}
							if training_args.do_eval:
														logger.info("""*** Evaluate ***"""   )

														lowerCamelCase__      :			Optional[Any] 						= trainer.evaluate()

														lowerCamelCase__      :			List[Any] 						= os.path.join(training_args.output_dir       ,    """eval_results.txt"""   )
														if trainer.is_world_process_zero():
																					with open(UpperCAmelCase__       ,    """w"""   ) as writer:
																												logger.info("""***** Eval results *****"""   )
																												for key, value in result.items():
																																			logger.info("""  %s = %s"""       ,    UpperCAmelCase__       ,    UpperCAmelCase__   )
																																			writer.write("""%s = %s\n""" % (key, value)   )

																					results.update(UpperCAmelCase__   )

																					 # Predict
							if training_args.do_predict:
														lowerCamelCase__      :			Dict 						= TokenClassificationDataset(
														    token_classification_task=UpperCAmelCase__       ,    data_dir=data_args.data_dir       ,    tokenizer=UpperCAmelCase__       ,    labels=UpperCAmelCase__       ,    model_type=config.model_type       ,    max_seq_length=data_args.max_seq_length       ,    overwrite_cache=data_args.overwrite_cache       ,    mode=Split.test       ,    )

														lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			List[Any] 						= trainer.predict(UpperCAmelCase__   )
														lowerCamelCase__						,	lowerCamelCase__      :			Optional[int] 						= align_predictions(UpperCAmelCase__       ,    UpperCAmelCase__   )

														lowerCamelCase__      :			str 						= os.path.join(training_args.output_dir       ,    """test_results.txt"""   )
														if trainer.is_world_process_zero():
																					with open(UpperCAmelCase__       ,    """w"""   ) as writer:
																												for key, value in metrics.items():
																																			logger.info("""  %s = %s"""       ,    UpperCAmelCase__       ,    UpperCAmelCase__   )
																																			writer.write("""%s = %s\n""" % (key, value)   )

																																			   # Save predictions
														lowerCamelCase__      :			List[str] 						= os.path.join(training_args.output_dir       ,    """test_predictions.txt"""   )
														if trainer.is_world_process_zero():
																					with open(UpperCAmelCase__       ,    """w"""   ) as writer:
																												with open(os.path.join(data_args.data_dir       ,    """test.txt"""   )       ,    """r"""   ) as f:
																																			token_classification_task.write_predictions_to_file(UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__   )

							return results





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> List[str]:
							main()


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 _lowercase			(       _lowercase	):




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Dict 						= self.config_class(**self.inputs_dict  )
														self.parent.assertTrue(hasattr(UpperCamelCase__    ,							"""width_multiplier"""  )  )



class _lowercase			:




							def __init__(							self:       str    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       str=13    ,							UpperCamelCase__:       Any=64    ,							UpperCamelCase__:       Optional[Any]=2    ,							UpperCamelCase__:       str=3    ,							UpperCamelCase__:       List[str]="swish"    ,							UpperCamelCase__:       Any=3    ,							UpperCamelCase__:       Optional[int]=32    ,							UpperCamelCase__:       Union[str, Any]=0.1    ,							UpperCamelCase__:       int=0.02    ,							UpperCamelCase__:       Dict=True    ,							UpperCamelCase__:       Dict=True    ,							UpperCamelCase__:       Any=10    ,							UpperCamelCase__:       int=None    ,							UpperCamelCase__:       List[Any]=0.25    ,							UpperCamelCase__:       str=0.0    ,							UpperCamelCase__:       Optional[int]=0.0    ,							):
														lowerCamelCase__      :			Any 						= parent
														lowerCamelCase__      :			Optional[Any] 						= batch_size
														lowerCamelCase__      :			Optional[int] 						= image_size
														lowerCamelCase__      :			str 						= patch_size
														lowerCamelCase__      :			Optional[int] 						= num_channels
														lowerCamelCase__      :			Optional[Any] 						= make_divisible(512 * width_multiplier    ,							divisor=8  )
														lowerCamelCase__      :			List[str] 						= hidden_act
														lowerCamelCase__      :			Any 						= conv_kernel_size
														lowerCamelCase__      :			Any 						= output_stride
														lowerCamelCase__      :			Union[str, Any] 						= classifier_dropout_prob
														lowerCamelCase__      :			List[str] 						= use_labels
														lowerCamelCase__      :			Optional[Any] 						= is_training
														lowerCamelCase__      :			List[str] 						= num_labels
														lowerCamelCase__      :			Dict 						= initializer_range
														lowerCamelCase__      :			List[Any] 						= scope
														lowerCamelCase__      :			Tuple 						= width_multiplier
														lowerCamelCase__      :			List[Any] 						= ffn_dropout
														lowerCamelCase__      :			Any 						= attn_dropout




