


"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


snake_case__						:   Optional[int]			 = {
    '''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__						:   List[Any]			 = [
     '''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''GPTBigCodeForSequenceClassification''',
     '''GPTBigCodeForTokenClassification''',
     '''GPTBigCodeForCausalLM''',
     '''GPTBigCodeModel''',
     '''GPTBigCodePreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_bigcode import (
      GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTBigCodeForCausalLM,
      GPTBigCodeForSequenceClassification,
      GPTBigCodeForTokenClassification,
      GPTBigCodeModel,
      GPTBigCodePreTrainedModel,
  )


else:
 import sys

 snake_case__						:   Dict			 = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
 import PIL

snake_case__						:   List[Any]			 = logging.get_logger(__name__)


def    _snake_case  (  _snake_case :	Tuple	):
      if isinstance(_snake_case       ,      (list, tuple)	) and isinstance(videos[0]       ,      (list, tuple)	) and is_valid_image(videos[0][0]	):
            return videos

      elif isinstance(_snake_case       ,      (list, tuple)	) and is_valid_image(videos[0]	):
            return [videos]

      elif is_valid_image(_snake_case	):
            return [[videos]]

      raise ValueError(f'''Could not make batched video from {videos}'''	)






class 						snake_case_(				a__	):
     __UpperCamelCase     =    ['''pixel_values''']






     def __init__(		self     :    Optional[int]    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    Dict[str, int] = None    , UpperCamelCase_     :    PILImageResampling = PILImageResampling.BILINEAR    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    Dict[str, int] = None    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    Union[int, float] = 1 / 2_5_5    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    Optional[Union[float, List[float]]] = None    , UpperCamelCase_     :    Optional[Union[float, List[float]]] = None    , **UpperCamelCase_     :    Tuple    , ):
           super().__init__(**UpperCamelCase_ )
           lowerCAmelCase    :		Optional[Any]				=      size if size is not None else {'''shortest_edge''': 2_5_6}
           lowerCAmelCase    :		Optional[Any]				=      get_size_dict(UpperCamelCase_    , default_to_square=UpperCamelCase_ )
           lowerCAmelCase    :		Tuple				=      crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
           lowerCAmelCase    :		Dict				=      get_size_dict(UpperCamelCase_    , param_name='''crop_size''' )

           lowerCAmelCase    :		Any				=      do_resize
           lowerCAmelCase    :		Union[str, Any]				=      size
           lowerCAmelCase    :		List[str]				=      do_center_crop
           lowerCAmelCase    :		int				=      crop_size
           lowerCAmelCase    :		Dict				=      resample
           lowerCAmelCase    :		Dict				=      do_rescale
           lowerCAmelCase    :		Any				=      rescale_factor
           lowerCAmelCase    :		List[Any]				=      offset
           lowerCAmelCase    :		Tuple				=      do_normalize
           lowerCAmelCase    :		Optional[Any]				=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           lowerCAmelCase    :		List[Any]				=      image_std if image_std is not None else IMAGENET_STANDARD_STD






     def    lowerCamelCase__   (		self     :    Tuple    , UpperCamelCase_     :    np.ndarray    , UpperCamelCase_     :    Dict[str, int]    , UpperCamelCase_     :    PILImageResampling = PILImageResampling.BILINEAR    , UpperCamelCase_     :    Optional[Union[str, ChannelDimension]] = None    , **UpperCamelCase_     :    Optional[Any]    , ):
           lowerCAmelCase    :		Optional[int]				=      get_size_dict(UpperCamelCase_    , default_to_square=UpperCamelCase_ )
           if "shortest_edge" in size:
                 lowerCAmelCase    :		List[str]				=      get_resize_output_image_size(UpperCamelCase_    , size['''shortest_edge''']    , default_to_square=UpperCamelCase_ )
           elif "height" in size and "width" in size:
                 lowerCAmelCase    :		Any				=      (size['''height'''], size['''width'''])
           else:
                 raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
           return resize(UpperCamelCase_    , size=UpperCamelCase_    , resample=UpperCamelCase_    , data_format=UpperCamelCase_    , **UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Optional[int]    , UpperCamelCase_     :    np.ndarray    , UpperCamelCase_     :    Dict[str, int]    , UpperCamelCase_     :    Optional[Union[str, ChannelDimension]] = None    , **UpperCamelCase_     :    Union[str, Any]    , ):
           lowerCAmelCase    :		Tuple				=      get_size_dict(UpperCamelCase_ )
           if "height" not in size or "width" not in size:
                 raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''' )
           return center_crop(UpperCamelCase_    , size=(size['''height'''], size['''width'''])    , data_format=UpperCamelCase_    , **UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Optional[Any]    , UpperCamelCase_     :    np.ndarray    , UpperCamelCase_     :    Union[int, float]    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    Optional[Union[str, ChannelDimension]] = None    , **UpperCamelCase_     :    Optional[Any]    , ):
           lowerCAmelCase    :		List[str]				=      image.astype(np.floataa )
           if offset:
                 lowerCAmelCase    :		Union[str, Any]				=      image - (scale / 2)
           return rescale(UpperCamelCase_    , scale=UpperCamelCase_    , data_format=UpperCamelCase_    , **UpperCamelCase_ )






