


'''simple docstring'''


import argparse
import os
import re

import packaging.version


__magic_name__			=      'examples/'
__magic_name__			=      {
    'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'),
    'doc': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
__magic_name__			=      {
    'init': 'src/diffusers/__init__.py',
    'setup': 'setup.py',
}
__magic_name__			=      'README.md'




def      lowerCamelCase					(      lowerCamelCase  :      Optional[Any]							,						lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      List[Any]):
      with open(lowerCamelCase							,						"""r"""							,						encoding="""utf-8"""							,						newline="""\n""") as f:
            A_						:  int          =				f.read()
      A_   ,					A_						:  Union[str, Any]          =				REPLACE_PATTERNS[pattern]
      A_						:  Union[str, Any]          =				replace.replace("""VERSION"""							,						lowerCamelCase)
      A_						:  List[Any]          =				re_pattern.sub(lowerCamelCase							,						lowerCamelCase)
      with open(lowerCamelCase							,						"""w"""							,						encoding="""utf-8"""							,						newline="""\n""") as f:
            f.write(lowerCamelCase)




def      lowerCamelCase					(      lowerCamelCase  :      str):
      for folder, directories, fnames in os.walk(lowerCamelCase):
            # Removing some of the folders with non-actively maintained examples from the walk
            if "research_projects" in directories:
                  directories.remove("""research_projects""")
            if "legacy" in directories:
                  directories.remove("""legacy""")
            for fname in fnames:
                  if fname.endswith(""".py"""):
                        update_version_in_file(os.path.join(lowerCamelCase							,						lowerCamelCase)							,						lowerCamelCase							,						pattern="""examples""")




def      lowerCamelCase					(      lowerCamelCase  :      Tuple							,						lowerCamelCase  :      Tuple=False):
      for pattern, fname in REPLACE_FILES.items():
            update_version_in_file(lowerCamelCase							,						lowerCamelCase							,						lowerCamelCase)
      if not patch:
            update_version_in_examples(lowerCamelCase)




def      lowerCamelCase					(      ):
      A_						:  Optional[Any]          =				"""🤗 Transformers currently provides the following architectures"""
      A_						:  Optional[int]          =				"""1. Want to contribute a new model?"""
      with open(lowerCamelCase							,						"""r"""							,						encoding="""utf-8"""							,						newline="""\n""") as f:
            A_						:  List[Any]          =				f.readlines()

      # Find the start of the list.
      A_						:  Optional[int]          =				0
      while not lines[start_index].startswith(_start_prompt):
            start_index += 1
      start_index += 1

      A_						:  List[str]          =				start_index
      # Update the lines in the model list.
      while not lines[index].startswith(_end_prompt):
            if lines[index].startswith("""1."""):
                  A_						:  Optional[int]          =				lines[index].replace(
                      """https://huggingface.co/docs/diffusers/main/model_doc"""							,						"""https://huggingface.co/docs/diffusers/model_doc"""							,						)
            index += 1

      with open(lowerCamelCase							,						"""w"""							,						encoding="""utf-8"""							,						newline="""\n""") as f:
            f.writelines(lowerCamelCase)




def      lowerCamelCase					(      ):
      with open(REPLACE_FILES["""init"""]							,						"""r""") as f:
            A_						:  Optional[Any]          =				f.read()
      A_						:  int          =				REPLACE_PATTERNS["""init"""][0].search(lowerCamelCase).groups()[0]
      return packaging.version.parse(lowerCamelCase)




def      lowerCamelCase					(      lowerCamelCase  :      Dict=False):
      A_						:  Dict          =				get_version()
      if patch and default_version.is_devrelease:
            raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!""")
      if default_version.is_devrelease:
            A_						:  Optional[int]          =				default_version.base_version
      elif patch:
            A_						:  int          =				F'{default_version.major}.{default_version.minor}.{default_version.micro + 1}'
      else:
            A_						:  Union[str, Any]          =				F'{default_version.major}.{default_version.minor + 1}.0'

      # Now let's ask nicely if that's the right one.
      A_						:  Tuple          =				input(F'Which version are you releasing? [{default_version}]')
      if len(lowerCamelCase) == 0:
            A_						:  List[str]          =				default_version

      print(F'Updating version to {version}.')
      global_version_update(lowerCamelCase							,						patch=lowerCamelCase)




def      lowerCamelCase					(      ):
      A_						:  Optional[Any]          =				get_version()
      A_						:  Tuple          =				F'{current_version.major}.{current_version.minor + 1}.0.dev0'
      A_						:  List[str]          =				current_version.base_version

      # Check with the user we got that right.
      A_						:  List[Any]          =				input(F'Which version are we developing now? [{dev_version}]')
      if len(lowerCamelCase) == 0:
            A_						:  Tuple          =				dev_version

      print(F'Updating version to {version}.')
      global_version_update(lowerCamelCase)


