


'''simple docstring'''
import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class        lowercase     :

		"""simple docstring"""

		def __init__(		self   ,       UpperCamelCase_   ,       UpperCamelCase_=sys.maxsize							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			'''bilinear'''
							UpperCamelCase__       :Optional[Any]							 =			max_size
							UpperCamelCase__       :List[str]							 =			short_edge_length




		def __call__(		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			[]
							for img in imgs:
												UpperCamelCase__       :Union[str, Any]							 =			img.shape[:2]
												# later: provide list and randomly choose index for resize
												UpperCamelCase__       :Optional[int]							 =			np.random.randint(self.short_edge_length[0]   ,       self.short_edge_length[1] + 1							)
												if size == 0:
																	return img
												UpperCamelCase__       :List[Any]							 =			size * 1.0 / min(lowerCamelCase__   ,       lowerCamelCase__							)
												if h < w:
																	UpperCamelCase__       :int							 =			size, scale * w
												else:
																	UpperCamelCase__       :int							 =			scale * h, size
												if max(lowerCamelCase__   ,       lowerCamelCase__							) > self.max_size:
																	UpperCamelCase__       :str							 =			self.max_size * 1.0 / max(lowerCamelCase__   ,       lowerCamelCase__							)
																	UpperCamelCase__       :Tuple							 =			newh * scale
																	UpperCamelCase__       :Tuple							 =			neww * scale
												UpperCamelCase__       :List[str]							 =			int(neww + 0.5							)
												UpperCamelCase__       :Dict							 =			int(newh + 0.5							)

												if img.dtype == np.uinta:
																	UpperCamelCase__       :Dict							 =			Image.fromarray(lowerCamelCase__							)
																	UpperCamelCase__       :Union[str, Any]							 =			pil_image.resize((neww, newh)   ,       PILImageResampling.BILINEAR							)
																	UpperCamelCase__       :List[str]							 =			np.asarray(lowerCamelCase__							)
												else:
																	UpperCamelCase__       :List[str]							 =			img.permute(2   ,       0   ,       1							).unsqueeze(0							)  # 3, 0, 1)  # hw(c) -> nchw
																	UpperCamelCase__       :str							 =			nn.functional.interpolate(
																	    lowerCamelCase__   ,       (newh, neww)   ,       mode=self.interp_method   ,       align_corners=lowerCamelCase__							).squeeze(0							)
												img_augs.append(lowerCamelCase__							)

							return img_augs



class        lowercase     :

		"""simple docstring"""

		def __init__(		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]   ,       cfg.INPUT.MAX_SIZE_TEST							)
							UpperCamelCase__       :Optional[int]							 =			cfg.INPUT.FORMAT
							UpperCamelCase__       :Optional[int]							 =			cfg.SIZE_DIVISIBILITY
							UpperCamelCase__       :Optional[int]							 =			cfg.PAD_VALUE
							UpperCamelCase__       :List[str]							 =			cfg.INPUT.MAX_SIZE_TEST
							UpperCamelCase__       :str							 =			cfg.MODEL.DEVICE
							UpperCamelCase__       :int							 =			torch.tensor(cfg.MODEL.PIXEL_STD							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)   ,       1   ,       1							)
							UpperCamelCase__       :int							 =			torch.tensor(cfg.MODEL.PIXEL_MEAN							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)   ,       1   ,       1							)
							UpperCamelCase__       :Optional[Any]							 =			lambda UpperCamelCase_							: (x - self.pixel_mean) / self.pixel_std




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			tuple(max(lowerCamelCase__							) for s in zip(*[img.shape for img in images]							)							)
							UpperCamelCase__       :Dict							 =			[im.shape[-2:] for im in images]
							UpperCamelCase__       :List[Any]							 =			[
							    nn.functional.pad(
							        lowerCamelCase__   ,       [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]   ,       value=self.pad_value   ,       )
							    for size, im in zip(lowerCamelCase__   ,       lowerCamelCase__							)
							]

							return torch.stack(lowerCamelCase__							), torch.tensor(lowerCamelCase__							)




		def __call__(		self   ,       UpperCamelCase_   ,       UpperCamelCase_=False							):

