


'''simple docstring'''







import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase     :  Tuple											=    logging.get_logger()






def       lowercase					(_A ,    _A ,    _A ,    _A ,    _A = True       ):







       """simple docstring"""







       print(f'Converting {name}...'       )

       with torch.no_grad():
              if hidden_sizes == 1_2_8:
                     if name[-1] == "S":
                            _lowerCAmelCase     :				Optional[Any]       =							timm.create_model('levit_128s' ,    pretrained=_A       )
                     else:
                            _lowerCAmelCase     :				Optional[Any]       =							timm.create_model('levit_128' ,    pretrained=_A       )
              if hidden_sizes == 1_9_2:
                     _lowerCAmelCase     :				Optional[Any]       =							timm.create_model('levit_192' ,    pretrained=_A       )
              if hidden_sizes == 2_5_6:
                     _lowerCAmelCase     :				List[Any]       =							timm.create_model('levit_256' ,    pretrained=_A       )
              if hidden_sizes == 3_8_4:
                     _lowerCAmelCase     :				Dict       =							timm.create_model('levit_384' ,    pretrained=_A       )

              from_model.eval()
              _lowerCAmelCase     :				List[Any]       =							LevitForImageClassificationWithTeacher(_A       ).eval()
              _lowerCAmelCase     :				List[Any]       =							OrderedDict()

              _lowerCAmelCase     :				Optional[int]       =							from_model.state_dict()
              _lowerCAmelCase     :				str       =							list(from_model.state_dict().keys()       )
              _lowerCAmelCase     :				Tuple       =							list(our_model.state_dict().keys()       )
              print(len(_A       ) ,    len(_A       )       )
              for i in range(len(_A       )       ):
                     _lowerCAmelCase     :				List[str]       =							weights[og_keys[i]]
              our_model.load_state_dict(_A       )

              _lowerCAmelCase     :				List[Any]       =							torch.randn((2, 3, 2_2_4, 2_2_4)       )
              _lowerCAmelCase     :				Dict       =							from_model(_A       )
              _lowerCAmelCase     :				Optional[int]       =							our_model(_A       ).logits

       assert torch.allclose(_A ,    _A       ), "The model logits don't match the original one."

       _lowerCAmelCase     :				Dict       =							name
       print(_A       )

       if push_to_hub:
              our_model.save_pretrained(save_directory / checkpoint_name       )
              _lowerCAmelCase     :				List[str]       =							LevitImageProcessor()
              image_processor.save_pretrained(save_directory / checkpoint_name       )

              print(f'Pushed {checkpoint_name}'       )






def       lowercase					(_A ,    _A = None ,    _A = True       ):







       """simple docstring"""







       _lowerCAmelCase     :				Union[str, Any]       =							'imagenet-1k-id2label.json'
       _lowerCAmelCase     :				Any       =							1_0_0_0
       _lowerCAmelCase     :				Dict       =							(1, num_labels)

       _lowerCAmelCase     :				Tuple       =							'huggingface/label-files'
       _lowerCAmelCase     :				Any       =							num_labels
       _lowerCAmelCase     :				int       =							json.load(open(hf_hub_download(_A ,    _A ,    repo_type='dataset'       ) ,    'r'       )       )
       _lowerCAmelCase     :				Tuple       =							{int(_A       ): v for k, v in idalabel.items()}

       _lowerCAmelCase     :				str       =							idalabel
       _lowerCAmelCase     :				int       =							{v: k for k, v in idalabel.items()}

       _lowerCAmelCase     :				Optional[int]       =							partial(_A ,    num_labels=_A ,    idalabel=_A ,    labelaid=_A       )

       _lowerCAmelCase     :				List[Any]       =							{
           'levit-128S': 1_2_8,
           'levit-128': 1_2_8,
           'levit-192': 1_9_2,
           'levit-256': 2_5_6,
           'levit-384': 3_8_4,
       }

       _lowerCAmelCase     :				List[Any]       =							{
           'levit-128S': ImageNetPreTrainedConfig(
               hidden_sizes=[1_2_8, 2_5_6, 3_8_4] ,    num_attention_heads=[4, 6, 8] ,    depths=[2, 3, 4] ,    key_dim=[1_6, 1_6, 1_6] ,    drop_path_rate=0 ,    ),
           'levit-128': ImageNetPreTrainedConfig(
               hidden_sizes=[1_2_8, 2_5_6, 3_8_4] ,    num_attention_heads=[4, 8, 1_2] ,    depths=[4, 4, 4] ,    key_dim=[1_6, 1_6, 1_6] ,    drop_path_rate=0 ,    ),
           'levit-192': ImageNetPreTrainedConfig(
               hidden_sizes=[1_9_2, 2_8_8, 3_8_4] ,    num_attention_heads=[3, 5, 6] ,    depths=[4, 4, 4] ,    key_dim=[3_2, 3_2, 3_2] ,    drop_path_rate=0 ,    ),
           'levit-256': ImageNetPreTrainedConfig(
               hidden_sizes=[2_5_6, 3_8_4, 5_1_2] ,    num_attention_heads=[4, 6, 8] ,    depths=[4, 4, 4] ,    key_dim=[3_2, 3_2, 3_2] ,    drop_path_rate=0 ,    ),
           'levit-384': ImageNetPreTrainedConfig(
               hidden_sizes=[3_8_4, 5_1_2, 7_6_8] ,    num_attention_heads=[6, 9, 1_2] ,    depths=[4, 4, 4] ,    key_dim=[3_2, 3_2, 3_2] ,    drop_path_rate=0.1 ,    ),
       }

