from typing import Dict, Iterable, List, Optional, Union

import numpy as np

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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A_      =      logging.get_logger(__name__)






class      __lowercase					(    _A    ):
						lowercase					 =     ['pixel_values']

						def __init__(	self				:      Optional[int]							,							__lowerCamelCase				:      bool = True							,							__lowerCamelCase				:      Dict[str, int] = None							,							__lowerCamelCase				:      PILImageResampling = PILImageResampling.BICUBIC							,							__lowerCamelCase				:      bool = True							,							__lowerCamelCase				:      Dict[str, int] = None							,							__lowerCamelCase				:      bool = True							,							__lowerCamelCase				:      Union[int, float] = 1 / 2_55							,							__lowerCamelCase				:      bool = True							,							__lowerCamelCase				:      Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN							,							__lowerCamelCase				:      Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD							,							**__lowerCamelCase				:      Dict							,							)							->		None:


									'''simple docstring'''

									super().__init__(**__lowerCamelCase   )
									lowercase							=		size if size is not None else {'''shortest_edge''': 2_24}
									lowercase							=		get_size_dict(__lowerCamelCase							,							default_to_square=__lowerCamelCase   )
									lowercase							=		crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
									lowercase							=		get_size_dict(__lowerCamelCase							,							param_name='''crop_size'''   )

									lowercase							=		do_resize
									lowercase							=		size
									lowercase							=		resample
									lowercase							=		do_center_crop
									lowercase							=		crop_size
									lowercase							=		do_rescale
									lowercase							=		rescale_factor
									lowercase							=		do_normalize
									lowercase							=		image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									lowercase							=		image_std if image_std is not None else IMAGENET_DEFAULT_STD

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


									'''simple docstring'''

									lowercase							=		get_size_dict(__lowerCamelCase							,							default_to_square=__lowerCamelCase   )
									# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
									if "shortest_edge" in size:
												lowercase							=		int((2_56 / 2_24) * size['''shortest_edge''']   )
												lowercase							=		get_resize_output_image_size(__lowerCamelCase							,							size=__lowerCamelCase							,							default_to_square=__lowerCamelCase   )
												lowercase							=		{'''height''': output_size[0], '''width''': output_size[1]}
									if "height" not in size_dict or "width" not in size_dict:
												raise ValueError(
												    f'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'   )
									return resize(
									    __lowerCamelCase							,							size=(size_dict['''height'''], size_dict['''width'''])							,							resample=__lowerCamelCase							,							data_format=__lowerCamelCase							,							**__lowerCamelCase   )

						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      np.ndarray							,							__lowerCamelCase				:      Dict[str, int]							,							__lowerCamelCase				:      Optional[Union[str, ChannelDimension]] = None							,							**__lowerCamelCase				:      str							,							)							->		np.ndarray:


									'''simple docstring'''

									lowercase							=		get_size_dict(__lowerCamelCase   )
									if "height" not in size or "width" not in size:
												raise ValueError(f'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'   )
									return center_crop(__lowerCamelCase							,							size=(size['''height'''], size['''width'''])							,							data_format=__lowerCamelCase							,							**__lowerCamelCase   )

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


									'''simple docstring'''

									return rescale(__lowerCamelCase							,							scale=__lowerCamelCase							,							data_format=__lowerCamelCase							,							**__lowerCamelCase   )

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


									'''simple docstring'''

									return normalize(__lowerCamelCase							,							mean=__lowerCamelCase							,							std=__lowerCamelCase							,							data_format=__lowerCamelCase							,							**__lowerCamelCase   )



						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      ImageInput							,							__lowerCamelCase				:      Optional[bool] = None							,							__lowerCamelCase				:      Optional[Dict[str, int]] = None							,							__lowerCamelCase				:      PILImageResampling = None							,							__lowerCamelCase				:      Optional[bool] = None							,							__lowerCamelCase				:      Optional[Dict[str, int]] = None							,							__lowerCamelCase				:      Optional[bool] = None							,							__lowerCamelCase				:      Optional[float] = None							,							__lowerCamelCase				:      Optional[bool] = None							,							__lowerCamelCase				:      Optional[Union[float, Iterable[float]]] = None							,							__lowerCamelCase				:      Optional[Union[float, Iterable[float]]] = None							,							__lowerCamelCase				:      Optional[TensorType] = None							,							__lowerCamelCase				:      ChannelDimension = ChannelDimension.FIRST							,							**__lowerCamelCase				:      str							,							)							->		BatchFeature:


									'''simple docstring'''

									lowercase							=		do_resize if do_resize is not None else self.do_resize
									lowercase							=		resample if resample is not None else self.resample
									lowercase							=		do_center_crop if do_center_crop is not None else self.do_center_crop
									lowercase							=		do_rescale if do_rescale is not None else self.do_rescale
									lowercase							=		rescale_factor if rescale_factor is not None else self.rescale_factor
									lowercase							=		do_normalize if do_normalize is not None else self.do_normalize
									lowercase							=		image_mean if image_mean is not None else self.image_mean
									lowercase							=		image_std if image_std is not None else self.image_std

									lowercase							=		size if size is not None else self.size
									lowercase							=		get_size_dict(__lowerCamelCase							,							default_to_square=__lowerCamelCase   )
									lowercase							=		crop_size if crop_size is not None else self.crop_size
									lowercase							=		get_size_dict(__lowerCamelCase							,							param_name='''crop_size'''   )

									lowercase							=		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:
												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.'''   )

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

									if do_resize:
												lowercase							=		[self.resize(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   ) for image in images]

									if do_center_crop:
												lowercase							=		[self.center_crop(__lowerCamelCase							,							__lowerCamelCase   ) for image in images]

									if do_rescale:
												lowercase							=		[self.rescale(__lowerCamelCase							,							__lowerCamelCase   ) for image in images]

									if do_normalize:
												lowercase							=		[self.normalize(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   ) for image in images]

									lowercase							=		[to_channel_dimension_format(__lowerCamelCase							,							__lowerCamelCase   ) for image in images]

									lowercase							=		{'''pixel_values''': images}
									return BatchFeature(data=__lowerCamelCase							,							tensor_type=__lowerCamelCase   )






from typing import TYPE_CHECKING

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


A_      =      {
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_      =      [
						    "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

						A_      =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





