



"""simple docstring"""

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
a_			   =					logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')

a_			   =					list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
a_			   =					tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class    __lowercase     :







						"""simple docstring"""







						_A		:							Optional[str]							       =		field(
						    default="""cifar10"""					,    metadata={"""help""": """Name of a dataset from the datasets package"""})
						_A		:							Optional[str]							       =		field(
						    default=_UpperCAmelCase					,    metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
						_A		:							Optional[str]							       =		field(
						    default=_UpperCAmelCase					,    metadata={"""help""": """The column name of the images in the files. If not set, will try to use 'image' or 'img'."""}					,    )
						_A		:							Optional[str]							       =		field(default=_UpperCAmelCase					,    metadata={"""help""": """A folder containing the training data."""})
						_A		:							Optional[str]							       =		field(default=_UpperCAmelCase					,    metadata={"""help""": """A folder containing the validation data."""})
						_A		:							Optional[float]							       =		field(
						    default=0.15					,    metadata={"""help""": """Percent to split off of train for validation."""})
						_A		:							int							       =		field(default=32					,    metadata={"""help""": """The size of the square patches to use for masking."""})
						_A		:							float							       =		field(
						    default=0.6					,    metadata={"""help""": """Percentage of patches to mask."""}					,    )
						_A		:							Optional[int]							       =		field(
						    default=_UpperCAmelCase					,    metadata={
						        """help""": (
						            """For debugging purposes or quicker training, truncate the number of training examples to this """
						            """value if set."""
						        )
						    }					,    )
						_A		:							Optional[int]							       =		field(
						    default=_UpperCAmelCase					,    metadata={
						        """help""": (
						            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
						            """value if set."""
						        )
						    }					,    )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Any 					=					{}
											if self.train_dir is not None:
																snake_case_					:		Any 					=					self.train_dir
											if self.validation_dir is not None:
																snake_case_					:		Tuple 					=					self.validation_dir
											snake_case_					:		int 					=					data_files if data_files else None







@dataclass
class    __lowercase     :







						"""simple docstring"""







						_A		:							str							       =		field(
						    default=_UpperCAmelCase					,    metadata={
						        """help""": (
						            """The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a """
						            """checkpoint identifier on the hub. """
						            """Don't set if you want to train a model from scratch."""
						        )
						    }					,    )
						_A		:							Optional[str]							       =		field(
						    default=_UpperCAmelCase					,    metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(_UpperCAmelCase)}					,    )
						_A		:							Optional[str]							       =		field(
						    default=_UpperCAmelCase					,    metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
						_A		:							Optional[str]							       =		field(
						    default=_UpperCAmelCase					,    metadata={
						        """help""": (
						            """Override some existing default config settings when a model is trained from scratch. Example: """
						            """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
						        )
						    }					,    )
						_A		:							Optional[str]							       =		field(
						    default=_UpperCAmelCase					,    metadata={"""help""": """Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"""}					,    )
						_A		:							str							       =		field(
						    default="""main"""					,    metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}					,    )
						_A		:							str							       =		field(default=_UpperCAmelCase					,    metadata={"""help""": """Name or path of preprocessor config."""})
						_A		:							bool							       =		field(
						    default=_UpperCAmelCase					,    metadata={
						        """help""": (
						            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
						            """with private models)."""
						        )
						    }					,    )
						_A		:							Optional[int]							       =		field(
						    default=_UpperCAmelCase					,    metadata={
						        """help""": (
						            """The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
						        )
						    }					,    )
						_A		:							Optional[int]							       =		field(
						    default=_UpperCAmelCase					,    metadata={
						        """help""": (
						            """The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
						        )
						    }					,    )
						_A		:							Optional[int]							       =		field(
						    default=_UpperCAmelCase					,    metadata={"""help""": """Stride to use for the encoder."""}					,    )







class    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		lowercase__=1_92   ,		lowercase__=32   ,		lowercase__=4   ,		lowercase__=0.6       ):
											snake_case_					:		Optional[Any] 					=					input_size
											snake_case_					:		List[Any] 					=					mask_patch_size
											snake_case_					:		Tuple 					=					model_patch_size
											snake_case_					:		Dict 					=					mask_ratio

											if self.input_size % self.mask_patch_size != 0:
																raise ValueError("""Input size must be divisible by mask patch size"""       )
											if self.mask_patch_size % self.model_patch_size != 0:
																raise ValueError("""Mask patch size must be divisible by model patch size"""       )

