






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()
UpperCAmelCase__							  =						logging.get_logger(__name__)
def 		_a       ( a					:Any      ,				a					:Any=False      ,				a					:Any=False					)				->     str:
 a						    = "backbone." if is_semantic else ""

 a						    = []
 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 		_a       ( a					:str      ,				a					:List[str]      ,				a					:List[Any]=False      ,				a					:int=False					)				->     Any:



 for i in range(config.num_hidden_layers					):
  a						    = "backbone." if is_semantic else ""
  # queries, keys and values
  a						    = state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""					)
  a						    = state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""					)
  a						    = state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""					)

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

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

  a						    = gamma_a
  a						    = gamma_a
def 		_a       ( a					:str      ,				a					:Dict      ,				a					:Tuple					)				->     Any:
 a						    = dct.pop(a					)
 a						    = val
def 		_a       ( )				->     Optional[Any]:
 a						    = "http://images.cocodataset.org/val2017/000000039769.jpg"
 a						    = Image.open(requests.get(a      ,				stream=a					).raw					)
 return im
@torch.no_grad()
def 		_a       ( a					:Any      ,				a					:str      ,				a					:Optional[Any]=False					)				->     str:
 a						    = False if "rvlcdip" in checkpoint_url else True
 a						    = BeitConfig(use_absolute_position_embeddings=a      ,				use_mask_token=a					)

 # size of the architecture
 if "large" in checkpoint_url or "dit-l" in checkpoint_url:
  a						    = 1_024
  a						    = 4_096
  a						    = 24
  a						    = 16

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

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

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

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

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

 a						    = image_processor(images=a      ,				return_tensors='''pt'''					)
 a						    = encoding["pixel_values"]

 a						    = model(a					)
 a						    = outputs.logits

 # verify logits
 a						    = [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8_192]
 assert logits.shape == torch.Size(a					), "Shape of logits not as expected"

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

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


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










"""simple docstring"""



import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


_lowerCAmelCase	:	Optional[int]				=      {
    """169M""": 12,
    """430M""": 24,
    """1B5""": 24,
    """3B""": 32,
    """7B""": 32,
    """14B""": 40,
}

_lowerCAmelCase	:	Optional[Any]				=      {
    """169M""": 768,
    """430M""": 1_024,
    """1B5""": 2_048,
    """3B""": 2_560,
    """7B""": 4_096,
    """14B""": 5_120,
}


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Any				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    str							=							list(state_dict.keys()				)
     for name in state_dict_keys:
          UpperCAmelCase__						:    Dict							=							state_dict.pop(snake_case				)
          # emb -> embedding
          if name.startswith("emb."				):
               UpperCAmelCase__						:    str							=							name.replace("emb."			,					"embeddings."				)
          # ln_0 -> pre_ln (only present at block 0)
          if name.startswith("blocks.0.ln0"				):
               UpperCAmelCase__						:    List[str]							=							name.replace("blocks.0.ln0"			,					"blocks.0.pre_ln"				)
          # att -> attention
          UpperCAmelCase__						:    Optional[int]							=							re.sub(r"blocks\.(\d+)\.att"			,					r"blocks.\1.attention"			,					snake_case				)
          # ffn -> feed_forward
          UpperCAmelCase__						:    Any							=							re.sub(r"blocks\.(\d+)\.ffn"			,					r"blocks.\1.feed_forward"			,					snake_case				)
          # time_mix_k -> time_mix_key and reshape
          if name.endswith(".time_mix_k"				):
               UpperCAmelCase__						:    List[Any]							=							name.replace(".time_mix_k"			,					".time_mix_key"				)
          # time_mix_v -> time_mix_value and reshape
          if name.endswith(".time_mix_v"				):
               UpperCAmelCase__						:    int							=							name.replace(".time_mix_v"			,					".time_mix_value"				)
          # time_mix_r -> time_mix_key and reshape
          if name.endswith(".time_mix_r"				):
               UpperCAmelCase__						:    Optional[Any]							=							name.replace(".time_mix_r"			,					".time_mix_receptance"				)

          if name != "head.weight":
               UpperCAmelCase__						:    int							=							"rwkv." + name

          UpperCAmelCase__						:    Dict							=							weight
     return state_dict


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[Any]			,					snake_case       : Optional[int]			,					snake_case       : Optional[Any]			,					snake_case       : Dict=None			,					snake_case       : List[Any]=None			,					snake_case       : List[str]=False			,					snake_case       : str=None				)->		int:






