"""simple docstring"""




from typing import TYPE_CHECKING

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


__lowerCamelCase							 =     {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

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

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     [
				    "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "FNetForMaskedLM",
				    "FNetForMultipleChoice",
				    "FNetForNextSentencePrediction",
				    "FNetForPreTraining",
				    "FNetForQuestionAnswering",
				    "FNetForSequenceClassification",
				    "FNetForTokenClassification",
				    "FNetLayer",
				    "FNetModel",
				    "FNetPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_fnet import FNetTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_fnet_fast import FNetTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_fnet import (
								    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FNetForMaskedLM,
								    FNetForMultipleChoice,
								    FNetForNextSentencePrediction,
								    FNetForPreTraining,
								    FNetForQuestionAnswering,
								    FNetForSequenceClassification,
								    FNetForTokenClassification,
								    FNetLayer,
								    FNetModel,
								    FNetPreTrainedModel,
								)


else:
				import sys

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






"""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


__lowerCamelCase							 =     logging.get_logger(__name__)






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							b.T
							A__		   =							np.sum(np.square(UpperCamelCase__ )				,			axis=1 )
							A__		   =							np.sum(np.square(UpperCamelCase__ )				,			axis=0 )
							A__		   =							np.matmul(UpperCamelCase__				,			UpperCamelCase__ )
							A__		   =							aa[:, None] - 2 * ab + ba[None, :]
							return d






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							x.reshape(-1				,			3 )
							A__		   =							squared_euclidean_distance(UpperCamelCase__				,			UpperCamelCase__ )
							return np.argmin(UpperCamelCase__				,			axis=1 )


class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Tuple  							=						['pixel_values']





						def __init__(				self						,__UpperCAmelCase = None						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = True						,__UpperCAmelCase = True						,**__UpperCAmelCase						,)    -> None:
													super().__init__(**__UpperCAmelCase   )
													A__		   =							size if size is not None else {'height': 2_56, 'width': 2_56}
													A__		   =							get_size_dict(__UpperCAmelCase   )
													A__		   =							np.array(__UpperCAmelCase   ) if clusters is not None else None
													A__		   =							do_resize
													A__		   =							size
													A__		   =							resample
													A__		   =							do_normalize
													A__		   =							do_color_quantize





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase   )
													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(
													    __UpperCAmelCase						,size=(size['height'], size['width'])						,resample=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,)    -> np.ndarray:
													A__		   =							rescale(image=__UpperCAmelCase						,scale=1 / 1_2_7.5						,data_format=__UpperCAmelCase   )
													A__		   =							image - 1
													return image





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,**__UpperCAmelCase						,)    -> PIL.Image.Image:
													A__		   =							do_resize if do_resize is not None else self.do_resize
													A__		   =							size if size is not None else self.size
													A__		   =							get_size_dict(__UpperCAmelCase   )
													A__		   =							resample if resample is not None else self.resample
													A__		   =							do_normalize if do_normalize is not None else self.do_normalize
													A__		   =							do_color_quantize if do_color_quantize is not None else self.do_color_quantize
													A__		   =							clusters if clusters is not None else self.clusters
													A__		   =							np.array(__UpperCAmelCase   )

													A__		   =							make_list_of_images(__UpperCAmelCase   )

													if not valid_images(__UpperCAmelCase   ):
																				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.
													A__		   =							[to_numpy_array(__UpperCAmelCase   ) for image in images]

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

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

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

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

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

													A__		   =							{'input_ids': images}
													return BatchFeature(data=__UpperCAmelCase						,tensor_type=__UpperCAmelCase   )





