

'''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  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__			)     ->     Optional[int]:
       A_		   = 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
                     ):
                            A_		   = 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
                     ):
                            A_		   = 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:
                                   A_		   = True
                     if attribute_used:
                            break
              if attribute_used:
                     break

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

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

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

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

       return attribute_used or case_allowed
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Tuple:
       A_		   = dict(inspect.signature(config_class.__init__			).parameters			)
       A_		   = [x for x in list(signature.keys()			) if x not in ["""self""", """kwargs"""]]
       A_		   = [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
       A_		   = {}
       if len(config_class.attribute_map			) > 0:
              A_		   = {v: k for k, v in config_class.attribute_map.items()}

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

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

       A_		   = []
       for config_param, default_value in zip(UpperCAmelCase__,    UpperCAmelCase__			):
              # `attributes` here is all the variant names for `config_param`
              A_		   = [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  UpperCAmelCase__      ( )     ->     Union[str, Any]:
       A_		   = {}
       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.)
              A_		   = [
                  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:
                     A_		   = check_config_attributes_being_used(UpperCAmelCase__			)
                     if len(UpperCAmelCase__			) > 0:
                            A_		   = unused_attributes

       if len(UpperCAmelCase__			) > 0:
              A_		   = """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'''







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import CLIPImageProcessor, CLIPProcessor


@require_vision
class    A__ ( unittest.TestCase     ):



    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = tempfile.mkdtemp()

           # fmt: off
           A_		   = ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
           # fmt: on
           A_		   = dict(zip(UpperCamelCase__						,			range(len(UpperCamelCase__    )    )    )    )
           A_		   = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
           A_		   = {"""unk_token""": """<unk>"""}

           A_		   = os.path.join(self.tmpdirname						,			VOCAB_FILES_NAMES["""vocab_file"""]    )
           A_		   = os.path.join(self.tmpdirname						,			VOCAB_FILES_NAMES["""merges_file"""]    )
           with open(self.vocab_file						,			"""w"""						,			encoding="""utf-8"""    ) as fp:
                  fp.write(json.dumps(UpperCamelCase__    ) + """\n"""    )
           with open(self.merges_file						,			"""w"""						,			encoding="""utf-8"""    ) as fp:
                  fp.write("""\n""".join(UpperCamelCase__    )    )

           A_		   = {
               """do_resize""": True,
               """size""": 20,
               """do_center_crop""": True,
               """crop_size""": 18,
               """do_normalize""": True,
               """image_mean""": [0.48145466, 0.4578275, 0.40821073],
               """image_std""": [0.26862954, 0.26130258, 0.27577711],
           }
           A_		   = os.path.join(self.tmpdirname						,			UpperCamelCase__    )
           with open(self.image_processor_file						,			"""w"""						,			encoding="""utf-8"""    ) as fp:
                  json.dump(UpperCamelCase__						,			UpperCamelCase__    )



    def    snake_case_     (   self						,			**UpperCamelCase__    )  -> str:





           '''simple docstring'''
           return CLIPTokenizer.from_pretrained(self.tmpdirname						,			**UpperCamelCase__    )



    def    snake_case_     (   self						,			**UpperCamelCase__    )  -> Any:





           '''simple docstring'''
           return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,			**UpperCamelCase__    )



    def    snake_case_     (   self						,			**UpperCamelCase__    )  -> Optional[Any]:





           '''simple docstring'''
           return CLIPImageProcessor.from_pretrained(self.tmpdirname						,			**UpperCamelCase__    )



    def    snake_case_     (   self    )  -> List[Any]:





           '''simple docstring'''
           shutil.rmtree(self.tmpdirname    )



    def    snake_case_     (   self    )  -> Any:





           '''simple docstring'''
           A_		   = [np.random.randint(255						,			size=(3, 30, 400)						,			dtype=np.uinta    )]

           A_		   = [Image.fromarray(np.moveaxis(UpperCamelCase__						,			0						,			-1    )    ) for x in image_inputs]

           return image_inputs



    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           A_		   = self.get_tokenizer()
           A_		   = self.get_rust_tokenizer()
           A_		   = self.get_image_processor()

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )
           processor_slow.save_pretrained(self.tmpdirname    )
           A_		   = CLIPProcessor.from_pretrained(self.tmpdirname						,			use_fast=UpperCamelCase__    )