							'''simple docstring'''


							with torch.no_grad():
												if not isinstance(lowerCamelCase__   ,       lowerCamelCase__							):
																	UpperCamelCase__       :Optional[Any]							 =			[images]
												if single_image:
																	assert len(lowerCamelCase__							) == 1
												for i in range(len(lowerCamelCase__							)							):
																	if isinstance(images[i]   ,       torch.Tensor							):
																						images.insert(lowerCamelCase__   ,       images.pop(lowerCamelCase__							).to(self.device							).float()							)
																	elif not isinstance(images[i]   ,       torch.Tensor							):
																						images.insert(
																						    lowerCamelCase__   ,       torch.as_tensor(img_tensorize(images.pop(lowerCamelCase__							)   ,       input_format=self.input_format							)							)
																						    .to(self.device							)
																						    .float()   ,       )
																						   # resize smallest edge
												UpperCamelCase__       :str							 =			torch.tensor([im.shape[:2] for im in images]							)
												UpperCamelCase__       :Any							 =			self.aug(lowerCamelCase__							)
												# transpose images and convert to torch tensors
												# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
												# now normalize before pad to avoid useless arithmetic
												UpperCamelCase__       :List[Any]							 =			[self.normalizer(lowerCamelCase__							) for x in images]
												# now pad them to do the following operations
												UpperCamelCase__       :Optional[Any]							 =			self.pad(lowerCamelCase__							)
												# Normalize

												if self.size_divisibility > 0:
																	raise NotImplementedError()
												# pad
												UpperCamelCase__       :Optional[int]							 =			torch.true_divide(lowerCamelCase__   ,       lowerCamelCase__							)
												if single_image:
																	return images[0], sizes[0], scales_yx[0]
												else:
																	return images, sizes, scales_yx



def       a		(      __a			, __a       )  ->     Union[str, Any]:



					'''simple docstring'''






					boxes[:, 0::2] *= scale_yx[:, 1]
					boxes[:, 1::2] *= scale_yx[:, 0]
					return boxes







def       a		(      __a			, __a       )  ->     Optional[int]:



					'''simple docstring'''






					assert torch.isfinite(__a       ).all(), "Box tensor contains infinite or NaN!"
					UpperCamelCase__       :Dict							 =			box_size
					tensor[:, 0].clamp_(min=0			, max=__a       )
					tensor[:, 1].clamp_(min=0			, max=__a       )
					tensor[:, 2].clamp_(min=0			, max=__a       )
					tensor[:, 3].clamp_(min=0			, max=__a       )



'''simple docstring'''
import argparse
from collections import defaultdict

import yaml


__snake_case											=					'''docs/source/en/_toctree.yml'''


def       a				(      __a       )  ->     int:



					'''simple docstring'''






					UpperCamelCase__       :int							 =			defaultdict(__a       )
					UpperCamelCase__       :int							 =			[]
					UpperCamelCase__       :int							 =			[]
					for doc in doc_list:
										if "local" in doc:
															counts[doc["local"]] += 1

										if doc["title"].lower() == "overview":
															overview_doc.append({'''local''': doc['''local'''], '''title''': doc['''title''']}       )
										else:
															new_doc_list.append(__a       )

					UpperCamelCase__       :Union[str, Any]							 =			new_doc_list
					UpperCamelCase__       :Tuple							 =			[key for key, value in counts.items() if value > 1]

					UpperCamelCase__       :Union[str, Any]							 =			[]
					for duplicate_key in duplicates:
										UpperCamelCase__       :Dict							 =			list({doc['''title'''] for doc in doc_list if doc['''local'''] == duplicate_key}       )
										if len(__a       ) > 1:
															raise ValueError(
															    f'''{duplicate_key} is present several times in the documentation table of content at '''
															    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
															    '''others.'''       )
										# Only add this once
										new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}       )

