import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
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 ,							A=False							):
     UpperCamelCase__										=      '''backbone.''' if is_semantic else ''''''

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

     # projection layer + position embeddings
     rename_keys.extend(
         [
             (f"{prefix}cls_token", '''beit.embeddings.cls_token'''),
             (f"{prefix}patch_embed.proj.weight", '''beit.embeddings.patch_embeddings.projection.weight'''),
             (f"{prefix}patch_embed.proj.bias", '''beit.embeddings.patch_embeddings.projection.bias'''),
             (f"{prefix}pos_embed", '''beit.embeddings.position_embeddings'''),
         ]							)

     if has_lm_head:
          # mask token + layernorm
          rename_keys.extend(
              [
                  ('''mask_token''', '''beit.embeddings.mask_token'''),
                  ('''norm.weight''', '''layernorm.weight'''),
                  ('''norm.bias''', '''layernorm.bias'''),
              ]							)
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
                  ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
                  ('''head.weight''', '''classifier.weight'''),
                  ('''head.bias''', '''classifier.bias'''),
              ]							)

     return rename_keys







def  __UpperCamelCase    (						A ,							A ,							A=False ,							A=False							):
     for i in range(config.num_hidden_layers							):
          UpperCamelCase__										=      '''backbone.''' if is_semantic else ''''''
          # queries, keys and values
          UpperCamelCase__										=      state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight"							)
          UpperCamelCase__										=      state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias"							)
          UpperCamelCase__										=      state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias"							)

          UpperCamelCase__										=      in_proj_weight[
              : config.hidden_size, :
          ]
          UpperCamelCase__										=      q_bias
          UpperCamelCase__										=      in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          UpperCamelCase__										=      in_proj_weight[
              -config.hidden_size :, :
          ]
          UpperCamelCase__										=      v_bias

          # gamma_1 and gamma_2
          # we call them lambda because otherwise they are renamed when using .from_pretrained
          UpperCamelCase__										=      state_dict.pop(f"{prefix}blocks.{i}.gamma_1"							)
          UpperCamelCase__										=      state_dict.pop(f"{prefix}blocks.{i}.gamma_2"							)

          UpperCamelCase__										=      gamma_a
          UpperCamelCase__										=      gamma_a







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







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







@torch.no_grad()
def  __UpperCamelCase    (						A ,							A ,							A=False							):
     UpperCamelCase__										=      False if '''rvlcdip''' in checkpoint_url else True
     UpperCamelCase__										=      BeitConfig(use_absolute_position_embeddings=SCREAMING_SNAKE_CASE_ ,							use_mask_token=SCREAMING_SNAKE_CASE_							)

     # size of the architecture
     if "large" in checkpoint_url or "dit-l" in checkpoint_url:
          UpperCamelCase__										=      1024
          UpperCamelCase__										=      4096
          UpperCamelCase__										=      24
          UpperCamelCase__										=      16

     # labels
     if "rvlcdip" in checkpoint_url:
          UpperCamelCase__										=      16
          UpperCamelCase__										=      '''huggingface/label-files'''
          UpperCamelCase__										=      '''rvlcdip-id2label.json'''
          UpperCamelCase__										=      json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							repo_type='''dataset'''							) ,							'''r'''							)							)
          UpperCamelCase__										=      {int(SCREAMING_SNAKE_CASE_							): v for k, v in idalabel.items()}
          UpperCamelCase__										=      idalabel
          UpperCamelCase__										=      {v: k for k, v in idalabel.items()}

     # load state_dict of original model, remove and rename some keys
     UpperCamelCase__										=      torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_ ,							map_location='''cpu'''							)['''model''']

     UpperCamelCase__										=      create_rename_keys(SCREAMING_SNAKE_CASE_ ,							has_lm_head=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_ ,							has_lm_head=SCREAMING_SNAKE_CASE_							)

     # load HuggingFace model
     UpperCamelCase__										=      BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE_							) if has_lm_head else BeitForImageClassification(SCREAMING_SNAKE_CASE_							)
     model.eval()
     model.load_state_dict(SCREAMING_SNAKE_CASE_							)

