



'''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
SCREAMING_SNAKE_CASE__      					=     "src/transformers"


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

SCREAMING_SNAKE_CASE__      					=     transformers.models.auto.configuration_auto.CONFIG_MAPPING

SCREAMING_SNAKE_CASE__      					=     {
    # 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        lowerCamelCase  (				_snake_case  :						Dict     ,_snake_case  :						List[str]     ,_snake_case  :						Union[str, Any]     ,_snake_case  :						Optional[int]       ):





    '''simple docstring'''


    lowercase__		       =						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
            ):
                lowercase__		       =						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
            ):
                lowercase__		       =						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:
                    lowercase__		       =						True
            if attribute_used:
                break
        if attribute_used:
            break

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

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

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

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

    return attribute_used or case_allowed




def        lowerCamelCase  (				_snake_case  :						Dict       ):





    '''simple docstring'''


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

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

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

    lowercase__		       =						[]
    for config_param, default_value in zip(_snake_case     ,_snake_case       ):
        # `attributes` here is all the variant names for `config_param`
        lowercase__		       =						[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        lowerCamelCase  (				):





    '''simple docstring'''


    lowercase__		       =						{}
    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.)
        lowercase__		       =						[
            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:
            lowercase__		       =						check_config_attributes_being_used(_snake_case       )
            if len(_snake_case       ) > 0:
                lowercase__		       =						unused_attributes

    if len(_snake_case       ) > 0:
        lowercase__		       =						"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()









'''simple docstring'''





from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

SCREAMING_SNAKE_CASE__      					=     {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

SCREAMING_SNAKE_CASE__      					=     {
    "facebook/blenderbot_small-90M": 512,
}


class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Union[str, Any]						   =					VOCAB_FILES_NAMES
       lowerCAmelCase__  :Any						   =					PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__  :str						   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__  :List[Any]						   =					BlenderbotSmallTokenizer



       def __init__(				self     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_="<|endoftext|>"     ,UpperCAmelCase_="<|endoftext|>"     ,UpperCAmelCase_="<|endoftext|>"     ,UpperCAmelCase_=False     ,UpperCAmelCase_=True     ,**UpperCAmelCase_     ,)  ->							Optional[Any]:
           super().__init__(
               ByteLevelBPETokenizer(
                   vocab=UpperCAmelCase_     ,merges=UpperCAmelCase_     ,add_prefix_space=UpperCAmelCase_     ,trim_offsets=UpperCAmelCase_     ,)     ,bos_token=UpperCAmelCase_     ,eos_token=UpperCAmelCase_     ,unk_token=UpperCAmelCase_     ,**UpperCAmelCase_     ,)
           lowercase__		       =						add_prefix_space



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=None      )  ->							Optional[int]:
           lowercase__		       =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
               return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_ = None      )  ->							List[int]:
           lowercase__		       =						[self.sep_token_id]
           lowercase__		       =						[self.cls_token_id]

           if token_ids_a is None:
               return len(cls + token_ids_a + sep      ) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]




