'''simple docstring'''


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

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image
from torch import nn
from torchvision import transforms
from torchvision.transforms import functional

import transformers
from transformers import (
    AutoConfig,
    AutoImageProcessor,
    AutoModelForSemanticSegmentation,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
)
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
snake_case__	:     List[Any]      =    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>=2.0.0''', '''To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt''')


def    _lowerCamelCase       ( lowerCamelCase_   :				Optional[Any]      ,	lowerCamelCase_   :				Any      ,	lowerCamelCase_   :				Union[str, Any]=0  ):



     """simple docstring"""





     UpperCAmelCase_				:  Any  = (size, size) if isinstance(lowerCamelCase_      ,	lowerCamelCase_  ) else size
     UpperCAmelCase_  ,     UpperCAmelCase_				:  Optional[Any]  = img.size
     UpperCAmelCase_				:  Dict  = size[1] - original_height if original_height < size[1] else 0
     UpperCAmelCase_				:  List[Any]  = size[0] - original_width if original_width < size[0] else 0
     UpperCAmelCase_				:  List[str]  = functional.pad(lowerCamelCase_      ,	(0, 0, pad_width, pad_height)      ,	fill=lowerCamelCase_  )
     return img






class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = transforms






  def __call__(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       for t in self.transforms:
            UpperCAmelCase_  ,     UpperCAmelCase_				:  Optional[int]  = t(snake_case_ ,						snake_case_		)
       return image, target







class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self		):




       '''simple docstring'''

       pass






  def __call__(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       return image, target







class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = size






  def __call__(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = functional.resize(snake_case_ ,						self.size		)
       UpperCAmelCase_				:  str  = functional.resize(snake_case_ ,						self.size ,						interpolation=transforms.InterpolationMode.NEAREST		)
       return image, target







class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_=None		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = min_size
       if max_size is None:
            UpperCAmelCase_				:  str  = min_size
       UpperCAmelCase_				:  int  = max_size






  def __call__(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[str]  = random.randint(self.min_size ,						self.max_size		)
       UpperCAmelCase_				:  str  = functional.resize(snake_case_ ,						snake_case_		)
       UpperCAmelCase_				:  Any  = functional.resize(snake_case_ ,						snake_case_ ,						interpolation=transforms.InterpolationMode.NEAREST		)
       return image, target







class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = size if isinstance(snake_case_ ,						snake_case_		) else (size, size)






  def __call__(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = pad_if_smaller(snake_case_ ,						self.size		)
       UpperCAmelCase_				:  Optional[int]  = pad_if_smaller(snake_case_ ,						self.size ,						fill=2_5_5		)
       UpperCAmelCase_				:  Optional[int]  = transforms.RandomCrop.get_params(snake_case_ ,						self.size		)
       UpperCAmelCase_				:  int  = functional.crop(snake_case_ ,						*snake_case_		)
       UpperCAmelCase_				:  Optional[Any]  = functional.crop(snake_case_ ,						*snake_case_		)
       return image, target







class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = flip_prob






  def __call__(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       if random.random() < self.flip_prob:
            UpperCAmelCase_				:  List[Any]  = functional.hflip(snake_case_		)
            UpperCAmelCase_				:  Optional[Any]  = functional.hflip(snake_case_		)
       return image, target







class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __call__(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = functional.pil_to_tensor(snake_case_		)
       UpperCAmelCase_				:  Optional[Any]  = torch.as_tensor(np.array(snake_case_		) ,						dtype=torch.intaa		)
       return image, target







class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = dtype






  def __call__(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = functional.convert_image_dtype(snake_case_ ,						self.dtype		)
       return image, target







class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = mean
       UpperCAmelCase_				:  Union[str, Any]  = std






  def __call__(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = functional.normalize(snake_case_ ,						mean=self.mean ,						std=self.std		)
       return image, target







class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __call__(					self ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       if not isinstance(snake_case_ ,						np.ndarray		):
            UpperCAmelCase_				:  Tuple  = np.array(snake_case_		).astype(np.uinta		)
       # avoid using underflow conversion
       UpperCAmelCase_				:  Dict  = 2_5_5
       UpperCAmelCase_				:  Union[str, Any]  = target - 1
       UpperCAmelCase_				:  int  = 2_5_5

