


"""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 (
    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


a__    :							Tuple            =					logging.get_logger(__name__)


if is_vision_available():
		import PIL




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  Tuple    =      ["pixel_values"]




	def __init__(			self      :    Optional[Any]      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    Dict[str, int] = None      ,      UpperCAmelCase__      :    PILImageResampling = PILImageResampling.BICUBIC      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    Dict[str, int] = None      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    Union[int, float] = 1 / 2_5_5      ,      UpperCAmelCase__      :    bool = True      ,      UpperCAmelCase__      :    Optional[Union[float, List[float]]] = None      ,      UpperCAmelCase__      :    Optional[Union[float, List[float]]] = None      ,      UpperCAmelCase__      :    bool = True      ,      **UpperCAmelCase__      :    str      ,      )	->     None:
			super().__init__(**UpperCAmelCase__       )
			__SCREAMING_SNAKE_CASE						       =    size if size is not None else {"shortest_edge": 2_2_4}
			__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__      ,      default_to_square=UpperCAmelCase__       )
			__SCREAMING_SNAKE_CASE						       =    crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
			__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__      ,      default_to_square=UpperCAmelCase__      ,      param_name="crop_size"       )

			__SCREAMING_SNAKE_CASE						       =    do_resize
			__SCREAMING_SNAKE_CASE						       =    size
			__SCREAMING_SNAKE_CASE						       =    resample
			__SCREAMING_SNAKE_CASE						       =    do_center_crop
			__SCREAMING_SNAKE_CASE						       =    crop_size
			__SCREAMING_SNAKE_CASE						       =    do_rescale
			__SCREAMING_SNAKE_CASE						       =    rescale_factor
			__SCREAMING_SNAKE_CASE						       =    do_normalize
			__SCREAMING_SNAKE_CASE						       =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
			__SCREAMING_SNAKE_CASE						       =    image_std if image_std is not None else OPENAI_CLIP_STD
			__SCREAMING_SNAKE_CASE						       =    do_convert_rgb




	def   UpperCAmelCase_				(			self      :    int      ,      UpperCAmelCase__      :    np.ndarray      ,      UpperCAmelCase__      :    Dict[str, int]      ,      UpperCAmelCase__      :    PILImageResampling = PILImageResampling.BICUBIC      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = None      ,      **UpperCAmelCase__      :    str      ,      )	->     np.ndarray:
			__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__      ,      default_to_square=UpperCAmelCase__       )
			if "shortest_edge" not in size:
					raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""       )
			__SCREAMING_SNAKE_CASE						       =    get_resize_output_image_size(UpperCAmelCase__      ,      size=size["shortest_edge"]      ,      default_to_square=UpperCAmelCase__       )
			return resize(UpperCAmelCase__      ,      size=UpperCAmelCase__      ,      resample=UpperCAmelCase__      ,      data_format=UpperCAmelCase__      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Union[str, Any]      ,      UpperCAmelCase__      :    np.ndarray      ,      UpperCAmelCase__      :    Dict[str, int]      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = None      ,      **UpperCAmelCase__      :    Optional[int]      ,      )	->     np.ndarray:
			__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__       )
			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(UpperCAmelCase__      ,      size=(size["height"], size["width"])      ,      data_format=UpperCAmelCase__      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    np.ndarray      ,      UpperCAmelCase__      :    Union[int, float]      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = None      ,      **UpperCAmelCase__      :    List[Any]      ,      )	->     Dict:
			return rescale(UpperCAmelCase__      ,      scale=UpperCAmelCase__      ,      data_format=UpperCAmelCase__      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    np.ndarray      ,      UpperCAmelCase__      :    Union[float, List[float]]      ,      UpperCAmelCase__      :    Union[float, List[float]]      ,      UpperCAmelCase__      :    Optional[Union[str, ChannelDimension]] = None      ,      **UpperCAmelCase__      :    Any      ,      )	->     np.ndarray:
			return normalize(UpperCAmelCase__      ,      mean=UpperCAmelCase__      ,      std=UpperCAmelCase__      ,      data_format=UpperCAmelCase__      ,      **UpperCAmelCase__       )




