
"""simple docstring"""







import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.12.2"):
       raise Exception("requires fairseq >= 0.12.2")
if version.parse(fairseq.__version__) > version.parse("2"):
       raise Exception("requires fairseq < v2")

logging.set_verbosity_info()
lowercase_         =       logging.get_logger(__name__)

lowercase_         =       "Hello, World!"
lowercase_         =       "en_XX"




def       A_      (       lowercase    ,			lowercase    ,			lowercase   )  ->				Optional[Any]:


     """simple docstring"""







     UpperCAmelCase_    :     Dict            =	Path("""data_bin"""   )
     UpperCAmelCase_    :     Tuple            =	FairseqXmodModel.from_pretrained(
         model_name_or_path=str(Path(lowercase   ).parent   )    ,			checkpoint_file=Path(lowercase   ).name    ,			_name="""xmod_base"""    ,			arch="""xmod_base"""    ,			task="""multilingual_masked_lm"""    ,			data_name_or_path=str(lowercase   )    ,			bpe="""sentencepiece"""    ,			sentencepiece_model=str(Path(lowercase   ).parent / """sentencepiece.bpe.model"""   )    ,			src_dict=str(data_dir / """dict.txt"""   )    ,			)
     xmod.eval()  # disable dropout
     print(lowercase   )

     UpperCAmelCase_    :     int            =	xmod.model.encoder.sentence_encoder
     UpperCAmelCase_    :     int            =	XmodConfig(
         vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings    ,			hidden_size=xmod.cfg.model.encoder_embed_dim    ,			num_hidden_layers=xmod.cfg.model.encoder_layers    ,			num_attention_heads=xmod.cfg.model.encoder_attention_heads    ,			intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim    ,			max_position_embeddings=514    ,			type_vocab_size=1    ,			layer_norm_eps=1E-5    ,			pre_norm=xmod.cfg.model.encoder_normalize_before    ,			adapter_reduction_factor=getattr(xmod.cfg.model    ,			"""bottleneck"""    ,			2   )    ,			adapter_layer_norm=xmod.cfg.model.adapter_layer_norm    ,			adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm    ,			ln_before_adapter=xmod.cfg.model.ln_before_adapter    ,			languages=xmod.cfg.model.languages    ,			)
     if classification_head:
          UpperCAmelCase_    :     int            =	xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

     print("""Our X-MOD config:"""    ,			lowercase   )

     UpperCAmelCase_    :     str            =	XmodForSequenceClassification(lowercase   ) if classification_head else XmodForMaskedLM(lowercase   )
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     UpperCAmelCase_    :     List[Any]            =	xmod_sent_encoder.embed_tokens.weight
     UpperCAmelCase_    :     Tuple            =	xmod_sent_encoder.embed_positions.weight
     UpperCAmelCase_    :     Tuple            =	torch.zeros_like(
         model.roberta.embeddings.token_type_embeddings.weight   )  # just zero them out b/c xmod doesn't use them.

     UpperCAmelCase_    :     Optional[Any]            =	xmod_sent_encoder.layernorm_embedding.weight
     UpperCAmelCase_    :     Optional[Any]            =	xmod_sent_encoder.layernorm_embedding.bias

     for i in range(config.num_hidden_layers   ):
          # Encoder: start of layer
          UpperCAmelCase_    :     List[str]            =	model.roberta.encoder.layer[i]
          UpperCAmelCase_    :     Any            =	xmod_sent_encoder.layers[i]

          # self attention
          UpperCAmelCase_    :     Union[str, Any]            =	layer.attention.self
          if not (
              xmod_layer.self_attn.k_proj.weight.data.shape
              == xmod_layer.self_attn.q_proj.weight.data.shape
              == xmod_layer.self_attn.v_proj.weight.data.shape
              == torch.Size((config.hidden_size, config.hidden_size)   )
          ):
               raise AssertionError("""Dimensions of self-attention weights do not match."""   )