       UpperCAmelCase_				:  Optional[int]  = Image.fromarray(snake_case_		)
       return image, target







@dataclass
class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  lowerCamelCase_						:Optional[str]		       =						field(
      default='''segments/sidewalk-semantic'''		,      metadata={
          '''help''': '''Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'''
      }		,      )
  lowerCamelCase_						:Optional[str]		       =						field(
      default=lowerCamelCase_		,      metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}			)
  lowerCamelCase_						:Optional[float]		       =						field(
      default=0.15		,      metadata={'''help''': '''Percent to split off of train for validation.'''}			)
  lowerCamelCase_						:Optional[int]		       =						field(
      default=lowerCamelCase_		,      metadata={
          '''help''': (
              '''For debugging purposes or quicker training, truncate the number of training examples to this '''
              '''value if set.'''
          )
      }		,      )
  lowerCamelCase_						:Optional[int]		       =						field(
      default=lowerCamelCase_		,      metadata={
          '''help''': (
              '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
              '''value if set.'''
          )
      }		,      )
  lowerCamelCase_						:Optional[bool]		       =						field(
      default=lowerCamelCase_		,      metadata={'''help''': '''Whether or not to reduce all labels by 1 and replace background by 255.'''}		,      )


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
            raise ValueError(
                'You must specify either a dataset name from the hub or a train and/or validation directory.'		)







@dataclass
class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  lowerCamelCase_						:str		       =						field(
      default='''nvidia/mit-b0'''		,      metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}		,      )
  lowerCamelCase_						:Optional[str]		       =						field(
      default=lowerCamelCase_		,      metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}			)
  lowerCamelCase_						:Optional[str]		       =						field(
      default=lowerCamelCase_		,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}			)
  lowerCamelCase_						:str		       =						field(
      default='''main'''		,      metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}		,      )
  lowerCamelCase_						:str		       =						field(default=lowerCamelCase_		,      metadata={'''help''': '''Name or path of preprocessor config.'''}			)
  lowerCamelCase_						:bool		       =						field(
      default=lowerCamelCase_		,      metadata={
          '''help''': (
              '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
              '''with private models).'''
          )
      }		,      )







def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Optional[int]  = 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.
          UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_				:  Any  = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
     else:
          UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_				:  Optional[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_semantic_segmentation'      ,	lowerCamelCase_      ,	lowerCamelCase_  )

     # 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()

     UpperCAmelCase_				:  Tuple  = training_args.get_process_log_level()
     logger.setLevel(lowerCamelCase_  )
     transformers.utils.logging.set_verbosity(lowerCamelCase_  )
     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.
     UpperCAmelCase_				:  Union[str, Any]  = None
     if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
          UpperCAmelCase_				:  int  = 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 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.'  )

    # Load dataset
    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
    # download the dataset.
    # TODO support datasets from local folders
     UpperCAmelCase_				:  Tuple  = load_dataset(data_args.dataset_name      ,	cache_dir=model_args.cache_dir  )

     # Rename column names to standardized names (only "image" and "label" need to be present)
     if "pixel_values" in dataset["train"].column_names:
          UpperCAmelCase_				:  Union[str, Any]  = dataset.rename_columns({'pixel_values': 'image'}  )
     if "annotation" in dataset["train"].column_names:
          UpperCAmelCase_				:  Any  = dataset.rename_columns({'annotation': 'label'}  )

     # If we don't have a validation split, split off a percentage of train as validation.
     UpperCAmelCase_				:  Tuple  = None if 'validation' in dataset.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split      ,	lowerCamelCase_  ) and data_args.train_val_split > 0.0:
          UpperCAmelCase_				:  Optional[int]  = dataset['train'].train_test_split(data_args.train_val_split  )
          UpperCAmelCase_				:  Any  = split['train']
          UpperCAmelCase_				:  List[Any]  = split['test']