     '''simple docstring'''



     if tokenizer_file is None:
          print("No `--tokenizer_file` provided, we will use the default tokenizer."				)
          UpperCAmelCase__						:    str							=							5_0277
          UpperCAmelCase__						:    List[Any]							=							AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"				)
     else:
          UpperCAmelCase__						:    Union[str, Any]							=							PreTrainedTokenizerFast(tokenizer_file=snake_case				)
          UpperCAmelCase__						:    Tuple							=							len(snake_case				)
     tokenizer.save_pretrained(snake_case				)

     # 2. Build the config
     UpperCAmelCase__						:    Dict							=							list(NUM_HIDDEN_LAYERS_MAPPING.keys()				)
     if size is None:
          # Try to infer size from the checkpoint name
          for candidate in possible_sizes:
               if candidate in checkpoint_file:
                    UpperCAmelCase__						:    List[Any]							=							candidate
                    break
          if size is None:
               raise ValueError("Could not infer the size, please provide it with the `--size` argument."				)
     if size not in possible_sizes:
          raise ValueError(f'`size` should be one of {possible_sizes}, got {size}.'				)

     UpperCAmelCase__						:    int							=							RwkvConfig(
         vocab_size=snake_case			,					num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]			,					hidden_size=HIDEN_SIZE_MAPPING[size]			,					)
     config.save_pretrained(snake_case				)

     # 3. Download model file then convert state_dict
     UpperCAmelCase__						:    Optional[int]							=							hf_hub_download(snake_case			,					snake_case				)
     UpperCAmelCase__						:    Optional[Any]							=							torch.load(snake_case			,					map_location="cpu"				)
     UpperCAmelCase__						:    Tuple							=							convert_state_dict(snake_case				)

     # 4. Split in shards and save
     UpperCAmelCase__							,				UpperCAmelCase__						:    List[Any]							=							shard_checkpoint(snake_case				)
     for shard_file, shard in shards.items():
          torch.save(snake_case			,					os.path.join(snake_case			,					snake_case				)				)

     if index is not None:
          UpperCAmelCase__						:    Tuple							=							os.path.join(snake_case			,					snake_case				)
          # Save the index as well
          with open(snake_case			,					"w"			,					encoding="utf-8"				) as f:
               UpperCAmelCase__						:    Any							=							json.dumps(snake_case			,					indent=2			,					sort_keys=snake_case				) + "\n"
               f.write(snake_case				)

          # 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
          print(
              "Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model."				)
          UpperCAmelCase__						:    List[str]							=							list(shards.keys()				)

          del state_dict
          del shards
          gc.collect()

          for shard_file in shard_files:
               UpperCAmelCase__						:    Any							=							torch.load(os.path.join(snake_case			,					snake_case				)				)
               torch.save({k: v.cpu().clone() for k, v in state_dict.items()}			,					os.path.join(snake_case			,					snake_case				)				)

     del state_dict
     gc.collect()

     if push_to_hub:
          if model_name is None:
               raise ValueError("Please provide a `model_name` to push the model to the Hub."				)
          UpperCAmelCase__						:    Optional[Any]							=							AutoModelForCausalLM.from_pretrained(snake_case				)
          model.push_to_hub(snake_case			,					max_shard_size="2GB"				)
          tokenizer.push_to_hub(snake_case				)


if __name__ == "__main__":
 _lowerCAmelCase	:	Optional[Any]				=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--repo_id""", default=None, type=str, required=True, help="""Repo ID from which to pull the checkpoint."""
 )
 parser.add_argument(
     """--checkpoint_file""", default=None, type=str, required=True, help="""Name of the checkpoint file in the repo."""
 )
 parser.add_argument(
     """--output_dir""", default=None, type=str, required=True, help="""Where to save the converted model."""
 )
 parser.add_argument(
     """--tokenizer_file""",
     default=None,
     type=str,
     help="""Path to the tokenizer file to use (if not provided, only the model is converted).""",
 )
 parser.add_argument(
     """--size""",
     default=None,
     type=str,
     help="""Size of the model. Will be inferred from the `checkpoint_file` if not passed.""",
 )
 parser.add_argument(
     """--push_to_hub""",
     action="""store_true""",
     help="""Push to the Hub the converted model.""",
 )
 parser.add_argument(
     """--model_name""",
     default=None,
     type=str,
     help="""Name of the pushed model on the Hub, including the username / organization.""",
 )

 _lowerCAmelCase	:	Dict				=      parser.parse_args()
 convert_rmkv_checkpoint_to_hf_format(
     args.repo_id,
     args.checkpoint_file,
     args.output_dir,
     size=args.size,
     tokenizer_file=args.tokenizer_file,
     push_to_hub=args.push_to_hub,
     model_name=args.model_name,
 )


