





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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
			import torch

if is_vision_available():
			import PIL


_a						=   logging.get_logger(__name__)





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   Tuple[int, int]:







	'''simple docstring'''







	def constraint_to_multiple_of(__snake_case ,__snake_case ,__snake_case=0 ,__snake_case=None							):
		lowerCamelCase__              =	round(val / multiple							) * multiple

		if max_val is not None and x > max_val:
			lowerCamelCase__              =	math.floor(val / multiple							) * multiple

		if x < min_val:
			lowerCamelCase__              =	math.ceil(val / multiple							) * multiple

		return x

	lowerCamelCase__              =	(output_size, output_size) if isinstance(__snake_case ,__snake_case							) else output_size

	lowerCamelCase__       ,			lowerCamelCase__              =	get_image_size(__snake_case							)
	lowerCamelCase__       ,			lowerCamelCase__              =	output_size

	# determine new height and width
	lowerCamelCase__              =	output_height / input_height
	lowerCamelCase__              =	output_width / input_width

	if keep_aspect_ratio:
		# scale as little as possible
		if abs(1 - scale_width							) < abs(1 - scale_height							):
			# fit width
			lowerCamelCase__              =	scale_width
		else:
			# fit height
			lowerCamelCase__              =	scale_height

	lowerCamelCase__              =	constraint_to_multiple_of(scale_height * input_height ,multiple=__snake_case							)
	lowerCamelCase__              =	constraint_to_multiple_of(scale_width * input_width ,multiple=__snake_case							)

	return (new_height, new_width)






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		["""pixel_values"""]





			def __init__(					self ,						__lowerCAmelCase = True ,						__lowerCAmelCase = None ,						__lowerCAmelCase = PILImageResampling.BILINEAR ,						__lowerCAmelCase = False ,						__lowerCAmelCase = 1 ,						__lowerCAmelCase = True ,						__lowerCAmelCase = 1 / 2_5_5 ,						__lowerCAmelCase = True ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						**__lowerCAmelCase ,						):



				'''simple docstring'''







				super().__init__(**__lowerCAmelCase							)
				lowerCamelCase__              =	size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
				lowerCamelCase__              =	get_size_dict(__lowerCAmelCase							)
				lowerCamelCase__              =	do_resize
				lowerCamelCase__              =	size
				lowerCamelCase__              =	keep_aspect_ratio
				lowerCamelCase__              =	ensure_multiple_of
				lowerCamelCase__              =	resample
				lowerCamelCase__              =	do_rescale
				lowerCamelCase__              =	rescale_factor
				lowerCamelCase__              =	do_normalize
				lowerCamelCase__              =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				lowerCamelCase__              =	image_std if image_std is not None else IMAGENET_STANDARD_STD





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase = False ,						__lowerCAmelCase = 1 ,						__lowerCAmelCase = PILImageResampling.BICUBIC ,						__lowerCAmelCase = None ,						**__lowerCAmelCase ,						):



				'''simple docstring'''







				lowerCamelCase__              =	get_size_dict(__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()}'							)
				lowerCamelCase__              =	get_resize_output_image_size(
				    __lowerCAmelCase ,						output_size=(size['''height'''], size['''width''']) ,						keep_aspect_ratio=__lowerCAmelCase ,						multiple=__lowerCAmelCase ,						)
				return resize(__lowerCAmelCase ,						size=__lowerCAmelCase ,						resample=__lowerCAmelCase ,						data_format=__lowerCAmelCase ,						**__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase = None ,						**__lowerCAmelCase ,						):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase = None ,						**__lowerCAmelCase ,						):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = ChannelDimension.FIRST ,						**__lowerCAmelCase ,						):



				'''simple docstring'''







				lowerCamelCase__              =	do_resize if do_resize is not None else self.do_resize
				lowerCamelCase__              =	size if size is not None else self.size
				lowerCamelCase__              =	get_size_dict(__lowerCAmelCase							)
				lowerCamelCase__              =	keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
				lowerCamelCase__              =	ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
				lowerCamelCase__              =	resample if resample is not None else self.resample
				lowerCamelCase__              =	do_rescale if do_rescale is not None else self.do_rescale
				lowerCamelCase__              =	rescale_factor if rescale_factor is not None else self.rescale_factor
				lowerCamelCase__              =	do_normalize if do_normalize is not None else self.do_normalize
				lowerCamelCase__              =	image_mean if image_mean is not None else self.image_mean
				lowerCamelCase__              =	image_std if image_std is not None else self.image_std

				lowerCamelCase__              =	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.'''							)

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

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

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

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

				lowerCamelCase__              =	[to_channel_dimension_format(__lowerCAmelCase ,						__lowerCAmelCase							) for image in images]

				lowerCamelCase__              =	{'''pixel_values''': images}
				return BatchFeature(data=__lowerCAmelCase ,						tensor_type=__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase = None							):



				'''simple docstring'''







				lowerCamelCase__              =	outputs.logits

				# Resize logits and compute semantic segmentation maps
				if target_sizes is not None:
					if len(__lowerCAmelCase							) != len(__lowerCAmelCase							):
						raise ValueError(
						    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''							)

					if is_torch_tensor(__lowerCAmelCase							):
						lowerCamelCase__              =	target_sizes.numpy()

					lowerCamelCase__              =	[]

					for idx in range(len(__lowerCAmelCase							)							):
						lowerCamelCase__              =	torch.nn.functional.interpolate(
						    logits[idx].unsqueeze(dim=0							) ,						size=target_sizes[idx] ,						mode='''bilinear''' ,						align_corners=__lowerCAmelCase							)
						lowerCamelCase__              =	resized_logits[0].argmax(dim=0							)
						semantic_segmentation.append(__lowerCAmelCase							)
				else:
					lowerCamelCase__              =	logits.argmax(dim=1							)
					lowerCamelCase__              =	[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]							)]

				return semantic_segmentation










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__)



