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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__=False     ):
       lowerCAmelCase__					     = []

       # fmt: off
       # stem:
       rename_keys.append(('cls_token', 'vit.embeddings.cls_token')     )
       rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings')     )

       rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight')     )
       rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias')     )

       # backbone
       rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight')     )
       rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight')     )
       rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias')     )

       for stage_idx in range(len(config.backbone_config.depths     )     ):
              for layer_idx in range(config.backbone_config.depths[stage_idx]     ):
                     rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")     )
                     rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")     )
                     rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")     )
                     rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")     )
                     rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")     )
                     rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")     )
                     rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")     )
                     rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")     )
                     rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")     )

              rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")     )
              rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")     )
              rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")     )

       # transformer encoder
       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""")     )

       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"
              lowerCAmelCase__					     = [(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'),
                  ]     )
       # fmt: on

       return rename_keys






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=False     ):





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






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = ['head.weight', 'head.bias']
       for k in ignore_keys:
              state_dict.pop(__snake_case     ,  __snake_case     )






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = dct.pop(__snake_case     )
       lowerCAmelCase__					     = val






def 				__lowerCamelCase		(						):
       lowerCAmelCase__					     = 'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowerCAmelCase__					     = Image.open(requests.get(__snake_case     ,  stream=__snake_case     ).raw     )
       return im






@torch.no_grad()
def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=False     ):
       lowerCAmelCase__					     = BitConfig(
           global_padding='same'     ,  layer_type='bottleneck'     ,  depths=(3, 4, 9)     ,  out_features=['stage3']     ,  embedding_dynamic_padding=__snake_case     ,  )
       lowerCAmelCase__					     = ViTHybridConfig(backbone_config=__snake_case     ,  image_size=3_8_4     ,  num_labels=1_0_0_0     )
       lowerCAmelCase__					     = False

       # load original model from timm
       lowerCAmelCase__					     = timm.create_model(__snake_case     ,  pretrained=__snake_case     )
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       lowerCAmelCase__					     = timm_model.state_dict()
       if base_model:
              remove_classification_head_(__snake_case     )
       lowerCAmelCase__					     = create_rename_keys(__snake_case     ,  __snake_case     )
       for src, dest in rename_keys:
              rename_key(__snake_case     ,  __snake_case     ,  __snake_case     )
       read_in_q_k_v(__snake_case     ,  __snake_case     ,  __snake_case     )

       lowerCAmelCase__					     = 'huggingface/label-files'
       lowerCAmelCase__					     = 'imagenet-1k-id2label.json'
       lowerCAmelCase__					     = json.load(open(hf_hub_download(__snake_case     ,  __snake_case     ,  repo_type='dataset'     )     ,  'r'     )     )
       lowerCAmelCase__					     = {int(__snake_case     ): v for k, v in idalabel.items()}
       lowerCAmelCase__					     = idalabel
       lowerCAmelCase__					     = {v: k for k, v in idalabel.items()}

       # load HuggingFace model
       if vit_name[-5:] == "in21k":
              lowerCAmelCase__					     = ViTHybridModel(__snake_case     ).eval()
       else:
              lowerCAmelCase__					     = ViTHybridForImageClassification(__snake_case     ).eval()
       model.load_state_dict(__snake_case     )

       # create image processor
       lowerCAmelCase__					     = create_transform(**resolve_data_config({}     ,  model=__snake_case     )     )
       lowerCAmelCase__					     = transform.transforms

       lowerCAmelCase__					     = {
           'bilinear': PILImageResampling.BILINEAR,
           'bicubic': PILImageResampling.BICUBIC,
           'nearest': PILImageResampling.NEAREST,
       }

       lowerCAmelCase__					     = ViTHybridImageProcessor(
           do_resize=__snake_case     ,  size={'shortest_edge': timm_transforms[0].size}     ,  resample=pillow_resamplings[timm_transforms[0].interpolation.value]     ,  do_center_crop=__snake_case     ,  crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}     ,  do_normalize=__snake_case     ,  image_mean=timm_transforms[-1].mean.tolist()     ,  image_std=timm_transforms[-1].std.tolist()     ,  )

       lowerCAmelCase__					     = prepare_img()
       lowerCAmelCase__					     = transform(__snake_case     ).unsqueeze(0     )
       lowerCAmelCase__					     = processor(__snake_case     ,  return_tensors='pt'     ).pixel_values