     # Prepare label mappings.
     # We'll include these in the model's config to get human readable labels in the Inference API.
     if data_args.dataset_name == "scene_parse_150":
          UpperCAmelCase_				:  Union[str, Any]  = 'huggingface/label-files'
          UpperCAmelCase_				:  Any  = 'ade20k-id2label.json'
     else:
          UpperCAmelCase_				:  List[str]  = data_args.dataset_name
          UpperCAmelCase_				:  str  = 'id2label.json'
     UpperCAmelCase_				:  List[str]  = json.load(open(hf_hub_download(lowerCamelCase_      ,	lowerCamelCase_      ,	repo_type='dataset'  )      ,	'r'  )  )
     UpperCAmelCase_				:  Optional[int]  = {int(lowerCamelCase_  ): v for k, v in idalabel.items()}
     UpperCAmelCase_				:  Optional[int]  = {v: str(lowerCamelCase_  ) for k, v in idalabel.items()}

     # Load the mean IoU metric from the datasets package
     UpperCAmelCase_				:  Dict  = evaluate.load('mean_iou'  )

     # Define our 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.
     @torch.no_grad()
     def compute_metrics(lowerCamelCase_   :				str  ):
          UpperCAmelCase_  ,     UpperCAmelCase_				:  List[Any]  = eval_pred
          UpperCAmelCase_				:  Optional[Any]  = torch.from_numpy(lowerCamelCase_  )
          # scale the logits to the size of the label
          UpperCAmelCase_				:  str  = nn.functional.interpolate(
              lowerCamelCase_      ,	size=labels.shape[-2:]      ,	mode='bilinear'      ,	align_corners=lowerCamelCase_      ,	).argmax(dim=1  )

          UpperCAmelCase_				:  int  = logits_tensor.detach().cpu().numpy()
          UpperCAmelCase_				:  Optional[int]  = metric.compute(
              predictions=lowerCamelCase_      ,	references=lowerCamelCase_      ,	num_labels=len(lowerCamelCase_  )      ,	ignore_index=0      ,	reduce_labels=image_processor.do_reduce_labels      ,	)
          # add per category metrics as individual key-value pairs
          UpperCAmelCase_				:  Optional[int]  = metrics.pop('per_category_accuracy'  ).tolist()
          UpperCAmelCase_				:  Optional[Any]  = metrics.pop('per_category_iou'  ).tolist()

          metrics.update({F'''accuracy_{idalabel[i]}''': v for i, v in enumerate(lowerCamelCase_  )}  )
          metrics.update({F'''iou_{idalabel[i]}''': v for i, v in enumerate(lowerCamelCase_  )}  )

          return metrics

     UpperCAmelCase_				:  List[Any]  = AutoConfig.from_pretrained(
         model_args.config_name or model_args.model_name_or_path      ,	labelaid=lowerCamelCase_      ,	idalabel=lowerCamelCase_      ,	cache_dir=model_args.cache_dir      ,	revision=model_args.model_revision      ,	use_auth_token=True if model_args.use_auth_token else None      ,	)
     UpperCAmelCase_				:  int  = AutoModelForSemanticSegmentation.from_pretrained(
         model_args.model_name_or_path      ,	from_tf=bool('.ckpt' in model_args.model_name_or_path  )      ,	config=lowerCamelCase_      ,	cache_dir=model_args.cache_dir      ,	revision=model_args.model_revision      ,	use_auth_token=True if model_args.use_auth_token else None      ,	)
     UpperCAmelCase_				:  Optional[int]  = AutoImageProcessor.from_pretrained(
         model_args.image_processor_name or model_args.model_name_or_path      ,	cache_dir=model_args.cache_dir      ,	revision=model_args.model_revision      ,	use_auth_token=True if model_args.use_auth_token else None      ,	)

