'''simple docstring'''






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

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

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    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


# 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>=1.8.0''', '''To fix: pip install -r examples/pytorch/text-classification/requirements.txt''')

_UpperCAmelCase    :     List[Any]         =       logging.getLogger(__name__)







@dataclass
class lowercase_       :







   """simple docstring"""

   __lowerCAmelCase         =   field(
       default=1_2_8    ,    metadata={
           "help": (
               "The maximum total input sequence length after tokenization. Sequences longer "
               "than this will be truncated, sequences shorter will be padded."
           )
       }    ,    )
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={"help": "Overwrite the cached preprocessed datasets or not."}			)
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={
           "help": (
               "Whether to pad all samples to `max_seq_length`. "
               "If False, will pad the samples dynamically when batching to the maximum length in the batch."
           )
       }    ,    )
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={
           "help": (
               "For debugging purposes or quicker training, truncate the number of training examples to this "
               "value if set."
           )
       }    ,    )
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={
           "help": (
               "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
               "value if set."
           )
       }    ,    )
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={
           "help": (
               "For debugging purposes or quicker training, truncate the number of prediction examples to this "
               "value if set."
           )
       }    ,    )







@dataclass
class lowercase_       :







   """simple docstring"""

   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}			)
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={"help": "Evaluation language. Also train language if `train_language` is set to None."}			)
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={"help": "Train language if it is different from the evaluation language."}			)
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={"help": "Pretrained config name or path if not the same as model_name"}			)
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}			)
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    ,    )
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"}    ,    )
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}    ,    )
   __lowerCAmelCase         =   field(
       default="main"    ,    metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}    ,    )
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={
           "help": (
               "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
               "with private models)."
           )
       }    ,    )
   __lowerCAmelCase         =   field(
       default=_UpperCamelCase    ,    metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}    ,    )






def  _SCREAMING_SNAKE_CASE		(      ):
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
     # We now keep distinct sets of args, for a cleaner separation of concerns.

     _A    		=  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
     _A		,       _A		,       _A    		=  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_xnli'   ,		__snake_case				)

     # 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()

     _A    		=  training_args.get_process_log_level()
     logger.setLevel(__snake_case				)
     datasets.utils.logging.set_verbosity(__snake_case				)
     transformers.utils.logging.set_verbosity(__snake_case				)
     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}'				)

     # Detecting last checkpoint.
     _A    		=  None
     if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
          _A    		=  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 overcome.'				)
          elif last_checkpoint is not 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.'				)

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

     # In distributed training, the load_dataset function guarantees that only one local process can concurrently
     # download the dataset.
     # Downloading and loading xnli dataset from the hub.
     if training_args.do_train:
          if model_args.train_language is None:
               _A    		=  load_dataset(
                   'xnli'   ,		model_args.language   ,		split='train'   ,		cache_dir=model_args.cache_dir   ,		use_auth_token=True if model_args.use_auth_token else None   ,		)
          else:
               _A    		=  load_dataset(
                   'xnli'   ,		model_args.train_language   ,		split='train'   ,		cache_dir=model_args.cache_dir   ,		use_auth_token=True if model_args.use_auth_token else None   ,		)
          _A    		=  train_dataset.features['label'].names

     if training_args.do_eval:
          _A    		=  load_dataset(
              'xnli'   ,		model_args.language   ,		split='validation'   ,		cache_dir=model_args.cache_dir   ,		use_auth_token=True if model_args.use_auth_token else None   ,		)
          _A    		=  eval_dataset.features['label'].names

     if training_args.do_predict:
          _A    		=  load_dataset(
              'xnli'   ,		model_args.language   ,		split='test'   ,		cache_dir=model_args.cache_dir   ,		use_auth_token=True if model_args.use_auth_token else None   ,		)
          _A    		=  predict_dataset.features['label'].names

     # Labels
     _A    		=  len(__snake_case				)

     # Load pretrained model and tokenizer
     # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     _A    		=  AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,		num_labels=__snake_case   ,		idalabel={str(__snake_case				): label for i, label in enumerate(__snake_case				)}   ,		labelaid={label: i for i, label in enumerate(__snake_case				)}   ,		finetuning_task='xnli'   ,		cache_dir=model_args.cache_dir   ,		revision=model_args.model_revision   ,		use_auth_token=True if model_args.use_auth_token else None   ,		)
     _A    		=  AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   ,		do_lower_case=model_args.do_lower_case   ,		cache_dir=model_args.cache_dir   ,		use_fast=model_args.use_fast_tokenizer   ,		revision=model_args.model_revision   ,		use_auth_token=True if model_args.use_auth_token else None   ,		)
     _A    		=  AutoModelForSequenceClassification.from_pretrained(
         model_args.model_name_or_path   ,		from_tf=bool('.ckpt' in model_args.model_name_or_path				)   ,		config=__snake_case   ,		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   ,		)

     # Preprocessing the datasets
     # Padding strategy
     if data_args.pad_to_max_length:
          _A    		=  'max_length'
     else:
          # We will pad later, dynamically at batch creation, to the max sequence length in each batch
          _A    		=  False

     def preprocess_function(__snake_case							:			List[Any]				):
          # Tokenize the texts
          return tokenizer(
              examples['premise']   ,		examples['hypothesis']   ,		padding=__snake_case   ,		max_length=data_args.max_seq_length   ,		truncation=__snake_case   ,		)

     if training_args.do_train:
          if data_args.max_train_samples is not None:
               _A    		=  min(len(__snake_case				)   ,		data_args.max_train_samples				)
               _A    		=  train_dataset.select(range(__snake_case				)				)
          with training_args.main_process_first(desc='train dataset map pre-processing'				):
               _A    		=  train_dataset.map(
                   __snake_case   ,		batched=__snake_case   ,		load_from_cache_file=not data_args.overwrite_cache   ,		desc='Running tokenizer on train dataset'   ,		)
          # Log a few random samples from the training set:
          for index in random.sample(range(len(__snake_case				)				)   ,		3				):
               logger.info(F'Sample {index} of the training set: {train_dataset[index]}.'				)

     if training_args.do_eval:
          if data_args.max_eval_samples is not None:
               _A    		=  min(len(__snake_case				)   ,		data_args.max_eval_samples				)
               _A    		=  eval_dataset.select(range(__snake_case				)				)
          with training_args.main_process_first(desc='validation dataset map pre-processing'				):
               _A    		=  eval_dataset.map(
                   __snake_case   ,		batched=__snake_case   ,		load_from_cache_file=not data_args.overwrite_cache   ,		desc='Running tokenizer on validation dataset'   ,		)

     if training_args.do_predict:
          if data_args.max_predict_samples is not None:
               _A    		=  min(len(__snake_case				)   ,		data_args.max_predict_samples				)
               _A    		=  predict_dataset.select(range(__snake_case				)				)
          with training_args.main_process_first(desc='prediction dataset map pre-processing'				):
               _A    		=  predict_dataset.map(
                   __snake_case   ,		batched=__snake_case   ,		load_from_cache_file=not data_args.overwrite_cache   ,		desc='Running tokenizer on prediction dataset'   ,		)

    # Get the metric function
     _A    		=  evaluate.load('xnli'				)

     # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
     # predictions and label_ids field) and has to return a dictionary string to float.
     def compute_metrics(__snake_case							:			EvalPrediction				):
          _A    		=  p.predictions[0] if isinstance(p.predictions   ,		__snake_case				) else p.predictions
          _A    		=  np.argmax(__snake_case   ,		axis=1				)
          return metric.compute(predictions=__snake_case   ,		references=p.label_ids				)

     # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
     if data_args.pad_to_max_length:
          _A    		=  default_data_collator
     elif training_args.fpaa:
          _A    		=  DataCollatorWithPadding(__snake_case   ,		pad_to_multiple_of=8				)
     else:
          _A    		=  None

     # Initialize our Trainer
     _A    		=  Trainer(
         model=__snake_case   ,		args=__snake_case   ,		train_dataset=train_dataset if training_args.do_train else None   ,		eval_dataset=eval_dataset if training_args.do_eval else None   ,		compute_metrics=__snake_case   ,		tokenizer=__snake_case   ,		data_collator=__snake_case   ,		)

     # Training
     if training_args.do_train:
          _A    		=  None
          if training_args.resume_from_checkpoint is not None:
               _A    		=  training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               _A    		=  last_checkpoint
          _A    		=  trainer.train(resume_from_checkpoint=__snake_case				)
          _A    		=  train_result.metrics
          _A    		=  (
              data_args.max_train_samples if data_args.max_train_samples is not None else len(__snake_case				)
          )
          _A    		=  min(__snake_case   ,		len(__snake_case				)				)

          trainer.save_model()  # Saves the tokenizer too for easy upload

          trainer.log_metrics('train'   ,		__snake_case				)
          trainer.save_metrics('train'   ,		__snake_case				)
          trainer.save_state()

     # Evaluation
     if training_args.do_eval:
          logger.info('*** Evaluate ***'				)
          _A    		=  trainer.evaluate(eval_dataset=__snake_case				)

          _A    		=  data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__snake_case				)
          _A    		=  min(__snake_case   ,		len(__snake_case				)				)

          trainer.log_metrics('eval'   ,		__snake_case				)
          trainer.save_metrics('eval'   ,		__snake_case				)

     # Prediction
     if training_args.do_predict:
          logger.info('*** Predict ***'				)
          _A		,       _A		,       _A    		=  trainer.predict(__snake_case   ,		metric_key_prefix='predict'				)

