
'''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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


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

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






    snake_case_ :    Optional[int]     			=						[]
    for i in range(config.num_hidden_layers       ):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((f'blocks.{i}.norm1.weight', f'vit.encoder.layer.{i}.layernorm_before.weight')       )
        rename_keys.append((f'blocks.{i}.norm1.bias', f'vit.encoder.layer.{i}.layernorm_before.bias')       )
        rename_keys.append((f'blocks.{i}.attn.proj.weight', f'vit.encoder.layer.{i}.attention.output.dense.weight')       )
        rename_keys.append((f'blocks.{i}.attn.proj.bias', f'vit.encoder.layer.{i}.attention.output.dense.bias')       )
        rename_keys.append((f'blocks.{i}.norm2.weight', f'vit.encoder.layer.{i}.layernorm_after.weight')       )
        rename_keys.append((f'blocks.{i}.norm2.bias', f'vit.encoder.layer.{i}.layernorm_after.bias')       )
        rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'vit.encoder.layer.{i}.intermediate.dense.weight')       )
        rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'vit.encoder.layer.{i}.intermediate.dense.bias')       )
        rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'vit.encoder.layer.{i}.output.dense.weight')       )
        rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'vit.encoder.layer.{i}.output.dense.bias')       )

    # projection layer + position embeddings
    rename_keys.extend(
        [
            ("cls_token", "vit.embeddings.cls_token"),
            ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
            ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
            ("pos_embed", "vit.embeddings.position_embeddings"),
        ]       )

    if base_model:
        # layernorm + pooler
        rename_keys.extend(
            [
                ("norm.weight", "layernorm.weight"),
                ("norm.bias", "layernorm.bias"),
                ("pre_logits.fc.weight", "pooler.dense.weight"),
                ("pre_logits.fc.bias", "pooler.dense.bias"),
            ]       )

        # if just the base model, we should remove "vit" from all keys that start with "vit"
        snake_case_ :    Dict     			=						[(pair[0], pair[1][4:]) if pair[1].startswith("vit"       ) else pair for pair in rename_keys]
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ("norm.weight", "vit.layernorm.weight"),
                ("norm.bias", "vit.layernorm.bias"),
                ("head.weight", "classifier.weight"),
                ("head.bias", "classifier.bias"),
            ]       )

    return rename_keys

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






    for i in range(config.num_hidden_layers       ):
        if base_model:
            snake_case_ :    Optional[int]     			=						""
        else:
            snake_case_ :    List[Any]     			=						"vit."
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        snake_case_ :    Tuple     			=						state_dict.pop(f'blocks.{i}.attn.qkv.weight'       )
        snake_case_ :    Optional[int]     			=						state_dict.pop(f'blocks.{i}.attn.qkv.bias'       )
        # next, add query, keys and values (in that order) to the state dict
        snake_case_ :    Dict     			=						in_proj_weight[
            : config.hidden_size, :
        ]
        snake_case_ :    Optional[Any]     			=						in_proj_bias[: config.hidden_size]
        snake_case_ :    str     			=						in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        snake_case_ :    Dict     			=						in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        snake_case_ :    List[Any]     			=						in_proj_weight[
            -config.hidden_size :, :
        ]
        snake_case_ :    Optional[Any]     			=						in_proj_bias[-config.hidden_size :]

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






    snake_case_ :    List[Any]     			=						["head.weight", "head.bias"]
    for k in ignore_keys:
        state_dict.pop(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

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






    snake_case_ :    Any     			=						dct.pop(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Tuple     			=						val

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






    snake_case_ :    List[Any]     			=						"http://images.cocodataset.org/val2017/000000039769.jpg"
    snake_case_ :    List[Any]     			=						Image.open(requests.get(__SCREAMING_SNAKE_CASE,		stream=__SCREAMING_SNAKE_CASE       ).raw       )
    return im

@torch.no_grad()
def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[Any]     			=						ViTConfig()
    snake_case_ :    List[str]     			=						False
    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    if vit_name[-5:] == "in21k":
        snake_case_ :    Tuple     			=						True
        snake_case_ :    Dict     			=						int(vit_name[-1_2:-1_0]       )
        snake_case_ :    List[Any]     			=						int(vit_name[-9:-6]       )
    else:
        snake_case_ :    str     			=						1_0_0_0
        snake_case_ :    str     			=						"huggingface/label-files"
        snake_case_ :    str     			=						"imagenet-1k-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_ :    Optional[int]     			=						idalabel
        snake_case_ :    Optional[int]     			=						{v: k for k, v in idalabel.items()}
        snake_case_ :    Optional[Any]     			=						int(vit_name[-6:-4]       )
        snake_case_ :    Dict     			=						int(vit_name[-3:]       )
    # size of the architecture
    if "deit" in vit_name:
        if vit_name[9:].startswith("tiny"       ):
            snake_case_ :    Tuple     			=						1_9_2
            snake_case_ :    Union[str, Any]     			=						7_6_8
            snake_case_ :    Any     			=						1_2
            snake_case_ :    List[Any]     			=						3
        elif vit_name[9:].startswith("small"       ):
            snake_case_ :    Optional[int]     			=						3_8_4
            snake_case_ :    Optional[int]     			=						1_5_3_6
            snake_case_ :    Optional[int]     			=						1_2
            snake_case_ :    int     			=						6
        else:
            pass
    else:
        if vit_name[4:].startswith("small"       ):
            snake_case_ :    Union[str, Any]     			=						7_6_8
            snake_case_ :    Optional[Any]     			=						2_3_0_4
            snake_case_ :    List[str]     			=						8
            snake_case_ :    Dict     			=						8
        elif vit_name[4:].startswith("base"       ):
            pass
        elif vit_name[4:].startswith("large"       ):
            snake_case_ :    List[str]     			=						1_0_2_4
            snake_case_ :    Dict     			=						4_0_9_6
            snake_case_ :    str     			=						2_4
            snake_case_ :    int     			=						1_6
        elif vit_name[4:].startswith("huge"       ):
            snake_case_ :    Any     			=						1_2_8_0
            snake_case_ :    List[Any]     			=						5_1_2_0
            snake_case_ :    Tuple     			=						3_2
            snake_case_ :    Dict     			=						1_6