     # Define torchvision transforms to be applied to each image + target.
     # Not that straightforward in torchvision: https://github.com/pytorch/vision/issues/9
     # Currently based on official torchvision references: https://github.com/pytorch/vision/blob/main/references/segmentation/transforms.py
     if "shortest_edge" in image_processor.size:
          # We instead set the target size as (shortest_edge, shortest_edge) to here to ensure all images are batchable.
          UpperCAmelCase_				:  Optional[Any]  = (image_processor.size['shortest_edge'], image_processor.size['shortest_edge'])
     else:
          UpperCAmelCase_				:  Tuple  = (image_processor.size['height'], image_processor.size['width'])
     UpperCAmelCase_				:  Tuple  = Compose(
         [
             ReduceLabels() if data_args.reduce_labels else Identity(),
             RandomCrop(size=lowerCamelCase_  ),
             RandomHorizontalFlip(flip_prob=0.5  ),
             PILToTensor(),
             ConvertImageDtype(torch.float  ),
             Normalize(mean=image_processor.image_mean      ,	std=image_processor.image_std  ),
         ]  )
     # Define torchvision transform to be applied to each image.
     # jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)
     UpperCAmelCase_				:  str  = Compose(
         [
             ReduceLabels() if data_args.reduce_labels else Identity(),
             Resize(size=lowerCamelCase_  ),
             PILToTensor(),
             ConvertImageDtype(torch.float  ),
             Normalize(mean=image_processor.image_mean      ,	std=image_processor.image_std  ),
         ]  )

     def preprocess_train(lowerCamelCase_   :				List[Any]  ):
          UpperCAmelCase_				:  Any  = []
          UpperCAmelCase_				:  List[str]  = []
          for image, target in zip(example_batch['image']      ,	example_batch['label']  ):
               UpperCAmelCase_  ,     UpperCAmelCase_				:  str  = train_transforms(image.convert('RGB'  )      ,	lowerCamelCase_  )
               pixel_values.append(lowerCamelCase_  )
               labels.append(lowerCamelCase_  )

          UpperCAmelCase_				:  Tuple  = {}
          UpperCAmelCase_				:  List[Any]  = torch.stack(lowerCamelCase_  )
          UpperCAmelCase_				:  List[str]  = torch.stack(lowerCamelCase_  )

          return encoding

     def preprocess_val(lowerCamelCase_   :				List[str]  ):
          UpperCAmelCase_				:  Any  = []
          UpperCAmelCase_				:  List[str]  = []
          for image, target in zip(example_batch['image']      ,	example_batch['label']  ):
               UpperCAmelCase_  ,     UpperCAmelCase_				:  List[Any]  = val_transforms(image.convert('RGB'  )      ,	lowerCamelCase_  )
               pixel_values.append(lowerCamelCase_  )
               labels.append(lowerCamelCase_  )

          UpperCAmelCase_				:  Optional[int]  = {}
          UpperCAmelCase_				:  Optional[int]  = torch.stack(lowerCamelCase_  )
          UpperCAmelCase_				:  Dict  = torch.stack(lowerCamelCase_  )

          return encoding

     if training_args.do_train:
          if "train" not in dataset:
               raise ValueError('--do_train requires a train dataset'  )
          if data_args.max_train_samples is not None:
               UpperCAmelCase_				:  Union[str, Any]  = (
                   dataset['train'].shuffle(seed=training_args.seed  ).select(range(data_args.max_train_samples  )  )
               )
          # Set the training transforms
          dataset["train"].set_transform(lowerCamelCase_  )

     if training_args.do_eval:
          if "validation" not in dataset:
               raise ValueError('--do_eval requires a validation dataset'  )
          if data_args.max_eval_samples is not None:
               UpperCAmelCase_				:  int  = (
                   dataset['validation'].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
               )
          # Set the validation transforms
          dataset["validation"].set_transform(lowerCamelCase_  )

     # Initalize our trainer
     UpperCAmelCase_				:  Any  = Trainer(
         model=lowerCamelCase_      ,	args=lowerCamelCase_      ,	train_dataset=dataset['train'] if training_args.do_train else None      ,	eval_dataset=dataset['validation'] if training_args.do_eval else None      ,	compute_metrics=lowerCamelCase_      ,	tokenizer=lowerCamelCase_      ,	data_collator=lowerCamelCase_      ,	)

     # Training
     if training_args.do_train:
          UpperCAmelCase_				:  Dict  = None
          if training_args.resume_from_checkpoint is not None:
               UpperCAmelCase_				:  str  = training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               UpperCAmelCase_				:  Tuple  = last_checkpoint
          UpperCAmelCase_				:  Union[str, Any]  = trainer.train(resume_from_checkpoint=lowerCamelCase_  )
          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:
          UpperCAmelCase_				:  int  = trainer.evaluate()
          trainer.log_metrics('eval'      ,	lowerCamelCase_  )
          trainer.save_metrics('eval'      ,	lowerCamelCase_  )

