




'''simple docstring'''



import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCamelCase_			=						'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
lowerCamelCase_			=						direct_transformers_import(PATH_TO_TRANSFORMERS)

lowerCamelCase_			=						transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowerCamelCase_			=						{
    # used to compute the property `self.chunk_length`
    'EncodecConfig': ['overlap'],
    # used as `self.bert_model = BertModel(config, ...)`
    'DPRConfig': True,
    # not used in modeling files, but it's an important information
    'FSMTConfig': ['langs'],
    # used internally in the configuration class file
    'GPTNeoConfig': ['attention_types'],
    # used internally in the configuration class file
    'EsmConfig': ['is_folding_model'],
    # used during training (despite we don't have training script for these models yet)
    'Mask2FormerConfig': ['ignore_value'],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    'OneFormerConfig': ['ignore_value', 'norm'],
    # used during preprocessing and collation, see `collating_graphormer.py`
    'GraphormerConfig': ['spatial_pos_max'],
    # used internally in the configuration class file
    'T5Config': ['feed_forward_proj'],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    'MT5Config': ['feed_forward_proj', 'tokenizer_class'],
    'UMT5Config': ['feed_forward_proj', 'tokenizer_class'],
    # used internally in the configuration class file
    'LongT5Config': ['feed_forward_proj'],
    # used internally in the configuration class file
    'SwitchTransformersConfig': ['feed_forward_proj'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'BioGptConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'GLPNConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'SegformerConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'CvtConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'PerceiverConfig': ['layer_norm_eps'],
    # used internally to calculate the feature size
    'InformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate the feature size
    'TimeSeriesTransformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate the feature size
    'AutoformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate `mlp_dim`
    'SamVisionConfig': ['mlp_ratio'],
    # For (head) training, but so far not implemented
    'ClapAudioConfig': ['num_classes'],
    # Not used, but providing useful information to users
    'SpeechT5HifiGanConfig': ['sampling_rate'],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        'CLIPSegConfig': True,
        'DeformableDetrConfig': True,
        'DetaConfig': True,
        'DinatConfig': True,
        'DonutSwinConfig': True,
        'EfficientFormerConfig': True,
        'FSMTConfig': True,
        'JukeboxConfig': True,
        'LayoutLMv2Config': True,
        'MaskFormerSwinConfig': True,
        'MT5Config': True,
        'NatConfig': True,
        'OneFormerConfig': True,
        'PerceiverConfig': True,
        'RagConfig': True,
        'SpeechT5Config': True,
        'SwinConfig': True,
        'Swin2SRConfig': True,
        'Swinv2Config': True,
        'SwitchTransformersConfig': True,
        'TableTransformerConfig': True,
        'TapasConfig': True,
        'TransfoXLConfig': True,
        'UniSpeechConfig': True,
        'UniSpeechSatConfig': True,
        'WavLMConfig': True,
        'WhisperConfig': True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        'JukeboxPriorConfig': True,
        # TODO: @Younes (for `is_decoder`)
        'Pix2StructTextConfig': True,
    }
)
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Dict	,					__A    :			List[str]	,					__A    :			Dict	,					__A    :			Dict  )			->			Optional[Any]:
   _SCREAMING_SNAKE_CASE	=				False
   for attribute in attributes:
      for modeling_source in source_strings:
         # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
         if (
             f"""config.{attribute}""" in modeling_source
             or f"""getattr(config, \"{attribute}\"""" in modeling_source
             or f"""getattr(self.config, \"{attribute}\"""" in modeling_source
         ):
            _SCREAMING_SNAKE_CASE	=				True
         # Deal with multi-line cases
         elif (
             re.search(
                 Rf"""getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*\"{attribute}\""""	,					lowerCAmelCase__	,					)
             is not None
         ):
            _SCREAMING_SNAKE_CASE	=				True
         # `SequenceSummary` is called with `SequenceSummary(config)`
         elif attribute in [
             "summary_type",
             "summary_use_proj",
             "summary_activation",
             "summary_last_dropout",
             "summary_proj_to_labels",
             "summary_first_dropout",
         ]:
            if "SequenceSummary" in modeling_source:
               _SCREAMING_SNAKE_CASE	=				True
         if attribute_used:
            break
      if attribute_used:
         break