											snake_case_					:		List[Any] 					=					self.input_size // self.mask_patch_size
											snake_case_					:		List[str] 					=					self.mask_patch_size // self.model_patch_size

											snake_case_					:		Optional[int] 					=					self.rand_size**2
											snake_case_					:		Union[str, Any] 					=					int(np.ceil(self.token_count * self.mask_ratio       )       )







						def __call__(self       ):
											snake_case_					:		Optional[int] 					=					np.random.permutation(self.token_count       )[: self.mask_count]
											snake_case_					:		Optional[Any] 					=					np.zeros(self.token_count   ,		dtype=lowercase__       )
											snake_case_					:		Optional[Any] 					=					1

											snake_case_					:		Any 					=					mask.reshape((self.rand_size, self.rand_size)       )
											snake_case_					:		List[Any] 					=					mask.repeat(self.scale   ,		axis=0       ).repeat(self.scale   ,		axis=1       )

											return torch.tensor(mask.flatten()       )







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					snake_case_					:		Any 					=					torch.stack([example["""pixel_values"""] for example in examples]			)
					snake_case_					:		Optional[int] 					=					torch.stack([example["""mask"""] for example in examples]			)
					return {"pixel_values": pixel_values, "bool_masked_pos": mask}







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		Any 					=					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
					if len(sys.argv			) == 2 and sys.argv[1].endswith(""".json"""			):
										# If we pass only one argument to the script and it's the path to a json file,
										# let's parse it to get our arguments.
										snake_case_					,			snake_case_					,			snake_case_					:		Tuple 					=					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
					else:
										snake_case_					,			snake_case_					,			snake_case_					:		List[Any] 					=					parser.parse_args_into_dataclasses()

					# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
					# information sent is the one passed as arguments along with your Python/PyTorch versions.
					send_example_telemetry("""run_mim"""				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)

					# Setup logging
					logging.basicConfig(
					    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""				,    datefmt="""%m/%d/%Y %H:%M:%S"""				,    handlers=[logging.StreamHandler(sys.stdout			)]				,    )

					if training_args.should_log:
										# The default of training_args.log_level is passive, so we set log level at info here to have that default.
										transformers.utils.logging.set_verbosity_info()

					snake_case_					:		Optional[int] 					=					training_args.get_process_log_level()
					logger.setLevel(SCREAMING_SNAKE_CASE__			)
					transformers.utils.logging.set_verbosity(SCREAMING_SNAKE_CASE__			)
					transformers.utils.logging.enable_default_handler()
					transformers.utils.logging.enable_explicit_format()

					# Log on each process the small summary:
					logger.warning(
					    f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
					    + f'distributed training: {bool(training_args.local_rank != -1			)}, 16-bits training: {training_args.fpaa}'			)
					logger.info(f'Training/evaluation parameters {training_args}'			)

					# Detecting last checkpoint.
					snake_case_					:		List[Any] 					=					None
					if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
										snake_case_					:		Dict 					=					get_last_checkpoint(training_args.output_dir			)
										if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
															raise ValueError(
															    f'Output directory ({training_args.output_dir}) already exists and is not empty. '
															    """Use --overwrite_output_dir to overcome."""			)
										elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
															logger.info(
															    f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
															    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""			)

    # Initialize our dataset.
					snake_case_					:		List[Any] 					=					load_dataset(
					    data_args.dataset_name				,    data_args.dataset_config_name				,    data_files=data_args.data_files				,    cache_dir=model_args.cache_dir				,    use_auth_token=True if model_args.use_auth_token else None				,    )

					# If we don't have a validation split, split off a percentage of train as validation.
					snake_case_					:		int 					=					None if """validation""" in ds.keys() else data_args.train_val_split
					if isinstance(data_args.train_val_split				,    SCREAMING_SNAKE_CASE__			) and data_args.train_val_split > 0.0:
										snake_case_					:		List[Any] 					=					ds["""train"""].train_test_split(data_args.train_val_split			)
										snake_case_					:		List[str] 					=					split["""train"""]
										snake_case_					:		List[str] 					=					split["""test"""]