					# Add none duplicate-keys
					new_doc.extend([doc for doc in doc_list if '''local''' not in counts or counts[doc['''local''']] == 1]       )
					UpperCamelCase__       :Union[str, Any]							 =			sorted(__a			, key=lambda __a       : s["title"].lower()       )

					# "overview" gets special treatment and is always first
					if len(__a       ) > 1:
										raise ValueError('''{doc_list} has two \'overview\' docs which is not allowed.'''       )

					overview_doc.extend(__a       )

					# Sort
					return overview_doc


def       a				(      __a=False       )  ->     Any:



					'''simple docstring'''






					with open(__a			, encoding='''utf-8'''       ) as f:
										UpperCamelCase__       :Any							 =			yaml.safe_load(f.read()       )

					# Get to the API doc
					UpperCamelCase__       :str							 =			0
					while content[api_idx]["title"] != "API":
										api_idx += 1
					UpperCamelCase__       :str							 =			content[api_idx]['''sections''']

					# Then to the model doc
					UpperCamelCase__       :Optional[Any]							 =			0
					while api_doc[scheduler_idx]["title"] != "Schedulers":
										scheduler_idx += 1

					UpperCamelCase__       :List[Any]							 =			api_doc[scheduler_idx]['''sections''']
					UpperCamelCase__       :Union[str, Any]							 =			clean_doc_toc(__a       )

					UpperCamelCase__       :List[Any]							 =			False
					if new_scheduler_doc != scheduler_doc:
										UpperCamelCase__       :Optional[int]							 =			True
										if overwrite:
															UpperCamelCase__       :Dict							 =			new_scheduler_doc

					if diff:
										if overwrite:
															UpperCamelCase__       :Any							 =			api_doc
															with open(__a			, '''w'''			, encoding='''utf-8'''       ) as f:
																				f.write(yaml.dump(__a			, allow_unicode=__a       )       )
										else:
															raise ValueError(
															    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''       )


def       a				(      __a=False       )  ->     Optional[Any]:



					'''simple docstring'''






					with open(__a			, encoding='''utf-8'''       ) as f:
										UpperCamelCase__       :str							 =			yaml.safe_load(f.read()       )

					# Get to the API doc
					UpperCamelCase__       :Optional[Any]							 =			0
					while content[api_idx]["title"] != "API":
										api_idx += 1
					UpperCamelCase__       :Any							 =			content[api_idx]['''sections''']

					# Then to the model doc
					UpperCamelCase__       :str							 =			0
					while api_doc[pipeline_idx]["title"] != "Pipelines":
										pipeline_idx += 1

					UpperCamelCase__       :Any							 =			False
					UpperCamelCase__       :Union[str, Any]							 =			api_doc[pipeline_idx]['''sections''']
					UpperCamelCase__       :Tuple							 =			[]

					# sort sub pipeline docs
					for pipeline_doc in pipeline_docs:
										if "section" in pipeline_doc:
															UpperCamelCase__       :Dict							 =			pipeline_doc['''section''']
															UpperCamelCase__       :Optional[Any]							 =			clean_doc_toc(__a       )
															if overwrite:
																				UpperCamelCase__       :Optional[int]							 =			new_sub_pipeline_doc
										new_pipeline_docs.append(__a       )

					# sort overall pipeline doc
					UpperCamelCase__       :Optional[Any]							 =			clean_doc_toc(__a       )

					if new_pipeline_docs != pipeline_docs:
										UpperCamelCase__       :int							 =			True
										if overwrite:
															UpperCamelCase__       :Union[str, Any]							 =			new_pipeline_docs

					if diff:
										if overwrite:
															UpperCamelCase__       :Dict							 =			api_doc
															with open(__a			, '''w'''			, encoding='''utf-8'''       ) as f:
																				f.write(yaml.dump(__a			, allow_unicode=__a       )       )
										else:
															raise ValueError(
															    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''       )


if __name__ == "__main__":
		__snake_case											=					argparse.ArgumentParser()
		parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
		__snake_case											=					parser.parse_args()

		check_scheduler_doc(args.fix_and_overwrite)
		check_pipeline_doc(args.fix_and_overwrite)