



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__snake_case       :	Any            ={
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case       :	Dict            =['SqueezeBertTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case       :	Dict            =[
							    'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SqueezeBertForMaskedLM',
							    'SqueezeBertForMultipleChoice',
							    'SqueezeBertForQuestionAnswering',
							    'SqueezeBertForSequenceClassification',
							    'SqueezeBertForTokenClassification',
							    'SqueezeBertModel',
							    'SqueezeBertModule',
							    'SqueezeBertPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_squeezebert import (
							    SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SqueezeBertConfig,
							    SqueezeBertOnnxConfig,
							)
							from .tokenization_squeezebert import SqueezeBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_squeezebert import (
														    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SqueezeBertForMaskedLM,
														    SqueezeBertForMultipleChoice,
														    SqueezeBertForQuestionAnswering,
														    SqueezeBertForSequenceClassification,
														    SqueezeBertForTokenClassification,
														    SqueezeBertModel,
														    SqueezeBertModule,
														    SqueezeBertPreTrainedModel,
														)

else:
							import sys

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






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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__snake_case       :	str            =logging.get_logger(__name__)


if is_vision_available():
							import PIL



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=["""pixel_values"""]



		def __init__(self	,__lowerCamelCase = True	,__lowerCamelCase = None	,__lowerCamelCase = PILImageResampling.BICUBIC	,__lowerCamelCase = True	,__lowerCamelCase = None	,__lowerCamelCase = True	,__lowerCamelCase = 1 / 2_55	,__lowerCamelCase = True	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = True	,**__lowerCamelCase	,)  ->    None:

						"""simple docstring"""






						super().__init__(**__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  size if size is not None else {'''shortest_edge''': 2_24}
						lowerCAmelCase__  :       Union[str, Any]				=  get_size_dict(__lowerCamelCase	,default_to_square=__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
						lowerCAmelCase__  :       Optional[int]				=  get_size_dict(__lowerCamelCase	,default_to_square=__lowerCamelCase	,param_name='''crop_size'''				)

						lowerCAmelCase__  :       Optional[int]				=  do_resize
						lowerCAmelCase__  :       Any				=  size
						lowerCAmelCase__  :       int				=  resample
						lowerCAmelCase__  :       Dict				=  do_center_crop
						lowerCAmelCase__  :       str				=  crop_size
						lowerCAmelCase__  :       Dict				=  do_rescale
						lowerCAmelCase__  :       Optional[Any]				=  rescale_factor
						lowerCAmelCase__  :       Dict				=  do_normalize
						lowerCAmelCase__  :       Tuple				=  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						lowerCAmelCase__  :       Union[str, Any]				=  image_std if image_std is not None else OPENAI_CLIP_STD
						lowerCAmelCase__  :       int				=  do_convert_rgb



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = PILImageResampling.BICUBIC	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    np.ndarray:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  get_size_dict(__lowerCamelCase	,default_to_square=__lowerCamelCase				)
						if "shortest_edge" not in size:
										raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""				)
						lowerCAmelCase__  :       Optional[int]				=  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 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    np.ndarray:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  get_size_dict(__lowerCamelCase				)
						if "height" not in size or "width" not in size:
										raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""				)
						return center_crop(__lowerCamelCase	,size=(size['''height'''], size['''width'''])	,data_format=__lowerCamelCase	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    int:

						"""simple docstring"""






						return rescale(__lowerCamelCase	,scale=__lowerCamelCase	,data_format=__lowerCamelCase	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    np.ndarray:

						"""simple docstring"""






						return normalize(__lowerCamelCase	,mean=__lowerCamelCase	,std=__lowerCamelCase	,data_format=__lowerCamelCase	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__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"""






						lowerCAmelCase__  :       Tuple				=  do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase__  :       Tuple				=  size if size is not None else self.size
						lowerCAmelCase__  :       Any				=  get_size_dict(__lowerCamelCase	,param_name='''size'''	,default_to_square=__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  resample if resample is not None else self.resample
						lowerCAmelCase__  :       Dict				=  do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCAmelCase__  :       List[Any]				=  crop_size if crop_size is not None else self.crop_size
						lowerCAmelCase__  :       str				=  get_size_dict(__lowerCamelCase	,param_name='''crop_size'''	,default_to_square=__lowerCamelCase				)
						lowerCAmelCase__  :       Dict				=  do_rescale if do_rescale is not None else self.do_rescale
						lowerCAmelCase__  :       int				=  rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCAmelCase__  :       List[Any]				=  do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase__  :       Dict				=  image_mean if image_mean is not None else self.image_mean
						lowerCAmelCase__  :       int				=  image_std if image_std is not None else self.image_std
						lowerCAmelCase__  :       Dict				=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						lowerCAmelCase__  :       Optional[int]				=  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_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.'''				)

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
										lowerCAmelCase__  :       Union[str, Any]				=  [convert_to_rgb(__lowerCamelCase				) for image in images]

						# All transformations expect numpy arrays.
						lowerCAmelCase__  :       Any				=  [to_numpy_array(__lowerCamelCase				) for image in images]

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

						if do_center_crop:
										lowerCAmelCase__  :       Dict				=  [self.center_crop(image=__lowerCamelCase	,size=__lowerCamelCase				) for image in images]

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

						if do_normalize:
										lowerCAmelCase__  :       Optional[Any]				=  [self.normalize(image=__lowerCamelCase	,mean=__lowerCamelCase	,std=__lowerCamelCase				) for image in images]

						lowerCAmelCase__  :       List[Any]				=  [to_channel_dimension_format(__lowerCamelCase	,__lowerCamelCase				) for image in images]

						lowerCAmelCase__  :       Optional[int]				=  {'''pixel_values''': images}
						return BatchFeature(data=__lowerCamelCase	,tensor_type=__lowerCamelCase				)