    # common and important attributes, even if they do not always appear in the modeling files
   _SCREAMING_SNAKE_CASE	=				[
       """bos_index""",
       """eos_index""",
       """pad_index""",
       """unk_index""",
       """mask_index""",
       """image_size""",
       """use_cache""",
       """out_features""",
       """out_indices""",
   ]
   _SCREAMING_SNAKE_CASE	=				["""encoder_no_repeat_ngram_size"""]

   # Special cases to be allowed
   _SCREAMING_SNAKE_CASE	=				True
   if not attribute_used:
      _SCREAMING_SNAKE_CASE	=				False
      for attribute in attributes:
         # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
         if attribute in ["is_encoder_decoder"] and default_value is True:
            _SCREAMING_SNAKE_CASE	=				True
         elif attribute in ["tie_word_embeddings"] and default_value is False:
            _SCREAMING_SNAKE_CASE	=				True

         # Allow cases without checking the default value in the configuration class
         elif attribute in attributes_to_allow + attributes_used_in_generation:
            _SCREAMING_SNAKE_CASE	=				True
         elif attribute.endswith("_token_id"  ):
            _SCREAMING_SNAKE_CASE	=				True

         # configuration class specific cases
         if not case_allowed:
            _SCREAMING_SNAKE_CASE	=				SPECIAL_CASES_TO_ALLOW.get(config_class.__name__	,					[]  )
            _SCREAMING_SNAKE_CASE	=				allowed_cases is True or attribute in allowed_cases

   return attribute_used or case_allowed
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[Any]  )			->			str:
   _SCREAMING_SNAKE_CASE	=				dict(inspect.signature(config_class.__init__  ).parameters  )
   _SCREAMING_SNAKE_CASE	=				[x for x in list(signature.keys()  ) if x not in ["""self""", """kwargs"""]]
   _SCREAMING_SNAKE_CASE	=				[signature[param].default for param in parameter_names]

   # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
   # as one variant is used, the test should pass
   _SCREAMING_SNAKE_CASE	=				{}
   if len(config_class.attribute_map  ) > 0:
      _SCREAMING_SNAKE_CASE	=				{v: k for k, v in config_class.attribute_map.items()}

   # Get the path to modeling source files
   _SCREAMING_SNAKE_CASE	=				inspect.getsourcefile(lowerCAmelCase__  )
   _SCREAMING_SNAKE_CASE	=				os.path.dirname(lowerCAmelCase__  )
   # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
   _SCREAMING_SNAKE_CASE	=				[os.path.join(lowerCAmelCase__	,					lowerCAmelCase__  ) for fn in os.listdir(lowerCAmelCase__  ) if fn.startswith("modeling_"  )]

   # Get the source code strings
   _SCREAMING_SNAKE_CASE	=				[]
   for path in modeling_paths:
      if os.path.isfile(lowerCAmelCase__  ):
         with open(lowerCAmelCase__  ) as fp:
            modeling_sources.append(fp.read()  )

   _SCREAMING_SNAKE_CASE	=				[]
   for config_param, default_value in zip(lowerCAmelCase__	,					lowerCAmelCase__  ):
      # `attributes` here is all the variant names for `config_param`
      _SCREAMING_SNAKE_CASE	=				[config_param]
      # some configuration classes have non-empty `attribute_map`, and both names could be used in the
      # corresponding modeling files. As long as one of them appears, it is fine.
      if config_param in reversed_attribute_map:
         attributes.append(reversed_attribute_map[config_param]  )

      if not check_attribute_being_used(lowerCAmelCase__	,					lowerCAmelCase__	,					lowerCAmelCase__	,					lowerCAmelCase__  ):
         unused_attributes.append(attributes[0]  )

