


"""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
__snake_case         =					"""src/transformers"""


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

__snake_case         =					transformers.models.auto.configuration_auto.CONFIG_MAPPING

__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		(		lowercase						:						Dict		,							lowercase						:						Dict		,							lowercase						:						Any		,							lowercase						:						Optional[int]				)			->		Optional[int]:


    """simple docstring"""





    snake_case					:   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
            ):
                snake_case					:   Any         =					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}"'		,							lowercase		,							)
                is not None
            ):
                snake_case					:   Tuple         =					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					:   Dict         =					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					:   str         =					[
        "bos_index",
        "eos_index",
        "pad_index",
        "unk_index",
        "mask_index",
        "image_size",
        "use_cache",
        "out_features",
        "out_indices",
    ]
    snake_case					:   Optional[Any]         =					["encoder_no_repeat_ngram_size"]

    # Special cases to be allowed
    snake_case					:   str         =					True
    if not attribute_used:
        snake_case					:   List[Any]         =					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					:   Optional[int]         =					True
            elif attribute in ["tie_word_embeddings"] and default_value is False:
                snake_case					:   Optional[int]         =					True

            # Allow cases without checking the default value in the configuration class
            elif attribute in attributes_to_allow + attributes_used_in_generation:
                snake_case					:   Optional[int]         =					True
            elif attribute.endswith("_token_id"				):
                snake_case					:   Optional[Any]         =					True

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

    return attribute_used or case_allowed







def 						__lowerCAmelCase		(		lowercase						:						Tuple				)			->		Optional[int]:


    """simple docstring"""





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

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

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

    snake_case					:   Dict         =					[]
    for config_param, default_value in zip(lowercase		,							lowercase				):
        # `attributes` here is all the variant names for `config_param`
        snake_case					:   Union[str, Any]         =					[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(lowercase		,							lowercase		,							lowercase		,							lowercase				):
            unused_attributes.append(attributes[0]				)

    return sorted(lowercase				)







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


    """simple docstring"""





    snake_case					:   Union[str, 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.)
        snake_case					:   Dict         =					[
            cls
            for name, cls in inspect.getmembers(
                inspect.getmodule(_config_class				)		,							lambda lowercase				: inspect.isclass(lowercase				)
                and issubclass(lowercase		,							lowercase				)
                and inspect.getmodule(lowercase				) == inspect.getmodule(_config_class				)		,							)
        ]
        for config_class in config_classes_in_module:
            snake_case					:   List[str]         =					check_config_attributes_being_used(lowercase				)
            if len(lowercase				) > 0:
                snake_case					:   Any         =					unused_attributes

    if len(lowercase				) > 0:
        snake_case					:   Any         =					"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(lowercase				)


if __name__ == "__main__":
  check_config_attributes()









"""simple docstring"""



import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__snake_case         =					16
__snake_case         =					32







def 						__lowerCAmelCase		(		lowercase						:						Accelerator		,							lowercase						:						int = 16				)			->		Any:


    """simple docstring"""





    snake_case					:   Optional[Any]         =					AutoTokenizer.from_pretrained("bert-base-cased"				)
    snake_case					:   Optional[int]         =					load_dataset("glue"		,							"mrpc"				)

    def tokenize_function(lowercase						:						int				):
        # max_length=None => use the model max length (it's actually the default)
        snake_case					:   List[str]         =					tokenizer(examples["sentence1"]		,							examples["sentence2"]		,							truncation=lowercase		,							max_length=lowercase				)
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        snake_case					:   Tuple         =					datasets.map(
            lowercase		,							batched=lowercase		,							remove_columns=["idx", "sentence1", "sentence2"]		,							)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    snake_case					:   Optional[Any]         =					tokenized_datasets.rename_column("label"		,							"labels"				)

    def collate_fn(lowercase						:						Optional[int]				):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        snake_case					:   Optional[int]         =					128 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            snake_case					:   Dict         =					16
        elif accelerator.mixed_precision != "no":
            snake_case					:   Union[str, Any]         =					8
        else:
            snake_case					:   Any         =					None

        return tokenizer.pad(
            lowercase		,							padding="longest"		,							max_length=lowercase		,							pad_to_multiple_of=lowercase		,							return_tensors="pt"		,							)

    # Instantiate dataloaders.
    snake_case					:   Dict         =					DataLoader(
        tokenized_datasets["train"]		,							shuffle=lowercase		,							collate_fn=lowercase		,							batch_size=lowercase				)
    snake_case					:   str         =					DataLoader(
        tokenized_datasets["validation"]		,							shuffle=lowercase		,							collate_fn=lowercase		,							batch_size=lowercase				)

    return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
  from accelerate.test_utils.training import mocked_dataloaders