					# Create config
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.
					snake_case_					:		Dict 					=					{
					    """cache_dir""": model_args.cache_dir,
					    """revision""": model_args.model_revision,
					    """use_auth_token""": True if model_args.use_auth_token else None,
					}
					if model_args.config_name_or_path:
										snake_case_					:		str 					=					AutoConfig.from_pretrained(model_args.config_name_or_path				,    **SCREAMING_SNAKE_CASE__			)
					elif model_args.model_name_or_path:
										snake_case_					:		str 					=					AutoConfig.from_pretrained(model_args.model_name_or_path				,    **SCREAMING_SNAKE_CASE__			)
					else:
										snake_case_					:		Dict 					=					CONFIG_MAPPING[model_args.model_type]()
										logger.warning("""You are instantiating a new config instance from scratch."""			)
										if model_args.config_overrides is not None:
															logger.info(f'Overriding config: {model_args.config_overrides}'			)
															config.update_from_string(model_args.config_overrides			)
															logger.info(f'New config: {config}'			)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
					if hasattr(SCREAMING_SNAKE_CASE__				,    """decoder_type"""			):
										snake_case_					:		str 					=					"""simmim"""

					# adapt config
					snake_case_					:		Tuple 					=					model_args.image_size if model_args.image_size is not None else config.image_size
					snake_case_					:		str 					=					model_args.patch_size if model_args.patch_size is not None else config.patch_size
					snake_case_					:		List[Any] 					=					(
					    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
					)

					config.update(
					    {
					        """image_size""": model_args.image_size,
					        """patch_size""": model_args.patch_size,
					        """encoder_stride""": model_args.encoder_stride,
					    }			)

					# create image processor
					if model_args.image_processor_name:
										snake_case_					:		Union[str, Any] 					=					AutoImageProcessor.from_pretrained(model_args.image_processor_name				,    **SCREAMING_SNAKE_CASE__			)
					elif model_args.model_name_or_path:
										snake_case_					:		Optional[Any] 					=					AutoImageProcessor.from_pretrained(model_args.model_name_or_path				,    **SCREAMING_SNAKE_CASE__			)
					else:
										snake_case_					:		str 					=					{
										    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
										}
										snake_case_					:		Tuple 					=					IMAGE_PROCESSOR_TYPES[model_args.model_type]()

					# create model
					if model_args.model_name_or_path:
										snake_case_					:		List[Any] 					=					AutoModelForMaskedImageModeling.from_pretrained(
										    model_args.model_name_or_path				,    from_tf=bool(""".ckpt""" in model_args.model_name_or_path			)				,    config=SCREAMING_SNAKE_CASE__				,    cache_dir=model_args.cache_dir				,    revision=model_args.model_revision				,    use_auth_token=True if model_args.use_auth_token else None				,    )
					else:
										logger.info("""Training new model from scratch"""			)
										snake_case_					:		Optional[int] 					=					AutoModelForMaskedImageModeling.from_config(SCREAMING_SNAKE_CASE__			)

					if training_args.do_train:
										snake_case_					:		str 					=					ds["""train"""].column_names
					else:
										snake_case_					:		Union[str, Any] 					=					ds["""validation"""].column_names

					if data_args.image_column_name is not None:
										snake_case_					:		Any 					=					data_args.image_column_name
					elif "image" in column_names:
										snake_case_					:		str 					=					"""image"""
					elif "img" in column_names:
										snake_case_					:		Dict 					=					"""img"""
					else:
										snake_case_					:		Optional[int] 					=					column_names[0]

					# transformations as done in original SimMIM paper
					# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
					snake_case_					:		List[str] 					=					Compose(
					    [
					        Lambda(lambda SCREAMING_SNAKE_CASE__			: img.convert("""RGB"""			) if img.mode != "RGB" else img			),
					        RandomResizedCrop(model_args.image_size				,    scale=(0.67, 1.0)				,    ratio=(3.0 / 4.0, 4.0 / 3.0)			),
					        RandomHorizontalFlip(),
					        ToTensor(),
					        Normalize(mean=image_processor.image_mean				,    std=image_processor.image_std			),
					    ]			)

