import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""1.0.0a"""):
  raise Exception("""requires fairseq >= 1.0.0a""")

logging.set_verbosity_info()
SCREAMING_SNAKE_CASE :	int							     =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		"""Hello world! cécé herlolip"""



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  FairseqRobertaModel.from_pretrained(_A					)
 roberta.eval()  # disable dropout
 __a       =  roberta.model.encoder.sentence_encoder
 __a       =  XLMRobertaConfig(
     vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings							,				hidden_size=roberta.cfg.model.encoder_embed_dim							,				num_hidden_layers=roberta.cfg.model.encoder_layers							,				num_attention_heads=roberta.cfg.model.encoder_attention_heads							,				intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim							,				max_position_embeddings=514							,				type_vocab_size=1							,				layer_norm_eps=1E-5							,				)
 if classification_head:
  __a       =  roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

 print("Our RoBERTa config:"							,				_A					)

 __a       =  XLMRobertaXLForSequenceClassification(_A					) if classification_head else XLMRobertaXLForMaskedLM(_A					)
 model.eval()

 # Now let's copy all the weights.
 # Embeddings
 __a       =  roberta_sent_encoder.embed_tokens.weight
 __a       =  roberta_sent_encoder.embed_positions.weight
 __a       =  torch.zeros_like(
     model.roberta.embeddings.token_type_embeddings.weight					)  # just zero them out b/c RoBERTa doesn't use them.

 __a       =  roberta_sent_encoder.layer_norm.weight
 __a       =  roberta_sent_encoder.layer_norm.bias

 for i in range(config.num_hidden_layers					):
  # Encoder: start of layer
  __a       =  model.roberta.encoder.layer[i]
  __a       =  roberta_sent_encoder.layers[i]

  __a       =  layer.attention
  __a       =  roberta_layer.self_attn_layer_norm.weight
  __a       =  roberta_layer.self_attn_layer_norm.bias

  # self attention
  __a       =  layer.attention.self
  assert (
      roberta_layer.self_attn.k_proj.weight.data.shape
      == roberta_layer.self_attn.q_proj.weight.data.shape
      == roberta_layer.self_attn.v_proj.weight.data.shape
      == torch.Size((config.hidden_size, config.hidden_size)					)
  )

  __a       =  roberta_layer.self_attn.q_proj.weight
  __a       =  roberta_layer.self_attn.q_proj.bias
  __a       =  roberta_layer.self_attn.k_proj.weight
  __a       =  roberta_layer.self_attn.k_proj.bias
  __a       =  roberta_layer.self_attn.v_proj.weight
  __a       =  roberta_layer.self_attn.v_proj.bias

  # self-attention output
  __a       =  layer.attention.output
  assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
  __a       =  roberta_layer.self_attn.out_proj.weight
  __a       =  roberta_layer.self_attn.out_proj.bias

  # this one is final layer norm
  __a       =  roberta_layer.final_layer_norm.weight
  __a       =  roberta_layer.final_layer_norm.bias

  # intermediate
  __a       =  layer.intermediate
  assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
  __a       =  roberta_layer.fca.weight
  __a       =  roberta_layer.fca.bias

  # output
  __a       =  layer.output
  assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
  __a       =  roberta_layer.fca.weight
  __a       =  roberta_layer.fca.bias
  # end of layer

 if classification_head:
  __a       =  roberta.model.classification_heads["mnli"].dense.weight
  __a       =  roberta.model.classification_heads["mnli"].dense.bias
  __a       =  roberta.model.classification_heads["mnli"].out_proj.weight
  __a       =  roberta.model.classification_heads["mnli"].out_proj.bias
 else:
  # LM Head
  __a       =  roberta.model.encoder.lm_head.dense.weight
  __a       =  roberta.model.encoder.lm_head.dense.bias
  __a       =  roberta.model.encoder.lm_head.layer_norm.weight
  __a       =  roberta.model.encoder.lm_head.layer_norm.bias
  __a       =  roberta.model.encoder.lm_head.weight
  __a       =  roberta.model.encoder.lm_head.bias

 # Let's check that we get the same results.
 __a       =  roberta.encode(_A					).unsqueeze(0					)  # batch of size 1

 __a       =  model(_A					)[0]
 if classification_head:
  __a       =  roberta.model.classification_heads["mnli"](roberta.extract_features(_A					)					)
 else:
  __a       =  roberta.model(_A					)[0]
 print(our_output.shape							,				their_output.shape					)
 __a       =  torch.max(torch.abs(our_output - their_output					)					).item()
 print(f"""max_absolute_diff = {max_absolute_diff}"""					)  # ~ 1e-7
 __a       =  torch.allclose(_A							,				_A							,				atol=1E-3					)
 print("Do both models output the same tensors?"							,				"🔥" if success else "💩"					)
 if not success:
  raise Exception("Something went wRoNg"					)

 pathlib.Path(_A					).mkdir(parents=_A							,				exist_ok=_A					)
 print(f"""Saving model to {pytorch_dump_folder_path}"""					)
 model.save_pretrained(_A					)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	int							     =		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--roberta_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  parser.add_argument(
      """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
  )
  SCREAMING_SNAKE_CASE :	List[Any]							     =		parser.parse_args()
  convert_xlm_roberta_xl_checkpoint_to_pytorch(
      args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )



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 :	str							     =		logging.get_logger(__name__)



def   __A		(			_A							,				_A=False							,				_A=False					):



 """simple docstring"""


 __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							,				_A							,				_A=False							,				_A=False					):



 """simple docstring"""


 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							,				_A							,				_A					):



 """simple docstring"""


 __a       =  dct.pop(_A					)
 __a       =  val



def   __A		(			):



 """simple docstring"""


 __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							,				_A							,				_A=False					):



 """simple docstring"""


 __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       =  1024
  __a       =  4096
  __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, 8192]
 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__":
  SCREAMING_SNAKE_CASE :	int							     =		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 :	Union[str, Any]							     =		parser.parse_args()
  convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


