




"""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()
SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	"Hello, World!"
SCREAMING_SNAKE_CASE    =	"en_XX"







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

 A__	=    xmod.model.encoder.sentence_encoder
 A__	=    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=5_14      ,			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:
  A__	=    xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

 A__	=    XmodForSequenceClassification(__lowerCamelCase						) if classification_head else XmodForMaskedLM(__lowerCamelCase						)
 model.eval()

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

 A__	=    xmod_sent_encoder.layernorm_embedding.weight
 A__	=    xmod_sent_encoder.layernorm_embedding.bias

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

  # self attention
  A__	=    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."						)

  A__	=    xmod_layer.self_attn.q_proj.weight
  A__	=    xmod_layer.self_attn.q_proj.bias
  A__	=    xmod_layer.self_attn.k_proj.weight
  A__	=    xmod_layer.self_attn.k_proj.bias
  A__	=    xmod_layer.self_attn.v_proj.weight
  A__	=    xmod_layer.self_attn.v_proj.bias

  # self-attention output
  A__	=    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."						)
  A__	=    xmod_layer.self_attn.out_proj.weight
  A__	=    xmod_layer.self_attn.out_proj.bias
  A__	=    xmod_layer.self_attn_layer_norm.weight
  A__	=    xmod_layer.self_attn_layer_norm.bias

  # intermediate
  A__	=    layer.intermediate
  if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
   raise AssertionError("Dimensions of intermediate weights do not match."						)
  A__	=    xmod_layer.fca.weight
  A__	=    xmod_layer.fca.bias

  # output
  A__	=    layer.output
  if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
   raise AssertionError("Dimensions of feed-forward weights do not match."						)
  A__	=    xmod_layer.fca.weight
  A__	=    xmod_layer.fca.bias
  A__	=    xmod_layer.final_layer_norm.weight
  A__	=    xmod_layer.final_layer_norm.bias
  if bert_output.adapter_layer_norm is not None:
   A__	=    xmod_layer.adapter_layer_norm.weight
   A__	=    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():
   A__	=    bert_output.adapter_modules[lang_code]
   A__	=    xmod_layer.adapter_modules[lang_code]
   A__	=    from_adapter.fca.weight
   A__	=    from_adapter.fca.bias
   A__	=    from_adapter.fca.weight
   A__	=    from_adapter.fca.bias

  # end of layer

 if xmod_sent_encoder.layer_norm is not None:
  A__	=    xmod_sent_encoder.layer_norm.weight
  A__	=    xmod_sent_encoder.layer_norm.bias

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

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

 A__	=    model(__lowerCamelCase						)[0]
 if classification_head:
  A__	=    xmod.model.classification_heads["mnli"](xmod.extract_features(__lowerCamelCase						)						)
 else:
  A__	=    xmod.model(__lowerCamelCase      ,			lang_id=[SAMPLE_LANGUAGE]						)[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(__lowerCamelCase      ,			__lowerCamelCase      ,			atol=1E-3						)
 print("Do both models output the same tensors?"      ,			"🔥" if success else "💩"						)
 if not success:
  raise Exception("Something went wRoNg"						)

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	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."
      )
      SCREAMING_SNAKE_CASE    =	parser.parse_args()
      convert_xmod_checkpoint_to_pytorch(
          args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE    =	{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	["MobileViTFeatureExtractor"]
      SCREAMING_SNAKE_CASE    =	["MobileViTImageProcessor"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	[
          "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "MobileViTForImageClassification",
          "MobileViTForSemanticSegmentation",
          "MobileViTModel",
          "MobileViTPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE    =	[
          "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFMobileViTForImageClassification",
          "TFMobileViTForSemanticSegmentation",
          "TFMobileViTModel",
          "TFMobileViTPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilevit import MobileViTFeatureExtractor
            from .image_processing_mobilevit import MobileViTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilevit import (
                MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileViTForImageClassification,
                MobileViTForSemanticSegmentation,
                MobileViTModel,
                MobileViTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mobilevit import (
                TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMobileViTForImageClassification,
                TFMobileViTForSemanticSegmentation,
                TFMobileViTModel,
                TFMobileViTPreTrainedModel,
            )


else:
      import sys

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