					# create mask generator
					snake_case_					:		Any 					=					MaskGenerator(
					    input_size=model_args.image_size				,    mask_patch_size=data_args.mask_patch_size				,    model_patch_size=model_args.patch_size				,    mask_ratio=data_args.mask_ratio				,    )

					def preprocess_images(SCREAMING_SNAKE_CASE__ :  List[Any]			):
										snake_case_					:		List[str] 					=					[transforms(SCREAMING_SNAKE_CASE__			) for image in examples[image_column_name]]
										snake_case_					:		Any 					=					[mask_generator() for i in range(len(examples[image_column_name]			)			)]

										return examples

					if training_args.do_train:
										if "train" not in ds:
															raise ValueError("""--do_train requires a train dataset"""			)
										if data_args.max_train_samples is not None:
															snake_case_					:		Optional[int] 					=					ds["""train"""].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
										# Set the training transforms
										ds["train"].set_transform(SCREAMING_SNAKE_CASE__			)

					if training_args.do_eval:
										if "validation" not in ds:
															raise ValueError("""--do_eval requires a validation dataset"""			)
										if data_args.max_eval_samples is not None:
															snake_case_					:		Union[str, Any] 					=					(
															    ds["""validation"""].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
															)
										# Set the validation transforms
										ds["validation"].set_transform(SCREAMING_SNAKE_CASE__			)

					# Initialize our trainer
					snake_case_					:		Tuple 					=					Trainer(
					    model=SCREAMING_SNAKE_CASE__				,    args=SCREAMING_SNAKE_CASE__				,    train_dataset=ds["""train"""] if training_args.do_train else None				,    eval_dataset=ds["""validation"""] if training_args.do_eval else None				,    tokenizer=SCREAMING_SNAKE_CASE__				,    data_collator=SCREAMING_SNAKE_CASE__				,    )

					# Training
					if training_args.do_train:
										snake_case_					:		Any 					=					None
										if training_args.resume_from_checkpoint is not None:
															snake_case_					:		str 					=					training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															snake_case_					:		int 					=					last_checkpoint
										snake_case_					:		List[Any] 					=					trainer.train(resume_from_checkpoint=SCREAMING_SNAKE_CASE__			)
										trainer.save_model()
										trainer.log_metrics("""train"""				,    train_result.metrics			)
										trainer.save_metrics("""train"""				,    train_result.metrics			)
										trainer.save_state()

					# Evaluation
					if training_args.do_eval:
										snake_case_					:		List[Any] 					=					trainer.evaluate()
										trainer.log_metrics("""eval"""				,    SCREAMING_SNAKE_CASE__			)
										trainer.save_metrics("""eval"""				,    SCREAMING_SNAKE_CASE__			)

					# Write model card and (optionally) push to hub
					snake_case_					:		Optional[Any] 					=					{
					    """finetuned_from""": model_args.model_name_or_path,
					    """tasks""": """masked-image-modeling""",
					    """dataset""": data_args.dataset_name,
					    """tags""": ["""masked-image-modeling"""],
					}
					if training_args.push_to_hub:
										trainer.push_to_hub(**SCREAMING_SNAKE_CASE__			)
					else:
										trainer.create_model_card(**SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
			main()









"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


a_			   =					r'''
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

    Args:
        title_sep (`str`, *optional*, defaults to  `" / "`):
            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
        doc_sep (`str`, *optional*, defaults to  `" // "`):
            Separator inserted between the text of the retrieved document and the original input when calling
            [`RagRetriever`].
        n_docs (`int`, *optional*, defaults to 5):
            Number of documents to retrieve.
        max_combined_length (`int`, *optional*, defaults to 300):
            Max length of contextualized input returned by [`~RagRetriever.__call__`].
        retrieval_vector_size (`int`, *optional*, defaults to 768):
            Dimensionality of the document embeddings indexed by [`RagRetriever`].
        retrieval_batch_size (`int`, *optional*, defaults to 8):
            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
            [`RagRetriever`].
        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):
            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
            using `datasets.list_datasets()`).
        dataset_split (`str`, *optional*, defaults to `"train"`)
            Which split of the `dataset` to load.
        index_name (`str`, *optional*, defaults to `"compressed"`)
            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and
            `"compressed"`.
        index_path (`str`, *optional*)
            The path to the serialized faiss index on disk.
        passages_path (`str`, *optional*):
            A path to text passages compatible with the faiss index. Required if using
            [`~models.rag.retrieval_rag.LegacyIndex`]
        use_dummy_dataset (`bool`, *optional*, defaults to `False`)
            Whether to load a "dummy" variant of the dataset specified by `dataset`.
        label_smoothing (`float`, *optional*, defaults to 0.0):
            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
            in the loss calculation. If set to 0, no label smoothing is performed.
        do_marginalize (`bool`, *optional*, defaults to `False`):
            If `True`, the logits are marginalized over all documents by making use of
            `torch.nn.functional.log_softmax`.
        reduce_loss (`bool`, *optional*, defaults to `False`):
            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
        do_deduplication (`bool`, *optional*, defaults to `True`):
            Whether or not to deduplicate the generations from different context documents for a given input. Has to be
            set to `False` if used while training with distributed backend.
        exclude_bos_score (`bool`, *optional*, defaults to `False`):
            Whether or not to disregard the BOS token when computing the loss.
        output_retrieved(`bool`, *optional*, defaults to `False`):
            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
            `context_attention_mask` are returned. See returned tensors for more detail.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        forced_eos_token_id (`int`, *optional*):
            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
            `eos_token_id`.
'''