     # Write model card and (optionally) push to hub
     UpperCAmelCase_				:  List[str]  = {
         'finetuned_from': model_args.model_name_or_path,
         'dataset': data_args.dataset_name,
         'tags': ['image-segmentation', 'vision'],
     }
     if training_args.push_to_hub:
          trainer.push_to_hub(**lowerCamelCase_  )
     else:
          trainer.create_model_card(**lowerCamelCase_  )


if __name__ == "__main__":
 main()







'''simple docstring'''


import inspect
import unittest

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

from ...test_backbone_common import BackboneTesterMixin
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 torch import nn

 from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
 from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor






class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_=3 ,						snake_case_=3_2 ,						snake_case_=3 ,						snake_case_=1_0 ,						snake_case_=[1_0, 2_0, 3_0, 4_0] ,						snake_case_=[1, 1, 2, 1] ,						snake_case_=True ,						snake_case_=True ,						snake_case_="relu" ,						snake_case_=3 ,						snake_case_=None ,						snake_case_=["stage2", "stage3", "stage4"] ,						snake_case_=[2, 3, 4] ,						):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = parent
       UpperCAmelCase_				:  int  = batch_size
       UpperCAmelCase_				:  Optional[int]  = image_size
       UpperCAmelCase_				:  List[str]  = num_channels
       UpperCAmelCase_				:  Dict  = embeddings_size
       UpperCAmelCase_				:  List[str]  = hidden_sizes
       UpperCAmelCase_				:  int  = depths
       UpperCAmelCase_				:  Dict  = is_training
       UpperCAmelCase_				:  Optional[int]  = use_labels
       UpperCAmelCase_				:  Union[str, Any]  = hidden_act
       UpperCAmelCase_				:  Optional[Any]  = num_labels
       UpperCAmelCase_				:  List[Any]  = scope
       UpperCAmelCase_				:  Tuple  = len(snake_case_		)
       UpperCAmelCase_				:  Dict  = out_features
       UpperCAmelCase_				:  Dict  = out_indices


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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

       UpperCAmelCase_				:  List[str]  = None
       if self.use_labels:
            UpperCAmelCase_				:  str  = ids_tensor([self.batch_size] ,						self.num_labels		)

       UpperCAmelCase_				:  List[str]  = self.get_config()

       return config, pixel_values, labels


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return ResNetConfig(
           num_channels=self.num_channels ,						embeddings_size=self.embeddings_size ,						hidden_sizes=self.hidden_sizes ,						depths=self.depths ,						hidden_act=self.hidden_act ,						num_labels=self.num_labels ,						out_features=self.out_features ,						out_indices=self.out_indices ,						)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = ResNetModel(config=snake_case_		)
       model.to(snake_case_		)
       model.eval()
       UpperCAmelCase_				:  List[Any]  = model(snake_case_		)
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape ,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) ,						)


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

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


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = ResNetBackbone(config=snake_case_		)
       model.to(snake_case_		)
       model.eval()
       UpperCAmelCase_				:  Dict  = model(snake_case_		)

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps		) ,						len(config.out_features		)		)
       self.parent.assertListEqual(list(result.feature_maps[0].shape		) ,						[self.batch_size, self.hidden_sizes[1], 4, 4]		)

       # verify channels
       self.parent.assertEqual(len(model.channels		) ,						len(config.out_features		)		)
       self.parent.assertListEqual(model.channels ,						config.hidden_sizes[1:]		)

       # verify backbone works with out_features=None
       UpperCAmelCase_				:  Tuple  = None
       UpperCAmelCase_				:  Tuple  = ResNetBackbone(config=snake_case_		)
       model.to(snake_case_		)
       model.eval()
       UpperCAmelCase_				:  List[Any]  = model(snake_case_		)

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps		) ,						1		)
       self.parent.assertListEqual(list(result.feature_maps[0].shape		) ,						[self.batch_size, self.hidden_sizes[-1], 1, 1]		)

