



"""simple docstring"""




from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
 import torch


_lowerCAmelCase							:   Union[str, Any]				=				logging.get_logger(__name__)






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  Optional[Any]						=					['pixel_values']



      def __init__(	self	:      Optional[int]	,      A	:      bool = True	,      A	:      Optional[Dict[str, int]] = None	,      A	:      PILImageResampling = PILImageResampling.BILINEAR	,      A	:      bool = True	,      A	:      Dict[str, int] = None	,      A	:      bool = True	,      A	:      Union[int, float] = 1 / 2_5_5	,      A	:      bool = True	,      A	:      Optional[Union[float, List[float]]] = None	,      A	:      Optional[Union[float, List[float]]] = None	,      **A	:      Any	,      ):
           super().__init__(**A		)
           _UpperCAmelCase     :      str														=       size if size is not None else {"shortest_edge": 2_5_6}
           _UpperCAmelCase     :      List[str]														=       get_size_dict(A	,      default_to_square=A		)
           _UpperCAmelCase     :      Union[str, Any]														=       crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
           _UpperCAmelCase     :      Optional[Any]														=       get_size_dict(A	,      param_name="crop_size"		)
           _UpperCAmelCase     :      List[Any]														=       do_resize
           _UpperCAmelCase     :      Tuple														=       size
           _UpperCAmelCase     :      Optional[int]														=       resample
           _UpperCAmelCase     :      Tuple														=       do_center_crop
           _UpperCAmelCase     :      str														=       crop_size
           _UpperCAmelCase     :      Optional[Any]														=       do_rescale
           _UpperCAmelCase     :      Optional[int]														=       rescale_factor
           _UpperCAmelCase     :      List[str]														=       do_normalize
           _UpperCAmelCase     :      str														=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           _UpperCAmelCase     :      Optional[int]														=       image_std if image_std is not None else IMAGENET_STANDARD_STD



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      np.ndarray	,      A	:      Dict[str, int]	,      A	:      PILImageResampling = PILImageResampling.BICUBIC	,      A	:      Optional[Union[str, ChannelDimension]] = None	,      **A	:      Optional[Any]	,      ):
           _UpperCAmelCase     :      str														=       get_size_dict(A	,      default_to_square=A		)
           if "shortest_edge" not in size:
                raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'		)
           _UpperCAmelCase     :      Any														=       get_resize_output_image_size(A	,      size=size["shortest_edge"]	,      default_to_square=A		)
           return resize(A	,      size=A	,      resample=A	,      data_format=A	,      **A		)



      def 		snake_case_   (	self	:      Optional[int]	,      A	:      np.ndarray	,      A	:      Dict[str, int]	,      A	:      Optional[Union[str, ChannelDimension]] = None	,      **A	:      Tuple	,      ):
           _UpperCAmelCase     :      Optional[int]														=       get_size_dict(A		)
           if "height" not in size or "width" not in size:
                raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'		)
           return center_crop(A	,      size=(size["height"], size["width"])	,      data_format=A	,      **A		)



      def 		snake_case_   (	self	:      List[Any]	,      A	:      np.ndarray	,      A	:      float	,      A	:      Optional[Union[str, ChannelDimension]] = None	,      **A	:      int		):
           return rescale(A	,      scale=A	,      data_format=A	,      **A		)



      def 		snake_case_   (	self	:      Optional[int]	,      A	:      np.ndarray	,      A	:      Union[float, List[float]]	,      A	:      Union[float, List[float]]	,      A	:      Optional[Union[str, ChannelDimension]] = None	,      **A	:      List[Any]	,      ):
           return normalize(A	,      mean=A	,      std=A	,      data_format=A	,      **A		)



