

"""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


_a							=							{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							:      Union[str, Any], UpperCAmelCase__							:      Union[str, Any]     ):
    super().__init__()
    __lowercase								=						torchvision.models.resnetaaa(pretrained=UpperCAmelCase__     )
    __lowercase								=						list(model.children()     )[:-2]
    __lowercase								=						nn.Sequential(*UpperCAmelCase__     )
    __lowercase								=						nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]     )



 def  _lowercase			(      self							:      List[str], UpperCAmelCase__							:      Tuple     ):
    # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
    __lowercase								=						self.pool(self.model(UpperCAmelCase__     )     )
    __lowercase								=						torch.flatten(UpperCAmelCase__, start_dim=2     )
    __lowercase								=						out.transpose(1, 2     ).contiguous()
    return out  # BxNx2048







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      Tuple, UpperCAmelCase__							:      List[Any], UpperCAmelCase__							:      int, UpperCAmelCase__							:      List[Any], UpperCAmelCase__							:      Any, UpperCAmelCase__							:      Dict     ):
    __lowercase								=						[json.loads(UpperCAmelCase__     ) for l in open(UpperCAmelCase__     )]
    __lowercase								=						os.path.dirname(UpperCAmelCase__     )
    __lowercase								=						tokenizer
    __lowercase								=						labels
    __lowercase								=						len(UpperCAmelCase__     )
    __lowercase								=						max_seq_length

    __lowercase								=						transforms


 def __len__(      self							:      Union[str, Any]     ):
    return len(self.data     )


 def __getitem__(      self							:      Optional[Any], UpperCAmelCase__							:      Optional[Any]     ):
    __lowercase								=						torch.LongTensor(self.tokenizer.encode(self.data[index]["text"], add_special_tokens=UpperCAmelCase__     )     )
    __lowercase		,__lowercase		,__lowercase								=						sentence[0], sentence[1:-1], sentence[-1]
    __lowercase								=						sentence[: self.max_seq_length]

    __lowercase								=						torch.zeros(self.n_classes     )
    __lowercase								=						1

    __lowercase								=						Image.open(os.path.join(self.data_dir, self.data[index]["img"]     )     ).convert("RGB"     )
    __lowercase								=						self.transforms(UpperCAmelCase__     )

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



 def  _lowercase			(      self							:      int     ):
    __lowercase								=						Counter()
    for row in self.data:
       label_freqs.update(row["label"]     )
    return label_freqs







def   _A  (  UpperCamelCase_					:	List[str])     ->						Union[str, Any]:
   '''simple docstring'''


   __lowercase								=						[len(row["sentence"]) for row in batch]
   __lowercase		,__lowercase								=						len(UpperCamelCase_), max(UpperCamelCase_)

   __lowercase								=						torch.zeros(UpperCamelCase_,      UpperCamelCase_,      dtype=torch.long)
   __lowercase								=						torch.zeros(UpperCamelCase_,      UpperCamelCase_,      dtype=torch.long)

   for i_batch, (input_row, length) in enumerate(zip(UpperCamelCase_,      UpperCamelCase_)):
      __lowercase								=						input_row["sentence"]
      __lowercase								=						1

   __lowercase								=						torch.stack([row["image"] for row in batch])
   __lowercase								=						torch.stack([row["label"] for row in batch])
   __lowercase								=						torch.stack([row["image_start_token"] for row in batch])
   __lowercase								=						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   _A  (  )     ->						List[str]:
   '''simple docstring'''


   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   _A  (  )     ->						List[Any]:
   '''simple docstring'''


   return transforms.Compose(
       [
           transforms.Resize(256),
           transforms.CenterCrop(224),
           transforms.ToTensor(),
           transforms.Normalize(
               mean=[0.46_777_044, 0.44_531_429, 0.40_661_017],      std=[0.12_221_994, 0.12_145_835, 0.14_380_469],      ),
       ])







"""simple docstring"""





import argparse
import os
import re


_a							=							'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_a							=							re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
_a							=							re.compile(r'\s*\(\s*"(\S[^"]+)"')




def   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	bool = False)     ->						Optional[Any]:
   '''simple docstring'''


   with open(UpperCamelCase_,      "r",      encoding="utf-8") as f:
      __lowercase								=						f.read()

   __lowercase								=						content.split("\n")
   __lowercase								=						[]
   __lowercase								=						0
   while line_idx < len(UpperCamelCase_):
      if _re_intro_mapping.search(lines[line_idx]) is not None:
         __lowercase								=						len(re.search(r"^(\s*)\S",      lines[line_idx]).groups()[0]) + 8
         # Start of a new mapping!
         while not lines[line_idx].startswith(" " * indent + "("):
            new_lines.append(lines[line_idx])
            line_idx += 1

         __lowercase								=						[]
         while lines[line_idx].strip() != "]":
            # Blocks either fit in one line or not
            if lines[line_idx].strip() == "(":
               __lowercase								=						line_idx
               while not lines[line_idx].startswith(" " * indent + ")"):
                  line_idx += 1
               blocks.append("\n".join(lines[start_idx : line_idx + 1]))
            else:
               blocks.append(lines[line_idx])
            line_idx += 1

         # Sort blocks by their identifiers
         __lowercase								=						sorted(UpperCamelCase_,      key=lambda UpperCamelCase_: _re_identifier.search(UpperCamelCase_).groups()[0])
         new_lines += blocks
      else:
         new_lines.append(lines[line_idx])
         line_idx += 1

   if overwrite:
      with open(UpperCamelCase_,      "w",      encoding="utf-8") as f:
         f.write("\n".join(UpperCamelCase_))
   elif "\n".join(UpperCamelCase_) != content:
      return True




def   _A  (  UpperCamelCase_					:	bool = False)     ->						Optional[Any]:
   '''simple docstring'''


   __lowercase								=						[os.path.join(UpperCamelCase_,      UpperCamelCase_) for f in os.listdir(UpperCamelCase_) if f.endswith(".py")]
   __lowercase								=						[sort_auto_mapping(UpperCamelCase_,      overwrite=UpperCamelCase_) for fname in fnames]

   if not overwrite and any(UpperCamelCase_):
      __lowercase								=						[f for f, d in zip(UpperCamelCase_,      UpperCamelCase_) if d]
      raise ValueError(
          F"""The following files have auto mappings that need sorting: {", ".join(UpperCamelCase_)}. Run `make style` to fix"""
          " this.")


if __name__ == "__main__":
   _a							=							argparse.ArgumentParser()
   parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
   _a							=							parser.parse_args()

   sort_all_auto_mappings(not args.check_only)




