





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE							     =			{
    "configuration_whisper": ["WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperConfig", "WhisperOnnxConfig"],
    "feature_extraction_whisper": ["WhisperFeatureExtractor"],
    "processing_whisper": ["WhisperProcessor"],
    "tokenization_whisper": ["WhisperTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE							     =			["WhisperTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE							     =			[
     "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "WhisperForConditionalGeneration",
     "WhisperModel",
     "WhisperPreTrainedModel",
     "WhisperForAudioClassification",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE							     =			[
     "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFWhisperForConditionalGeneration",
     "TFWhisperModel",
     "TFWhisperPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE							     =			[
     "FlaxWhisperForConditionalGeneration",
     "FlaxWhisperModel",
     "FlaxWhisperPreTrainedModel",
     "FlaxWhisperForAudioClassification",
 ]


if TYPE_CHECKING:
 from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
 from .feature_extraction_whisper import WhisperFeatureExtractor
 from .processing_whisper import WhisperProcessor
 from .tokenization_whisper import WhisperTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_whisper_fast import WhisperTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_whisper import (
      WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      WhisperForAudioClassification,
      WhisperForConditionalGeneration,
      WhisperModel,
      WhisperPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_whisper import (
      TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFWhisperForConditionalGeneration,
      TFWhisperModel,
      TFWhisperPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_whisper import (
      FlaxWhisperForAudioClassification,
      FlaxWhisperForConditionalGeneration,
      FlaxWhisperModel,
      FlaxWhisperPreTrainedModel,
  )

else:
 import sys

 _SCREAMING_SNAKE_CASE							     =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''simple docstring'''




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()
_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Tuple      ,    __lowerCAmelCase       :							Tuple=False      ,    __lowerCAmelCase       :							Any=False		)		->  Optional[Any]:
   snake_case 						=   """backbone.""" if is_semantic else """"""

   snake_case 						=   []
   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 	__lowerCamelCase			(      __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							str=False      ,    __lowerCAmelCase       :							List[Any]=False		)		->  Optional[int]:
   for i in range(config.num_hidden_layers		):
      snake_case 						=   """backbone.""" if is_semantic else """"""
      # queries, keys and values
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''		)
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''		)
      snake_case 						=   state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''		)

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

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

      snake_case 						=   gamma_a
      snake_case 						=   gamma_a





def 	__lowerCamelCase			(      __lowerCAmelCase       :							List[str]      ,    __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							Tuple		)		->  Any:
   snake_case 						=   dct.pop(__lowerCAmelCase		)
   snake_case 						=   val





def 	__lowerCamelCase			(      )		->  Dict:
   snake_case 						=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
   snake_case 						=   Image.open(requests.get(__lowerCAmelCase      ,    stream=__lowerCAmelCase		).raw		)
   return im





@torch.no_grad()
def 	__lowerCamelCase			(      __lowerCAmelCase       :							Dict      ,    __lowerCAmelCase       :							Union[str, Any]      ,    __lowerCAmelCase       :							Any=False		)		->  str:
   snake_case 						=   False if """rvlcdip""" in checkpoint_url else True
   snake_case 						=   BeitConfig(use_absolute_position_embeddings=__lowerCAmelCase      ,    use_mask_token=__lowerCAmelCase		)

   # size of the architecture
   if "large" in checkpoint_url or "dit-l" in checkpoint_url:
      snake_case 						=   10_24
      snake_case 						=   40_96
      snake_case 						=   24
      snake_case 						=   16

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

   # load state_dict of original model, remove and rename some keys
   snake_case 						=   torch.hub.load_state_dict_from_url(__lowerCAmelCase      ,    map_location="""cpu"""		)["""model"""]

   snake_case 						=   create_rename_keys(__lowerCAmelCase      ,    has_lm_head=__lowerCAmelCase		)
   for src, dest in rename_keys:
      rename_key(__lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
   read_in_q_k_v(__lowerCAmelCase      ,    __lowerCAmelCase      ,    has_lm_head=__lowerCAmelCase		)

   # load HuggingFace model
   snake_case 						=   BeitForMaskedImageModeling(__lowerCAmelCase		) if has_lm_head else BeitForImageClassification(__lowerCAmelCase		)
   model.eval()
   model.load_state_dict(__lowerCAmelCase		)

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

   snake_case 						=   image_processor(images=__lowerCAmelCase      ,    return_tensors="""pt"""		)
   snake_case 						=   encoding["""pixel_values"""]

   snake_case 						=   model(__lowerCAmelCase		)
   snake_case 						=   outputs.logits

   # verify logits
   snake_case 						=   [1, 16] if """rvlcdip""" in checkpoint_url else [1, 1_96, 81_92]
   assert logits.shape == torch.Size(__lowerCAmelCase		), "Shape of logits not as expected"

   Path(__lowerCAmelCase		).mkdir(exist_ok=__lowerCAmelCase		)
   print(F'''Saving model to {pytorch_dump_folder_path}'''		)
   model.save_pretrained(__lowerCAmelCase		)
   print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
   image_processor.save_pretrained(__lowerCAmelCase		)

   if push_to_hub:
      if has_lm_head:
         snake_case 						=   """dit-base""" if """base""" in checkpoint_url else """dit-large"""
      else:
         snake_case 						=   """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(__lowerCAmelCase      ,    __lowerCAmelCase		)      ,    organization="""nielsr"""      ,    commit_message="""Add image processor"""      ,    use_temp_dir=__lowerCAmelCase      ,    )
      model.push_to_hub(
          repo_path_or_name=Path(__lowerCAmelCase      ,    __lowerCAmelCase		)      ,    organization="""nielsr"""      ,    commit_message="""Add model"""      ,    use_temp_dir=__lowerCAmelCase      ,    )


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			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",
 )
 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)

