



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import 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_vision_available, logging


if is_vision_available():
    import PIL


UpperCAmelCase_									=							logging.get_logger(__name__)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[Any]     ,				_SCREAMING_SNAKE_CASE   :	Union[str, Any]				)->Optional[int]:
      _lowerCAmelCase 			=  b.T
      _lowerCAmelCase 			=  np.sum(np.square(__SCREAMING_SNAKE_CASE				)     ,				axis=1				)
      _lowerCAmelCase 			=  np.sum(np.square(__SCREAMING_SNAKE_CASE				)     ,				axis=0				)
      _lowerCAmelCase 			=  np.matmul(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  aa[:, None] - 2 * ab + ba[None, :]
      return d
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]				)->Dict:
      _lowerCAmelCase 			=  x.reshape(-1     ,				3				)
      _lowerCAmelCase 			=  squared_euclidean_distance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE				)
      return np.argmin(__SCREAMING_SNAKE_CASE     ,				axis=1				)





class 		UpperCAmelCase						(						snake_case__				):
   SCREAMING_SNAKE_CASE__							=     ['''pixel_values''']





   def __init__(	self       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = PILImageResampling.BILINEAR       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = True       ,     **_lowerCAmelCase       ,     ):
         super().__init__(**lowercase_			)
         _lowerCAmelCase 			=  size if size is not None else {"height": 256, "width": 256}
         _lowerCAmelCase 			=  get_size_dict(lowercase_			)
         _lowerCAmelCase 			=  np.array(lowercase_			) if clusters is not None else None
         _lowerCAmelCase 			=  do_resize
         _lowerCAmelCase 			=  size
         _lowerCAmelCase 			=  resample
         _lowerCAmelCase 			=  do_normalize
         _lowerCAmelCase 			=  do_color_quantize





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = PILImageResampling.BILINEAR       ,     _lowerCAmelCase = None       ,     **_lowerCAmelCase       ,     ):
         _lowerCAmelCase 			=  get_size_dict(lowercase_			)
         if "height" not in size or "width" not in size:
               raise ValueError(F'''Size dictionary must contain both height and width keys. Got {size.keys()}'''			)
         return resize(
             lowercase_       ,     size=(size['''height'''], size['''width'''])       ,     resample=lowercase_       ,     data_format=lowercase_       ,     **lowercase_			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None       ,     ):
         _lowerCAmelCase 			=  rescale(image=lowercase_       ,     scale=1 / 127.5       ,     data_format=lowercase_			)
         _lowerCAmelCase 			=  image - 1
         return image





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = ChannelDimension.FIRST       ,     **_lowerCAmelCase       ,     ):
         _lowerCAmelCase 			=  do_resize if do_resize is not None else self.do_resize
         _lowerCAmelCase 			=  size if size is not None else self.size
         _lowerCAmelCase 			=  get_size_dict(lowercase_			)
         _lowerCAmelCase 			=  resample if resample is not None else self.resample
         _lowerCAmelCase 			=  do_normalize if do_normalize is not None else self.do_normalize
         _lowerCAmelCase 			=  do_color_quantize if do_color_quantize is not None else self.do_color_quantize
         _lowerCAmelCase 			=  clusters if clusters is not None else self.clusters
         _lowerCAmelCase 			=  np.array(lowercase_			)

         _lowerCAmelCase 			=  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 or resample is None:
               raise ValueError('''Size and resample must be specified if do_resize is True.'''			)

         if do_color_quantize and clusters is None:
               raise ValueError('''Clusters must be specified if do_color_quantize is True.'''			)

         # All transformations expect numpy arrays.
         _lowerCAmelCase 			=  [to_numpy_array(lowercase_			) for image in images]

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

         if do_normalize:
               _lowerCAmelCase 			=  [self.normalize(image=lowercase_			) for image in images]

         if do_color_quantize:
               _lowerCAmelCase 			=  [to_channel_dimension_format(lowercase_       ,     ChannelDimension.LAST			) for image in images]
               # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
               _lowerCAmelCase 			=  np.array(lowercase_			)
               _lowerCAmelCase 			=  color_quantize(lowercase_       ,     lowercase_			).reshape(images.shape[:-1]			)

               # flatten to (batch_size, height*width)
               _lowerCAmelCase 			=  images.shape[0]
               _lowerCAmelCase 			=  images.reshape(lowercase_       ,     -1			)

               # We need to convert back to a list of images to keep consistent behaviour across processors.
               _lowerCAmelCase 			=  list(lowercase_			)
         else:
               _lowerCAmelCase 			=  [to_channel_dimension_format(lowercase_       ,     lowercase_			) for image in images]

         _lowerCAmelCase 			=  {"input_ids": images}
         return BatchFeature(data=lowercase_       ,     tensor_type=lowercase_			)




# Copyright 2022 The HuggingFace Team and The OpenBMB 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_									=							{
    "configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CpmAntConfig"],
    "tokenization_cpmant": ["CpmAntTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "CpmAntForCausalLM",
        "CpmAntModel",
        "CpmAntPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
    from .tokenization_cpmant import CpmAntTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_cpmant import (
            CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CpmAntForCausalLM,
            CpmAntModel,
            CpmAntPreTrainedModel,
        )


else:
    import sys

    UpperCAmelCase_									=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)