   return sorted(lowerCAmelCase__  )
def 			SCREAMING_SNAKE_CASE_			(							)			->			Optional[int]:
   _SCREAMING_SNAKE_CASE	=				{}
   for _config_class in list(CONFIG_MAPPING.values()  ):
      # Skip deprecated models
      if "models.deprecated" in _config_class.__module__:
         continue
      # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
      _SCREAMING_SNAKE_CASE	=				[
          cls
          for name, cls in inspect.getmembers(
              inspect.getmodule(_config_class  )	,					lambda __A  : inspect.isclass(lowerCAmelCase__  )
              and issubclass(lowerCAmelCase__	,					lowerCAmelCase__  )
              and inspect.getmodule(lowerCAmelCase__  ) == inspect.getmodule(_config_class  )	,					)
      ]
      for config_class in config_classes_in_module:
         _SCREAMING_SNAKE_CASE	=				check_config_attributes_being_used(lowerCAmelCase__  )
         if len(lowerCAmelCase__  ) > 0:
            _SCREAMING_SNAKE_CASE	=				unused_attributes

   if len(lowerCAmelCase__  ) > 0:
      _SCREAMING_SNAKE_CASE	=				"""The following configuration classes contain unused attributes in the corresponding modeling files:\n"""
      for name, attributes in configs_with_unused_attributes.items():
         error += f"""{name}: {attributes}\n"""

      raise ValueError(lowerCAmelCase__  )


if __name__ == "__main__":
 check_config_attributes()







'''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


lowerCamelCase_			=						{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 		lowercase_   (			nn.Module       ):


   """simple docstring"""

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

      """simple docstring"""
      super().__init__()
      _SCREAMING_SNAKE_CASE	=				torchvision.models.resnetaaa(pretrained=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				list(model.children()   )[:-2]
      _SCREAMING_SNAKE_CASE	=				nn.Sequential(*__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )



   def 							lowerCAmelCase_      (   self :     Optional[int]  ,				__lowerCamelCase :     Optional[Any]   ):

      """simple docstring"""
      # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
      _SCREAMING_SNAKE_CASE	=				self.pool(self.model(__lowerCamelCase   )   )
      _SCREAMING_SNAKE_CASE	=				torch.flatten(__lowerCamelCase  ,				start_dim=2   )
      _SCREAMING_SNAKE_CASE	=				out.transpose(1  ,				2   ).contiguous()
      return out  # BxNx2048







class 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     Tuple  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[json.loads(__lowerCamelCase   ) for l in open(__lowerCamelCase   )]
      _SCREAMING_SNAKE_CASE	=				os.path.dirname(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				tokenizer
      _SCREAMING_SNAKE_CASE	=				labels
      _SCREAMING_SNAKE_CASE	=				len(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				max_seq_length

      _SCREAMING_SNAKE_CASE	=				transforms



   def __len__(   self :     int   ):

      """simple docstring"""
      return len(self.data   )



   def __getitem__(   self :     str  ,				__lowerCamelCase :     Any   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]  ,				add_special_tokens=__lowerCamelCase   )   )
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				sentence[0], sentence[1:-1], sentence[-1]
      _SCREAMING_SNAKE_CASE	=				sentence[: self.max_seq_length]

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

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

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



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				Counter()
      for row in self.data:
         label_freqs.update(row["label"]   )
      return label_freqs


def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[Any]  )			->			Dict:
   _SCREAMING_SNAKE_CASE	=				[len(row["sentence"]  ) for row in batch]
   _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				len(__A  ), max(__A  )

   _SCREAMING_SNAKE_CASE	=				torch.zeros(__A	,					__A	,					dtype=torch.long  )
   _SCREAMING_SNAKE_CASE	=				torch.zeros(__A	,					__A	,					dtype=torch.long  )

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

   _SCREAMING_SNAKE_CASE	=				torch.stack([row["image"] for row in batch]  )
   _SCREAMING_SNAKE_CASE	=				torch.stack([row["label"] for row in batch]  )
   _SCREAMING_SNAKE_CASE	=				torch.stack([row["image_start_token"] for row in batch]  )
   _SCREAMING_SNAKE_CASE	=				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 			SCREAMING_SNAKE_CASE_			(							)			->			Optional[int]:
   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 			SCREAMING_SNAKE_CASE_			(							)			->			Optional[int]:
   return transforms.Compose(
       [
           transforms.Resize(2_56  ),
           transforms.CenterCrop(2_24  ),
           transforms.ToTensor(),
           transforms.Normalize(
               mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]	,					std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]	,					),
       ]  )

