




'''simple docstring'''
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 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[int]     			=						SwinvaConfig()
    snake_case_ :    Union[str, Any]     			=						swinva_name.split("_"       )

    snake_case_ :    Tuple     			=						name_split[1]
    if "to" in name_split[3]:
        snake_case_ :    List[str]     			=						int(name_split[3][-3:]       )
    else:
        snake_case_ :    Optional[Any]     			=						int(name_split[3]       )
    if "to" in name_split[2]:
        snake_case_ :    List[Any]     			=						int(name_split[2][-2:]       )
    else:
        snake_case_ :    int     			=						int(name_split[2][6:]       )

    if model_size == "tiny":
        snake_case_ :    Any     			=						9_6
        snake_case_ :    Tuple     			=						(2, 2, 6, 2)
        snake_case_ :    Dict     			=						(3, 6, 1_2, 2_4)
    elif model_size == "small":
        snake_case_ :    List[Any]     			=						9_6
        snake_case_ :    Tuple     			=						(2, 2, 1_8, 2)
        snake_case_ :    List[Any]     			=						(3, 6, 1_2, 2_4)
    elif model_size == "base":
        snake_case_ :    Dict     			=						1_2_8
        snake_case_ :    List[str]     			=						(2, 2, 1_8, 2)
        snake_case_ :    Union[str, Any]     			=						(4, 8, 1_6, 3_2)
    else:
        snake_case_ :    Union[str, Any]     			=						1_9_2
        snake_case_ :    Any     			=						(2, 2, 1_8, 2)
        snake_case_ :    Optional[Any]     			=						(6, 1_2, 2_4, 4_8)

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

    if ("22k" in swinva_name) and ("to" not in swinva_name):
        snake_case_ :    List[str]     			=						2_1_8_4_1
        snake_case_ :    List[Any]     			=						"huggingface/label-files"
        snake_case_ :    List[Any]     			=						"imagenet-22k-id2label.json"
        snake_case_ :    Dict     			=						json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		repo_type="dataset"       ),		"r"       )       )
        snake_case_ :    List[str]     			=						{int(__SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
        snake_case_ :    Tuple     			=						idalabel
        snake_case_ :    Any     			=						{v: k for k, v in idalabel.items()}

    else:
        snake_case_ :    Dict     			=						1_0_0_0
        snake_case_ :    str     			=						"huggingface/label-files"
        snake_case_ :    str     			=						"imagenet-1k-id2label.json"
        snake_case_ :    int     			=						json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		repo_type="dataset"       ),		"r"       )       )
        snake_case_ :    int     			=						{int(__SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
        snake_case_ :    List[Any]     			=						idalabel
        snake_case_ :    List[str]     			=						{v: k for k, v in idalabel.items()}

    snake_case_ :    int     			=						img_size
    snake_case_ :    Tuple     			=						num_classes
    snake_case_ :    Dict     			=						embed_dim
    snake_case_ :    str     			=						depths
    snake_case_ :    Any     			=						num_heads
    snake_case_ :    int     			=						window_size

    return config

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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

    if "head" in name:
        snake_case_ :    List[str]     			=						name.replace("head",		"classifier"       )
    else:
        snake_case_ :    List[Any]     			=						"swinv2." + name

    return name

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    for key in orig_state_dict.copy().keys():
        snake_case_ :    List[str]     			=						orig_state_dict.pop(__SCREAMING_SNAKE_CASE       )

        if "mask" in key:
            continue
        elif "qkv" in key:
            snake_case_ :    List[str]     			=						key.split("."       )
            snake_case_ :    Optional[int]     			=						int(key_split[1]       )
            snake_case_ :    Dict     			=						int(key_split[3]       )
            snake_case_ :    Union[str, Any]     			=						model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                snake_case_ :    List[str]     			=						val[:dim, :]
                snake_case_ :    Optional[int]     			=						val[dim : dim * 2, :]
                snake_case_ :    Tuple     			=						val[-dim:, :]
            else:
                snake_case_ :    Optional[Any]     			=						val[:dim]
                snake_case_ :    List[Any]     			=						val[
                    dim : dim * 2
                ]
                snake_case_ :    Optional[int]     			=						val[-dim:]
        else:
            snake_case_ :    Optional[int]     			=						val

    return orig_state_dict

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[int]     			=						timm.create_model(__SCREAMING_SNAKE_CASE,		pretrained=__SCREAMING_SNAKE_CASE       )
    timm_model.eval()

    snake_case_ :    int     			=						get_swinva_config(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    int     			=						SwinvaForImageClassification(__SCREAMING_SNAKE_CASE       )
    model.eval()

    snake_case_ :    List[Any]     			=						convert_state_dict(timm_model.state_dict(),		__SCREAMING_SNAKE_CASE       )
    model.load_state_dict(__SCREAMING_SNAKE_CASE       )

    snake_case_ :    Optional[int]     			=						"http://images.cocodataset.org/val2017/000000039769.jpg"

    snake_case_ :    List[Any]     			=						AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_",		"-"       )       )       )
    snake_case_ :    str     			=						Image.open(requests.get(__SCREAMING_SNAKE_CASE,		stream=__SCREAMING_SNAKE_CASE       ).raw       )
    snake_case_ :    Union[str, Any]     			=						image_processor(images=__SCREAMING_SNAKE_CASE,		return_tensors="pt"       )

    snake_case_ :    int     			=						timm_model(inputs["pixel_values"]       )
    snake_case_ :    Tuple     			=						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__":
  a_											=						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."
  )

  a_											=						parser.parse_args()
  convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)






'''simple docstring'''
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


a_											=						["small", "medium", "large"]

a_											=						"lm_head.decoder.weight"
a_											=						"lm_head.weight"

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						torch.load(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[Any]     			=						d.pop(__SCREAMING_SNAKE_CASE       )
    os.makedirs(__SCREAMING_SNAKE_CASE,		exist_ok=__SCREAMING_SNAKE_CASE       )
    torch.save(__SCREAMING_SNAKE_CASE,		os.path.join(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )       )


if __name__ == "__main__":
  a_											=						argparse.ArgumentParser()
  parser.add_argument("--dialogpt_path", default=".", type=str)
  a_											=						parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    a_											=						os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
    a_											=						f"""./DialoGPT-{MODEL}"""
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )




