




'''simple docstring'''

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


a    :							Union[str, Any]										=    logging.get_logger(__name__)


if is_vision_available():
							import PIL






class 		a							(  _lowerCamelCase	):
				snake_case_           =						['pixel_values']


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

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


				def     A_					(						self       :   Tuple			,							lowercase_       :   Any			,							lowercase_       :   Tuple			,							lowercase_       :   Optional[Any] = PILImageResampling.BICUBIC			,							lowercase_       :   List[Any] = None			,							**lowercase_       :   List[str]			,							):
									snake_case_		     =							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()}"   )
									snake_case_		     =							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     A_					(						self       :   Dict			,							lowercase_       :   int			,							lowercase_       :   Optional[Any]			,							lowercase_       :   Tuple = None			,							**lowercase_       :   int			,							):
									snake_case_		     =							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     A_					(						self       :   Any			,							lowercase_       :   Optional[Any]			,							lowercase_       :   Tuple			,							lowercase_       :   str = None			,							**lowercase_       :   Dict			,							):
									return rescale(lowercase_			,							scale=lowercase_			,							data_format=lowercase_			,							**lowercase_   )


				def     A_					(						self       :   Optional[Any]			,							lowercase_       :   int			,							lowercase_       :   Dict			,							lowercase_       :   Optional[Any]			,							lowercase_       :   int = None			,							**lowercase_       :   Dict			,							):
									return normalize(lowercase_			,							mean=lowercase_			,							std=lowercase_			,							data_format=lowercase_			,							**lowercase_   )


				def     A_					(						self       :   List[Any]			,							lowercase_       :   int			,							lowercase_       :   str = None			,							lowercase_       :   Dict = None			,							lowercase_       :   Union[str, Any] = None			,							lowercase_       :   Optional[int] = None			,							lowercase_       :   Union[str, Any] = None			,							lowercase_       :   Tuple = None			,							lowercase_       :   Any = None			,							lowercase_       :   List[str] = None			,							lowercase_       :   Union[str, Any] = None			,							lowercase_       :   Tuple = None			,							lowercase_       :   List[Any] = None			,							lowercase_       :   Any = None			,							lowercase_       :   Tuple = ChannelDimension.FIRST			,							**lowercase_       :   str			,							):
									snake_case_		     =							do_resize if do_resize is not None else self.do_resize
									snake_case_		     =							size if size is not None else self.size
									snake_case_		     =							get_size_dict(lowercase_			,							param_name='''size'''			,							default_to_square=lowercase_   )
									snake_case_		     =							resample if resample is not None else self.resample
									snake_case_		     =							do_center_crop if do_center_crop is not None else self.do_center_crop
									snake_case_		     =							crop_size if crop_size is not None else self.crop_size
									snake_case_		     =							get_size_dict(lowercase_			,							param_name='''crop_size'''			,							default_to_square=lowercase_   )
									snake_case_		     =							do_rescale if do_rescale is not None else self.do_rescale
									snake_case_		     =							rescale_factor if rescale_factor is not None else self.rescale_factor
									snake_case_		     =							do_normalize if do_normalize is not None else self.do_normalize
									snake_case_		     =							image_mean if image_mean is not None else self.image_mean
									snake_case_		     =							image_std if image_std is not None else self.image_std
									snake_case_		     =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									snake_case_		     =							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:
														snake_case_		     =							[convert_to_rgb(lowercase_   ) for image in images]

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

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

									if do_center_crop:
														snake_case_		     =							[self.center_crop(image=lowercase_			,							size=lowercase_   ) for image in images]

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

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

									snake_case_		     =							[to_channel_dimension_format(lowercase_			,							lowercase_   ) for image in images]

									snake_case_		     =							{'''pixel_values''': images}
									return BatchFeature(data=lowercase_			,							tensor_type=lowercase_   )



"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


A_    							=					{
    """configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnxConfig"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """GPTNeoForCausalLM""",
						    """GPTNeoForQuestionAnswering""",
						    """GPTNeoForSequenceClassification""",
						    """GPTNeoForTokenClassification""",
						    """GPTNeoModel""",
						    """GPTNeoPreTrainedModel""",
						    """load_tf_weights_in_gpt_neo""",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """FlaxGPTNeoForCausalLM""",
						    """FlaxGPTNeoModel""",
						    """FlaxGPTNeoPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_gpt_neo import (
												    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GPTNeoForCausalLM,
												    GPTNeoForQuestionAnswering,
												    GPTNeoForSequenceClassification,
												    GPTNeoForTokenClassification,
												    GPTNeoModel,
												    GPTNeoPreTrainedModel,
												    load_tf_weights_in_gpt_neo,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
						import sys

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



