


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,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
 import PIL

if is_torch_available():
 import torch


SCREAMING_SNAKE_CASE			: int				   =					logging.get_logger(__name__)






class _lowerCamelCase(		_a  ):
       lowercase_					:      Union[str, Any]       					=     ["""pixel_values"""]
       def __init__(   self, lowerCamelCase = True, lowerCamelCase = None, lowerCamelCase = PILImageResampling.BILINEAR, lowerCamelCase = True, lowerCamelCase = 1 / 2_55, lowerCamelCase = True, lowerCamelCase = None, lowerCamelCase = True, **lowerCamelCase, )			->  None:




           """simple docstring"""

           super().__init__(**lowerCamelCase)
           _lowercase			:				List[str] 						=						size if size is not None else {'shortest_edge': 2_24}
           _lowercase			:				Dict 						=						get_size_dict(lowerCamelCase, default_to_square=lowerCamelCase)
           _lowercase			:				Dict 						=						crop_size if crop_size is not None else {'height': 2_56, 'width': 2_56}
           _lowercase			:				Tuple 						=						get_size_dict(lowerCamelCase, param_name='crop_size')

           _lowercase			:				Optional[int] 						=						do_resize
           _lowercase			:				List[str] 						=						size
           _lowercase			:				List[str] 						=						resample
           _lowercase			:				Dict 						=						do_rescale
           _lowercase			:				str 						=						rescale_factor
           _lowercase			:				Dict 						=						do_center_crop
           _lowercase			:				Optional[int] 						=						crop_size
           _lowercase			:				Tuple 						=						do_flip_channel_order
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase = PIL.Image.BILINEAR, lowerCamelCase = None, **lowerCamelCase, )			->  np.ndarray:




           """simple docstring"""

           _lowercase			:				int 						=						get_size_dict(lowerCamelCase, default_to_square=lowerCamelCase)
           if "shortest_edge" not in size:
               raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}''')
           _lowercase			:				Tuple 						=						get_resize_output_image_size(lowerCamelCase, size=size['shortest_edge'], default_to_square=lowerCamelCase)
           return resize(lowerCamelCase, size=lowerCamelCase, resample=lowerCamelCase, data_format=lowerCamelCase, **lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase = None, **lowerCamelCase, )			->  np.ndarray:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						get_size_dict(lowerCamelCase)
           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()}''')
           return center_crop(lowerCamelCase, size=(size['height'], size['width']), data_format=lowerCamelCase, **lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase, lowerCamelCase = None, **lowerCamelCase, )			->  List[Any]:




           """simple docstring"""

           return rescale(lowerCamelCase, scale=lowerCamelCase, data_format=lowerCamelCase, **lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None)			->  np.ndarray:




           """simple docstring"""

           return flip_channel_order(lowerCamelCase, data_format=lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = None, lowerCamelCase = ChannelDimension.FIRST, **lowerCamelCase, )			->  PIL.Image.Image:




           """simple docstring"""

           _lowercase			:				str 						=						do_resize if do_resize is not None else self.do_resize
           _lowercase			:				Tuple 						=						resample if resample is not None else self.resample
           _lowercase			:				Optional[int] 						=						do_rescale if do_rescale is not None else self.do_rescale
           _lowercase			:				str 						=						rescale_factor if rescale_factor is not None else self.rescale_factor
           _lowercase			:				Tuple 						=						do_center_crop if do_center_crop is not None else self.do_center_crop
           _lowercase			:				int 						=						(
               do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
           )

           _lowercase			:				List[str] 						=						size if size is not None else self.size
           _lowercase			:				Any 						=						get_size_dict(lowerCamelCase, default_to_square=lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						crop_size if crop_size is not None else self.crop_size
           _lowercase			:				Optional[Any] 						=						get_size_dict(lowerCamelCase, param_name='crop_size')

           _lowercase			:				Union[str, Any] 						=						make_list_of_images(lowerCamelCase)

           if not valid_images(lowerCamelCase):
               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_rescale and rescale_factor is None:
               raise ValueError('Rescale factor must be specified if do_rescale is True.')

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

           # All transformations expect numpy arrays.
           _lowercase			:				Tuple 						=						[to_numpy_array(lowerCamelCase) for image in images]

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

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

           if do_rescale:
               _lowercase			:				Union[str, Any] 						=						[self.rescale(image=lowerCamelCase, scale=lowerCamelCase) for image in images]

           # the pretrained checkpoints assume images are BGR, not RGB
           if do_flip_channel_order:
               _lowercase			:				List[str] 						=						[self.flip_channel_order(image=lowerCamelCase) for image in images]

           _lowercase			:				Any 						=						[to_channel_dimension_format(lowerCamelCase, lowerCamelCase) for image in images]

           _lowercase			:				Optional[Any] 						=						{'pixel_values': images}
           return BatchFeature(data=lowerCamelCase, tensor_type=lowerCamelCase)




       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase = None)			->  Tuple:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						outputs.logits

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

               if is_torch_tensor(lowerCamelCase):
                   _lowercase			:				List[Any] 						=						target_sizes.numpy()

               _lowercase			:				List[str] 						=						[]

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

           return semantic_segmentation









'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__							:  int    ={
    '''configuration_xmod''': [
        '''XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XmodConfig''',
        '''XmodOnnxConfig''',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  List[str]    =[
          '''XMOD_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''XmodForCausalLM''',
          '''XmodForMaskedLM''',
          '''XmodForMultipleChoice''',
          '''XmodForQuestionAnswering''',
          '''XmodForSequenceClassification''',
          '''XmodForTokenClassification''',
          '''XmodModel''',
          '''XmodPreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xmod import (
                XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
                XmodForCausalLM,
                XmodForMaskedLM,
                XmodForMultipleChoice,
                XmodForQuestionAnswering,
                XmodForSequenceClassification,
                XmodForTokenClassification,
                XmodModel,
                XmodPreTrainedModel,
            )

else:
      import sys

      A__							:  str    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






