





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_     							= 'src/transformers'


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

UpperCAmelCase_     							= transformers.models.auto.configuration_auto.CONFIG_MAPPING

UpperCAmelCase_     							= {
    # 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_    (  __UpperCAmelCase:			Optional[Any]		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			Tuple      )      ->	int:
    UpperCamelCase__							:				List[str]          =      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__							:				str          =      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}\""		,   SCREAMING_SNAKE_CASE__		,   )
                is not None
            ):
                UpperCamelCase__							:				Any          =      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__							:				Union[str, Any]          =      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__							:				Optional[int]          =      [
        '''bos_index''',
        '''eos_index''',
        '''pad_index''',
        '''unk_index''',
        '''mask_index''',
        '''image_size''',
        '''use_cache''',
        '''out_features''',
        '''out_indices''',
    ]
    UpperCamelCase__							:				Optional[Any]          =      ['''encoder_no_repeat_ngram_size''']

    # Special cases to be allowed
    UpperCamelCase__							:				str          =      True
    if not attribute_used:
        UpperCamelCase__							:				str          =      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__							:				Dict          =      True
            elif attribute in ["tie_word_embeddings"] and default_value is False:
                UpperCamelCase__							:				Union[str, Any]          =      True

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

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

    return attribute_used or case_allowed




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Union[str, Any]      )      ->	Any:
    UpperCamelCase__							:				List[Any]          =      dict(inspect.signature(config_class.__init__      ).parameters      )
    UpperCamelCase__							:				Tuple          =      [x for x in list(signature.keys()      ) if x not in ['''self''', '''kwargs''']]
    UpperCamelCase__							:				Optional[Any]          =      [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__							:				Any          =      {}
    if len(config_class.attribute_map      ) > 0:
        UpperCamelCase__							:				Tuple          =      {v: k for k, v in config_class.attribute_map.items()}

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

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

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

    return sorted(SCREAMING_SNAKE_CASE__      )




def 		lowerCAmelCase_    (  )      ->	Dict:
    UpperCamelCase__							:				List[Any]          =      {}
    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__							:				Any          =      [
            cls
            for name, cls in inspect.getmembers(
                inspect.getmodule(_config_class      )		,   lambda __UpperCAmelCase      : inspect.isclass(SCREAMING_SNAKE_CASE__      )
                and issubclass(SCREAMING_SNAKE_CASE__		,   SCREAMING_SNAKE_CASE__      )
                and inspect.getmodule(SCREAMING_SNAKE_CASE__      ) == inspect.getmodule(_config_class      )		,   )
        ]
        for config_class in config_classes_in_module:
            UpperCamelCase__							:				Any          =      check_config_attributes_being_used(SCREAMING_SNAKE_CASE__      )
            if len(SCREAMING_SNAKE_CASE__      ) > 0:
                UpperCamelCase__							:				int          =      unused_attributes

    if len(SCREAMING_SNAKE_CASE__      ) > 0:
        UpperCamelCase__							:				List[str]          =      '''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(SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
  check_config_attributes()




import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


lowercase_       		=  "true"
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	List[Any]=82    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]=16      )							->      Optional[Any]:





    '''simple docstring'''






    set_seed(42      )
    A__          =   RegressionModel()
    A__          =   deepcopy(SCREAMING_SNAKE_CASE__      )
    A__          =   RegressionDataset(length=SCREAMING_SNAKE_CASE__      )
    A__          =   DataLoader(SCREAMING_SNAKE_CASE__    ,			batch_size=SCREAMING_SNAKE_CASE__      )
    model.to(accelerator.device      )
    A__      ,						A__          =   accelerator.prepare(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    return model, ddp_model, dataloader
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Accelerator    ,			SCREAMING_SNAKE_CASE__			:	Tuple=False      )							->      int:





    '''simple docstring'''






    A__          =   AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'      )
    A__          =   load_dataset('glue'    ,			'mrpc'    ,			split='validation'      )

    def tokenize_function(SCREAMING_SNAKE_CASE__			:	List[Any]      ):
        A__          =   tokenizer(examples['sentence1']    ,			examples['sentence2']    ,			truncation=SCREAMING_SNAKE_CASE__    ,			max_length=SCREAMING_SNAKE_CASE__      )
        return outputs

    with accelerator.main_process_first():
        A__          =   dataset.map(
            SCREAMING_SNAKE_CASE__    ,			batched=SCREAMING_SNAKE_CASE__    ,			remove_columns=['idx', 'sentence1', 'sentence2']    ,			)

    A__          =   tokenized_datasets.rename_column('label'    ,			'labels'      )

    def collate_fn(SCREAMING_SNAKE_CASE__			:	Dict      ):
        if use_longest:
            return tokenizer.pad(SCREAMING_SNAKE_CASE__    ,			padding='longest'    ,			return_tensors='pt'      )
        return tokenizer.pad(SCREAMING_SNAKE_CASE__    ,			padding='max_length'    ,			max_length=128    ,			return_tensors='pt'      )

    return DataLoader(SCREAMING_SNAKE_CASE__    ,			shuffle=SCREAMING_SNAKE_CASE__    ,			collate_fn=SCREAMING_SNAKE_CASE__    ,			batch_size=16      )
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Any      )							->      str:





