




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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


lowerCamelCase__					:  Dict      							=				logging.get_logger(__name__)

def    UpperCAmelCase_				( __UpperCAmelCase		:      Optional[Any]     ,     __UpperCAmelCase		:      List[Any]				)   ->					Union[str, Any]:
      SCREAMING_SNAKE_CASE_      		=     b.T
      SCREAMING_SNAKE_CASE_      		=     np.sum(np.square(__UpperCAmelCase				)     ,     axis=1				)
      SCREAMING_SNAKE_CASE_      		=     np.sum(np.square(__UpperCAmelCase				)     ,     axis=0				)
      SCREAMING_SNAKE_CASE_      		=     np.matmul(__UpperCAmelCase     ,     __UpperCAmelCase				)
      SCREAMING_SNAKE_CASE_      		=     aa[:, None] - 2 * ab + ba[None, :]
      return d

def    UpperCAmelCase_				( __UpperCAmelCase		:      Dict     ,     __UpperCAmelCase		:      Optional[Any]				)   ->					Optional[int]:
      SCREAMING_SNAKE_CASE_      		=     x.reshape(-1     ,     3				)
      SCREAMING_SNAKE_CASE_      		=     squared_euclidean_distance(__UpperCAmelCase     ,     __UpperCAmelCase				)
      return np.argmin(__UpperCAmelCase     ,     axis=1				)





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       ["pixel_values"]



 def __init__(				self    :						List[Any]							,							_lowerCAmelCase    :						Optional[Union[List[List[int]], np.ndarray]] = None							,							_lowerCAmelCase    :						bool = True							,							_lowerCAmelCase    :						Dict[str, int] = None							,							_lowerCAmelCase    :						PILImageResampling = PILImageResampling.BILINEAR							,							_lowerCAmelCase    :						bool = True							,							_lowerCAmelCase    :						bool = True							,							**_lowerCAmelCase    :						List[Any]							,							):
       super().__init__(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     size if size is not None else {'height': 256, 'width': 256}
       SCREAMING_SNAKE_CASE_      		=     get_size_dict(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     np.array(_lowerCAmelCase		) if clusters is not None else None
       SCREAMING_SNAKE_CASE_      		=     do_resize
       SCREAMING_SNAKE_CASE_      		=     size
       SCREAMING_SNAKE_CASE_      		=     resample
       SCREAMING_SNAKE_CASE_      		=     do_normalize
       SCREAMING_SNAKE_CASE_      		=     do_color_quantize



 def  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						np.ndarray							,							_lowerCAmelCase    :						Dict[str, int]							,							_lowerCAmelCase    :						PILImageResampling = PILImageResampling.BILINEAR							,							_lowerCAmelCase    :						Optional[Union[str, ChannelDimension]] = None							,							**_lowerCAmelCase    :						Tuple							,							):
       SCREAMING_SNAKE_CASE_      		=     get_size_dict(_lowerCAmelCase		)
       if "height" not in size or "width" not in size:
             raise ValueError(F"Size dictionary must contain both height and width keys. Got {size.keys()}"		)
       return resize(
           _lowerCAmelCase							,							size=(size['height'], size['width'])							,							resample=_lowerCAmelCase							,							data_format=_lowerCAmelCase							,							**_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						np.ndarray							,							_lowerCAmelCase    :						Optional[Union[str, ChannelDimension]] = None							,							):
       SCREAMING_SNAKE_CASE_      		=     rescale(image=_lowerCAmelCase							,							scale=1 / 127.5							,							data_format=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     image - 1
       return image




 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						ImageInput							,							_lowerCAmelCase    :						bool = None							,							_lowerCAmelCase    :						Dict[str, int] = None							,							_lowerCAmelCase    :						PILImageResampling = None							,							_lowerCAmelCase    :						bool = None							,							_lowerCAmelCase    :						Optional[bool] = None							,							_lowerCAmelCase    :						Optional[Union[List[List[int]], np.ndarray]] = None							,							_lowerCAmelCase    :						Optional[Union[str, TensorType]] = None							,							_lowerCAmelCase    :						Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST							,							**_lowerCAmelCase    :						Optional[Any]							,							):
       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(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE_      		=     do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE_      		=     do_color_quantize if do_color_quantize is not None else self.do_color_quantize
       SCREAMING_SNAKE_CASE_      		=     clusters if clusters is not None else self.clusters
       SCREAMING_SNAKE_CASE_      		=     np.array(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     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 or resample is None:
             raise ValueError('Size and resample must be specified if do_resize is True.'		)

       if do_color_quantize and clusters is None:
             raise ValueError('Clusters must be specified if do_color_quantize is True.'		)

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

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

       if do_normalize:
             SCREAMING_SNAKE_CASE_      		=     [self.normalize(image=_lowerCAmelCase		) for image in images]

       if do_color_quantize:
             SCREAMING_SNAKE_CASE_      		=     [to_channel_dimension_format(_lowerCAmelCase							,							ChannelDimension.LAST		) for image in images]
             # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
             SCREAMING_SNAKE_CASE_      		=     np.array(_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     color_quantize(_lowerCAmelCase							,							_lowerCAmelCase		).reshape(images.shape[:-1]		)

             # flatten to (batch_size, height*width)
             SCREAMING_SNAKE_CASE_      		=     images.shape[0]
             SCREAMING_SNAKE_CASE_      		=     images.reshape(_lowerCAmelCase							,							-1		)

             # We need to convert back to a list of images to keep consistent behaviour across processors.
             SCREAMING_SNAKE_CASE_      		=     list(_lowerCAmelCase		)
       else:
             SCREAMING_SNAKE_CASE_      		=     [to_channel_dimension_format(_lowerCAmelCase							,							_lowerCAmelCase		) for image in images]

       SCREAMING_SNAKE_CASE_      		=     {'input_ids': images}
       return BatchFeature(data=_lowerCAmelCase							,							tensor_type=_lowerCAmelCase		)





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__					:  List[Any]      							=				TaTokenizer

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

lowerCamelCase__					:  Union[str, Any]      							=				TaTokenizerFast

lowerCamelCase__					:  Dict      							=				{'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  Tuple      							=				[
       '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__					:  Tuple      							=				['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__					:  int      							=				_LazyModule(
       __name__,
       globals()['__file__'],
       _import_structure,
       extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
       module_spec=__spec__,
   )