          _A    		=  (
              data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__snake_case				)
          )
          _A    		=  min(__snake_case   ,		len(__snake_case				)				)

          trainer.log_metrics('predict'   ,		__snake_case				)
          trainer.save_metrics('predict'   ,		__snake_case				)

          _A    		=  np.argmax(__snake_case   ,		axis=1				)
          _A    		=  os.path.join(training_args.output_dir   ,		'predictions.txt'				)
          if trainer.is_world_process_zero():
               with open(__snake_case   ,		'w'				) as writer:
                    writer.write('index\tprediction\n'				)
                    for index, item in enumerate(__snake_case				):
                         _A    		=  label_list[item]
                         writer.write(F'{index}\t{item}\n'				)


if __name__ == "__main__":
   main()





"""simple docstring"""




import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

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


if is_torch_available():
  import torch

  from transformers import UperNetForSemanticSegmentation
  from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor


class       __magic_name__							:
       def __init__(  self			,					A_			,					A_=13			,					A_=32			,					A_=3			,					A_=4			,					A_=[10, 20, 30, 40]			,					A_=[2, 2, 3, 2]			,					A_=True			,					A_=True			,					A_=37			,					A_="gelu"			,					A_=10			,					A_=0.02			,					A_=["stage2", "stage3", "stage4"]			,					A_=3			,					A_=None			,					)  ->						List[str]:





         """simple docstring"""




         _lowercase:      str   			=  parent
         _lowercase:      Union[str, Any]   			=  batch_size
         _lowercase:      Tuple   			=  image_size
         _lowercase:      Tuple   			=  num_channels
         _lowercase:      Optional[int]   			=  num_stages
         _lowercase:      List[Any]   			=  hidden_sizes
         _lowercase:      Dict   			=  depths
         _lowercase:      int   			=  is_training
         _lowercase:      Tuple   			=  use_labels
         _lowercase:      Any   			=  intermediate_size
         _lowercase:      Union[str, Any]   			=  hidden_act
         _lowercase:      str   			=  type_sequence_label_size
         _lowercase:      int   			=  initializer_range
         _lowercase:      List[str]   			=  out_features
         _lowercase:      Any   			=  num_labels
         _lowercase:      Any   			=  scope
         _lowercase:      str   			=  num_stages
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         _lowercase:      Any   			=  None
         if self.use_labels:
           _lowercase:      Dict   			=  ids_tensor([self.batch_size]			,					self.type_sequence_label_size							)

         _lowercase:      Tuple   			=  self.get_config()

         return config, pixel_values, labels
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         return ConvNextConfig(
             num_channels=self.num_channels			,					num_stages=self.num_stages			,					hidden_sizes=self.hidden_sizes			,					depths=self.depths			,					is_training=self.is_training			,					intermediate_size=self.intermediate_size			,					hidden_act=self.hidden_act			,					out_features=self.out_features			,					)
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         return UperNetConfig(
             backbone_config=self.get_backbone_config()			,					hidden_size=512			,					pool_scales=[1, 2, 3, 6]			,					use_auxiliary_head=A_			,					auxiliary_loss_weight=0.4			,					auxiliary_in_channels=40			,					auxiliary_channels=256			,					auxiliary_num_convs=1			,					auxiliary_concat_input=A_			,					loss_ignore_index=255			,					num_labels=self.num_labels			,					)
       def 				lowercase_				(  self			,					A_			,					A_			,					A_							)  ->						int:





         """simple docstring"""




         _lowercase:      List[str]   			=  UperNetForSemanticSegmentation(config=A_							)
         model.to(A_							)
         model.eval()
         _lowercase:      int   			=  model(A_							)
         self.parent.assertEqual(
             result.logits.shape			,					(self.batch_size, self.num_labels, self.image_size, self.image_size)							)



       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         _lowercase:      Optional[int]   			=  self.prepare_config_and_inputs()
         (
             (
             _lowercase
         )			,					(
             _lowercase
         )			,					(
             _lowercase
         )			,					
         ):      Tuple   			=  config_and_inputs
         _lowercase:      str   			=  {'''pixel_values''': pixel_values}
         return config, inputs_dict


@require_torch
class       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		(UperNetForSemanticSegmentation,) if is_torch_available() else ()
       UpperCamelCase_ =		{'''image-segmentation''': UperNetForSemanticSegmentation} if is_torch_available() else {}
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       UpperCamelCase_ =		False
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      List[Any]   			=  UperNetModelTester(self							)
         _lowercase:      Optional[Any]   			=  ConfigTester(self			,					config_class=A_			,					has_text_modality=A_			,					hidden_size=37							)
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         return
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