      def 		snake_case_   (	self	:      int	,      A	:      ImageInput	,      A	:      Optional[bool] = None	,      A	:      Dict[str, int] = None	,      A	:      PILImageResampling = None	,      A	:      bool = None	,      A	:      Dict[str, int] = None	,      A	:      Optional[bool] = None	,      A	:      Optional[float] = None	,      A	:      Optional[bool] = None	,      A	:      Optional[Union[float, List[float]]] = None	,      A	:      Optional[Union[float, List[float]]] = None	,      A	:      Optional[Union[str, TensorType]] = None	,      A	:      Union[str, ChannelDimension] = ChannelDimension.FIRST	,      **A	:      int	,      ):
           _UpperCAmelCase     :      Dict														=       do_resize if do_resize is not None else self.do_resize
           _UpperCAmelCase     :      int														=       size if size is not None else self.size
           _UpperCAmelCase     :      Tuple														=       get_size_dict(A	,      default_to_square=A		)
           _UpperCAmelCase     :      Optional[int]														=       resample if resample is not None else self.resample
           _UpperCAmelCase     :      int														=       do_center_crop if do_center_crop is not None else self.do_center_crop
           _UpperCAmelCase     :      Optional[int]														=       crop_size if crop_size is not None else self.crop_size
           _UpperCAmelCase     :      Optional[Any]														=       get_size_dict(A	,      param_name="crop_size"		)
           _UpperCAmelCase     :      Any														=       do_rescale if do_rescale is not None else self.do_rescale
           _UpperCAmelCase     :      Any														=       rescale_factor if rescale_factor is not None else self.rescale_factor
           _UpperCAmelCase     :      List[str]														=       do_normalize if do_normalize is not None else self.do_normalize
           _UpperCAmelCase     :      List[str]														=       image_mean if image_mean is not None else self.image_mean
           _UpperCAmelCase     :      List[Any]														=       image_std if image_std is not None else self.image_std

           _UpperCAmelCase     :      Optional[Any]														=       make_list_of_images(A		)

           if not valid_images(A		):
                raise ValueError(
                    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                    "torch.Tensor, tf.Tensor or jax.ndarray."		)

           if do_resize and size is None:
                raise ValueError("Size must be specified if do_resize is True."		)

           if do_center_crop and crop_size is None:
                raise ValueError("Crop size must be specified if do_center_crop is True."		)

           if do_rescale and rescale_factor is None:
                raise ValueError("Rescale factor must be specified if do_rescale is True."		)

           if do_normalize and (image_mean is None or image_std is None):
                raise ValueError("Image mean and std must be specified if do_normalize is True."		)

           # All transformations expect numpy arrays.
           _UpperCAmelCase     :      Dict														=       [to_numpy_array(A		) for image in images]

           if do_resize:
                _UpperCAmelCase     :      int														=       [self.resize(image=A	,      size=A	,      resample=A		) for image in images]

           if do_center_crop:
                _UpperCAmelCase     :      List[Any]														=       [self.center_crop(image=A	,      size=A		) for image in images]

           if do_rescale:
                _UpperCAmelCase     :      Dict														=       [self.rescale(image=A	,      scale=A		) for image in images]

           if do_normalize:
                _UpperCAmelCase     :      str														=       [self.normalize(image=A	,      mean=A	,      std=A		) for image in images]

           _UpperCAmelCase     :      Tuple														=       [to_channel_dimension_format(A	,      A		) for image in images]

           _UpperCAmelCase     :      Union[str, Any]														=       {"pixel_values": images}
           return BatchFeature(data=A	,      tensor_type=A		)



      def 		snake_case_   (	self	:      List[str]	,      A	:      List[str]	,      A	:      List[Tuple] = None		):
           _UpperCAmelCase     :      Any														=       outputs.logits

           # Resize logits and compute semantic segmentation maps
           if target_sizes is not None:
                if len(A		) != len(A		):
                     raise ValueError(
                         "Make sure that you pass in as many target sizes as the batch dimension of the logits"		)

                if is_torch_tensor(A		):
                     _UpperCAmelCase     :      Any														=       target_sizes.numpy()

                _UpperCAmelCase     :      List[Any]														=       []

                for idx in range(len(A		)		):
                     _UpperCAmelCase     :      Union[str, Any]														=       torch.nn.functional.interpolate(
                         logits[idx].unsqueeze(dim=0		)	,      size=target_sizes[idx]	,      mode="bilinear"	,      align_corners=A		)
                     _UpperCAmelCase     :      Tuple														=       resized_logits[0].argmax(dim=0		)
                     semantic_segmentation.append(A		)
           else:
                _UpperCAmelCase     :      int														=       logits.argmax(dim=1		)
                _UpperCAmelCase     :      Tuple														=       [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

           return semantic_segmentation





"""simple docstring"""




import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  List[str]:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       test_file.split(os.path.sep							)
     if components[0:2] != ["tests", "models"]:
          raise ValueError(
              "`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got "
              f'{test_file} instead.'							)
     _UpperCAmelCase     :      int														=       components[-1]
     if not test_fn.endswith("py"							):
          raise ValueError(f'`test_file` should be a python file. Got {test_fn} instead.'							)
     if not test_fn.startswith("test_modeling_"							):
          raise ValueError(
              f'`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'							)