							def 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			Dict 						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

														lowerCamelCase__      :			Union[str, Any] 						= self.get_config()

														return config, pixel_values, labels, pixel_labels




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														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 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Optional[int]  ):
														lowerCamelCase__      :			Union[str, Any] 						= MobileViTVaModel(config=UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			str 						= model(UpperCamelCase__  )
														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 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Tuple  ):
														lowerCamelCase__      :			Tuple 						= self.num_labels
														lowerCamelCase__      :			Dict 						= MobileViTVaForImageClassification(UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			int 						= model(UpperCamelCase__    ,							labels=UpperCamelCase__  )
														self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_labels)  )




							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			List[str] 						= self.num_labels
														lowerCamelCase__      :			Union[str, Any] 						= MobileViTVaForSemanticSegmentation(UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			Tuple 						= model(UpperCamelCase__  )
														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__      :			List[Any] 						= model(UpperCamelCase__    ,							labels=UpperCamelCase__  )
														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 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__      :			Any 						= self.prepare_config_and_inputs()
														lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__						,	lowerCamelCase__      :			Union[str, Any] 						= config_and_inputs
														lowerCamelCase__      :			Optional[Any] 						= {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_torch
class _lowercase			(       _lowercase       ,   _lowercase       ,   unittest.TestCase	):
							a          = (
							    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
							    if is_torch_available()
							    else ()
							)

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

							a          = False
							a          = False
							a          = False
							a          = False




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			Tuple 						= MobileViTVaModelTester(self  )
														lowerCamelCase__      :			List[str] 						= MobileViTVaConfigTester(self    ,							config_class=UpperCamelCase__    ,							has_text_modality=UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Tuple  ):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""MobileViTV2 does not use inputs_embeds"""  )
							def 	lowerCamelCase_       (							self:       int  ):
														pass




							@unittest.skip(reason="""MobileViTV2 does not support input and output embeddings"""  )
							def 	lowerCamelCase_       (							self:       List[str]  ):
														pass




							@unittest.skip(reason="""MobileViTV2 does not output attentions"""  )
							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														pass




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




							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
							def 	lowerCamelCase_       (							self:       Tuple  ):
														pass




							def 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__						,	lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__      :			Union[str, Any] 						= model_class(UpperCamelCase__  )
																					lowerCamelCase__      :			int 						= inspect.signature(model.forward  )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCamelCase__      :			Tuple 						= [*signature.parameters.keys()]

																					lowerCamelCase__      :			str 						= ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       List[str]  ):
														def check_hidden_states_output(UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       Optional[Any]  ):
																					lowerCamelCase__      :			List[Any] 						= model_class(UpperCamelCase__  )
																					model.to(UpperCamelCase__  )
																					model.eval()

																					with torch.no_grad():
																												lowerCamelCase__      :			Tuple 						= model(**self._prepare_for_class(UpperCamelCase__    ,							UpperCamelCase__  )  )

																					lowerCamelCase__      :			Optional[int] 						= outputs.hidden_states

																					lowerCamelCase__      :			List[Any] 						= 5
																					self.assertEqual(len(UpperCamelCase__  )    ,							UpperCamelCase__  )

