

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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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


A_			:					Tuple    			=     logging.get_logger(__name__)



class        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  int    				=			['''pixel_values''']






					def __init__(					self       ,			A__ = True       ,			A__ = None       ,			A__ = PIL.Image.BICUBIC       ,			A__ = True       ,			A__ = None       ,			A__ = 1 / 255       ,			A__ = True       ,			A__ = True       ,			A__ = None       ,			A__ = None       ,			**A__       ,			):
						super().__init__(**A__     )
						A__				:       List[Any]	  =						size if size is not None else {"""height""": 256, """width""": 256}
						A__				:       Dict	  =						get_size_dict(A__     )
						A__				:       int	  =						crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
						A__				:       Optional[Any]	  =						get_size_dict(A__       ,			param_name="""crop_size"""     )

						A__				:       Tuple	  =						do_resize
						A__				:       List[Any]	  =						size
						A__				:       Union[str, Any]	  =						resample
						A__				:       int	  =						do_center_crop
						A__				:       List[Any]	  =						crop_size
						A__				:       Union[str, Any]	  =						do_rescale
						A__				:       str	  =						rescale_factor
						A__				:       Union[str, Any]	  =						do_normalize
						A__				:       Optional[int]	  =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						A__				:       List[str]	  =						image_std if image_std is not None else IMAGENET_STANDARD_STD






					def     __A     (					self       ,			A__       ,			A__       ,			A__ = PIL.Image.BICUBIC       ,			A__ = None       ,			**A__       ,			):
						A__				:       Any	  =						get_size_dict(A__     )
						if "height" not in size or "width" not in size:
							raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""     )
						return resize(
						    A__       ,			size=(size["""height"""], size["""width"""])       ,			resample=A__       ,			data_format=A__       ,			**A__     )






					def     __A     (					self       ,			A__       ,			A__       ,			A__ = None       ,			**A__       ,			):
						A__				:       List[str]	  =						get_size_dict(A__     )
						if "height" not in size or "width" not in size:
							raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""     )
						return center_crop(A__       ,			size=(size["""height"""], size["""width"""])       ,			data_format=A__       ,			**A__     )






					def     __A     (					self       ,			A__       ,			A__       ,			A__ = None       ,			**A__       ,			):
						return rescale(A__       ,			scale=A__       ,			data_format=A__       ,			**A__     )






					def     __A     (					self       ,			A__       ,			A__       ,			A__       ,			A__ = None       ,			**A__       ,			):
						return normalize(A__       ,			mean=A__       ,			std=A__       ,			data_format=A__       ,			**A__     )






					def     __A     (					self       ,			A__       ,			A__ = None       ,			A__ = None       ,			A__=None       ,			A__ = None       ,			A__ = None       ,			A__ = None       ,			A__ = None       ,			A__ = None       ,			A__ = None       ,			A__ = None       ,			A__ = None       ,			A__ = ChannelDimension.FIRST       ,			**A__       ,			):
						A__				:       Dict	  =						do_resize if do_resize is not None else self.do_resize
						A__				:       Optional[int]	  =						resample if resample is not None else self.resample
						A__				:       str	  =						do_center_crop if do_center_crop is not None else self.do_center_crop
						A__				:       Optional[Any]	  =						do_rescale if do_rescale is not None else self.do_rescale
						A__				:       int	  =						rescale_factor if rescale_factor is not None else self.rescale_factor
						A__				:       Optional[Any]	  =						do_normalize if do_normalize is not None else self.do_normalize
						A__				:       List[Any]	  =						image_mean if image_mean is not None else self.image_mean
						A__				:       Optional[int]	  =						image_std if image_std is not None else self.image_std

						A__				:       Optional[Any]	  =						size if size is not None else self.size
						A__				:       str	  =						get_size_dict(A__     )
						A__				:       Any	  =						crop_size if crop_size is not None else self.crop_size
						A__				:       Any	  =						get_size_dict(A__       ,			param_name="""crop_size"""     )

						A__				:       str	  =						make_list_of_images(A__     )

						if not valid_images(A__     ):
							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_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."""     )

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

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

						if do_center_crop:
							A__				:       Tuple	  =						[self.center_crop(image=A__       ,			size=A__     ) for image in images]

						if do_rescale:
							A__				:       Dict	  =						[self.rescale(image=A__       ,			scale=A__     ) for image in images]

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

						A__				:       str	  =						[to_channel_dimension_format(A__       ,			A__     ) for image in images]

						A__				:       Dict	  =						{"""pixel_values""": images}
						return BatchFeature(data=A__       ,			tensor_type=A__     )






from typing import TYPE_CHECKING

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


A_			:					Tuple    			=     {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_			:					Dict    			=     [
	    'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'XLMForMultipleChoice',
	    'XLMForQuestionAnswering',
	    'XLMForQuestionAnsweringSimple',
	    'XLMForSequenceClassification',
	    'XLMForTokenClassification',
	    'XLMModel',
	    'XLMPreTrainedModel',
	    'XLMWithLMHeadModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_			:					str    			=     [
	    'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFXLMForMultipleChoice',
	    'TFXLMForQuestionAnsweringSimple',
	    'TFXLMForSequenceClassification',
	    'TFXLMForTokenClassification',
	    'TFXLMMainLayer',
	    'TFXLMModel',
	    'TFXLMPreTrainedModel',
	    'TFXLMWithLMHeadModel',
	]


if TYPE_CHECKING:
	from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
	from .tokenization_xlm import XLMTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm import (
		    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMForMultipleChoice,
		    XLMForQuestionAnswering,
		    XLMForQuestionAnsweringSimple,
		    XLMForSequenceClassification,
		    XLMForTokenClassification,
		    XLMModel,
		    XLMPreTrainedModel,
		    XLMWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm import (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMMainLayer,
		    TFXLMModel,
		    TFXLMPreTrainedModel,
		    TFXLMWithLMHeadModel,
		)

else:
	import sys

	A_			:					Tuple    			=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



