



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


__lowerCamelCase      =    logging.get_logger(__name__)


if is_vision_available():
    import PIL



class 						_snake_case					(	__snake_case ):





 """simple docstring"""



 lowerCamelCase_								=	['pixel_values']
 def __init__(  self		,			a = True		,			a = None		,			a = PILImageResampling.BICUBIC		,			a = True		,			a = None		,			a = True		,			a = 1 / 2_5_5		,			a = True		,			a = None		,			a = None		,			a = True		,			**a		,			)		->							None:




   """simple docstring"""







   super().__init__(**a    )
   _A							=			size if size is not None else {'''shortest_edge''': 2_2_4}
   _A							=			get_size_dict(a		,			default_to_square=a    )
   _A							=			crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
   _A							=			get_size_dict(a    )

   _A							=			do_resize
   _A							=			size
   _A							=			resample
   _A							=			do_center_crop
   _A							=			crop_size
   _A							=			do_rescale
   _A							=			rescale_factor
   _A							=			do_normalize
   _A							=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   _A							=			image_std if image_std is not None else OPENAI_CLIP_STD
   _A							=			do_convert_rgb
 def lowercase_						(  self		,			a		,			a		,			a = PILImageResampling.BICUBIC		,			a = None		,			**a		,			)		->							np.ndarray:




   """simple docstring"""







   _A							=			get_size_dict(a		,			default_to_square=a    )
   _A							=			get_resize_output_image_size(
       a		,			size=(size['''height'''], size['''width'''])		,			default_to_square=a    )
   return resize(a		,			size=a		,			resample=a		,			data_format=a		,			**a    )
 def lowercase_						(  self		,			a		,			a		,			a = None		,			**a		,			)		->							np.ndarray:




   """simple docstring"""







   _A							=			get_size_dict(a    )
   return center_crop(a		,			size=(size['''height'''], size['''width'''])		,			data_format=a		,			**a    )
 def lowercase_						(  self		,			a		,			a		,			a = None		,			**a		,			)		->							Dict:




   """simple docstring"""







   return rescale(a		,			scale=a		,			data_format=a		,			**a    )
 def lowercase_						(  self		,			a		,			a		,			a		,			a = None		,			**a		,			)		->							np.ndarray:




   """simple docstring"""







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



 def lowercase_						(  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 = None		,			a = ChannelDimension.FIRST		,			**a		,			)		->							PIL.Image.Image:




   """simple docstring"""







   _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(a		,			default_to_square=a    )
   _A							=			resample if resample is not None else self.resample
   _A							=			do_center_crop if do_center_crop is not None else self.do_center_crop
   _A							=			crop_size if crop_size is not None else self.crop_size
   _A							=			get_size_dict(a    )
   _A							=			do_rescale if do_rescale is not None else self.do_rescale
   _A							=			rescale_factor if rescale_factor is not None else self.rescale_factor
   _A							=			do_normalize if do_normalize is not None else self.do_normalize
   _A							=			image_mean if image_mean is not None else self.image_mean
   _A							=			image_std if image_std is not None else self.image_std
   _A							=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   _A							=			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:
     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:
     _A							=			[convert_to_rgb(a    ) for image in images]

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

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

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

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

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

   _A							=			[to_channel_dimension_format(a		,			a    ) for image in images]

   _A							=			{'''pixel_values''': images}
   return BatchFeature(data=a		,			tensor_type=a    )
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase_				:		int       						=       {
    """configuration_fsmt""": ["""FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FSMTConfig"""],
    """tokenization_fsmt""": ["""FSMTTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_				:		Dict       						=       ["""FSMTForConditionalGeneration""", """FSMTModel""", """PretrainedFSMTModel"""]


if TYPE_CHECKING:
	from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
	from .tokenization_fsmt import FSMTTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
	import sys

	lowerCamelCase_				:		Dict       						=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




