




'''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


A     =logging.get_logger(__name__)


class 			_a  (	__a ):
       __a				:     str   				=					["""pixel_values"""]
       def __init__(	self						:	Optional[int]   ,				lowercase						:	bool = True   ,				lowercase						:	Optional[Dict[str, int]] = None   ,				lowercase						:	PILImageResampling = PILImageResampling.BILINEAR   ,				lowercase						:	bool = True   ,				lowercase						:	Dict[str, int] = None   ,				lowercase						:	bool = True   ,				lowercase						:	Union[int, float] = 1 / 255   ,				lowercase						:	bool = True   ,				lowercase						:	Optional[Union[float, List[float]]] = None   ,				lowercase						:	Optional[Union[float, List[float]]] = None   ,				**lowercase						:	Union[str, Any]   ,				):



           '''simple docstring'''

           super().__init__(**lowercase				)
           UpperCAmelCase			       =		size if size is not None else {'''shortest_edge''': 256}
           UpperCAmelCase			       =		get_size_dict(lowercase   ,				default_to_square=lowercase				)
           UpperCAmelCase			       =		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
           UpperCAmelCase			       =		get_size_dict(lowercase   ,				param_name='''crop_size'''				)
           UpperCAmelCase			       =		do_resize
           UpperCAmelCase			       =		size
           UpperCAmelCase			       =		resample
           UpperCAmelCase			       =		do_center_crop
           UpperCAmelCase			       =		crop_size
           UpperCAmelCase			       =		do_rescale
           UpperCAmelCase			       =		rescale_factor
           UpperCAmelCase			       =		do_normalize
           UpperCAmelCase			       =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           UpperCAmelCase			       =		image_std if image_std is not None else IMAGENET_STANDARD_STD
       def     A      (	self						:	Union[str, Any]   ,				lowercase						:	np.ndarray   ,				lowercase						:	Dict[str, int]   ,				lowercase						:	PILImageResampling = PILImageResampling.BICUBIC   ,				lowercase						:	Optional[Union[str, ChannelDimension]] = None   ,				**lowercase						:	Any   ,				):



           '''simple docstring'''

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



           '''simple docstring'''

           UpperCAmelCase			       =		get_size_dict(lowercase				)
           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(lowercase   ,				size=(size['''height'''], size['''width'''])   ,				data_format=lowercase   ,				**lowercase				)
       def     A      (	self						:	Tuple   ,				lowercase						:	np.ndarray   ,				lowercase						:	float   ,				lowercase						:	Optional[Union[str, ChannelDimension]] = None   ,				**lowercase						:	List[str]				):



           '''simple docstring'''

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



           '''simple docstring'''

           return normalize(lowercase   ,				mean=lowercase   ,				std=lowercase   ,				data_format=lowercase   ,				**lowercase				)
       def     A      (	self						:	Optional[int]   ,				lowercase						:	ImageInput   ,				lowercase						:	Optional[bool] = None   ,				lowercase						:	Dict[str, int] = None   ,				lowercase						:	PILImageResampling = None   ,				lowercase						:	bool = None   ,				lowercase						:	Dict[str, int] = None   ,				lowercase						:	Optional[bool] = None   ,				lowercase						:	Optional[float] = None   ,				lowercase						:	Optional[bool] = None   ,				lowercase						:	Optional[Union[float, List[float]]] = None   ,				lowercase						:	Optional[Union[float, List[float]]] = None   ,				lowercase						:	Optional[Union[str, TensorType]] = None   ,				lowercase						:	Union[str, ChannelDimension] = ChannelDimension.FIRST   ,				**lowercase						:	Dict   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		do_resize if do_resize is not None else self.do_resize
           UpperCAmelCase			       =		size if size is not None else self.size
           UpperCAmelCase			       =		get_size_dict(lowercase   ,				default_to_square=lowercase				)
           UpperCAmelCase			       =		resample if resample is not None else self.resample
           UpperCAmelCase			       =		do_center_crop if do_center_crop is not None else self.do_center_crop
           UpperCAmelCase			       =		crop_size if crop_size is not None else self.crop_size
           UpperCAmelCase			       =		get_size_dict(lowercase   ,				param_name='''crop_size'''				)
           UpperCAmelCase			       =		do_rescale if do_rescale is not None else self.do_rescale
           UpperCAmelCase			       =		rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCAmelCase			       =		do_normalize if do_normalize is not None else self.do_normalize
           UpperCAmelCase			       =		image_mean if image_mean is not None else self.image_mean
           UpperCAmelCase			       =		image_std if image_std is not None else self.image_std

           UpperCAmelCase			       =		make_list_of_images(lowercase				)

           if not valid_images(lowercase				):
               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			       =		[to_numpy_array(lowercase				) for image in images]

           if do_resize:
               UpperCAmelCase			       =		[self.resize(image=lowercase   ,				size=lowercase   ,				resample=lowercase				) for image in images]

           if do_center_crop:
               UpperCAmelCase			       =		[self.center_crop(image=lowercase   ,				size=lowercase				) for image in images]

           if do_rescale:
               UpperCAmelCase			       =		[self.rescale(image=lowercase   ,				scale=lowercase				) for image in images]

           if do_normalize:
               UpperCAmelCase			       =		[self.normalize(image=lowercase   ,				mean=lowercase   ,				std=lowercase				) for image in images]

           UpperCAmelCase			       =		[to_channel_dimension_format(lowercase   ,				lowercase				) for image in images]

           UpperCAmelCase			       =		{'''pixel_values''': images}
           return BatchFeature(data=lowercase   ,				tensor_type=lowercase				)







       def     A      (	self						:	Tuple   ,				lowercase						:	str   ,				lowercase						:	List[Tuple] = None				):



           '''simple docstring'''

           UpperCAmelCase			       =		outputs.logits

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

               if is_torch_tensor(lowercase				):
                   UpperCAmelCase			       =		target_sizes.numpy()

               UpperCAmelCase			       =		[]

               for idx in range(len(lowercase				)				):
                   UpperCAmelCase			       =		torch.nn.functional.interpolate(
                       logits[idx].unsqueeze(dim=0				)   ,				size=target_sizes[idx]   ,				mode='''bilinear'''   ,				align_corners=lowercase				)
                   UpperCAmelCase			       =		resized_logits[0].argmax(dim=0				)
                   semantic_segmentation.append(lowercase				)
           else:
               UpperCAmelCase			       =		logits.argmax(dim=1				)
               UpperCAmelCase			       =		[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

           return semantic_segmentation





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


a_    					=       {'configuration_encoder_decoder': ['EncoderDecoderConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       ['EncoderDecoderModel']

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       ['TFEncoderDecoderModel']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       ['FlaxEncoderDecoderModel']

if TYPE_CHECKING:
   from .configuration_encoder_decoder import EncoderDecoderConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_encoder_decoder import EncoderDecoderModel

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
   import sys

   a_    					=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



