

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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
	import PIL

if is_torch_available():
	import torch


UpperCAmelCase							 =					logging.get_logger(__name__)

class  __snake_case      (   __UpperCAmelCase):







							'''simple docstring'''






							UpperCamelCase__				:      List[Any]         =					['''pixel_values''']


							def __init__(							self  ,  a_ = True  ,  a_ = None  ,  a_ = PILImageResampling.BILINEAR  ,  a_ = True  ,  a_ = 1 / 255  ,  a_ = True  ,  a_ = None  ,  a_ = True  ,  **a_  ,  ):
											super().__init__(**__SCREAMING_SNAKE_CASE		)
											a__				      =		size if size is not None else {"""shortest_edge""": 224}
											a__				      =		get_size_dict(__SCREAMING_SNAKE_CASE  ,  default_to_square=__SCREAMING_SNAKE_CASE		)
											a__				      =		crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
											a__				      =		get_size_dict(__SCREAMING_SNAKE_CASE  ,  param_name="""crop_size"""		)

											a__				      =		do_resize
											a__				      =		size
											a__				      =		resample
											a__				      =		do_rescale
											a__				      =		rescale_factor
											a__				      =		do_center_crop
											a__				      =		crop_size
											a__				      =		do_flip_channel_order


							def _a      (							self  ,  a_  ,  a_  ,  a_ = PIL.Image.BILINEAR  ,  a_ = None  ,  **a_  ,  ):
											a__				      =		get_size_dict(__SCREAMING_SNAKE_CASE  ,  default_to_square=__SCREAMING_SNAKE_CASE		)
											if "shortest_edge" not in size:
															raise ValueError(F'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''		)
											a__				      =		get_resize_output_image_size(__SCREAMING_SNAKE_CASE  ,  size=size["""shortest_edge"""]  ,  default_to_square=__SCREAMING_SNAKE_CASE		)
											return resize(__SCREAMING_SNAKE_CASE  ,  size=__SCREAMING_SNAKE_CASE  ,  resample=__SCREAMING_SNAKE_CASE  ,  data_format=__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE		)


							def _a      (							self  ,  a_  ,  a_  ,  a_ = None  ,  **a_  ,  ):
											a__				      =		get_size_dict(__SCREAMING_SNAKE_CASE		)
											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()}'''		)
											return center_crop(__SCREAMING_SNAKE_CASE  ,  size=(size["""height"""], size["""width"""])  ,  data_format=__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE		)


							def _a      (							self  ,  a_  ,  a_  ,  a_ = None  ,  **a_  ,  ):
											return rescale(__SCREAMING_SNAKE_CASE  ,  scale=__SCREAMING_SNAKE_CASE  ,  data_format=__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE		)


							def _a      (							self  ,  a_  ,  a_ = None		):
											return flip_channel_order(__SCREAMING_SNAKE_CASE  ,  data_format=__SCREAMING_SNAKE_CASE		)


							def _a      (							self  ,  a_  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = ChannelDimension.FIRST  ,  **a_  ,  ):
											a__				      =		do_resize if do_resize is not None else self.do_resize
											a__				      =		resample if resample is not None else self.resample
											a__				      =		do_rescale if do_rescale is not None else self.do_rescale
											a__				      =		rescale_factor if rescale_factor is not None else self.rescale_factor
											a__				      =		do_center_crop if do_center_crop is not None else self.do_center_crop
											a__				      =		(
											    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
											)

											a__				      =		size if size is not None else self.size
											a__				      =		get_size_dict(__SCREAMING_SNAKE_CASE  ,  default_to_square=__SCREAMING_SNAKE_CASE		)
											a__				      =		crop_size if crop_size is not None else self.crop_size
											a__				      =		get_size_dict(__SCREAMING_SNAKE_CASE  ,  param_name="""crop_size"""		)

											a__				      =		make_list_of_images(__SCREAMING_SNAKE_CASE		)

											if not valid_images(__SCREAMING_SNAKE_CASE		):
															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_rescale and rescale_factor is None:
															raise ValueError("""Rescale factor must be specified if do_rescale is True."""		)

											if do_center_crop and crop_size is None:
															raise ValueError("""Crop size must be specified if do_center_crop is True."""		)

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

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

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

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

											# the pretrained checkpoints assume images are BGR, not RGB
											if do_flip_channel_order:
															a__				      =		[self.flip_channel_order(image=__SCREAMING_SNAKE_CASE		) for image in images]

											a__				      =		[to_channel_dimension_format(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE		) for image in images]

											a__				      =		{"""pixel_values""": images}
											return BatchFeature(data=__SCREAMING_SNAKE_CASE  ,  tensor_type=__SCREAMING_SNAKE_CASE		)







							def _a      (							self  ,  a_  ,  a_ = None		):
											a__				      =		outputs.logits

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

															if is_torch_tensor(__SCREAMING_SNAKE_CASE		):
																			a__				      =		target_sizes.numpy()

															a__				      =		[]

															for idx in range(len(__SCREAMING_SNAKE_CASE		)		):
																			a__				      =		torch.nn.functional.interpolate(
																			    logits[idx].unsqueeze(dim=0		)  ,  size=target_sizes[idx]  ,  mode="""bilinear"""  ,  align_corners=__SCREAMING_SNAKE_CASE		)
																			a__				      =		resized_logits[0].argmax(dim=0		)
																			semantic_segmentation.append(__SCREAMING_SNAKE_CASE		)
											else:
															a__				      =		logits.argmax(dim=1		)
															a__				      =		[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_torch_available,
)


UpperCAmelCase							 =					{
    """configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					[
	    """FALCON_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """FalconForCausalLM""",
	    """FalconModel""",
	    """FalconPreTrainedModel""",
	    """FalconForSequenceClassification""",
	    """FalconForTokenClassification""",
	    """FalconForQuestionAnswering""",
	]


if TYPE_CHECKING:
	from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_falcon import (
		    FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FalconForCausalLM,
		    FalconForQuestionAnswering,
		    FalconForSequenceClassification,
		    FalconForTokenClassification,
		    FalconModel,
		    FalconPreTrainedModel,
		)


else:
	import sys

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

