





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase 					=      logging.get_logger(__name__)

_lowerCamelCase 					=      torch.device('cpu')






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   'http://images.cocodataset.org/val2017/000000039769.jpg'
		SCREAMING_SNAKE_CASE__								=   Image.open(requests.get(_A       , stream=_A     ).raw     )
		return im






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]     ):







		if swiftformer_name == "swiftformer_xs":
				return torch.tensor([-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0, 8.8_6_8_5e-0_1, 2.4_3_6_0e-0_1]     )

		elif swiftformer_name == "swiftformer_s":
				return torch.tensor([3.9_6_3_6e-0_1, 2.3_4_7_8e-0_1, -1.6_9_6_3e0_0, -1.7_3_8_1e0_0, -8.6_3_3_7e-0_1]     )

		elif swiftformer_name == "swiftformer_l1":
				return torch.tensor([-4.2_7_6_8e-0_1, -4.7_4_2_9e-0_1, -1.0_8_9_7e0_0, -1.0_2_4_8e0_0, 3.5_5_2_3e-0_2]     )

		elif swiftformer_name == "swiftformer_l3":
				return torch.tensor([-2.5_3_3_0e-0_1, 2.4_2_1_1e-0_1, -6.0_1_8_5e-0_1, -8.2_7_8_9e-0_1, -6.0_4_4_6e-0_2]     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]       , UpperCamelCase__:				Union[str, Any]       , UpperCamelCase__:				Tuple     ):
		SCREAMING_SNAKE_CASE__								=   dct.pop(_A     )
		SCREAMING_SNAKE_CASE__								=   val






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str     ):
		SCREAMING_SNAKE_CASE__								=   []
		for k in state_dict.keys():
				SCREAMING_SNAKE_CASE__								=   k
				if ".pwconv" in k:
						SCREAMING_SNAKE_CASE__								=   k_new.replace(""".pwconv"""       , """.point_wise_conv"""     )
				if ".dwconv" in k:
						SCREAMING_SNAKE_CASE__								=   k_new.replace(""".dwconv"""       , """.depth_wise_conv"""     )
				if ".Proj." in k:
						SCREAMING_SNAKE_CASE__								=   k_new.replace(""".Proj."""       , """.proj."""     )
				if "patch_embed" in k_new:
						SCREAMING_SNAKE_CASE__								=   k_new.replace("""patch_embed"""       , """swiftformer.patch_embed.patch_embedding"""     )
				if "network" in k_new:
						SCREAMING_SNAKE_CASE__								=   k_new.split("""."""     )
						if ls[2].isdigit():
								SCREAMING_SNAKE_CASE__								=   'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]     )
						else:
								SCREAMING_SNAKE_CASE__								=   k_new.replace("""network"""       , """swiftformer.encoder.network"""     )
				rename_keys.append((k, k_new)     )
		return rename_keys






@torch.no_grad()
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]       , UpperCamelCase__:				List[Any]       , UpperCamelCase__:				Any     ):
		SCREAMING_SNAKE_CASE__								=   SwiftFormerConfig()

		# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
		SCREAMING_SNAKE_CASE__								=   1_000
		SCREAMING_SNAKE_CASE__								=   'huggingface/label-files'
		SCREAMING_SNAKE_CASE__								=   'imagenet-1k-id2label.json'
		SCREAMING_SNAKE_CASE__								=   json.load(open(hf_hub_download(_A       , _A       , repo_type="""dataset"""     )       , """r"""     )     )
		SCREAMING_SNAKE_CASE__								=   {int(_A     ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE__								=   idalabel
		SCREAMING_SNAKE_CASE__								=   {v: k for k, v in idalabel.items()}

		# size of the architecture
		if swiftformer_name == "swiftformer_xs":
				SCREAMING_SNAKE_CASE__								=   [3, 3, 6, 4]
				SCREAMING_SNAKE_CASE__								=   [48, 56, 112, 220]

		elif swiftformer_name == "swiftformer_s":
				SCREAMING_SNAKE_CASE__								=   [3, 3, 9, 6]
				SCREAMING_SNAKE_CASE__								=   [48, 64, 168, 224]

		elif swiftformer_name == "swiftformer_l1":
				SCREAMING_SNAKE_CASE__								=   [4, 3, 10, 5]
				SCREAMING_SNAKE_CASE__								=   [48, 96, 192, 384]

		elif swiftformer_name == "swiftformer_l3":
				SCREAMING_SNAKE_CASE__								=   [4, 4, 12, 6]
				SCREAMING_SNAKE_CASE__								=   [64, 128, 320, 512]

		# load state_dict of original model, remove and rename some keys
		if original_ckpt:
				if original_ckpt.startswith("""https"""     ):
						SCREAMING_SNAKE_CASE__								=   torch.hub.load_state_dict_from_url(_A       , map_location="""cpu"""       , check_hash=_A     )
				else:
						SCREAMING_SNAKE_CASE__								=   torch.load(_A       , map_location="""cpu"""     )
		SCREAMING_SNAKE_CASE__								=   checkpoint

		SCREAMING_SNAKE_CASE__								=   create_rename_keys(_A     )
		for rename_key_src, rename_key_dest in rename_keys:
				rename_key(_A       , _A       , _A     )

		# load HuggingFace model
		SCREAMING_SNAKE_CASE__								=   SwiftFormerForImageClassification(_A     ).eval()
		hf_model.load_state_dict(_A     )

		# prepare test inputs
		SCREAMING_SNAKE_CASE__								=   prepare_img()
		SCREAMING_SNAKE_CASE__								=   ViTImageProcessor.from_pretrained("""preprocessor_config"""     )
		SCREAMING_SNAKE_CASE__								=   processor(images=_A       , return_tensors="""pt"""     )

		# compare outputs from both models
		SCREAMING_SNAKE_CASE__								=   get_expected_output(_A     )
		SCREAMING_SNAKE_CASE__								=   hf_model(inputs["""pixel_values"""]     ).logits

		assert hf_logits.shape == torch.Size([1, 1_000]     )
		assert torch.allclose(hf_logits[0, 0:5]       , _A       , atol=1e-3     )

		Path(_A     ).mkdir(exist_ok=_A     )
		print(f'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''     )
		hf_model.save_pretrained(_A     )


if __name__ == "__main__":
	_lowerCamelCase 					=      argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--swiftformer_name',
	    default='swiftformer_xs',
	    choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
	    type=str,
	    help='Name of the SwiftFormer model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default='./converted_outputs/',
	    type=str,
	    help='Path to the output PyTorch model directory.',
	)
	parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')

	_lowerCamelCase 					=      parser.parse_args()
	convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)



'''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