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )
           processor_fast.save_pretrained(self.tmpdirname    )
           A_		   = CLIPProcessor.from_pretrained(self.tmpdirname    )

           self.assertEqual(processor_slow.tokenizer.get_vocab()						,			tokenizer_slow.get_vocab()    )
           self.assertEqual(processor_fast.tokenizer.get_vocab()						,			tokenizer_fast.get_vocab()    )
           self.assertEqual(tokenizer_slow.get_vocab()						,			tokenizer_fast.get_vocab()    )
           self.assertIsInstance(processor_slow.tokenizer						,			UpperCamelCase__    )
           self.assertIsInstance(processor_fast.tokenizer						,			UpperCamelCase__    )

           self.assertEqual(processor_slow.image_processor.to_json_string()						,			image_processor.to_json_string()    )
           self.assertEqual(processor_fast.image_processor.to_json_string()						,			image_processor.to_json_string()    )
           self.assertIsInstance(processor_slow.image_processor						,			UpperCamelCase__    )
           self.assertIsInstance(processor_fast.image_processor						,			UpperCamelCase__    )



    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           A_		   = CLIPProcessor(tokenizer=self.get_tokenizer()						,			image_processor=self.get_image_processor()    )
           processor.save_pretrained(self.tmpdirname    )

           A_		   = self.get_tokenizer(bos_token="""(BOS)"""						,			eos_token="""(EOS)"""    )
           A_		   = self.get_image_processor(do_normalize=UpperCamelCase__						,			padding_value=1.0    )

           A_		   = CLIPProcessor.from_pretrained(
               self.tmpdirname						,			bos_token="""(BOS)"""						,			eos_token="""(EOS)"""						,			do_normalize=UpperCamelCase__						,			padding_value=1.0    )

           self.assertEqual(processor.tokenizer.get_vocab()						,			tokenizer_add_kwargs.get_vocab()    )
           self.assertIsInstance(processor.tokenizer						,			UpperCamelCase__    )

           self.assertEqual(processor.image_processor.to_json_string()						,			image_processor_add_kwargs.to_json_string()    )
           self.assertIsInstance(processor.image_processor						,			UpperCamelCase__    )



    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           A_		   = self.get_image_processor()
           A_		   = self.get_tokenizer()

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )

           A_		   = self.prepare_image_inputs()

           A_		   = image_processor(UpperCamelCase__						,			return_tensors="""np"""    )
           A_		   = processor(images=UpperCamelCase__						,			return_tensors="""np"""    )

           for key in input_image_proc.keys():
                  self.assertAlmostEqual(input_image_proc[key].sum()						,			input_processor[key].sum()						,			delta=1e-2    )



    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           A_		   = self.get_image_processor()
           A_		   = self.get_tokenizer()

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )

           A_		   = """lower newer"""

           A_		   = processor(text=UpperCamelCase__    )

           A_		   = tokenizer(UpperCamelCase__    )

           for key in encoded_tok.keys():
                  self.assertListEqual(encoded_tok[key]						,			encoded_processor[key]    )



    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           A_		   = self.get_image_processor()
           A_		   = self.get_tokenizer()

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )

           A_		   = """lower newer"""
           A_		   = self.prepare_image_inputs()

           A_		   = processor(text=UpperCamelCase__						,			images=UpperCamelCase__    )

           self.assertListEqual(list(inputs.keys()    )						,			["""input_ids""", """attention_mask""", """pixel_values"""]    )

           # test if it raises when no input is passed
           with pytest.raises(UpperCamelCase__    ):
                  processor()



    def    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           A_		   = self.get_image_processor()
           A_		   = self.get_tokenizer()

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )

           A_		   = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           A_		   = processor.batch_decode(UpperCamelCase__    )
           A_		   = tokenizer.batch_decode(UpperCamelCase__    )

           self.assertListEqual(UpperCamelCase__						,			UpperCamelCase__    )







    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           A_		   = self.get_image_processor()
           A_		   = self.get_tokenizer()

           A_		   = CLIPProcessor(tokenizer=UpperCamelCase__						,			image_processor=UpperCamelCase__    )

           A_		   = """lower newer"""
           A_		   = self.prepare_image_inputs()

           A_		   = processor(text=UpperCamelCase__						,			images=UpperCamelCase__    )

           self.assertListEqual(list(inputs.keys()    )						,			processor.model_input_names    )