    # load original model from timm
    snake_case_ :    Dict     			=						timm.create_model(__SCREAMING_SNAKE_CASE,		pretrained=__SCREAMING_SNAKE_CASE       )
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    snake_case_ :    Union[str, Any]     			=						timm_model.state_dict()
    if base_model:
        remove_classification_head_(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Union[str, Any]     			=						create_rename_keys(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    for src, dest in rename_keys:
        rename_key(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    read_in_q_k_v(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        snake_case_ :    Union[str, Any]     			=						ViTModel(__SCREAMING_SNAKE_CASE       ).eval()
    else:
        snake_case_ :    int     			=						ViTForImageClassification(__SCREAMING_SNAKE_CASE       ).eval()
    model.load_state_dict(__SCREAMING_SNAKE_CASE       )

    # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
    if "deit" in vit_name:
        snake_case_ :    Union[str, Any]     			=						DeiTImageProcessor(size=config.image_size       )
    else:
        snake_case_ :    List[str]     			=						ViTImageProcessor(size=config.image_size       )
    snake_case_ :    Any     			=						image_processor(images=prepare_img(),		return_tensors="pt"       )
    snake_case_ :    str     			=						encoding["pixel_values"]
    snake_case_ :    Tuple     			=						model(__SCREAMING_SNAKE_CASE       )

    if base_model:
        snake_case_ :    Union[str, Any]     			=						timm_model.forward_features(__SCREAMING_SNAKE_CASE       )
        assert timm_pooled_output.shape == outputs.pooler_output.shape
        assert torch.allclose(__SCREAMING_SNAKE_CASE,		outputs.pooler_output,		atol=1E-3       )
    else:
        snake_case_ :    Tuple     			=						timm_model(__SCREAMING_SNAKE_CASE       )
        assert timm_logits.shape == outputs.logits.shape
        assert torch.allclose(__SCREAMING_SNAKE_CASE,		outputs.logits,		atol=1E-3       )

    Path(__SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE       )
    print(f'Saving model {vit_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       )


if __name__ == "__main__":
  a											=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--vit_name",
      default="vit_base_patch16_224",
      type=str,
      help="Name of the ViT 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_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)






'''simple docstring'''
import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


a_											=						logging.get_logger(__name__)






class    UpperCAmelCase_				:
 UpperCAmelCase_		    =						None


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






    if ParallelBackendConfig.backend_name is None:
        return _map_with_multiprocessing_pool(
            __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

    return _map_with_joblib(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

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






    snake_case_ :    str     			=						num_proc if num_proc <= len(__SCREAMING_SNAKE_CASE       ) else len(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Optional[int]     			=						[]  # We organize the splits ourselve (contiguous splits)
    for index in range(__SCREAMING_SNAKE_CASE       ):
        snake_case_ :    List[str]     			=						len(__SCREAMING_SNAKE_CASE       ) // num_proc
        snake_case_ :    Optional[Any]     			=						len(__SCREAMING_SNAKE_CASE       ) % num_proc
        snake_case_ :    Optional[Any]     			=						div * index + min(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
        snake_case_ :    Union[str, Any]     			=						start + div + (1 if index < mod else 0)
        split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)       )

    if len(__SCREAMING_SNAKE_CASE       ) != sum(len(i[1]       ) for i in split_kwds       ):
        raise ValueError(
            f'Error dividing inputs iterable among processes. '
            f'Total number of objects {len(__SCREAMING_SNAKE_CASE       )}, '
            f'length: {sum(len(i[1]       ) for i in split_kwds       )}'       )

    logger.info(
        f'Spawning {num_proc} processes for {len(__SCREAMING_SNAKE_CASE       )} objects in slices of {[len(i[1]       ) for i in split_kwds]}'       )
    snake_case_			, snake_case_ :    Tuple     			=						None, None
    if not disable_tqdm:
        snake_case_			, snake_case_ :    int     			=						(RLock(),), tqdm.set_lock
    with Pool(__SCREAMING_SNAKE_CASE,		initargs=__SCREAMING_SNAKE_CASE,		initializer=__SCREAMING_SNAKE_CASE       ) as pool:
        snake_case_ :    Union[str, Any]     			=						pool.map(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    logger.info(f'Finished {num_proc} processes'       )
    snake_case_ :    str     			=						[obj for proc_res in mapped for obj in proc_res]
    logger.info(f'Unpacked {len(__SCREAMING_SNAKE_CASE       )} objects'       )

    return mapped

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






    import joblib

    with joblib.parallel_backend(ParallelBackendConfig.backend_name,		n_jobs=__SCREAMING_SNAKE_CASE       ):
        return joblib.Parallel()(
            joblib.delayed(__SCREAMING_SNAKE_CASE       )((function, obj, types, None, True, None)       ) for obj in iterable       )


@experimental
@contextlib.contextmanager
def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Union[str, Any]     			=						backend_name

    if backend_name == "spark":
        from joblibspark import register_spark

        register_spark()

        # TODO: call create_cache_and_write_probe if "download" in steps
        # TODO: raise NotImplementedError when Dataset.map etc is called

    try:
        yield
    finally:
        snake_case_ :    Any     			=						None




