

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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int				)							->				Dict:





    '''simple docstring'''






    A__   				=       []
    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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     List[str]				)							->				Optional[int]:





    '''simple docstring'''






    A__   				=       []
    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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Dict				)							->				Union[str, Any]:





    '''simple docstring'''






    A__   				=       []
    token.append((f'cvt.encoder.stages.{idx}.cls_token', 'stage2.cls_token')				)
    return token
def 						_snake_case(   )							->				Any:





    '''simple docstring'''






    A__   				=       []
    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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     List[Any]   ,      SCREAMING_SNAKE_CASE__      :     Union[str, Any]				)							->				Optional[int]:





    '''simple docstring'''






    A__   				=       'imagenet-1k-id2label.json'
    A__   				=       1000

    A__   				=       'huggingface/label-files'
    A__   				=       num_labels
    A__   				=       json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      repo_type='dataset'				)				)   ,      'r'				)				)
    A__   				=       {int(SCREAMING_SNAKE_CASE__				): v for k, v in idalabel.items()}

    A__   				=       idalabel
    A__   				=       {v: k for k, v in idalabel.items()}

    A__   				=       A__   				=       CvtConfig(num_labels=SCREAMING_SNAKE_CASE__   ,      idalabel=SCREAMING_SNAKE_CASE__   ,      labelaid=SCREAMING_SNAKE_CASE__				)

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

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

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        A__   				=       [2, 2, 20]
        A__   				=       [3, 12, 16]
        A__   				=       [192, 768, 1024]

    A__   				=       CvtForImageClassification(SCREAMING_SNAKE_CASE__				)
    A__   				=       AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'				)
    A__   				=       image_size
    A__   				=       torch.load(SCREAMING_SNAKE_CASE__   ,      map_location=torch.device('cpu'				)				)

    A__   				=       OrderedDict()
    A__   				=       []

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

    A__   				=       list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(SCREAMING_SNAKE_CASE__				)
    for i in range(len(SCREAMING_SNAKE_CASE__				)				):
        A__   				=       original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(SCREAMING_SNAKE_CASE__				)
    model.save_pretrained(SCREAMING_SNAKE_CASE__				)
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE__				)


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

if __name__ == "__main__":
     lowercase_			=  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=384,
         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."
     )

     lowercase_			=  parser.parse_args()
     convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
     if not (is_transformers_available() and is_torch_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from ...utils.dummy_torch_and_transformers_objects import (
         ImageTextPipelineOutput,
         UniDiffuserPipeline,
     )
else:
     from .modeling_text_decoder import UniDiffuserTextDecoder
     from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
     from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline

