from typing import TYPE_CHECKING

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


_lowerCamelCase   :				Any     			=						{
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCamelCase   :				str     			=						[
							    '''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:
							_lowerCamelCase   :				Tuple     			=						[
							    '''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

							_lowerCamelCase   :				Tuple     			=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





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,
    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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


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


class   lowercase      (       a       ):
							lowercase__  :    List[str]									=							["""pixel_values"""]




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



												'''simple docstring'''

												super().__init__(**_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    size if size is not None else {"shortest_edge": 384}
												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase		,							default_to_square=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    do_resize
												SCREAMING_SNAKE_CASE           =    size
												# Default value set here for backwards compatibility where the value in config is None
												SCREAMING_SNAKE_CASE           =    crop_pct if crop_pct is not None else 224 / 256
												SCREAMING_SNAKE_CASE           =    resample
												SCREAMING_SNAKE_CASE           =    do_rescale
												SCREAMING_SNAKE_CASE           =    rescale_factor
												SCREAMING_SNAKE_CASE           =    do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												SCREAMING_SNAKE_CASE           =    image_std if image_std is not None else IMAGENET_STANDARD_STD




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase		,							default_to_square=_UpperCamelCase )
												if "shortest_edge" not in size:
																	raise ValueError(F"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}" )
												SCREAMING_SNAKE_CASE           =    size["shortest_edge"]

												if shortest_edge < 384:
																	# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																	SCREAMING_SNAKE_CASE           =    int(shortest_edge / crop_pct )
																	SCREAMING_SNAKE_CASE           =    get_resize_output_image_size(_UpperCamelCase		,							size=_UpperCamelCase		,							default_to_square=_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    resize(image=_UpperCamelCase		,							size=_UpperCamelCase		,							resample=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )
																	# then crop to (shortest_edge, shortest_edge)
																	return center_crop(image=_UpperCamelCase		,							size=(shortest_edge, shortest_edge)		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )
												else:
																	# warping (no cropping) when evaluated at 384 or larger
																	return resize(
																	    _UpperCamelCase		,							size=(shortest_edge, shortest_edge)		,							resample=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )




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



												'''simple docstring'''

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




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



												'''simple docstring'''

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





							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   ImageInput		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   Dict[str, int] = None		,							_UpperCamelCase				:   float = None		,							_UpperCamelCase				:   PILImageResampling = None		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   float = 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				:   Any		,							)   ->			PIL.Image.Image:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE           =    crop_pct if crop_pct is not None else self.crop_pct
												SCREAMING_SNAKE_CASE           =    resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE           =    do_rescale if do_rescale is not None else self.do_rescale
												SCREAMING_SNAKE_CASE           =    rescale_factor if rescale_factor is not None else self.rescale_factor
												SCREAMING_SNAKE_CASE           =    do_normalize if do_normalize is not None else self.do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean if image_mean is not None else self.image_mean
												SCREAMING_SNAKE_CASE           =    image_std if image_std is not None else self.image_std

												SCREAMING_SNAKE_CASE           =    size if size is not None else self.size
												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase		,							default_to_square=_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    make_list_of_images(_UpperCamelCase )

												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." )

												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_resize and size["shortest_edge"] < 384 and crop_pct is None:
																	raise ValueError("crop_pct must be specified if size < 384." )

												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.
												SCREAMING_SNAKE_CASE           =    [to_numpy_array(_UpperCamelCase ) for image in images]

												if do_resize:
																	SCREAMING_SNAKE_CASE           =    [self.resize(image=_UpperCamelCase		,							size=_UpperCamelCase		,							crop_pct=_UpperCamelCase		,							resample=_UpperCamelCase ) for image in images]

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

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

												SCREAMING_SNAKE_CASE           =    [to_channel_dimension_format(_UpperCamelCase		,							_UpperCamelCase ) for image in images]

												SCREAMING_SNAKE_CASE           =    {"pixel_values": images}
												return BatchFeature(data=_UpperCamelCase		,							tensor_type=_UpperCamelCase )