       # verify pixel values
       assert torch.allclose(__snake_case     ,  __snake_case     )

       # verify logits
       with torch.no_grad():
              lowerCAmelCase__					     = model(__snake_case     )
              lowerCAmelCase__					     = outputs.logits

       print('Predicted class:'     ,  logits.argmax(-1     ).item()     )
       if base_model:
              lowerCAmelCase__					     = timm_model.forward_features(__snake_case     )
              assert timm_pooled_output.shape == outputs.pooler_output.shape
              assert torch.allclose(__snake_case     ,  outputs.pooler_output     ,  atol=1e-3     )
       else:
              lowerCAmelCase__					     = timm_model(__snake_case     )
              assert timm_logits.shape == outputs.logits.shape
              assert torch.allclose(__snake_case     ,  outputs.logits     ,  atol=1e-3     )
       print('Looks ok!'     )

       if pytorch_dump_folder_path is not None:
              Path(__snake_case     ).mkdir(exist_ok=__snake_case     )
              print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}"""     )
              model.save_pretrained(__snake_case     )
              print(F"""Saving processor to {pytorch_dump_folder_path}"""     )
              processor.save_pretrained(__snake_case     )

       if push_to_hub:
              print(F"""Pushing model and processor to the hub {vit_name}"""     )
              model.push_to_hub(F"""ybelkada/{vit_name}"""     )
              processor.push_to_hub(F"""ybelkada/{vit_name}"""     )


if __name__ == "__main__":
    _a     						=			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--vit_name',
        default='vit_base_r50_s16_384',
        type=str,
        help='Name of the hybrid 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.'
    )
    parser.add_argument(
        '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
    )

    _a     						=			parser.parse_args()
    convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)

import argparse
from collections import defaultdict

import yaml


lowerCAmelCase__     						=			'docs/source/en/_toctree.yml'






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = defaultdict(lowerCAmelCase__     )
       for doc in model_doc:
              counts[doc["local"]] += 1
       lowerCAmelCase__					     = [key for key, value in counts.items() if value > 1]

       lowerCAmelCase__					     = []
       for duplicate_key in duplicates:
              lowerCAmelCase__					     = list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}     )
              if len(lowerCAmelCase__     ) > 1:
                     raise ValueError(
                         F"""{duplicate_key} is present several times in the documentation table of content at """
                         '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
                         'others.'     )
              # Only add this once
              new_doc.append({'local': duplicate_key, 'title': titles[0]}     )

       # Add none duplicate-keys
       new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1]     )

       # Sort
       return sorted(lowerCAmelCase__     ,  key=lambda lowerCAmelCase__     : s["title"].lower()     )






def 				__lowerCamelCase		(						lowerCAmelCase__=False     ):
       with open(lowerCAmelCase__     ,  encoding='utf-8'     ) as f:
              lowerCAmelCase__					     = yaml.safe_load(f.read()     )

       # Get to the API doc
       lowerCAmelCase__					     = 0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       lowerCAmelCase__					     = content[api_idx]['sections']

       # Then to the model doc
       lowerCAmelCase__					     = 0
       while api_doc[model_idx]["title"] != "Models":
              model_idx += 1

       lowerCAmelCase__					     = api_doc[model_idx]['sections']

       lowerCAmelCase__					     = [(idx, section) for idx, section in enumerate(lowerCAmelCase__     ) if 'sections' in section]
       lowerCAmelCase__					     = False
       for idx, modality_doc in modalities_docs:
              lowerCAmelCase__					     = modality_doc['sections']
              lowerCAmelCase__					     = clean_model_doc_toc(lowerCAmelCase__     )

              if old_modality_doc != new_modality_doc:
                     lowerCAmelCase__					     = True
                     if overwrite:
                            lowerCAmelCase__					     = new_modality_doc

       if diff:
              if overwrite:
                     lowerCAmelCase__					     = model_doc
                     lowerCAmelCase__					     = api_doc
                     with open(lowerCAmelCase__     ,  'w'     ,  encoding='utf-8'     ) as f:
                            f.write(yaml.dump(lowerCAmelCase__     ,  allow_unicode=lowerCAmelCase__     )     )
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'     )


if __name__ == "__main__":
    lowerCAmelCase__     						=			argparse.ArgumentParser()
    parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
    lowerCAmelCase__     						=			parser.parse_args()

    check_model_doc(args.fix_and_overwrite)