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
     __magic_name__			=      argparse.ArgumentParser()
     parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
     parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
     __magic_name__			=      parser.parse_args()
     if not args.post_release:
          pre_release_work(patch=args.patch)
     elif args.patch:
          print('Nothing to do after a patch :-)')
     else:
          post_release_work()




'''simple docstring'''


import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


__magic_name__			=      {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}



class 	__lowerCAmelCase		(							nn.Module    ):


  '''simple docstring'''







  def __init__(							self :       Dict	,_a :       Dict ):

        '''simple docstring'''




        super().__init__()
        A_						:  List[str]          =				torchvision.models.resnetaaa(pretrained=_a )
        A_						:  int          =				list(model.children() )[:-2]
        A_						:  int          =				nn.Sequential(*_a )
        A_						:  Optional[int]          =				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )






  def        _a							(							self :       str	,_a :       Optional[int] ):

        '''simple docstring'''




        A_						:  Tuple          =				self.pool(self.model(_a ) )
        A_						:  Any          =				torch.flatten(_a	,start_dim=2 )
        A_						:  str          =				out.transpose(1	,2 ).contiguous()
        return out  # BxNx2048







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  def __init__(							self :       int	,_a :       Optional[Any]	,_a :       Optional[Any]	,_a :       Dict	,_a :       Dict	,_a :       Optional[Any] ):

        '''simple docstring'''




        A_						:  Dict          =				[json.loads(_a ) for l in open(_a )]
        A_						:  Optional[int]          =				os.path.dirname(_a )
        A_						:  Optional[Any]          =				tokenizer
        A_						:  Optional[Any]          =				labels
        A_						:  List[Any]          =				len(_a )
        A_						:  str          =				max_seq_length

        A_						:  str          =				transforms






  def __len__(							self :       str ):

        '''simple docstring'''




        return len(self.data )






  def __getitem__(							self :       Tuple	,_a :       Optional[Any] ):

        '''simple docstring'''




        A_						:  Optional[int]          =				torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]	,add_special_tokens=_a ) )
        A_   ,					A_   ,					A_						:  Dict          =				sentence[0], sentence[1:-1], sentence[-1]
        A_						:  Optional[int]          =				sentence[: self.max_seq_length]

        A_						:  Any          =				torch.zeros(self.n_classes )
        A_						:  Tuple          =				1

        A_						:  Optional[Any]          =				Image.open(os.path.join(self.data_dir	,self.data[index]["""img"""] ) ).convert("""RGB""" )
        A_						:  Union[str, Any]          =				self.transforms(_a )

        return {
            "image_start_token": start_token,
            "image_end_token": end_token,
            "sentence": sentence,
            "image": image,
            "label": label,
        }






  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  str          =				Counter()
        for row in self.data:
              label_freqs.update(row["""label"""] )
        return label_freqs







def      lowerCamelCase					(      lowerCamelCase  :      str):
      A_						:  List[Any]          =				[len(row["""sentence"""]) for row in batch]
      A_   ,					A_						:  Dict          =				len(lowerCamelCase), max(lowerCamelCase)

      A_						:  Optional[int]          =				torch.zeros(lowerCamelCase							,						lowerCamelCase							,						dtype=torch.long)
      A_						:  Tuple          =				torch.zeros(lowerCamelCase							,						lowerCamelCase							,						dtype=torch.long)

      for i_batch, (input_row, length) in enumerate(zip(lowerCamelCase							,						lowerCamelCase)):
            A_						:  str          =				input_row["""sentence"""]
            A_						:  Tuple          =				1

      A_						:  int          =				torch.stack([row["""image"""] for row in batch])
      A_						:  str          =				torch.stack([row["""label"""] for row in batch])
      A_						:  List[Any]          =				torch.stack([row["""image_start_token"""] for row in batch])
      A_						:  Tuple          =				torch.stack([row["""image_end_token"""] for row in batch])

      return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor




def      lowerCamelCase					(      ):
      return [
          "Crime",
          "Drama",
          "Thriller",
          "Action",
          "Comedy",
          "Romance",
          "Documentary",
          "Short",
          "Mystery",
          "History",
          "Family",
          "Adventure",
          "Fantasy",
          "Sci-Fi",
          "Western",
          "Horror",
          "Sport",
          "War",
          "Music",
          "Musical",
          "Animation",
          "Biography",
          "Film-Noir",
      ]






def      lowerCamelCase					(      ):
      return transforms.Compose(
          [
              transforms.Resize(256),
              transforms.CenterCrop(224),
              transforms.ToTensor(),
              transforms.Normalize(
                  mean=[0.4677_7044, 0.4453_1429, 0.4066_1017]							,						std=[0.1222_1994, 0.1214_5835, 0.1438_0469]							,						),
          ])
