




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
a													=			"src/transformers"


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

a													=			transformers.models.auto.configuration_auto.CONFIG_MAPPING

a													=			{
    # 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      (		snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				Dict:
      _UpperCAmelCase      =			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
                  ):
                        _UpperCAmelCase      =			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}\""			,     snake_case			,     )
                      is not None
                  ):
                        _UpperCAmelCase      =			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:
                              _UpperCAmelCase      =			True
                  if attribute_used:
                        break
            if attribute_used:
                  break

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

      # Special cases to be allowed
      _UpperCAmelCase      =			True
      if not attribute_used:
            _UpperCAmelCase      =			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:
                        _UpperCAmelCase      =			True
                  elif attribute in ["tie_word_embeddings"] and default_value is False:
                        _UpperCAmelCase      =			True

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

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

      return attribute_used or case_allowed




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				List[Any]:
      _UpperCAmelCase      =			dict(inspect.signature(config_class.__init__       ).parameters       )
      _UpperCAmelCase      =			[x for x in list(signature.keys()       ) if x not in ["""self""", """kwargs"""]]
      _UpperCAmelCase      =			[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
      _UpperCAmelCase      =			{}
      if len(config_class.attribute_map       ) > 0:
            _UpperCAmelCase      =			{v: k for k, v in config_class.attribute_map.items()}

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

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

      _UpperCAmelCase      =			[]
      for config_param, default_value in zip(snake_case			,     snake_case       ):
            # `attributes` here is all the variant names for `config_param`
            _UpperCAmelCase      =			[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(snake_case			,     snake_case			,     snake_case			,     snake_case       ):
                  unused_attributes.append(attributes[0]       )

      return sorted(snake_case       )




def        _SCREAMING_SNAKE_CASE      (		)						->				Any:
      _UpperCAmelCase      =			{}
      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.)
            _UpperCAmelCase      =			[
                cls
                for name, cls in inspect.getmembers(
                    inspect.getmodule(_config_class       )			,     lambda snake_case       : inspect.isclass(snake_case       )
                    and issubclass(snake_case			,     snake_case       )
                    and inspect.getmodule(snake_case       ) == inspect.getmodule(_config_class       )			,     )
            ]
            for config_class in config_classes_in_module:
                  _UpperCAmelCase      =			check_config_attributes_being_used(snake_case       )
                  if len(snake_case       ) > 0:
                        _UpperCAmelCase      =			unused_attributes

      if len(snake_case       ) > 0:
            _UpperCAmelCase      =			"""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(snake_case       )


if __name__ == "__main__":
    check_config_attributes()





import csv

import tweepy

# Twitter API credentials
a													=			""
a													=			""
a													=			""
a													=			""




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				None:
						# authorize twitter, initialize tweepy
						_UpperCAmelCase      =			tweepy.OAuthHandler(snake_case			,     snake_case       )
						auth.set_access_token(snake_case			,     snake_case       )
						_UpperCAmelCase      =			tweepy.API(snake_case       )

						# initialize a list to hold all the tweepy Tweets
						_UpperCAmelCase      =			[]

						# make initial request for most recent tweets (200 is the maximum allowed count)
						_UpperCAmelCase      =			api.user_timeline(screen_name=snake_case			,     count=2_0_0       )

						# save most recent tweets
						alltweets.extend(snake_case       )

						# save the id of the oldest tweet less one
						_UpperCAmelCase      =			alltweets[-1].id - 1

						# keep grabbing tweets until there are no tweets left to grab
						while len(snake_case       ) > 0:
												print(f"getting tweets before {oldest}"       )

												# all subsequent requests use the max_id param to prevent duplicates
												_UpperCAmelCase      =			api.user_timeline(
												    screen_name=snake_case			,     count=2_0_0			,     max_id=snake_case       )

												# save most recent tweets
												alltweets.extend(snake_case       )

												# update the id of the oldest tweet less one
												_UpperCAmelCase      =			alltweets[-1].id - 1

												print(f"...{len(snake_case       )} tweets downloaded so far"       )

						# transform the tweepy tweets into a 2D array that will populate the csv
						_UpperCAmelCase      =			[[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]

						# write the csv
						with open(f"new_{screen_name}_tweets.csv"			,     """w"""       ) as f:
												_UpperCAmelCase      =			csv.writer(snake_case       )
												writer.writerow(["""id""", """created_at""", """text"""]       )
												writer.writerows(snake_case       )


if __name__ == "__main__":
				# pass in the username of the account you want to download
				get_all_tweets("FirePing32")