	def   UpperCAmelCase_				(			self      :    Any      ,      UpperCAmelCase__      :    ImageInput      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    Dict[str, int] = None      ,      UpperCAmelCase__      :    PILImageResampling = None      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    int = None      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    float = None      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    Optional[Union[float, List[float]]] = None      ,      UpperCAmelCase__      :    Optional[Union[float, List[float]]] = None      ,      UpperCAmelCase__      :    bool = None      ,      UpperCAmelCase__      :    Optional[Union[str, TensorType]] = None      ,      UpperCAmelCase__      :    Optional[ChannelDimension] = ChannelDimension.FIRST      ,      **UpperCAmelCase__      :    str      ,      )	->     PIL.Image.Image:
			__SCREAMING_SNAKE_CASE						       =    do_resize if do_resize is not None else self.do_resize
			__SCREAMING_SNAKE_CASE						       =    size if size is not None else self.size
			__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__      ,      param_name="size"      ,      default_to_square=UpperCAmelCase__       )
			__SCREAMING_SNAKE_CASE						       =    resample if resample is not None else self.resample
			__SCREAMING_SNAKE_CASE						       =    do_center_crop if do_center_crop is not None else self.do_center_crop
			__SCREAMING_SNAKE_CASE						       =    crop_size if crop_size is not None else self.crop_size
			__SCREAMING_SNAKE_CASE						       =    get_size_dict(UpperCAmelCase__      ,      param_name="crop_size"      ,      default_to_square=UpperCAmelCase__       )
			__SCREAMING_SNAKE_CASE						       =    do_rescale if do_rescale is not None else self.do_rescale
			__SCREAMING_SNAKE_CASE						       =    rescale_factor if rescale_factor is not None else self.rescale_factor
			__SCREAMING_SNAKE_CASE						       =    do_normalize if do_normalize is not None else self.do_normalize
			__SCREAMING_SNAKE_CASE						       =    image_mean if image_mean is not None else self.image_mean
			__SCREAMING_SNAKE_CASE						       =    image_std if image_std is not None else self.image_std
			__SCREAMING_SNAKE_CASE						       =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

			__SCREAMING_SNAKE_CASE						       =    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:
					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:
					__SCREAMING_SNAKE_CASE						       =    [convert_to_rgb(UpperCAmelCase__       ) for image in images]

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

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

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

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

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

			__SCREAMING_SNAKE_CASE						       =    [to_channel_dimension_format(UpperCAmelCase__      ,      UpperCAmelCase__       ) for image in images]

			__SCREAMING_SNAKE_CASE						       =    {"pixel_values": images}
			return BatchFeature(data=UpperCAmelCase__      ,      tensor_type=UpperCAmelCase__       )




"""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,
)


if is_sentencepiece_available():
		from ..ta.tokenization_ta import TaTokenizer
else:
		from ...utils.dummy_sentencepiece_objects import TaTokenizer

a__    :							Dict            =					TaTokenizer

if is_tokenizers_available():
		from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
		from ...utils.dummy_tokenizers_objects import TaTokenizerFast

a__    :							Any            =					TaTokenizerFast

a__    :							Tuple            =					{'''configuration_mt5''': ['''MT5Config''', '''MT5OnnxConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							int            =					[
		    '''MT5EncoderModel''',
		    '''MT5ForConditionalGeneration''',
		    '''MT5ForQuestionAnswering''',
		    '''MT5Model''',
		    '''MT5PreTrainedModel''',
		    '''MT5Stack''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							str            =					['''TFMT5EncoderModel''', '''TFMT5ForConditionalGeneration''', '''TFMT5Model''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							Union[str, Any]            =					['''FlaxMT5EncoderModel''', '''FlaxMT5ForConditionalGeneration''', '''FlaxMT5Model''']


if TYPE_CHECKING:
		from .configuration_mta import MTaConfig, MTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mta import (
				    MTaEncoderModel,
				    MTaForConditionalGeneration,
				    MTaForQuestionAnswering,
				    MTaModel,
				    MTaPreTrainedModel,
				    MTaStack,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
		import sys

		a__    :							str            =					_LazyModule(
		    __name__,
		    globals()['''__file__'''],
		    _import_structure,
		    extra_objects={'''MT5Tokenizer''': MTaTokenizer, '''MT5TokenizerFast''': MTaTokenizerFast},
		    module_spec=__spec__,
		)
