






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 convert_to_rgb, 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


if is_vision_available():
  import PIL


__a:     Dict   					=					logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  ['''pixel_values''']


  def __init__(      self		:			Any       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Dict[str, int] = None       ,				lowerCamelCase		:			PILImageResampling = PILImageResampling.BICUBIC       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Union[int, float] = 1 / 255       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Optional[Union[float, List[float]]] = None       ,				lowerCamelCase		:			Optional[Union[float, List[float]]] = None       ,				lowerCamelCase		:			bool = True       ,				**lowerCamelCase		:			List[Any]       ,				)						->	None:


         """simple docstring"""
         super().__init__(**lowerCamelCase							)
         _UpperCAmelCase							=    size if size is not None else {"""height""": 384, """width""": 384}
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase       ,				default_to_square=lowerCamelCase							)

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


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			np.ndarray       ,				lowerCamelCase		:			Dict[str, int]       ,				lowerCamelCase		:			PILImageResampling = PILImageResampling.BICUBIC       ,				lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None       ,				**lowerCamelCase		:			str       ,				)						->	np.ndarray:


         """simple docstring"""
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase       ,				default_to_square=lowerCamelCase							)
         if "height" not in size or "width" not in size:
                raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""							)
         _UpperCAmelCase							=    (size["""height"""], size["""width"""])
         return resize(lowerCamelCase       ,				size=lowerCamelCase       ,				resample=lowerCamelCase       ,				data_format=lowerCamelCase       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			np.ndarray       ,				lowerCamelCase		:			Union[int, float]       ,				lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None       ,				**lowerCamelCase		:			Dict       ,				)						->	Union[str, Any]:


         """simple docstring"""
         return rescale(lowerCamelCase       ,				scale=lowerCamelCase       ,				data_format=lowerCamelCase       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			np.ndarray       ,				lowerCamelCase		:			Union[float, List[float]]       ,				lowerCamelCase		:			Union[float, List[float]]       ,				lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None       ,				**lowerCamelCase		:			List[Any]       ,				)						->	np.ndarray:


         """simple docstring"""
         return normalize(lowerCamelCase       ,				mean=lowerCamelCase       ,				std=lowerCamelCase       ,				data_format=lowerCamelCase       ,				**lowerCamelCase							)





  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			ImageInput       ,				lowerCamelCase		:			Optional[bool] = None       ,				lowerCamelCase		:			Optional[Dict[str, int]] = None       ,				lowerCamelCase		:			PILImageResampling = None       ,				lowerCamelCase		:			Optional[bool] = None       ,				lowerCamelCase		:			Optional[float] = None       ,				lowerCamelCase		:			Optional[bool] = None       ,				lowerCamelCase		:			Optional[Union[float, List[float]]] = None       ,				lowerCamelCase		:			Optional[Union[float, List[float]]] = None       ,				lowerCamelCase		:			Optional[Union[str, TensorType]] = None       ,				lowerCamelCase		:			bool = None       ,				lowerCamelCase		:			ChannelDimension = ChannelDimension.FIRST       ,				**lowerCamelCase		:			List[str]       ,				)						->	PIL.Image.Image:


         """simple docstring"""
         _UpperCAmelCase							=    do_resize if do_resize is not None else self.do_resize
         _UpperCAmelCase							=    resample if resample is not None else self.resample
         _UpperCAmelCase							=    do_rescale if do_rescale is not None else self.do_rescale
         _UpperCAmelCase							=    rescale_factor if rescale_factor is not None else self.rescale_factor
         _UpperCAmelCase							=    do_normalize if do_normalize is not None else self.do_normalize
         _UpperCAmelCase							=    image_mean if image_mean is not None else self.image_mean
         _UpperCAmelCase							=    image_std if image_std is not None else self.image_std
         _UpperCAmelCase							=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         _UpperCAmelCase							=    size if size is not None else self.size
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase       ,				default_to_square=lowerCamelCase							)

         _UpperCAmelCase							=    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_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							=    [convert_to_rgb(lowerCamelCase							) for image in images]

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

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

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

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

         _UpperCAmelCase							=    [to_channel_dimension_format(lowerCamelCase       ,				lowerCamelCase							) for image in images]

         _UpperCAmelCase							=    BatchFeature(data={"""pixel_values""": images}       ,				tensor_type=lowerCamelCase							)

         return encoded_outputs







import os
import sys

__a:     Union[str, Any]   					=					os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


__a:     Union[str, Any]   					=					[
    '''torch''',
    '''numpy''',
    '''tokenizers''',
    '''filelock''',
    '''requests''',
    '''tqdm''',
    '''regex''',
    '''sentencepiece''',
    '''sacremoses''',
    '''importlib_metadata''',
    '''huggingface_hub''',
]




@add_start_docstrings(AutoConfig.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Union[str, Any]:
       return AutoConfig.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoTokenizer.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Any:
       return AutoTokenizer.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModel.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Tuple:
       return AutoModel.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Tuple:
       return AutoModelForCausalLM.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						Optional[Any]:
       return AutoModelForMaskedLM.from_pretrained(*__snake_case   ,      **__snake_case )




@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						List[str]:
       return AutoModelForSequenceClassification.from_pretrained(*__snake_case   ,      **__snake_case )





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def       _SCREAMING_SNAKE_CASE (							*__snake_case   ,      **__snake_case )				->						List[Any]:
       return AutoModelForQuestionAnswering.from_pretrained(*__snake_case   ,      **__snake_case )