@add_start_docstrings(_UpperCAmelCase)
class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Optional[int]							       =		"""rag"""
						_A		:							Optional[Any]							       =		True







						def __init__(self   ,		lowercase__=None   ,		lowercase__=True   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=" / "   ,		lowercase__=" // "   ,		lowercase__=5   ,		lowercase__=3_00   ,		lowercase__=7_68   ,		lowercase__=8   ,		lowercase__="wiki_dpr"   ,		lowercase__="train"   ,		lowercase__="compressed"   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=False   ,		lowercase__=False   ,		lowercase__=0.0   ,		lowercase__=True   ,		lowercase__=False   ,		lowercase__=False   ,		lowercase__=False   ,		lowercase__=True   ,		lowercase__=None   ,		**lowercase__   ,		):
											super().__init__(
											    bos_token_id=lowercase__   ,		pad_token_id=lowercase__   ,		eos_token_id=lowercase__   ,		decoder_start_token_id=lowercase__   ,		forced_eos_token_id=lowercase__   ,		is_encoder_decoder=lowercase__   ,		prefix=lowercase__   ,		vocab_size=lowercase__   ,		**lowercase__   ,		)
											assert (
											    "question_encoder" in kwargs and "generator" in kwargs
											), "Config has to be initialized with question_encoder and generator config"
											snake_case_					:		List[Any] 					=					kwargs.pop("""question_encoder"""       )
											snake_case_					:		Tuple 					=					question_encoder_config.pop("""model_type"""       )
											snake_case_					:		List[str] 					=					kwargs.pop("""generator"""       )
											snake_case_					:		List[str] 					=					decoder_config.pop("""model_type"""       )

											from ..auto.configuration_auto import AutoConfig

											snake_case_					:		List[str] 					=					AutoConfig.for_model(lowercase__   ,		**lowercase__       )
											snake_case_					:		Tuple 					=					AutoConfig.for_model(lowercase__   ,		**lowercase__       )

											snake_case_					:		int 					=					reduce_loss
											snake_case_					:		Optional[int] 					=					label_smoothing
											snake_case_					:		Dict 					=					exclude_bos_score
											snake_case_					:		Union[str, Any] 					=					do_marginalize

											snake_case_					:		Union[str, Any] 					=					title_sep
											snake_case_					:		int 					=					doc_sep
											snake_case_					:		int 					=					n_docs
											snake_case_					:		List[str] 					=					max_combined_length

											snake_case_					:		Tuple 					=					dataset
											snake_case_					:		int 					=					dataset_split
											snake_case_					:		str 					=					index_name

											snake_case_					:		List[str] 					=					retrieval_vector_size
											snake_case_					:		Dict 					=					retrieval_batch_size
											snake_case_					:		str 					=					passages_path
											snake_case_					:		Union[str, Any] 					=					index_path
											snake_case_					:		Tuple 					=					use_dummy_dataset

											snake_case_					:		Dict 					=					output_retrieved

											snake_case_					:		str 					=					do_deduplication

											snake_case_					:		Any 					=					use_cache

											if self.forced_eos_token_id is None:
																snake_case_					:		Any 					=					getattr(self.generator   ,		"""forced_eos_token_id"""   ,		lowercase__       )







						@classmethod
						def 						__UpperCamelCase							(cls   ,		lowercase__   ,		lowercase__   ,		**lowercase__       ):
											return cls(question_encoder=question_encoder_config.to_dict()   ,		generator=generator_config.to_dict()   ,		**lowercase__       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[Any] 					=					copy.deepcopy(self.__dict__       )
											snake_case_					:		Any 					=					self.question_encoder.to_dict()
											snake_case_					:		Dict 					=					self.generator.to_dict()
											snake_case_					:		Union[str, Any] 					=					self.__class__.model_type
											return output