          UpperCAmelCase_    :     Any            =	xmod_layer.self_attn.q_proj.weight
          UpperCAmelCase_    :     List[str]            =	xmod_layer.self_attn.q_proj.bias
          UpperCAmelCase_    :     Union[str, Any]            =	xmod_layer.self_attn.k_proj.weight
          UpperCAmelCase_    :     Optional[Any]            =	xmod_layer.self_attn.k_proj.bias
          UpperCAmelCase_    :     Optional[int]            =	xmod_layer.self_attn.v_proj.weight
          UpperCAmelCase_    :     int            =	xmod_layer.self_attn.v_proj.bias

          # self-attention output
          UpperCAmelCase_    :     Tuple            =	layer.attention.output
          if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
               raise AssertionError("""Dimensions of self-attention output weights do not match."""   )
          UpperCAmelCase_    :     Tuple            =	xmod_layer.self_attn.out_proj.weight
          UpperCAmelCase_    :     List[Any]            =	xmod_layer.self_attn.out_proj.bias
          UpperCAmelCase_    :     int            =	xmod_layer.self_attn_layer_norm.weight
          UpperCAmelCase_    :     Tuple            =	xmod_layer.self_attn_layer_norm.bias

          # intermediate
          UpperCAmelCase_    :     List[Any]            =	layer.intermediate
          if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError("""Dimensions of intermediate weights do not match."""   )
          UpperCAmelCase_    :     Tuple            =	xmod_layer.fca.weight
          UpperCAmelCase_    :     Optional[Any]            =	xmod_layer.fca.bias

          # output
          UpperCAmelCase_    :     str            =	layer.output
          if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError("""Dimensions of feed-forward weights do not match."""   )
          UpperCAmelCase_    :     str            =	xmod_layer.fca.weight
          UpperCAmelCase_    :     int            =	xmod_layer.fca.bias
          UpperCAmelCase_    :     Optional[Any]            =	xmod_layer.final_layer_norm.weight
          UpperCAmelCase_    :     Tuple            =	xmod_layer.final_layer_norm.bias
          if bert_output.adapter_layer_norm is not None:
               UpperCAmelCase_    :     Tuple            =	xmod_layer.adapter_layer_norm.weight
               UpperCAmelCase_    :     Any            =	xmod_layer.adapter_layer_norm.bias

          if sorted(bert_output.adapter_modules.keys()   ) != sorted(xmod_layer.adapter_modules.keys()   ):
               raise AssertionError("""Lists of language adapters do not match."""   )
          for lang_code, adapter in xmod_layer.adapter_modules.items():
               UpperCAmelCase_    :     List[str]            =	bert_output.adapter_modules[lang_code]
               UpperCAmelCase_    :     Dict            =	xmod_layer.adapter_modules[lang_code]
               UpperCAmelCase_    :     List[Any]            =	from_adapter.fca.weight
               UpperCAmelCase_    :     Union[str, Any]            =	from_adapter.fca.bias
               UpperCAmelCase_    :     str            =	from_adapter.fca.weight
               UpperCAmelCase_    :     Tuple            =	from_adapter.fca.bias

          # end of layer

     if xmod_sent_encoder.layer_norm is not None:
          UpperCAmelCase_    :     int            =	xmod_sent_encoder.layer_norm.weight
          UpperCAmelCase_    :     Optional[Any]            =	xmod_sent_encoder.layer_norm.bias

     if classification_head:
          UpperCAmelCase_    :     List[Any]            =	xmod.model.classification_heads["""mnli"""].dense.weight
          UpperCAmelCase_    :     Dict            =	xmod.model.classification_heads["""mnli"""].dense.bias
          UpperCAmelCase_    :     Union[str, Any]            =	xmod.model.classification_heads["""mnli"""].out_proj.weight
          UpperCAmelCase_    :     Optional[int]            =	xmod.model.classification_heads["""mnli"""].out_proj.bias
     else:
          # LM Head
          UpperCAmelCase_    :     List[str]            =	xmod.model.encoder.lm_head.dense.weight
          UpperCAmelCase_    :     Optional[Any]            =	xmod.model.encoder.lm_head.dense.bias
          UpperCAmelCase_    :     Any            =	xmod.model.encoder.lm_head.layer_norm.weight
          UpperCAmelCase_    :     List[str]            =	xmod.model.encoder.lm_head.layer_norm.bias
          UpperCAmelCase_    :     Union[str, Any]            =	xmod.model.encoder.lm_head.weight
          UpperCAmelCase_    :     Union[str, Any]            =	xmod.model.encoder.lm_head.bias

