

import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification


def  _UpperCAmelCase							(		a__):

			'''simple docstring'''



			a_		:		Optional[Any]      				=      []
			embed.append(
			    (
			        f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
			        f'''stage{idx}.patch_embed.proj.weight''',
			    ))
			embed.append(
			    (
			        f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
			        f'''stage{idx}.patch_embed.proj.bias''',
			    ))
			embed.append(
			    (
			        f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
			        f'''stage{idx}.patch_embed.norm.weight''',
			    ))
			embed.append(
			    (
			        f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
			        f'''stage{idx}.patch_embed.norm.bias''',
			    ))
			return embed


def  _UpperCAmelCase							(		a__   ,     a__):

			'''simple docstring'''



			a_		:		Tuple      				=      []
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
			        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
			        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
			        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
			        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
			        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
			        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
			        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
			        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
			        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
			        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
			        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
			        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
			        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
			        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
			        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
			        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
			        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
			        f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
			        f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
			        f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
			        f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
			        f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
			        f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
			        f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
			        f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
			    ))
			attention_weights.append(
			    (
			        f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
			        f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
			    ))
			attention_weights.append(
			    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight'''))
			attention_weights.append(
			    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias'''))
			attention_weights.append(
			    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight'''))
			attention_weights.append(
			    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias'''))
			attention_weights.append(
			    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight'''))
			attention_weights.append(
			    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias'''))
			attention_weights.append(
			    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight'''))
			attention_weights.append(
			    (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias'''))
			return attention_weights


def  _UpperCAmelCase							(		a__):

			'''simple docstring'''



			a_		:		Optional[int]      				=      []
			token.append((f'''cvt.encoder.stages.{idx}.cls_token''', """stage2.cls_token"""))
			return token


def  _UpperCAmelCase							(		):

			'''simple docstring'''



			a_		:		Union[str, Any]      				=      []
			head.append(("""layernorm.weight""", """norm.weight"""))
			head.append(("""layernorm.bias""", """norm.bias"""))
			head.append(("""classifier.weight""", """head.weight"""))
			head.append(("""classifier.bias""", """head.bias"""))
			return head


def  _UpperCAmelCase							(		a__   ,     a__   ,     a__   ,     a__):

			'''simple docstring'''



			a_		:		Optional[int]      				=      """imagenet-1k-id2label.json"""
			a_		:		str      				=      1_0_0_0

			a_		:		Union[str, Any]      				=      """huggingface/label-files"""
			a_		:		List[str]      				=      num_labels
			a_		:		Union[str, Any]      				=      json.load(open(cached_download(hf_hub_url(A__   ,     A__   ,     repo_type="""dataset"""))   ,     """r"""))
			a_		:		Union[str, Any]      				=      {int(A__): v for k, v in idalabel.items()}

			a_		:		Any      				=      idalabel
			a_		:		Dict      				=      {v: k for k, v in idalabel.items()}

			a_		:		str      				=      CvtConfig(num_labels=A__   ,     idalabel=A__   ,     labelaid=A__)

			# For depth size 13 (13 = 1+2+10)
			if cvt_model.rsplit("""/"""   ,     1)[-1][4:6] == "13":
						a_		:		Any      				=      [1, 2, 1_0]

			# For depth size 21 (21 = 1+4+16)
			elif cvt_model.rsplit("""/"""   ,     1)[-1][4:6] == "21":
						a_		:		str      				=      [1, 4, 1_6]

			# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
			else:
						a_		:		Any      				=      [2, 2, 2_0]
						a_		:		Optional[Any]      				=      [3, 1_2, 1_6]
						a_		:		Union[str, Any]      				=      [1_9_2, 7_6_8, 1_0_2_4]

			a_		:		List[Any]      				=      CvtForImageClassification(A__)
			a_		:		Union[str, Any]      				=      AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""")
			a_		:		Union[str, Any]      				=      image_size
			a_		:		int      				=      torch.load(A__   ,     map_location=torch.device("""cpu"""))

			a_		:		List[str]      				=      OrderedDict()
			a_		:		List[Any]      				=      []

			for idx in range(len(config.depth)):
						if config.cls_token[idx]:
									a_		:		Dict      				=      list_of_state_dict + cls_token(A__)
						a_		:		int      				=      list_of_state_dict + embeddings(A__)
						for cnt in range(config.depth[idx]):
									a_		:		str      				=      list_of_state_dict + attention(A__   ,     A__)

			a_		:		Optional[int]      				=      list_of_state_dict + final()
			for gg in list_of_state_dict:
						print(A__)
			for i in range(len(A__)):
						a_		:		Union[str, Any]      				=      original_weights[list_of_state_dict[i][1]]

			model.load_state_dict(A__)
			model.save_pretrained(A__)
			image_processor.save_pretrained(A__)


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
	__snake_case							:	Tuple											=       argparse.ArgumentParser()
	parser.add_argument(
	    """--cvt_model""",
	    default="""cvt-w24""",
	    type=str,
	    help="""Name of the cvt model you'd like to convert.""",
	)
	parser.add_argument(
	    """--image_size""",
	    default=3_84,
	    type=int,
	    help="""Input Image Size""",
	)
	parser.add_argument(
	    """--cvt_file_name""",
	    default=r"""cvtmodels\CvT-w24-384x384-IN-22k.pth""",
	    type=str,
	    help="""Input Image Size""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
	)

	__snake_case							:	Any											=       parser.parse_args()
	convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)







'''simple docstring'''


from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)