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

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

           _lowercase:      int   			=  ['''pixel_values''']
           self.assertListEqual(arg_names[:1]			,					A_							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[str]   			=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*A_							)
       @unittest.skip(reason='''UperNet does not use inputs_embeds'''							)
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         pass
       @unittest.skip(reason='''UperNet does not support input and output embeddings'''							)
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         pass
       @unittest.skip(reason='''UperNet does not have a base model'''							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         pass
       @unittest.skip(reason='''UperNet does not have a base model'''							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         pass
       @require_torch_multi_gpu
       @unittest.skip(reason='''UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''							)
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         pass
       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''							)
       def 				lowercase_				(  self							)  ->						List[Any]:





         """simple docstring"""




         pass
       def 				lowercase_				(  self							)  ->						str:





         """simple docstring"""




         def check_hidden_states_output(A_			,					A_			,					A_							):
           _lowercase:      Dict   			=  model_class(A_							)
           model.to(A_							)
           model.eval()

           with torch.no_grad():
             _lowercase:      List[str]   			=  model(**self._prepare_for_class(A_			,					A_							)							)

           _lowercase:      Tuple   			=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           _lowercase:      Union[str, Any]   			=  self.model_tester.num_stages
           self.assertEqual(len(A_							)			,					expected_num_stages + 1							)

           # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
           self.assertListEqual(
               list(hidden_states[0].shape[-2:]							)			,					[self.model_tester.image_size // 4, self.model_tester.image_size // 4]			,					)

         _lowercase			,					_lowercase:      Dict   			=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           _lowercase:      Optional[int]   			=  True
           check_hidden_states_output(A_			,					A_			,					A_							)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _lowercase:      Union[str, Any]   			=  True

           check_hidden_states_output(A_			,					A_			,					A_							)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase			,					_lowercase:      Any   			=  self.model_tester.prepare_config_and_inputs_for_common()

         _lowercase:      int   			=  _config_zero_init(A_							)
         _lowercase:      Optional[Any]   			=  _config_zero_init(configs_no_init.backbone_config							)
         for model_class in self.all_model_classes:
           _lowercase:      Any   			=  model_class(config=A_							)
           for name, param in model.named_parameters():
             if param.requires_grad:
               self.assertIn(
                   ((param.data.mean() * 1E9).round() / 1E9).item()			,					[0.0, 1.0]			,					msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''			,					)
       @unittest.skip(reason='''UperNet does not have tied weights'''							)
       def 				lowercase_				(  self							)  ->						List[str]:





         """simple docstring"""




         pass



       @slow
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowercase:      Union[str, Any]   			=  UperNetForSemanticSegmentation.from_pretrained(A_							)
           self.assertIsNotNone(A_							)




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      List[str]   			=  hf_hub_download(
      repo_id='''hf-internal-testing/fixtures_ade20k'''			,					repo_type='''dataset'''			,					filename='''ADE_val_00000001.jpg'''    )
  _lowercase:      Optional[int]   			=  Image.open(_UpperCamelCase    ).convert('''RGB'''    )
  return image







@require_torch
@require_vision
@slow
class       __magic_name__							(							unittest.TestCase			):
       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  AutoImageProcessor.from_pretrained('''openmmlab/upernet-swin-tiny'''							)
         _lowercase:      Optional[Any]   			=  UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-swin-tiny'''							).to(A_							)

         _lowercase:      List[Any]   			=  prepare_img()
         _lowercase:      Any   			=  processor(images=A_			,					return_tensors='''pt'''							).to(A_							)

         with torch.no_grad():
           _lowercase:      str   			=  model(**A_							)

         _lowercase:      Union[str, Any]   			=  torch.Size((1, model.config.num_labels, 512, 512)							)
         self.assertEqual(outputs.logits.shape			,					A_							)

         _lowercase:      Optional[Any]   			=  torch.tensor(
             [[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]]							).to(A_							)
         self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]			,					A_			,					atol=1E-4							)							)



       def 				lowercase_				(  self							)  ->						int:





         """simple docstring"""




         _lowercase:      Tuple   			=  AutoImageProcessor.from_pretrained('''openmmlab/upernet-convnext-tiny'''							)
         _lowercase:      str   			=  UperNetForSemanticSegmentation.from_pretrained('''openmmlab/upernet-convnext-tiny'''							).to(A_							)

         _lowercase:      Union[str, Any]   			=  prepare_img()
         _lowercase:      Optional[Any]   			=  processor(images=A_			,					return_tensors='''pt'''							).to(A_							)

         with torch.no_grad():
           _lowercase:      str   			=  model(**A_							)

         _lowercase:      Optional[Any]   			=  torch.Size((1, model.config.num_labels, 512, 512)							)
         self.assertEqual(outputs.logits.shape			,					A_							)

         _lowercase:      Tuple   			=  torch.tensor(
             [[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]]							).to(A_							)
         self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]			,					A_			,					atol=1E-4							)							)