																					# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
																					# with the width and height being successively divided by 2.
																					lowerCamelCase__      :			int 						= 2
																					for i in range(len(UpperCamelCase__  )  ):
																												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__      :			Tuple 						= self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__      :			int 						= True
																					check_hidden_states_output(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )

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

																					check_hidden_states_output(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			str 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase__  )




							@slow
							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__      :			Union[str, Any] 						= MobileViTVaModel.from_pretrained(UpperCamelCase__  )
																					self.assertIsNotNone(UpperCamelCase__  )





def 				SCREAMING_SNAKE_CASE_ ()   -> Optional[int]:
							lowerCamelCase__      :			str 						= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
							return image



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




							@cached_property
							def 	lowerCamelCase_       (							self:       Tuple  ):
														return (
														    MobileViTImageProcessor.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""  )
														    if is_vision_available()
														    else None
														)




							@slow
							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Optional[Any] 						= MobileViTVaForImageClassification.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""  ).to(
														    UpperCamelCase__  )

														lowerCamelCase__      :			Union[str, Any] 						= self.default_image_processor
														lowerCamelCase__      :			List[Any] 						= prepare_img()
														lowerCamelCase__      :			Any 						= image_processor(images=UpperCamelCase__    ,							return_tensors="""pt"""  ).to(UpperCamelCase__  )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__      :			int 						= model(**UpperCamelCase__  )

														# verify the logits
														lowerCamelCase__      :			str 						= torch.Size((1, 1_000)  )
														self.assertEqual(outputs.logits.shape    ,							UpperCamelCase__  )

														lowerCamelCase__      :			Optional[int] 						= torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]  ).to(UpperCamelCase__  )

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




							@slow
							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														lowerCamelCase__      :			int 						= MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""  )
														lowerCamelCase__      :			Optional[Any] 						= model.to(UpperCamelCase__  )

														lowerCamelCase__      :			Any 						= MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""  )

														lowerCamelCase__      :			Union[str, Any] 						= prepare_img()
														lowerCamelCase__      :			Dict 						= image_processor(images=UpperCamelCase__    ,							return_tensors="""pt"""  ).to(UpperCamelCase__  )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__      :			Optional[Any] 						= model(**UpperCamelCase__  )
														lowerCamelCase__      :			str 						= outputs.logits

														# verify the logits
														lowerCamelCase__      :			List[str] 						= torch.Size((1, 21, 32, 32)  )
														self.assertEqual(logits.shape    ,							UpperCamelCase__  )

														lowerCamelCase__      :			Any 						= torch.tensor(
														    [
														        [[7.0_863, 7.1_525, 6.8_201], [6.6_931, 6.8_770, 6.8_933], [6.2_978, 7.0_366, 6.9_636]],
														        [[-3.7_134, -3.6_712, -3.6_675], [-3.5_825, -3.3_549, -3.4_777], [-3.3_435, -3.3_979, -3.2_857]],
														        [[-2.9_329, -2.8_003, -2.7_369], [-3.0_564, -2.4_780, -2.0_207], [-2.6_889, -1.9_298, -1.7_640]],
														    ]    ,							device=UpperCamelCase__    ,							)

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




							@slow
							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			Optional[Any] 						= MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""  )
														lowerCamelCase__      :			List[Any] 						= model.to(UpperCamelCase__  )

														lowerCamelCase__      :			Union[str, Any] 						= MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""  )

														lowerCamelCase__      :			Optional[Any] 						= prepare_img()
														lowerCamelCase__      :			Dict 						= image_processor(images=UpperCamelCase__    ,							return_tensors="""pt"""  ).to(UpperCamelCase__  )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__      :			Dict 						= model(**UpperCamelCase__  )

														lowerCamelCase__      :			List[str] 						= outputs.logits.detach().cpu()

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

														lowerCamelCase__      :			Optional[Any] 						= image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase__  )
														lowerCamelCase__      :			int 						= torch.Size((32, 32)  )
														self.assertEqual(segmentation[0].shape    ,							UpperCamelCase__  )