       if model_name:
              convert_weight_and_push(
                  names_to_hidden_sizes[model_name] ,    _A ,    names_to_config[model_name] ,    _A ,    _A       )
       else:
              for model_name, config in names_to_config.items():
                     convert_weight_and_push(names_to_hidden_sizes[model_name] ,    _A ,    _A ,    _A ,    _A       )
       return config, expected_shape


if __name__ == "__main__":
       lowerCAmelCase     :  Optional[Any]											=    argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--model_name""",
           default=None,
           type=str,
           help="""The name of the model you wish to convert, it must be one of the supported Levit* architecture,""",
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""",
           default="""levit-dump-folder/""",
           type=Path,
           required=False,
           help="""Path to the output PyTorch model directory.""",
       )
       parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
       parser.add_argument(
           """--no-push_to_hub""",
           dest="""push_to_hub""",
           action="""store_false""",
           help="""Do not push model and image processor to the hub""",
       )

       lowerCAmelCase     :  Union[str, Any]											=    parser.parse_args()
       lowerCAmelCase     :  Path											=    args.pytorch_dump_folder_path
       pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
       convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





'''simple docstring'''







import copy

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


lowerCAmelCase     :  Tuple											=    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(SCREAMING_SNAKE_CASE_						)
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     "rag"
     __magic_name__           =     True







     def __init__(			self   ,						snake_case__=None   ,						snake_case__=True   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=" / "   ,						snake_case__=" // "   ,						snake_case__=5   ,						snake_case__=300   ,						snake_case__=768   ,						snake_case__=8   ,						snake_case__="wiki_dpr"   ,						snake_case__="train"   ,						snake_case__="compressed"   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=False   ,						snake_case__=False   ,						snake_case__=0.0   ,						snake_case__=True   ,						snake_case__=False   ,						snake_case__=False   ,						snake_case__=False   ,						snake_case__=True   ,						snake_case__=None   ,						**snake_case__   ,						):

            '''simple docstring'''

            super().__init__(
                bos_token_id=snake_case__   ,						pad_token_id=snake_case__   ,						eos_token_id=snake_case__   ,						decoder_start_token_id=snake_case__   ,						forced_eos_token_id=snake_case__   ,						is_encoder_decoder=snake_case__   ,						prefix=snake_case__   ,						vocab_size=snake_case__   ,						**snake_case__   ,						)
            assert (
                "question_encoder" in kwargs and "generator" in kwargs
            ), "Config has to be initialized with question_encoder and generator config"
            _lowerCAmelCase     :				Optional[Any]       =							kwargs.pop('question_encoder' )
            _lowerCAmelCase     :				List[Any]       =							question_encoder_config.pop('model_type' )
            _lowerCAmelCase     :				List[str]       =							kwargs.pop('generator' )
            _lowerCAmelCase     :				Tuple       =							decoder_config.pop('model_type' )

            from ..auto.configuration_auto import AutoConfig

            _lowerCAmelCase     :				Union[str, Any]       =							AutoConfig.for_model(snake_case__   ,						**snake_case__ )
            _lowerCAmelCase     :				List[str]       =							AutoConfig.for_model(snake_case__   ,						**snake_case__ )

            _lowerCAmelCase     :				Optional[int]       =							reduce_loss
            _lowerCAmelCase     :				Optional[Any]       =							label_smoothing
            _lowerCAmelCase     :				Dict       =							exclude_bos_score
            _lowerCAmelCase     :				Optional[int]       =							do_marginalize

            _lowerCAmelCase     :				List[str]       =							title_sep
            _lowerCAmelCase     :				Optional[Any]       =							doc_sep
            _lowerCAmelCase     :				str       =							n_docs
            _lowerCAmelCase     :				Optional[int]       =							max_combined_length

            _lowerCAmelCase     :				Dict       =							dataset
            _lowerCAmelCase     :				Optional[int]       =							dataset_split
            _lowerCAmelCase     :				str       =							index_name

            _lowerCAmelCase     :				Tuple       =							retrieval_vector_size
            _lowerCAmelCase     :				Any       =							retrieval_batch_size
            _lowerCAmelCase     :				Any       =							passages_path
            _lowerCAmelCase     :				Tuple       =							index_path
            _lowerCAmelCase     :				Any       =							use_dummy_dataset

            _lowerCAmelCase     :				Union[str, Any]       =							output_retrieved

            _lowerCAmelCase     :				List[str]       =							do_deduplication

            _lowerCAmelCase     :				Optional[int]       =							use_cache

            if self.forced_eos_token_id is None:
                   _lowerCAmelCase     :				Tuple       =							getattr(self.generator   ,						'forced_eos_token_id'   ,						snake_case__ )







     @classmethod
     def a						(			cls   ,						snake_case__   ,						snake_case__   ,						**snake_case__ ):

            '''simple docstring'''

            return cls(question_encoder=question_encoder_config.to_dict()   ,						generator=generator_config.to_dict()   ,						**snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Union[str, Any]       =							copy.deepcopy(self.__dict__ )
            _lowerCAmelCase     :				List[str]       =							self.question_encoder.to_dict()
            _lowerCAmelCase     :				Optional[Any]       =							self.generator.to_dict()
            _lowerCAmelCase     :				Optional[int]       =							self.__class__.model_type
            return output

