

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

__A						       =      "Hello, World!"
__A						       =      "en_XX"






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[int]:
  __lowerCAmelCase:	Optional[Any]   				=      Path("data_bin"			)
  __lowerCAmelCase:	Optional[int]   				=      FairseqXmodModel.from_pretrained(
      model_name_or_path=str(Path(__SCREAMING_SNAKE_CASE			).parent			) ,			checkpoint_file=Path(__SCREAMING_SNAKE_CASE			).name ,			_name="xmod_base" ,			arch="xmod_base" ,			task="multilingual_masked_lm" ,			data_name_or_path=str(__SCREAMING_SNAKE_CASE			) ,			bpe="sentencepiece" ,			sentencepiece_model=str(Path(__SCREAMING_SNAKE_CASE			).parent / "sentencepiece.bpe.model"			) ,			src_dict=str(data_dir / "dict.txt"			) ,			)
  xmod.eval()  # disable dropout
  print(__SCREAMING_SNAKE_CASE			)

  __lowerCAmelCase:	Tuple   				=      xmod.model.encoder.sentence_encoder
  __lowerCAmelCase:	Dict   				=      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_1_4 ,			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:
    __lowerCAmelCase:	str   				=      xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

  __lowerCAmelCase:	List[Any]   				=      XmodForSequenceClassification(__SCREAMING_SNAKE_CASE			) if classification_head else XmodForMaskedLM(__SCREAMING_SNAKE_CASE			)
  model.eval()

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

  __lowerCAmelCase:	List[str]   				=      xmod_sent_encoder.layernorm_embedding.weight
  __lowerCAmelCase:	int   				=      xmod_sent_encoder.layernorm_embedding.bias

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

    # self attention
    __lowerCAmelCase:	int   				=      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."			)

    __lowerCAmelCase:	int   				=      xmod_layer.self_attn.q_proj.weight
    __lowerCAmelCase:	Tuple   				=      xmod_layer.self_attn.q_proj.bias
    __lowerCAmelCase:	Dict   				=      xmod_layer.self_attn.k_proj.weight
    __lowerCAmelCase:	Any   				=      xmod_layer.self_attn.k_proj.bias
    __lowerCAmelCase:	Tuple   				=      xmod_layer.self_attn.v_proj.weight
    __lowerCAmelCase:	str   				=      xmod_layer.self_attn.v_proj.bias

    # self-attention output
    __lowerCAmelCase:	List[str]   				=      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."			)
    __lowerCAmelCase:	List[Any]   				=      xmod_layer.self_attn.out_proj.weight
    __lowerCAmelCase:	List[Any]   				=      xmod_layer.self_attn.out_proj.bias
    __lowerCAmelCase:	Optional[int]   				=      xmod_layer.self_attn_layer_norm.weight
    __lowerCAmelCase:	List[Any]   				=      xmod_layer.self_attn_layer_norm.bias

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

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

    # end of layer

  if xmod_sent_encoder.layer_norm is not None:
    __lowerCAmelCase:	Dict   				=      xmod_sent_encoder.layer_norm.weight
    __lowerCAmelCase:	Any   				=      xmod_sent_encoder.layer_norm.bias

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

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

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

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


if __name__ == "__main__":
   __A						       =      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."
   )
   __A						       =      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_torch_available


__A						       =      {
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						       =      [
			    "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "Swinv2ForImageClassification",
			    "Swinv2ForMaskedImageModeling",
			    "Swinv2Model",
			    "Swinv2PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

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