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






     def    lowerCamelCase__   (		self     :    Union[str, Any]    , UpperCamelCase_     :    ImageInput    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    Dict[str, int] = None    , UpperCamelCase_     :    PILImageResampling = None    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    Dict[str, int] = None    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    float = None    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    Optional[Union[float, List[float]]] = None    , UpperCamelCase_     :    Optional[Union[float, List[float]]] = None    , UpperCamelCase_     :    Optional[ChannelDimension] = ChannelDimension.FIRST    , ):
           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_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.''' )

           if offset and not do_rescale:
                 raise ValueError('''For offset, do_rescale must also be set to True.''' )

           # All transformations expect numpy arrays.
           lowerCAmelCase    :		List[str]				=      to_numpy_array(UpperCamelCase_ )

           if do_resize:
                 lowerCAmelCase    :		Optional[int]				=      self.resize(image=UpperCamelCase_    , size=UpperCamelCase_    , resample=UpperCamelCase_ )

           if do_center_crop:
                 lowerCAmelCase    :		List[str]				=      self.center_crop(UpperCamelCase_    , size=UpperCamelCase_ )

           if do_rescale:
                 lowerCAmelCase    :		str				=      self.rescale(image=UpperCamelCase_    , scale=UpperCamelCase_    , offset=UpperCamelCase_ )

           if do_normalize:
                 lowerCAmelCase    :		Optional[int]				=      self.normalize(image=UpperCamelCase_    , mean=UpperCamelCase_    , std=UpperCamelCase_ )

           lowerCAmelCase    :		str				=      to_channel_dimension_format(UpperCamelCase_    , UpperCamelCase_ )
           return image






     def    lowerCamelCase__   (		self     :    List[str]    , UpperCamelCase_     :    ImageInput    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    Dict[str, int] = None    , UpperCamelCase_     :    PILImageResampling = None    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    Dict[str, int] = None    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    float = None    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    bool = None    , UpperCamelCase_     :    Optional[Union[float, List[float]]] = None    , UpperCamelCase_     :    Optional[Union[float, List[float]]] = None    , UpperCamelCase_     :    Optional[Union[str, TensorType]] = None    , UpperCamelCase_     :    ChannelDimension = ChannelDimension.FIRST    , **UpperCamelCase_     :    List[str]    , ):
           lowerCAmelCase    :		str				=      do_resize if do_resize is not None else self.do_resize
           lowerCAmelCase    :		Any				=      resample if resample is not None else self.resample
           lowerCAmelCase    :		int				=      do_center_crop if do_center_crop is not None else self.do_center_crop
           lowerCAmelCase    :		int				=      do_rescale if do_rescale is not None else self.do_rescale
           lowerCAmelCase    :		int				=      rescale_factor if rescale_factor is not None else self.rescale_factor
           lowerCAmelCase    :		str				=      offset if offset is not None else self.offset
           lowerCAmelCase    :		Optional[int]				=      do_normalize if do_normalize is not None else self.do_normalize
           lowerCAmelCase    :		Dict				=      image_mean if image_mean is not None else self.image_mean
           lowerCAmelCase    :		Any				=      image_std if image_std is not None else self.image_std

           lowerCAmelCase    :		List[str]				=      size if size is not None else self.size
           lowerCAmelCase    :		Tuple				=      get_size_dict(UpperCamelCase_    , default_to_square=UpperCamelCase_ )
           lowerCAmelCase    :		Optional[int]				=      crop_size if crop_size is not None else self.crop_size
           lowerCAmelCase    :		Any				=      get_size_dict(UpperCamelCase_    , param_name='''crop_size''' )

           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.''' )

           lowerCAmelCase    :		List[str]				=      make_batched(UpperCamelCase_ )

           lowerCAmelCase    :		Dict				=      [
               [
                   self._preprocess_image(
                       image=UpperCamelCase_    , do_resize=UpperCamelCase_    , size=UpperCamelCase_    , resample=UpperCamelCase_    , do_center_crop=UpperCamelCase_    , crop_size=UpperCamelCase_    , do_rescale=UpperCamelCase_    , rescale_factor=UpperCamelCase_    , offset=UpperCamelCase_    , do_normalize=UpperCamelCase_    , image_mean=UpperCamelCase_    , image_std=UpperCamelCase_    , data_format=UpperCamelCase_    , )
                   for img in video
               ]
               for video in videos
           ]

           lowerCAmelCase    :		Optional[Any]				=      {'''pixel_values''': videos}
           return BatchFeature(data=UpperCamelCase_    , tensor_type=UpperCamelCase_ )


