




'''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
UpperCamelCase_		:     Dict          =		'''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
UpperCamelCase_		:     Optional[int]          =		direct_transformers_import(PATH_TO_TRANSFORMERS)

UpperCamelCase_		:     Tuple          =		transformers.models.auto.configuration_auto.CONFIG_MAPPING

UpperCamelCase_		:     Tuple          =		{
    # 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:       Tuple   ,		_UpperCamelCase:       Dict   ,		_UpperCamelCase:       Tuple   ,		_UpperCamelCase:       List[Any]       )		->					List[str]:

     """simple docstring"""




     _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
               ):
                    _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}\""""   ,		_UpperCamelCase   ,		)
                   is not None
               ):
                    _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:
                         _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
     _snake_case				= [
         "bos_index",
         "eos_index",
         "pad_index",
         "unk_index",
         "mask_index",
         "image_size",
         "use_cache",
         "out_features",
         "out_indices",
     ]
     _snake_case				= ["encoder_no_repeat_ngram_size"]

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

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

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

     return attribute_used or case_allowed





def       __a      (  _UpperCamelCase:       Optional[Any]       )		->					Optional[int]:

     """simple docstring"""




     _snake_case				= dict(inspect.signature(config_class.__init__       ).parameters       )
     _snake_case				= [x for x in list(signature.keys()       ) if x not in ["self", "kwargs"]]
     _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
     _snake_case				= {}
     if len(config_class.attribute_map       ) > 0:
          _snake_case				= {v: k for k, v in config_class.attribute_map.items()}

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

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

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

     return sorted(_UpperCamelCase       )





def       __a      (  )		->					str:

     """simple docstring"""




     _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.)
          _snake_case				= [
              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:
               _snake_case				= check_config_attributes_being_used(_UpperCamelCase       )
               if len(_UpperCamelCase       ) > 0:
                    _snake_case				= unused_attributes

     if len(_UpperCamelCase       ) > 0:
          _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(_UpperCamelCase       )


if __name__ == "__main__":
   check_config_attributes()








'''simple docstring'''





from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image


UpperCamelCase_		:     int          =		['''text''', '''image''', '''audio''']





def       __a      (  _UpperCamelCase:       List[str]       )		->					Dict:

     """simple docstring"""




     _snake_case				= []

     for input_type in input_types:
          if input_type == "text":
               inputs.append("Text input"       )
          elif input_type == "image":
               inputs.append(
                   Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"       )       ) / "000000039769.png"       ).resize((512, 512)       )       )
          elif input_type == "audio":
               inputs.append(torch.ones(3_000       )       )
          elif isinstance(_UpperCamelCase   ,		_UpperCamelCase       ):
               inputs.append(create_inputs(_UpperCamelCase       )       )
          else:
               raise ValueError(F"""Invalid type requested: {input_type}"""       )

     return inputs





def       __a      (  _UpperCamelCase:       List       )		->					Dict:

     """simple docstring"""




     _snake_case				= []

     for output in outputs:
          if isinstance(_UpperCamelCase   ,		(str, AgentText)       ):
               output_types.append("text"       )
          elif isinstance(_UpperCamelCase   ,		(Image.Image, AgentImage)       ):
               output_types.append("image"       )
          elif isinstance(_UpperCamelCase   ,		(torch.Tensor, AgentAudio)       ):
               output_types.append("audio"       )
          else:
               raise ValueError(F"""Invalid output: {output}"""       )

     return output_types





@is_tool_test
class   _a							:






     def 							_lowercase      (					self       )				->     Any:
          self.assertTrue(hasattr(self.tool   ,"inputs"       )       )
          self.assertTrue(hasattr(self.tool   ,"outputs"       )       )

          _snake_case				= self.tool.inputs
          for _input in inputs:
               if isinstance(_input   ,_SCREAMING_SNAKE_CASE       ):
                    for __input in _input:
                         self.assertTrue(__input in authorized_types       )
               else:
                    self.assertTrue(_input in authorized_types       )

          _snake_case				= self.tool.outputs
          for _output in outputs:
               self.assertTrue(_output in authorized_types       )






     def 							_lowercase      (					self       )				->     Any:
          _snake_case				= create_inputs(self.tool.inputs       )
          _snake_case				= self.tool(*_SCREAMING_SNAKE_CASE       )

          # There is a single output
          if len(self.tool.outputs       ) == 1:
               _snake_case				= [outputs]

          self.assertListEqual(output_types(_SCREAMING_SNAKE_CASE       )   ,self.tool.outputs       )






     def 							_lowercase      (					self       )				->     str:
          self.assertTrue(hasattr(self.tool   ,"description"       )       )
          self.assertTrue(hasattr(self.tool   ,"default_checkpoint"       )       )
          self.assertTrue(self.tool.description.startswith("This is a tool that"       )       )






     def 							_lowercase      (					self       )				->     Tuple:
          _snake_case				= create_inputs(self.tool.inputs       )
          _snake_case				= self.tool(*_SCREAMING_SNAKE_CASE       )

          if not isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= [outputs]

          self.assertEqual(len(_SCREAMING_SNAKE_CASE       )   ,len(self.tool.outputs       )       )

          for output, output_type in zip(_SCREAMING_SNAKE_CASE   ,self.tool.outputs       ):
               _snake_case				= AGENT_TYPE_MAPPING[output_type]
               self.assertTrue(isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )       )






     def 							_lowercase      (					self       )				->     Optional[Any]:
          _snake_case				= create_inputs(self.tool.inputs       )

          _snake_case				= []

          for _input, input_type in zip(_SCREAMING_SNAKE_CASE   ,self.tool.inputs       ):
               if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
                    _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input       ) for _input_type in input_type]       )
               else:
                    _inputs.append(AGENT_TYPE_MAPPING[input_type](_input       )       )

        # Should not raise an error
          _snake_case				= self.tool(*_SCREAMING_SNAKE_CASE       )

          if not isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= [outputs]

          self.assertEqual(len(_SCREAMING_SNAKE_CASE       )   ,len(self.tool.outputs       )       )


