






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
      from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


SCREAMING_SNAKE_CASE__		:							Union[str, Any] 				=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:							List[Any] 				=				{
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}







class 				UpperCAmelCase_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  'deberta-v2'




     def __init__(				self	,						_lowerCAmelCase=128100	,						_lowerCAmelCase=1536	,						_lowerCAmelCase=24	,						_lowerCAmelCase=24	,						_lowerCAmelCase=6144	,						_lowerCAmelCase="gelu"	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=0.1	,						_lowerCAmelCase=512	,						_lowerCAmelCase=0	,						_lowerCAmelCase=0.0_2	,						_lowerCAmelCase=1e-7	,						_lowerCAmelCase=False	,						_lowerCAmelCase=-1	,						_lowerCAmelCase=0	,						_lowerCAmelCase=True	,						_lowerCAmelCase=None	,						_lowerCAmelCase=0	,						_lowerCAmelCase="gelu"	,						**_lowerCAmelCase	,						):
          super().__init__(**_lowerCAmelCase		)

          UpperCAmelCase__      :  List[str]				      =			hidden_size
          UpperCAmelCase__      :  List[str]				      =			num_hidden_layers
          UpperCAmelCase__      :  Any				      =			num_attention_heads
          UpperCAmelCase__      :  List[str]				      =			intermediate_size
          UpperCAmelCase__      :  Optional[Any]				      =			hidden_act
          UpperCAmelCase__      :  Any				      =			hidden_dropout_prob
          UpperCAmelCase__      :  Tuple				      =			attention_probs_dropout_prob
          UpperCAmelCase__      :  Optional[Any]				      =			max_position_embeddings
          UpperCAmelCase__      :  Tuple				      =			type_vocab_size
          UpperCAmelCase__      :  Any				      =			initializer_range
          UpperCAmelCase__      :  List[Any]				      =			relative_attention
          UpperCAmelCase__      :  Tuple				      =			max_relative_positions
          UpperCAmelCase__      :  List[str]				      =			pad_token_id
          UpperCAmelCase__      :  Any				      =			position_biased_input

          # Backwards compatibility
          if type(_lowerCAmelCase		) == str:
               UpperCAmelCase__      :  Tuple				      =			[x.strip() for x in pos_att_type.lower().split("""|"""		)]

          UpperCAmelCase__      :  Dict				      =			pos_att_type
          UpperCAmelCase__      :  Tuple				      =			vocab_size
          UpperCAmelCase__      :  Dict				      =			layer_norm_eps

          UpperCAmelCase__      :  Any				      =			kwargs.get("""pooler_hidden_size"""	,						_lowerCAmelCase		)
          UpperCAmelCase__      :  Optional[int]				      =			pooler_dropout
          UpperCAmelCase__      :  int				      =			pooler_hidden_act







class 				UpperCAmelCase_  (		__lowerCamelCase		):




     @property
     def   __UpperCAmelCase  (				self		):
          if self.task == "multiple-choice":
               UpperCAmelCase__      :  Union[str, Any]				      =			{0: """batch""", 1: """choice""", 2: """sequence"""}
          else:
               UpperCAmelCase__      :  Tuple				      =			{0: """batch""", 1: """sequence"""}
          if self._config.type_vocab_size > 0:
               return OrderedDict(
                   [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)]		)
          else:
               return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)]		)




     @property
     def   __UpperCAmelCase  (				self		):
          return 12




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = -1	,						_lowerCAmelCase = -1	,						_lowerCAmelCase = -1	,						_lowerCAmelCase = False	,						_lowerCAmelCase = None	,						_lowerCAmelCase = 3	,						_lowerCAmelCase = 40	,						_lowerCAmelCase = 40	,						_lowerCAmelCase = None	,						):
          UpperCAmelCase__      :  int				      =			super().generate_dummy_inputs(preprocessor=_lowerCAmelCase	,						framework=_lowerCAmelCase		)
          if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
               del dummy_inputs["token_type_ids"]
          return dummy_inputs









import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Optional[int]:



    """simple docstring"""