     # Let's check that we get the same results.
     UpperCAmelCase_    :     Any            =	xmod.encode(lowercase   ).unsqueeze(0   )  # batch of size 1
     model.roberta.set_default_language(lowercase   )

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

     Path(lowercase   ).mkdir(parents=lowercase    ,			exist_ok=lowercase   )
     print(f'''Saving model to {pytorch_dump_folder_path}'''   )
     model.save_pretrained(lowercase   )


if __name__ == "__main__":
       lowercase_         =       argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--xmod_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."
       )
       lowercase_         =       parser.parse_args()
       convert_xmod_checkpoint_to_pytorch(
           args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )



"""simple docstring"""







import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)




def       A_      (       lowercase    ,			lowercase=False   )  ->				Any:


     """simple docstring"""







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

     # projection layer + position embeddings
     rename_keys.extend(
         [
             ("""module.cls_token""", """vit.embeddings.cls_token"""),
             ("""module.patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
             ("""module.patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
             ("""module.pos_embed""", """vit.embeddings.position_embeddings"""),
         ]   )

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ("""module.norm.weight""", """layernorm.weight"""),
                  ("""module.norm.bias""", """layernorm.bias"""),
              ]   )

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          UpperCAmelCase_    :     Union[str, Any]            =	[(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"""),
              ]   )

     return rename_keys




def       A_      (       lowercase    ,			lowercase    ,			lowercase=False   )  ->				Union[str, Any]:


     """simple docstring"""







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




def       A_      (       lowercase   )  ->				Dict:


     """simple docstring"""







     UpperCAmelCase_    :     List[Any]            =	["""head.weight""", """head.bias"""]
     for k in ignore_keys:
          state_dict.pop(lowercase    ,			lowercase   )




def       A_      (       lowercase   )  ->				Union[str, Any]:


     """simple docstring"""







     UpperCAmelCase_    :     Optional[Any]            =	[
         """module.fc.fc1.weight""",
         """module.fc.fc1.bias""",
         """module.fc.bn1.weight""",
         """module.fc.bn1.bias""",
         """module.fc.bn1.running_mean""",
         """module.fc.bn1.running_var""",
         """module.fc.bn1.num_batches_tracked""",
         """module.fc.fc2.weight""",
         """module.fc.fc2.bias""",
         """module.fc.bn2.weight""",
         """module.fc.bn2.bias""",
         """module.fc.bn2.running_mean""",
         """module.fc.bn2.running_var""",
         """module.fc.bn2.num_batches_tracked""",
         """module.fc.fc3.weight""",
         """module.fc.fc3.bias""",
     ]
     for k in ignore_keys:
          state_dict.pop(lowercase    ,			lowercase   )




def       A_      (       lowercase    ,			lowercase    ,			lowercase   )  ->				Any:


     """simple docstring"""







     UpperCAmelCase_    :     List[str]            =	dct.pop(lowercase   )
     UpperCAmelCase_    :     Dict            =	val




def       A_      (       lowercase    ,			lowercase   )  ->				Dict:


     """simple docstring"""







     UpperCAmelCase_    :     str            =	ViTMSNConfig()
     UpperCAmelCase_    :     List[str]            =	1000