       # verify channels
       self.parent.assertEqual(len(model.channels		) ,						1		)
       self.parent.assertListEqual(model.channels ,						[config.hidden_sizes[-1]]		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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







@require_torch
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_		,      lowerCamelCase_		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:str		       =						(
      (
          ResNetModel,
          ResNetForImageClassification,
          ResNetBackbone,
      )
      if is_torch_available()
      else ()
  )
  lowerCamelCase_						:List[str]		       =						(
      {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
      if is_torch_available()
      else {}
  )

  lowerCamelCase_						:int		       =						True
  lowerCamelCase_						:Optional[Any]		       =						False
  lowerCamelCase_						:Union[str, Any]		       =						False
  lowerCamelCase_						:int		       =						False
  lowerCamelCase_						:str		       =						False


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = ResNetModelTester(self		)
       UpperCAmelCase_				:  int  = ConfigTester(self ,						config_class=snake_case_ ,						has_text_modality=snake_case_		)


  def _UpperCamelCase	(					self		):




       '''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 _UpperCamelCase	(					self		):




       '''simple docstring'''

       return


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




       '''simple docstring'''

       pass


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




       '''simple docstring'''

       pass


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_				:  List[Any]  = self.model_tester.prepare_config_and_inputs_for_common()

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

            UpperCAmelCase_				:  List[str]  = ['pixel_values']
            self.assertListEqual(arg_names[:1] ,						snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_				:  List[str]  = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            UpperCAmelCase_				:  List[Any]  = model_class(config=snake_case_		)
            for name, module in model.named_modules():
                 if isinstance(snake_case_ ,						(nn.BatchNormad, nn.GroupNorm)		):
                      self.assertTrue(
                          torch.all(module.weight == 1		) ,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' ,						)
                      self.assertTrue(
                          torch.all(module.bias == 0		) ,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' ,						)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       def check_hidden_states_output(snake_case_ ,						snake_case_ ,						snake_case_		):
            UpperCAmelCase_				:  int  = model_class(snake_case_		)
            model.to(snake_case_		)
            model.eval()

            with torch.no_grad():
                 UpperCAmelCase_				:  str  = model(**self._prepare_for_class(snake_case_ ,						snake_case_		)		)

            UpperCAmelCase_				:  Union[str, Any]  = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            UpperCAmelCase_				:  str  = self.model_tester.num_stages
            self.assertEqual(len(snake_case_		) ,						expected_num_stages + 1		)

            # ResNet'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] ,						)

       UpperCAmelCase_  ,     UpperCAmelCase_				:  Any  = self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_				:  Optional[Any]  = ['basic', 'bottleneck']
       for model_class in self.all_model_classes:
            for layer_type in layers_type:
                 UpperCAmelCase_				:  Any  = layer_type
                 UpperCAmelCase_				:  str  = True
                 check_hidden_states_output(snake_case_ ,						snake_case_ ,						snake_case_		)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 UpperCAmelCase_				:  List[Any]  = True

                 check_hidden_states_output(snake_case_ ,						snake_case_ ,						snake_case_		)


  @unittest.skip(reason='ResNet does not use feedforward chunking'		)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       pass


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*snake_case_		)






  @slow
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase_				:  str  = ResNetModel.from_pretrained(snake_case_		)
            self.assertIsNotNone(snake_case_		)







def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Union[str, Any]  = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
     return image






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







  '''simple docstring'''






  @cached_property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       return (
           AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
           if is_vision_available()
           else None
       )






  @slow
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  str  = ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(snake_case_		)

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

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

       # verify the logits
       UpperCAmelCase_				:  Union[str, Any]  = torch.Size((1, 1_0_0_0)		)
       self.assertEqual(outputs.logits.shape ,						snake_case_		)

       UpperCAmelCase_				:  Optional[int]  = torch.tensor([-11.10_69, -9.78_77, -8.37_77]		).to(snake_case_		)

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







@require_torch
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:Any		       =						(ResNetBackbone,) if is_torch_available() else ()
  lowerCamelCase_						:List[str]		       =						False
  lowerCamelCase_						:Optional[Any]		       =						ResNetConfig


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = ResNetModelTester(self		)