    UpperCamelCase_			  =						SwinvaConfig()
    UpperCamelCase_			  =						swinva_name.split("_"			)

    UpperCamelCase_			  =						name_split[1]
    if "to" in name_split[3]:
        UpperCamelCase_			  =						int(name_split[3][-3:]			)
    else:
        UpperCamelCase_			  =						int(name_split[3]			)
    if "to" in name_split[2]:
        UpperCamelCase_			  =						int(name_split[2][-2:]			)
    else:
        UpperCamelCase_			  =						int(name_split[2][6:]			)

    if model_size == "tiny":
        UpperCamelCase_			  =						9_6
        UpperCamelCase_			  =						(2, 2, 6, 2)
        UpperCamelCase_			  =						(3, 6, 1_2, 2_4)
    elif model_size == "small":
        UpperCamelCase_			  =						9_6
        UpperCamelCase_			  =						(2, 2, 1_8, 2)
        UpperCamelCase_			  =						(3, 6, 1_2, 2_4)
    elif model_size == "base":
        UpperCamelCase_			  =						1_2_8
        UpperCamelCase_			  =						(2, 2, 1_8, 2)
        UpperCamelCase_			  =						(4, 8, 1_6, 3_2)
    else:
        UpperCamelCase_			  =						1_9_2
        UpperCamelCase_			  =						(2, 2, 1_8, 2)
        UpperCamelCase_			  =						(6, 1_2, 2_4, 4_8)

    if "to" in swinva_name:
        UpperCamelCase_			  =						(1_2, 1_2, 1_2, 6)

    if ("22k" in swinva_name) and ("to" not in swinva_name):
        UpperCamelCase_			  =						2_1_8_4_1
        UpperCamelCase_			  =						"huggingface/label-files"
        UpperCamelCase_			  =						"imagenet-22k-id2label.json"
        UpperCamelCase_			  =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						repo_type="dataset"			)			,						"r"			)			)
        UpperCamelCase_			  =						{int(SCREAMING_SNAKE_CASE_			): v for k, v in idalabel.items()}
        UpperCamelCase_			  =						idalabel
        UpperCamelCase_			  =						{v: k for k, v in idalabel.items()}

    else:
        UpperCamelCase_			  =						1_0_0_0
        UpperCamelCase_			  =						"huggingface/label-files"
        UpperCamelCase_			  =						"imagenet-1k-id2label.json"
        UpperCamelCase_			  =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						repo_type="dataset"			)			,						"r"			)			)
        UpperCamelCase_			  =						{int(SCREAMING_SNAKE_CASE_			): v for k, v in idalabel.items()}
        UpperCamelCase_			  =						idalabel
        UpperCamelCase_			  =						{v: k for k, v in idalabel.items()}

    UpperCamelCase_			  =						img_size
    UpperCamelCase_			  =						num_classes
    UpperCamelCase_			  =						embed_dim
    UpperCamelCase_			  =						depths
    UpperCamelCase_			  =						num_heads
    UpperCamelCase_			  =						window_size

    return config




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						Optional[Any]:



    """simple docstring"""






    if "patch_embed.proj" in name:
        UpperCamelCase_			  =						name.replace("patch_embed.proj"			,						"embeddings.patch_embeddings.projection"			)
    if "patch_embed.norm" in name:
        UpperCamelCase_			  =						name.replace("patch_embed.norm"			,						"embeddings.norm"			)
    if "layers" in name:
        UpperCamelCase_			  =						"encoder." + name
    if "attn.proj" in name:
        UpperCamelCase_			  =						name.replace("attn.proj"			,						"attention.output.dense"			)
    if "attn" in name:
        UpperCamelCase_			  =						name.replace("attn"			,						"attention.self"			)
    if "norm1" in name:
        UpperCamelCase_			  =						name.replace("norm1"			,						"layernorm_before"			)
    if "norm2" in name:
        UpperCamelCase_			  =						name.replace("norm2"			,						"layernorm_after"			)
    if "mlp.fc1" in name:
        UpperCamelCase_			  =						name.replace("mlp.fc1"			,						"intermediate.dense"			)
    if "mlp.fc2" in name:
        UpperCamelCase_			  =						name.replace("mlp.fc2"			,						"output.dense"			)
    if "q_bias" in name:
        UpperCamelCase_			  =						name.replace("q_bias"			,						"query.bias"			)
    if "k_bias" in name:
        UpperCamelCase_			  =						name.replace("k_bias"			,						"key.bias"			)
    if "v_bias" in name:
        UpperCamelCase_			  =						name.replace("v_bias"			,						"value.bias"			)
    if "cpb_mlp" in name:
        UpperCamelCase_			  =						name.replace("cpb_mlp"			,						"continuous_position_bias_mlp"			)
    if name == "norm.weight":
        UpperCamelCase_			  =						"layernorm.weight"
    if name == "norm.bias":
        UpperCamelCase_			  =						"layernorm.bias"

