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()
__magic_name__								=logging.get_logger(__name__)







def  __UpperCamelCase    (						A ,							A=False							):
     UpperCamelCase__										=      []

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







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      ['''head.weight''', '''head.bias''']
     for k in ignore_keys:
          state_dict.pop(A ,							A							)







def  __UpperCamelCase    (						A ,							A ,							A							):
     UpperCamelCase__										=      dct.pop(A							)
     UpperCamelCase__										=      val







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







@torch.no_grad()
def  __UpperCamelCase    (						A ,							A ,							A=False							):
     UpperCamelCase__										=      BitConfig(
         global_padding='''same''' ,							layer_type='''bottleneck''' ,							depths=(3, 4, 9) ,							out_features=['''stage3'''] ,							embedding_dynamic_padding=A ,							)
     UpperCamelCase__										=      ViTHybridConfig(backbone_config=A ,							image_size=384 ,							num_labels=1000							)
     UpperCamelCase__										=      False

     # load original model from timm
     UpperCamelCase__										=      timm.create_model(A ,							pretrained=A							)
     timm_model.eval()

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

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

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

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

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

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

     UpperCamelCase__										=      prepare_img()
     UpperCamelCase__										=      transform(A							).unsqueeze(0							)
     UpperCamelCase__										=      processor(A ,							return_tensors='''pt'''							).pixel_values

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

     # verify logits
     with torch.no_grad():
          UpperCamelCase__										=      model(A							)
          UpperCamelCase__										=      outputs.logits

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

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

     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__":
     __magic_name__								=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.'''
     )

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




from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def  __UpperCamelCase    (						A							):
     UpperCamelCase__						,       UpperCamelCase__										=      analyze_text(A							)
     UpperCamelCase__										=      list(''' ''' + ascii_lowercase							)
     # what is our total sum of probabilities.
     UpperCamelCase__										=      sum(single_char_strings.values()							)

     # one length string
     UpperCamelCase__										=      0
     # for each alpha we go in our dict and if it is in it we calculate entropy
     for ch in my_alphas:
          if ch in single_char_strings:
               UpperCamelCase__										=      single_char_strings[ch]
               UpperCamelCase__										=      my_str / all_sum
               my_fir_sum += prob * math.loga(A							)  # entropy formula.

    # print entropy
     print(f"{round(-1 * my_fir_sum							):.1f}"							)

     # two len string
     UpperCamelCase__										=      sum(two_char_strings.values()							)
     UpperCamelCase__										=      0
     # for each alpha (two in size) calculate entropy.
     for cha in my_alphas:
          for cha in my_alphas:
               UpperCamelCase__										=      cha + cha
               if sequence in two_char_strings:
                    UpperCamelCase__										=      two_char_strings[sequence]
                    UpperCamelCase__										=      int(A							) / all_sum
                    my_sec_sum += prob * math.loga(A							)

    # print second entropy
     print(f"{round(-1 * my_sec_sum							):.1f}"							)

     # print the difference between them
     print(f"{round((-1 * my_sec_sum) - (-1 * my_fir_sum)							):.1f}"							)







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      Counter()  # type: ignore
     UpperCamelCase__										=      Counter()  # type: ignore
     single_char_strings[text[-1]] += 1

     # first case when we have space at start.
     two_char_strings[" " + text[0]] += 1
     for i in range(0 ,							len(A							) - 1							):
          single_char_strings[text[i]] += 1
          two_char_strings[text[i : i + 2]] += 1
     return single_char_strings, two_char_strings







def  __UpperCamelCase    (						):
     import doctest

     doctest.testmod()
     # text = (
     #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
     #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
     #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
     #     "beloved evident through no service elderly is. Blind there if every no so "
     #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
     #     "of message cottage windows do besides against uncivil.  Delightful "
     #     "unreserved impossible few estimating men favourable see entreaties. She "
     #     "propriety immediate was improving. He or entrance humoured likewise "
     #     "moderate. Much nor game son say feel. Fat make met can must form into "
     #     "gate. Me we offending prevailed discovery. "
     # )

     # calculate_prob(text)


if __name__ == "__main__":
     main()



