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


__lowercase							 =					logging.get_logger(__name__)


if is_vision_available():
    import PIL


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Any						=      ["""pixel_values"""]







    def __init__(	self , __lowercase = True , __lowercase = None , __lowercase = PILImageResampling.BICUBIC , __lowercase = True , __lowercase = None , __lowercase = True , __lowercase = 1 / 255 , __lowercase = True , __lowercase = None , __lowercase = None , __lowercase = True , **__lowercase , )       ->	None:
        super().__init__(**__lowercase)
        __UpperCamelCase     :Optional[Any]			  =		size if size is not None else {'''shortest_edge''': 224}
        __UpperCamelCase     :Optional[int]			  =		get_size_dict(__lowercase , default_to_square=__lowercase)
        __UpperCamelCase     :Union[str, Any]			  =		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        __UpperCamelCase     :Optional[Any]			  =		get_size_dict(__lowercase , default_to_square=__lowercase , param_name='''crop_size''')

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







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase = PILImageResampling.BICUBIC , __lowercase = None , **__lowercase , )       ->	np.ndarray:
        __UpperCamelCase     :Any			  =		get_size_dict(__lowercase , default_to_square=__lowercase)
        if "shortest_edge" not in size:
            raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""")
        __UpperCamelCase     :Any			  =		get_resize_output_image_size(__lowercase , size=size['''shortest_edge'''] , default_to_square=__lowercase)
        return resize(__lowercase , size=__lowercase , resample=__lowercase , data_format=__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase = None , **__lowercase , )       ->	np.ndarray:
        __UpperCamelCase     :List[Any]			  =		get_size_dict(__lowercase)
        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(__lowercase , size=(size['''height'''], size['''width''']) , data_format=__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase = None , **__lowercase , )       ->	Any:
        return rescale(__lowercase , scale=__lowercase , data_format=__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase = None , **__lowercase , )       ->	np.ndarray:
        return normalize(__lowercase , mean=__lowercase , std=__lowercase , data_format=__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = ChannelDimension.FIRST , **__lowercase , )       ->	PIL.Image.Image:
        __UpperCamelCase     :str			  =		do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase     :Any			  =		size if size is not None else self.size
        __UpperCamelCase     :int			  =		get_size_dict(__lowercase , param_name='''size''' , default_to_square=__lowercase)
        __UpperCamelCase     :List[Any]			  =		resample if resample is not None else self.resample
        __UpperCamelCase     :List[str]			  =		do_center_crop if do_center_crop is not None else self.do_center_crop
        __UpperCamelCase     :Union[str, Any]			  =		crop_size if crop_size is not None else self.crop_size
        __UpperCamelCase     :Dict			  =		get_size_dict(__lowercase , param_name='''crop_size''' , default_to_square=__lowercase)
        __UpperCamelCase     :str			  =		do_rescale if do_rescale is not None else self.do_rescale
        __UpperCamelCase     :List[str]			  =		rescale_factor if rescale_factor is not None else self.rescale_factor
        __UpperCamelCase     :int			  =		do_normalize if do_normalize is not None else self.do_normalize
        __UpperCamelCase     :Optional[int]			  =		image_mean if image_mean is not None else self.image_mean
        __UpperCamelCase     :int			  =		image_std if image_std is not None else self.image_std
        __UpperCamelCase     :List[Any]			  =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        __UpperCamelCase     :List[str]			  =		make_list_of_images(__lowercase)

        if not valid_images(__lowercase):
            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:
            __UpperCamelCase     :Optional[int]			  =		[convert_to_rgb(__lowercase) for image in images]

        # All transformations expect numpy arrays.
        __UpperCamelCase     :str			  =		[to_numpy_array(__lowercase) for image in images]

        if do_resize:
            __UpperCamelCase     :Optional[Any]			  =		[self.resize(image=__lowercase , size=__lowercase , resample=__lowercase) for image in images]

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

        if do_rescale:
            __UpperCamelCase     :Optional[Any]			  =		[self.rescale(image=__lowercase , scale=__lowercase) for image in images]

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

        __UpperCamelCase     :Optional[Any]			  =		[to_channel_dimension_format(__lowercase , __lowercase) for image in images]

        __UpperCamelCase     :List[str]			  =		{'''pixel_values''': images}
        return BatchFeature(data=__lowercase , tensor_type=__lowercase)








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

lowerCAmelCase							:  Any   	=						TaTokenizer

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

lowerCAmelCase							:  Dict   	=						TaTokenizerFast

lowerCAmelCase							:  Optional[int]   	=						{"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

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

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Tuple   	=						["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  List[str]   	=						["""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

						lowerCAmelCase							:  Optional[Any]   	=						_LazyModule(
						    __name__,
						    globals()["""__file__"""],
						    _import_structure,
						    extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
						    module_spec=__spec__,
						)