    if "head" in name:
        UpperCamelCase_			  =						name.replace("head"			,						"classifier"			)
    else:
        UpperCamelCase_			  =						"swinv2." + name

    return name




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Optional[int]:



    """simple docstring"""






    for key in orig_state_dict.copy().keys():
        UpperCamelCase_			  =						orig_state_dict.pop(SCREAMING_SNAKE_CASE_			)

        if "mask" in key:
            continue
        elif "qkv" in key:
            UpperCamelCase_			  =						key.split("."			)
            UpperCamelCase_			  =						int(key_split[1]			)
            UpperCamelCase_			  =						int(key_split[3]			)
            UpperCamelCase_			  =						model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                UpperCamelCase_			  =						val[:dim, :]
                UpperCamelCase_			  =						val[dim : dim * 2, :]
                UpperCamelCase_			  =						val[-dim:, :]
            else:
                UpperCamelCase_			  =						val[:dim]
                UpperCamelCase_			  =						val[
                    dim : dim * 2
                ]
                UpperCamelCase_			  =						val[-dim:]
        else:
            UpperCamelCase_			  =						val

    return orig_state_dict




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Any:



    """simple docstring"""






    UpperCamelCase_			  =						timm.create_model(SCREAMING_SNAKE_CASE_			,						pretrained=SCREAMING_SNAKE_CASE_			)
    timm_model.eval()

    UpperCamelCase_			  =						get_swinva_config(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						SwinvaForImageClassification(SCREAMING_SNAKE_CASE_			)
    model.eval()

    UpperCamelCase_			  =						convert_state_dict(timm_model.state_dict()			,						SCREAMING_SNAKE_CASE_			)
    model.load_state_dict(SCREAMING_SNAKE_CASE_			)

    UpperCamelCase_			  =						"http://images.cocodataset.org/val2017/000000039769.jpg"

    UpperCamelCase_			  =						AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"			,						"-"			)			)			)
    UpperCamelCase_			  =						Image.open(requests.get(SCREAMING_SNAKE_CASE_			,						stream=SCREAMING_SNAKE_CASE_			).raw			)
    UpperCamelCase_			  =						image_processor(images=SCREAMING_SNAKE_CASE_			,						return_tensors="pt"			)

    UpperCamelCase_			  =						timm_model(inputs["pixel_values"]			)
    UpperCamelCase_			  =						model(**SCREAMING_SNAKE_CASE_			).logits

    assert torch.allclose(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						atol=1E-3			)

    print(f"Saving model {swinva_name} to {pytorch_dump_folder_path}"			)
    model.save_pretrained(SCREAMING_SNAKE_CASE_			)

    print(f"Saving image processor to {pytorch_dump_folder_path}"			)
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE_			)

    model.push_to_hub(
        repo_path_or_name=Path(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)			,						organization="nandwalritik"			,						commit_message="Add model"			,						)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:Tuple						=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--swinv2_name""",
     default="""swinv2_tiny_patch4_window8_256""",
     type=str,
     help="""Name of the Swinv2 timm model you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
 )

 SCREAMING_SNAKE_CASE		:int						=     parser.parse_args()
 convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
