



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 		a__      (		_UpperCamelCase      :			Optional[Any]			,_UpperCamelCase      :			Optional[int]			,_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			Optional[Any]				):
   __lowerCamelCase    				=     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
         ):
            __lowerCamelCase    				=     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}\""""			,_UpperCamelCase			,)
             is not None
         ):
            __lowerCamelCase    				=     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:
               __lowerCamelCase    				=     True
         if attribute_used:
            break
      if attribute_used:
         break

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

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

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

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

   return attribute_used or case_allowed





def 		a__      (		_UpperCamelCase      :			Union[str, Any]				):
   __lowerCamelCase    				=     dict(inspect.signature(config_class.__init__				).parameters				)
   __lowerCamelCase    				=     [x for x in list(signature.keys()				) if x not in ['''self''', '''kwargs''']]
   __lowerCamelCase    				=     [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
   __lowerCamelCase    				=     {}
   if len(config_class.attribute_map				) > 0:
      __lowerCamelCase    				=     {v: k for k, v in config_class.attribute_map.items()}

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

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

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

   return sorted(_UpperCamelCase				)





def 		a__      (		):
   __lowerCamelCase    				=     {}
   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.)
      __lowerCamelCase    				=     [
          cls
          for name, cls in inspect.getmembers(
              inspect.getmodule(_config_class				)			,lambda _UpperCamelCase				: inspect.isclass(_UpperCamelCase				)
              and issubclass(_UpperCamelCase			,_UpperCamelCase				)
              and inspect.getmodule(_UpperCamelCase				) == inspect.getmodule(_config_class				)			,)
      ]
      for config_class in config_classes_in_module:
         __lowerCamelCase    				=     check_config_attributes_being_used(_UpperCamelCase				)
         if len(_UpperCamelCase				) > 0:
            __lowerCamelCase    				=     unused_attributes

   if len(_UpperCamelCase				) > 0:
      __lowerCamelCase    				=     '''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(_UpperCamelCase				)


if __name__ == "__main__":
  check_config_attributes()











import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 			__lowerCAmelCase  (						lowerCAmelCase__	,						unittest.TestCase ):
 lowerCAmelCase__							       =	XLMTokenizer
 lowerCAmelCase__							       =	False




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    __lowerCamelCase    				=     [
        '''l''',
        '''o''',
        '''w''',
        '''e''',
        '''r''',
        '''s''',
        '''t''',
        '''i''',
        '''d''',
        '''n''',
        '''w</w>''',
        '''r</w>''',
        '''t</w>''',
        '''lo''',
        '''low''',
        '''er</w>''',
        '''low</w>''',
        '''lowest</w>''',
        '''newer</w>''',
        '''wider</w>''',
        '''<unk>''',
    ]
    __lowerCamelCase    				=     dict(zip(__UpperCAmelCase						,					range(len(__UpperCAmelCase					)					)					)					)
    __lowerCamelCase    				=     ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

    __lowerCamelCase    				=     os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['''vocab_file''']					)
    __lowerCamelCase    				=     os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['''merges_file''']					)
    with open(self.vocab_file						,					'''w'''					) as fp:
       fp.write(json.dumps(__UpperCAmelCase					)					)
    with open(self.merges_file						,					'''w'''					) as fp:
       fp.write('''\n'''.join(__UpperCAmelCase					)					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     '''lower newer'''
    __lowerCamelCase    				=     '''lower newer'''
    return input_text, output_text




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     XLMTokenizer(self.vocab_file						,					self.merges_file					)

    __lowerCamelCase    				=     '''lower'''
    __lowerCamelCase    				=     ['''low''', '''er</w>''']
    __lowerCamelCase    				=     tokenizer.tokenize(__UpperCAmelCase					)
    self.assertListEqual(__UpperCAmelCase						,					__UpperCAmelCase					)

    __lowerCamelCase    				=     tokens + ['''<unk>''']
    __lowerCamelCase    				=     [14, 15, 20]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase					)						,					__UpperCAmelCase					)




 @slow
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''					)

    __lowerCamelCase    				=     tokenizer.encode('''sequence builders'''						,					add_special_tokens=__UpperCAmelCase					)
    __lowerCamelCase    				=     tokenizer.encode('''multi-sequence build'''						,					add_special_tokens=__UpperCAmelCase					)

    __lowerCamelCase    				=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase					)
    __lowerCamelCase    				=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase						,					__UpperCAmelCase					)

    assert encoded_sentence == [0] + text + [1]
    assert encoded_pair == [0] + text + [1] + text_a + [1]