    '''simple docstring'''






    A__          =   Accelerator(dispatch_batches=SCREAMING_SNAKE_CASE__    ,			split_batches=SCREAMING_SNAKE_CASE__      )
    A__          =   get_dataloader(SCREAMING_SNAKE_CASE__    ,			not dispatch_batches      )
    A__          =   AutoModelForSequenceClassification.from_pretrained(
        'hf-internal-testing/mrpc-bert-base-cased'    ,			return_dict=SCREAMING_SNAKE_CASE__      )
    A__      ,						A__          =   accelerator.prepare(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      List[str]:





    '''simple docstring'''






    A__          =   []
    for batch in dataloader:
        A__      ,						A__          =   batch.values()
        with torch.no_grad():
            A__          =   model(SCREAMING_SNAKE_CASE__      )
            A__      ,						A__          =   accelerator.gather_for_metrics((logit, target)      )
            logits_and_targets.append((logit, target)      )
    A__      ,						A__          =   [], []
    for logit, targ in logits_and_targets:
        logits.append(SCREAMING_SNAKE_CASE__      )
        targs.append(SCREAMING_SNAKE_CASE__      )
    A__      ,						A__          =   torch.cat(SCREAMING_SNAKE_CASE__      ), torch.cat(SCREAMING_SNAKE_CASE__      )
    return logits, targs
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Accelerator    ,			SCREAMING_SNAKE_CASE__			:	int=82    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]=False    ,			SCREAMING_SNAKE_CASE__			:	Any=False    ,			SCREAMING_SNAKE_CASE__			:	Tuple=16      )							->      List[Any]:





    '''simple docstring'''






    A__      ,						A__      ,						A__          =   get_basic_setup(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    A__      ,						A__          =   generate_predictions(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    assert (
        len(SCREAMING_SNAKE_CASE__      ) == num_samples
    ), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(SCREAMING_SNAKE_CASE__      )}'
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	bool = False    ,			SCREAMING_SNAKE_CASE__			:	bool = False      )							->      str:





    '''simple docstring'''






    A__          =   evaluate.load('glue'    ,			'mrpc'      )
    A__      ,						A__          =   get_mrpc_setup(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    # First do baseline
    A__      ,						A__      ,						A__          =   setup['no']
    model.to(SCREAMING_SNAKE_CASE__      )
    model.eval()
    for batch in dataloader:
        batch.to(SCREAMING_SNAKE_CASE__      )
        with torch.inference_mode():
            A__          =   model(**SCREAMING_SNAKE_CASE__      )
        A__          =   outputs.logits.argmax(dim=-1      )
        metric.add_batch(predictions=SCREAMING_SNAKE_CASE__    ,			references=batch['labels']      )
    A__          =   metric.compute()

    # Then do distributed
    A__      ,						A__      ,						A__          =   setup['ddp']
    model.eval()
    for batch in dataloader:
        with torch.inference_mode():
            A__          =   model(**SCREAMING_SNAKE_CASE__      )
        A__          =   outputs.logits.argmax(dim=-1      )
        A__          =   batch['labels']
        A__      ,						A__          =   accelerator.gather_for_metrics((preds, references)      )
        metric.add_batch(predictions=SCREAMING_SNAKE_CASE__    ,			references=SCREAMING_SNAKE_CASE__      )
    A__          =   metric.compute()

    for key in "accuracy f1".split():
        assert math.isclose(
            baseline[key]    ,			distributed[key]      ), f'Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'
def 						_snake_case(    )							->      Optional[Any]:





    '''simple docstring'''






    A__          =   Accelerator(split_batches=SCREAMING_SNAKE_CASE__    ,			dispatch_batches=SCREAMING_SNAKE_CASE__      )
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_warning()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()
    # These are a bit slower so they should only be ran on the GPU or TPU
    if torch.cuda.is_available() or is_tpu_available():
        if accelerator.is_local_main_process:
            print('**Testing gather_for_metrics**'      )
        for split_batches in [True, False]:
            for dispatch_batches in [True, False]:
                if accelerator.is_local_main_process:
                    print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'      )
                test_mrpc(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
                accelerator.state._reset_state()
    if accelerator.is_local_main_process:
        print('**Test torch metrics**'      )
    for split_batches in [True, False]:
        for dispatch_batches in [True, False]:
            A__          =   Accelerator(split_batches=SCREAMING_SNAKE_CASE__    ,			dispatch_batches=SCREAMING_SNAKE_CASE__      )
            if accelerator.is_local_main_process:
                print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'      )
            test_torch_metrics(SCREAMING_SNAKE_CASE__    ,			99      )
            accelerator.state._reset_state()
    if accelerator.is_local_main_process:
        print('**Test last batch is not dropped when perfectly divisible**'      )
    A__          =   Accelerator()
    test_torch_metrics(SCREAMING_SNAKE_CASE__    ,			512      )
    accelerator.state._reset_state()
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[Any]      )							->      Union[str, Any]:





    '''simple docstring'''






    main()


if __name__ == "__main__":
     main()