     UpperCAmelCase_    :     str            =	"""datasets/huggingface/label-files"""
     UpperCAmelCase_    :     str            =	"""imagenet-1k-id2label.json"""
     UpperCAmelCase_    :     Union[str, Any]            =	json.load(open(hf_hub_download(lowercase    ,			lowercase   )    ,			"""r"""   )   )
     UpperCAmelCase_    :     Optional[int]            =	{int(lowercase   ): v for k, v in idalabel.items()}
     UpperCAmelCase_    :     Any            =	idalabel
     UpperCAmelCase_    :     Any            =	{v: k for k, v in idalabel.items()}

     if "s16" in checkpoint_url:
          UpperCAmelCase_    :     str            =	384
          UpperCAmelCase_    :     List[Any]            =	1536
          UpperCAmelCase_    :     Optional[Any]            =	6
     elif "l16" in checkpoint_url:
          UpperCAmelCase_    :     Any            =	1024
          UpperCAmelCase_    :     Dict            =	4096
          UpperCAmelCase_    :     Tuple            =	24
          UpperCAmelCase_    :     List[Any]            =	16
          UpperCAmelCase_    :     Optional[int]            =	0.1
     elif "b4" in checkpoint_url:
          UpperCAmelCase_    :     Union[str, Any]            =	4
     elif "l7" in checkpoint_url:
          UpperCAmelCase_    :     Optional[int]            =	7
          UpperCAmelCase_    :     Union[str, Any]            =	1024
          UpperCAmelCase_    :     Optional[int]            =	4096
          UpperCAmelCase_    :     Union[str, Any]            =	24
          UpperCAmelCase_    :     str            =	16
          UpperCAmelCase_    :     List[Any]            =	0.1

     UpperCAmelCase_    :     Optional[Any]            =	ViTMSNModel(lowercase   )

     UpperCAmelCase_    :     str            =	torch.hub.load_state_dict_from_url(lowercase    ,			map_location="""cpu"""   )["""target_encoder"""]

     UpperCAmelCase_    :     Optional[Any]            =	ViTImageProcessor(size=config.image_size   )

     remove_projection_head(lowercase   )
     UpperCAmelCase_    :     List[str]            =	create_rename_keys(lowercase    ,			base_model=lowercase   )

     for src, dest in rename_keys:
          rename_key(lowercase    ,			lowercase    ,			lowercase   )
     read_in_q_k_v(lowercase    ,			lowercase    ,			base_model=lowercase   )

     model.load_state_dict(lowercase   )
     model.eval()

     UpperCAmelCase_    :     str            =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""

     UpperCAmelCase_    :     Any            =	Image.open(requests.get(lowercase    ,			stream=lowercase   ).raw   )
     UpperCAmelCase_    :     int            =	ViTImageProcessor(
         size=config.image_size    ,			image_mean=lowercase    ,			image_std=lowercase   )
     UpperCAmelCase_    :     Tuple            =	image_processor(images=lowercase    ,			return_tensors="""pt"""   )

     # forward pass
     torch.manual_seed(2   )
     UpperCAmelCase_    :     int            =	model(**lowercase   )
     UpperCAmelCase_    :     str            =	outputs.last_hidden_state

     # The following Colab Notebook was used to generate these outputs:
     # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
     if "s16" in checkpoint_url:
          UpperCAmelCase_    :     Optional[int]            =	torch.tensor([[-1.0915, -1.4876, -1.1809]]   )
     elif "b16" in checkpoint_url:
          UpperCAmelCase_    :     str            =	torch.tensor([[14.2889, -18.9045, 11.7281]]   )
     elif "l16" in checkpoint_url:
          UpperCAmelCase_    :     List[str]            =	torch.tensor([[41.5028, -22.8681, 45.6475]]   )
     elif "b4" in checkpoint_url:
          UpperCAmelCase_    :     Any            =	torch.tensor([[-4.3868, 5.2932, -0.4137]]   )
     else:
          UpperCAmelCase_    :     Dict            =	torch.tensor([[-0.1792, -0.6465, 2.4263]]   )

     # verify logits
     assert torch.allclose(last_hidden_state[:, 0, :3]    ,			lowercase    ,			atol=1E-4   )

     print(f'''Saving model to {pytorch_dump_folder_path}'''   )
     model.save_pretrained(lowercase   )

     print(f'''Saving image processor to {pytorch_dump_folder_path}'''   )
     image_processor.save_pretrained(lowercase   )


if __name__ == "__main__":
       lowercase_         =       argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--checkpoint_url",
           default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
           type=str,
           help="URL of the checkpoint you'd like to convert.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )

       lowercase_         =       parser.parse_args()
       convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