  __snake_case         =					mocked_dataloaders  # noqa: F811







def 						__lowerCAmelCase		(		lowercase						:						List[str]		,							lowercase						:						Any				)			->		int:


    """simple docstring"""





    if os.environ.get("TESTING_MOCKED_DATALOADERS"		,							lowercase				) == "1":
        snake_case					:   Optional[Any]         =					2
    # New Code #
    snake_case					:   Tuple         =					int(args.gradient_accumulation_steps				)
    snake_case					:   Any         =					int(args.local_sgd_steps				)
    # Initialize accelerator
    snake_case					:   Optional[int]         =					Accelerator(
        cpu=args.cpu		,							mixed_precision=args.mixed_precision		,							gradient_accumulation_steps=lowercase				)
    if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
        raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"				)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    snake_case					:   List[str]         =					config["lr"]
    snake_case					:   Tuple         =					int(config["num_epochs"]				)
    snake_case					:   List[str]         =					int(config["seed"]				)
    snake_case					:   Optional[int]         =					int(config["batch_size"]				)

    snake_case					:   Optional[int]         =					evaluate.load("glue"		,							"mrpc"				)

    set_seed(lowercase				)
    snake_case      ,snake_case					:   Optional[Any]         =					get_dataloaders(lowercase		,							lowercase				)
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case					:   Optional[Any]         =					AutoModelForSequenceClassification.from_pretrained("bert-base-cased"		,							return_dict=lowercase				)

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    snake_case					:   Optional[int]         =					model.to(accelerator.device				)

    # Instantiate optimizer
    snake_case					:   Tuple         =					AdamW(params=model.parameters()		,							lr=lowercase				)

    # Instantiate scheduler
    snake_case					:   Any         =					get_linear_schedule_with_warmup(
        optimizer=lowercase		,							num_warmup_steps=100		,							num_training_steps=(len(lowercase				) * num_epochs)		,							)

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    snake_case      ,snake_case      ,snake_case      ,snake_case      ,snake_case					:   int         =					accelerator.prepare(
        lowercase		,							lowercase		,							lowercase		,							lowercase		,							lowercase				)

    # Now we train the model
    for epoch in range(lowercase				):
        model.train()
        with LocalSGD(
            accelerator=lowercase		,							model=lowercase		,							local_sgd_steps=lowercase		,							enabled=local_sgd_steps is not None				) as local_sgd:
            for step, batch in enumerate(lowercase				):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device				)
                # New code #
                # We use the new `accumulate` context manager to perform gradient accumulation
                # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                with accelerator.accumulate(lowercase				):
                    snake_case					:   Optional[int]         =					model(**lowercase				)
                    snake_case					:   List[Any]         =					output.loss
                    accelerator.backward(lowercase				)
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
                    # LocalSGD-specific line
                    local_sgd.step()

        model.eval()
        for step, batch in enumerate(lowercase				):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device				)
            with torch.no_grad():
                snake_case					:   List[str]         =					model(**lowercase				)
            snake_case					:   str         =					outputs.logits.argmax(dim=-1				)
            snake_case      ,snake_case					:   Dict         =					accelerator.gather_for_metrics((predictions, batch["labels"])				)
            metric.add_batch(
                predictions=lowercase		,							references=lowercase		,							)

        snake_case					:   int         =					metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F'epoch {epoch}:'		,							lowercase				)







def 						__lowerCAmelCase		(		)			->		int:


    """simple docstring"""





    snake_case					:   Optional[int]         =					argparse.ArgumentParser(description="Simple example of training script."				)
    parser.add_argument(
        "--mixed_precision"		,							type=lowercase		,							default=lowercase		,							choices=["no", "fp16", "bf16", "fp8"]		,							help="Whether to use mixed precision. Choose"
        "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
        "and an Nvidia Ampere GPU."		,							)
    # New Code #
    parser.add_argument(
        "--gradient_accumulation_steps"		,							type=lowercase		,							default=1		,							help="The number of minibatches to be ran before gradients are accumulated."		,							)
    parser.add_argument(
        "--local_sgd_steps"		,							type=lowercase		,							default=8		,							help="Number of local SGD steps or None to disable local SGD"				)
    parser.add_argument("--cpu"		,							action="store_true"		,							help="If passed, will train on the CPU."				)
    snake_case					:   int         =					parser.parse_args()
    snake_case					:   List[Any]         =					{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(lowercase		,							lowercase				)


if __name__ == "__main__":
  main()





