


'''simple docstring'''



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

import numpy as np

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


if is_torch_available():
	import torch

if is_vision_available():
	import PIL


__lowercase	:						Optional[Any]       =	logging.get_logger(__name__)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   np.ndarray						,      _SCREAMING_SNAKE_CASE      :   Union[int, Iterable[int]]						,      _SCREAMING_SNAKE_CASE      :   bool						,      _SCREAMING_SNAKE_CASE      :   int   ):
					def constraint_to_multiple_of(_SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   Optional[int]						,      _SCREAMING_SNAKE_CASE      :   str=0						,      _SCREAMING_SNAKE_CASE      :   Optional[int]=None   ):
										__a			:       Tuple					    =  round(val / multiple   ) * multiple

										if max_val is not None and x > max_val:
															__a			:       Optional[int]					    =  math.floor(val / multiple   ) * multiple

										if x < min_val:
															__a			:       int					    =  math.ceil(val / multiple   ) * multiple

										return x

					__a			:       str					    =  (output_size, output_size) if isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   ) else output_size

					__a    ,				__a			:       List[Any]					    =  get_image_size(_SCREAMING_SNAKE_CASE   )
					__a    ,				__a			:       List[Any]					    =  output_size

					# determine new height and width
					__a			:       Optional[Any]					    =  output_height / input_height
					__a			:       str					    =  output_width / input_width

					if keep_aspect_ratio:
										# scale as little as possible
										if abs(1 - scale_width   ) < abs(1 - scale_height   ):
															# fit width
															__a			:       Optional[Any]					    =  scale_width
										else:
															# fit height
															__a			:       List[Any]					    =  scale_height

					__a			:       Any					    =  constraint_to_multiple_of(scale_height * input_height						,      multiple=_SCREAMING_SNAKE_CASE   )
					__a			:       Dict					    =  constraint_to_multiple_of(scale_width * input_width						,      multiple=_SCREAMING_SNAKE_CASE   )

					return (new_height, new_width)





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    ["pixel_values"]




	def __init__(   self						,				__a = True						,				__a = None						,				__a = PILImageResampling.BILINEAR						,				__a = False						,				__a = 1						,				__a = True						,				__a = 1 / 255						,				__a = True						,				__a = None						,				__a = None						,				**__a						,				):

						'''simple docstring'''





						super().__init__(**__a							)
						__a			:       Optional[Any]					    =  size if size is not None else {'height': 384, 'width': 384}
						__a			:       Optional[int]					    =  get_size_dict(__a							)
						__a			:       str					    =  do_resize
						__a			:       Optional[Any]					    =  size
						__a			:       Optional[int]					    =  keep_aspect_ratio
						__a			:       int					    =  ensure_multiple_of
						__a			:       List[str]					    =  resample
						__a			:       str					    =  do_rescale
						__a			:       int					    =  rescale_factor
						__a			:       Optional[int]					    =  do_normalize
						__a			:       Dict					    =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__a			:       Dict					    =  image_std if image_std is not None else IMAGENET_STANDARD_STD




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a = False						,				__a = 1						,				__a = PILImageResampling.BICUBIC						,				__a = None						,				**__a						,				):

						'''simple docstring'''





						__a			:       int					    =  get_size_dict(__a							)
						if "height" not in size or "width" not in size:
											raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""							)
						__a			:       str					    =  get_resize_output_image_size(
						    __a						,				output_size=(size['height'], size['width'])						,				keep_aspect_ratio=__a						,				multiple=__a						,				)
						return resize(__a						,				size=__a						,				resample=__a						,				data_format=__a						,				**__a							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a = None						,				**__a						,				):

						'''simple docstring'''





						return rescale(__a						,				scale=__a						,				data_format=__a						,				**__a							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a						,				__a = None						,				**__a						,				):

						'''simple docstring'''





						return normalize(__a						,				mean=__a						,				std=__a						,				data_format=__a						,				**__a							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a = None						,				__a = None						,				__a = None						,				__a = None						,				__a = None						,				__a = None						,				__a = None						,				__a = None						,				__a = None						,				__a = None						,				__a = None						,				__a = ChannelDimension.FIRST						,				**__a						,				):

						'''simple docstring'''





						__a			:       int					    =  do_resize if do_resize is not None else self.do_resize
						__a			:       Union[str, Any]					    =  size if size is not None else self.size
						__a			:       List[str]					    =  get_size_dict(__a							)
						__a			:       Tuple					    =  keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
						__a			:       Tuple					    =  ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
						__a			:       int					    =  resample if resample is not None else self.resample
						__a			:       Dict					    =  do_rescale if do_rescale is not None else self.do_rescale
						__a			:       Tuple					    =  rescale_factor if rescale_factor is not None else self.rescale_factor
						__a			:       Dict					    =  do_normalize if do_normalize is not None else self.do_normalize
						__a			:       Union[str, Any]					    =  image_mean if image_mean is not None else self.image_mean
						__a			:       Optional[int]					    =  image_std if image_std is not None else self.image_std

						__a			:       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 or resample is None:
											raise ValueError('Size and resample must be specified if do_resize 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.
						__a			:       Optional[int]					    =  [to_numpy_array(__a							) for image in images]

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

						if do_rescale:
											__a			:       List[str]					    =  [self.rescale(image=__a						,				scale=__a							) for image in images]

						if do_normalize:
											__a			:       Optional[Any]					    =  [self.normalize(image=__a						,				mean=__a						,				std=__a							) for image in images]

						__a			:       List[Any]					    =  [to_channel_dimension_format(__a						,				__a							) for image in images]

						__a			:       Dict					    =  {'pixel_values': images}
						return BatchFeature(data=__a						,				tensor_type=__a							)





	def       __UpperCAmelCase							(   self						,				__a						,				__a = None							):

						'''simple docstring'''





						__a			:       Optional[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							):
																__a			:       Tuple					    =  target_sizes.numpy()

											__a			:       Optional[int]					    =  []

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

					__a			:       List[str]					    =  components[:-1] + [test_fn.replace('.py'						,      ''   )]
					__a			:       Optional[Any]					    =  '.'.join(_SCREAMING_SNAKE_CASE   )

					return test_module_path






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       List[str]					    =  get_module_path(_SCREAMING_SNAKE_CASE   )
					__a			:       Dict					    =  importlib.import_module(_SCREAMING_SNAKE_CASE   )

					return test_module






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       List[str]					    =  []
					__a			:       List[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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple   ):
					__a			:       Any					    =  []
					__a			:       str					    =  get_test_module(_SCREAMING_SNAKE_CASE   )
					for attr in dir(_SCREAMING_SNAKE_CASE   ):
										__a			:       int					    =  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).
										__a			:       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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       Any					    =  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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       Tuple					    =  test_class()
					if hasattr(_SCREAMING_SNAKE_CASE						,      'setUp'   ):
										test.setUp()

					__a			:       List[Any]					    =  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:
															__a			:       List[str]					    =  test.model_tester.__class__

					return model_tester






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )

					__a			:       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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       List[Any]					    =  get_test_classes_for_model(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					__a			:       Any					    =  []
					for test_class in test_classes:
										__a			:       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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					__a			:       str					    =  get_test_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       int					    =  {test_class: get_model_tester_from_test_class(_SCREAMING_SNAKE_CASE   ) for test_class in test_classes}
					return test_tester_mapping






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  get_model_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       Optional[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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   List[str]   ):
					__a			:       Optional[Any]					    =  get_model_classes(_SCREAMING_SNAKE_CASE   )
					__a			:       str					    =  {
					    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   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str   ):
					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



