





'''simple docstring'''


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


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)


def 	__a(SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					Any    ):



    '''simple docstring'''





    _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 	__a(SCREAMING_SNAKE_CASE_  :					Union[str, Any]		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      x.reshape(-1		,       3    )
    _lowerCAmelCase				=      squared_euclidean_distance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )
    return np.argmin(SCREAMING_SNAKE_CASE_		,       axis=1    )





class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							Tuple         =					["pixel_values"]




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




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase = PILImageResampling.BILINEAR			,     _lowerCAmelCase = None			,     **_lowerCAmelCase			,     )    ->							np.ndarray:
     _lowerCAmelCase				=      get_size_dict(_lowerCAmelCase		)
     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(
         _lowerCAmelCase			,     size=(size["height"], size["width"])			,     resample=_lowerCAmelCase			,     data_format=_lowerCAmelCase			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = None			,     )    ->							np.ndarray:
     _lowerCAmelCase				=      rescale(image=_lowerCAmelCase			,     scale=1 / 127.5			,     data_format=_lowerCAmelCase		)
     _lowerCAmelCase				=      image - 1
     return image





 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = ChannelDimension.FIRST			,     **_lowerCAmelCase			,     )    ->							PIL.Image.Image:
     _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(_lowerCAmelCase		)
     _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(_lowerCAmelCase		)

     _lowerCAmelCase				=      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 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(_lowerCAmelCase		) for image in images]

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

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

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

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

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

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









'''simple docstring'''


from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE	 =							{"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							["XGLMTokenizer"]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							["XGLMTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "XGLMForCausalLM",
           "XGLMModel",
           "XGLMPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "FlaxXGLMForCausalLM",
           "FlaxXGLMModel",
           "FlaxXGLMPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE	 =							[
           "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFXGLMForCausalLM",
           "TFXGLMModel",
           "TFXGLMPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm import XGLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_xglm_fast import XGLMTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_xglm import (
                  TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFXGLMForCausalLM,
                  TFXGLMModel,
                  TFXGLMPreTrainedModel,
              )


else:
       import sys

       _SCREAMING_SNAKE_CASE	 =							_LazyModule(__name__, globals()["__file__"], _import_structure)