     _UpperCAmelCase     :      Any														=       components[:-1] + [test_fn.replace(".py"		,    ""							)]
     _UpperCAmelCase     :      List[str]														=       ".".join(SCREAMING_SNAKE_CASE__							)

     return test_module_path

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[int]														=       get_module_path(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      List[Any]														=       importlib.import_module(SCREAMING_SNAKE_CASE__							)

     return test_module

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Union[str, Any]							)    ->  List[str]:





     '''simple docstring'''

     _UpperCAmelCase     :      Union[str, Any]														=       []
     _UpperCAmelCase     :      str														=       get_test_module(SCREAMING_SNAKE_CASE__							)
     for attr in dir(SCREAMING_SNAKE_CASE__							):
          if attr.endswith("ModelTester"							):
               tester_classes.append(getattr(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)							)

    # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      Union[str, Any]														=       []
     _UpperCAmelCase     :      Tuple														=       get_test_module(SCREAMING_SNAKE_CASE__							)
     for attr in dir(SCREAMING_SNAKE_CASE__							):
          _UpperCAmelCase     :      List[Any]														=       getattr(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
          # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
          # `all_model_classes` is not empty (which also excludes other special classes).
          _UpperCAmelCase     :      Optional[Any]														=       getattr(SCREAMING_SNAKE_CASE__		,    "all_model_classes"		,    []							)
          if len(SCREAMING_SNAKE_CASE__							) > 0:
               test_classes.append(SCREAMING_SNAKE_CASE__							)

    # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      Union[str, Any]														=       get_test_classes(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Tuple														=       set()
     for test_class in test_classes:
          model_classes.update(test_class.all_model_classes							)

     # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple							)    ->  Any:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       test_class()
     if hasattr(SCREAMING_SNAKE_CASE__		,    "setUp"							):
          test.setUp()

     _UpperCAmelCase     :      int														=       None
     if hasattr(SCREAMING_SNAKE_CASE__		,    "model_tester"							):
          # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
          if test.model_tester is not None:
               _UpperCAmelCase     :      List[str]														=       test.model_tester.__class__

     return model_tester

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  Union[str, Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       get_test_classes(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Optional[int]														=       []
     for test_class in test_classes:
          if model_class in test_class.all_model_classes:
               target_test_classes.append(SCREAMING_SNAKE_CASE__							)

    # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  Optional[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       get_test_classes_for_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      int														=       []
     for test_class in test_classes:
          _UpperCAmelCase     :      Optional[Any]														=       get_model_tester_from_test_class(SCREAMING_SNAKE_CASE__							)
          if tester_class is not None:
               tester_classes.append(SCREAMING_SNAKE_CASE__							)

    # sort with class names
     return sorted(SCREAMING_SNAKE_CASE__		,    key=lambda SCREAMING_SNAKE_CASE__							: x.__name__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       get_test_classes(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      List[Any]														=       {test_class: get_model_tester_from_test_class(SCREAMING_SNAKE_CASE__							) for test_class in test_classes}
     return test_tester_mapping

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  Dict:





     '''simple docstring'''

     _UpperCAmelCase     :      Optional[Any]														=       get_model_classes(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      int														=       {
         model_class: get_test_classes_for_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							) for model_class in model_classes
     }
     return model_test_mapping

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  Tuple:





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       get_model_classes(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Union[str, Any]														=       {
         model_class: get_tester_classes_for_model(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							) for model_class in model_classes
     }
     return model_to_tester_mapping

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  List[str]:





     '''simple docstring'''

     if isinstance(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
          return o
     elif isinstance(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
          return o.__name__
     elif isinstance(SCREAMING_SNAKE_CASE__		,    (list, tuple)							):
          return [to_json(SCREAMING_SNAKE_CASE__							) for x in o]
     elif isinstance(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							):
          return {to_json(SCREAMING_SNAKE_CASE__							): to_json(SCREAMING_SNAKE_CASE__							) for k, v in o.items()}
     else:
          return o
