

import argparse
from collections import defaultdict

import yaml


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







def 						lowerCamelCase__      ( __A    :Dict		):


       """simple docstring"""




       __snake_case       	=							defaultdict(__A		)
       __snake_case       	=							[]
       __snake_case       	=							[]
       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		)

       __snake_case       	=							new_doc_list
       __snake_case       	=							[key for key, value in counts.items() if value > 1]

       __snake_case       	=							[]
       for duplicate_key in duplicates:
              __snake_case       	=							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]		)
       __snake_case       	=							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 						lowerCamelCase__      ( __A    :Union[str, Any]=False		):


       """simple docstring"""




       with open(__A	,encoding="""utf-8"""		) as f:
              __snake_case       	=							yaml.safe_load(f.read()		)

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

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

       __snake_case       	=							api_doc[scheduler_idx]["""sections"""]
       __snake_case       	=							clean_doc_toc(__A		)

       __snake_case       	=							False
       if new_scheduler_doc != scheduler_doc:
              __snake_case       	=							True
              if overwrite:
                     __snake_case       	=							new_scheduler_doc

       if diff:
              if overwrite:
                     __snake_case       	=							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 						lowerCamelCase__      ( __A    :List[Any]=False		):


       """simple docstring"""




       with open(__A	,encoding="""utf-8"""		) as f:
              __snake_case       	=							yaml.safe_load(f.read()		)

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

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

       __snake_case       	=							False
       __snake_case       	=							api_doc[pipeline_idx]["""sections"""]
       __snake_case       	=							[]

       # sort sub pipeline docs
       for pipeline_doc in pipeline_docs:
              if "section" in pipeline_doc:
                     __snake_case       	=							pipeline_doc["""section"""]
                     __snake_case       	=							clean_doc_toc(__A		)
                     if overwrite:
                            __snake_case       	=							new_sub_pipeline_doc
              new_pipeline_docs.append(__A		)

       # sort overall pipeline doc
       __snake_case       	=							clean_doc_toc(__A		)

       if new_pipeline_docs != pipeline_docs:
              __snake_case       	=							True
              if overwrite:
                     __snake_case       	=							new_pipeline_docs

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

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




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     __snake_case    :
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase=sys.maxsize   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							"""bilinear"""
              __snake_case       	=							max_size
              __snake_case       	=							short_edge_length






       def __call__(		self      , _UpperCamelCase   )      ->							Optional[Any]:






              """simple docstring"""


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

                     if img.dtype == np.uinta:
                            __snake_case       	=							Image.fromarray(_UpperCamelCase   )
                            __snake_case       	=							pil_image.resize((neww, newh)      , PILImageResampling.BILINEAR   )
                            __snake_case       	=							np.asarray(_UpperCamelCase   )
                     else:
                            __snake_case       	=							img.permute(2      , 0      , 1   ).unsqueeze(0   )  # 3, 0, 1)  # hw(c) -> nchw
                            __snake_case       	=							nn.functional.interpolate(
                                _UpperCamelCase      , (newh, neww)      , mode=self.interp_method      , align_corners=_UpperCamelCase   ).squeeze(0   )
                     img_augs.append(_UpperCamelCase   )

              return img_augs






class     __snake_case    :
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase   )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]      , cfg.INPUT.MAX_SIZE_TEST   )
              __snake_case       	=							cfg.INPUT.FORMAT
              __snake_case       	=							cfg.SIZE_DIVISIBILITY
              __snake_case       	=							cfg.PAD_VALUE
              __snake_case       	=							cfg.INPUT.MAX_SIZE_TEST
              __snake_case       	=							cfg.MODEL.DEVICE
              __snake_case       	=							torch.tensor(cfg.MODEL.PIXEL_STD   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   )      , 1      , 1   )
              __snake_case       	=							torch.tensor(cfg.MODEL.PIXEL_MEAN   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   )      , 1      , 1   )
              __snake_case       	=							lambda _UpperCamelCase   : (x - self.pixel_mean) / self.pixel_std


       def 			a		(		self      , _UpperCamelCase   )      ->							int:






              """simple docstring"""


              __snake_case       	=							tuple(max(_UpperCamelCase   ) for s in zip(*[img.shape for img in images]   )   )
              __snake_case       	=							[im.shape[-2:] for im in images]
              __snake_case       	=							[
                  nn.functional.pad(
                      _UpperCamelCase      , [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]      , value=self.pad_value      , )
                  for size, im in zip(_UpperCamelCase      , _UpperCamelCase   )
              ]

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






       def __call__(		self      , _UpperCamelCase      , _UpperCamelCase=False   )      ->							Union[str, Any]:






              """simple docstring"""


              with torch.no_grad():
                     if not isinstance(_UpperCamelCase      , _UpperCamelCase   ):
                            __snake_case       	=							[images]
                     if single_image:
                            assert len(_UpperCamelCase   ) == 1
                     for i in range(len(_UpperCamelCase   )   ):
                            if isinstance(images[i]      , torch.Tensor   ):
                                   images.insert(_UpperCamelCase      , images.pop(_UpperCamelCase   ).to(self.device   ).float()   )
                            elif not isinstance(images[i]      , torch.Tensor   ):
                                   images.insert(
                                       _UpperCamelCase      , torch.as_tensor(img_tensorize(images.pop(_UpperCamelCase   )      , input_format=self.input_format   )   )
                                       .to(self.device   )
                                       .float()      , )
            # resize smallest edge
                     __snake_case       	=							torch.tensor([im.shape[:2] for im in images]   )
                     __snake_case       	=							self.aug(_UpperCamelCase   )
                     # 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
                     __snake_case       	=							[self.normalizer(_UpperCamelCase   ) for x in images]
                     # now pad them to do the following operations
                     __snake_case	,    __snake_case       	=							self.pad(_UpperCamelCase   )
                     # Normalize

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







def 						lowerCamelCase__      ( __A    :Union[str, Any]	,__A    :List[Any]		):


       """simple docstring"""




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







def 						lowerCamelCase__      ( __A    :Tuple	,__A    :Tuple[int, int]		):


       """simple docstring"""




       assert torch.isfinite(__A		).all(), "Box tensor contains infinite or NaN!"
       __snake_case	,    __snake_case       	=							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		)