     # Check outputs on an image
     UpperCamelCase__										=      BeitImageProcessor(
         size=config.image_size ,							resample=PILImageResampling.BILINEAR ,							do_center_crop=SCREAMING_SNAKE_CASE_							)
     UpperCamelCase__										=      prepare_img()

     UpperCamelCase__										=      image_processor(images=SCREAMING_SNAKE_CASE_ ,							return_tensors='''pt'''							)
     UpperCamelCase__										=      encoding['''pixel_values''']

     UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_							)
     UpperCamelCase__										=      outputs.logits

     # verify logits
     UpperCamelCase__										=      [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8192]
     assert logits.shape == torch.Size(SCREAMING_SNAKE_CASE_							), "Shape of logits not as expected"

     Path(SCREAMING_SNAKE_CASE_							).mkdir(exist_ok=SCREAMING_SNAKE_CASE_							)
     print(f"Saving model 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 push_to_hub:
          if has_lm_head:
               UpperCamelCase__										=      '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
          else:
               UpperCamelCase__										=      '''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
          image_processor.push_to_hub(
              repo_path_or_name=Path(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_							) ,							organization='''nielsr''' ,							commit_message='''Add image processor''' ,							use_temp_dir=SCREAMING_SNAKE_CASE_ ,							)
          model.push_to_hub(
              repo_path_or_name=Path(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_							) ,							organization='''nielsr''' ,							commit_message='''Add model''' ,							use_temp_dir=SCREAMING_SNAKE_CASE_ ,							)


if __name__ == "__main__":
     __magic_name__								=					argparse.ArgumentParser()

     parser.add_argument(
         '''--checkpoint_url''',
         default='''https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth''',
         type=str,
         help='''URL to the original PyTorch checkpoint (.pth file).''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
     )
     parser.add_argument(
         '''--push_to_hub''',
         action='''store_true''',
     )
     __magic_name__								=					parser.parse_args()
     convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




import math

import tensorflow as tf
from packaging import version







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0							) ,							x.dtype							)							))

     return x * cdf







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(math.pi ,							x.dtype							)
     UpperCamelCase__										=      tf.cast(0.04_47_15 ,							x.dtype							)
     UpperCamelCase__										=      0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi							) * (x + coeff * tf.pow(A ,							3							))							))

     return x * cdf







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)

     return x * tf.tanh(tf.math.softplus(A							)							)







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(0.04_47_15 ,							x.dtype							)
     UpperCamelCase__										=      tf.cast(0.79_78_84_56_08 ,							x.dtype							)

     return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)							))







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(1.7_02 ,							x.dtype							)
     return x * tf.math.sigmoid(coeff * x							)







def  __UpperCamelCase    (						A							):
     return tf.clip_by_value(_gelu(A							) ,							-10 ,							10							)







def  __UpperCamelCase    (						A ,							A=-1							):
     UpperCamelCase__						,       UpperCamelCase__										=      tf.split(A ,							2 ,							axis=A							)
     return a * tf.math.sigmoid(A							)


if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):







     def  __UpperCamelCase    (						A							):
          return tf.keras.activations.gelu(A ,							approximate=A							)

     __magic_name__								=tf.keras.activations.gelu



     __magic_name__								=approximate_gelu_wrap
else:
     __magic_name__								=_gelu
     __magic_name__								=_gelu_new


__magic_name__								={
    '''gelu''': gelu,
    '''gelu_10''': gelu_aa,
    '''gelu_fast''': gelu_fast,
    '''gelu_new''': gelu_new,
    '''glu''': glu,
    '''mish''': mish,
    '''quick_gelu''': quick_gelu,
    '''relu''': tf.keras.activations.relu,
    '''sigmoid''': tf.keras.activations.sigmoid,
    '''silu''': tf.keras.activations.swish,
    '''swish''': tf.keras.activations.swish,
    '''tanh''': tf.keras.activations.tanh,
}







def  __UpperCamelCase    (						A							):
     if activation_string in ACTaFN:
          return ACTaFN[activation_string]
     else:
          raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()							)}"							)



