


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

__snake_case       =       """Hello, World!"""
__snake_case       =       """en_XX"""







def     __lowerCAmelCase		(		lowercase    :    str		,				lowercase    :    str		,				lowercase    :    bool					)      ->		Optional[Any]:


    """simple docstring"""





    snake_case       : List[str]    =       Path("data_bin"					)
    snake_case       : 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					)

    snake_case       : Dict    =       xmod.model.encoder.sentence_encoder
    snake_case       : 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=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:
        snake_case       : Optional[int]    =       xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

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

    snake_case       : Dict    =       XmodForSequenceClassification(lowercase					) if classification_head else XmodForMaskedLM(lowercase					)
    model.eval()

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

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

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

        # self attention
        snake_case       : Optional[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."					)

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

        # self-attention output
        snake_case       : Union[str, Any]    =       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."					)
        snake_case       : Dict    =       xmod_layer.self_attn.out_proj.weight
        snake_case       : Optional[int]    =       xmod_layer.self_attn.out_proj.bias
        snake_case       : Dict    =       xmod_layer.self_attn_layer_norm.weight
        snake_case       : str    =       xmod_layer.self_attn_layer_norm.bias

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

        # output
        snake_case       : 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."					)
        snake_case       : Optional[Any]    =       xmod_layer.fca.weight
        snake_case       : Tuple    =       xmod_layer.fca.bias
        snake_case       : Dict    =       xmod_layer.final_layer_norm.weight
        snake_case       : Optional[Any]    =       xmod_layer.final_layer_norm.bias
        if bert_output.adapter_layer_norm is not None:
            snake_case       : Tuple    =       xmod_layer.adapter_layer_norm.weight
            snake_case       : 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():
            snake_case       : str    =       bert_output.adapter_modules[lang_code]
            snake_case       : Tuple    =       xmod_layer.adapter_modules[lang_code]
            snake_case       : List[Any]    =       from_adapter.fca.weight
            snake_case       : List[str]    =       from_adapter.fca.bias
            snake_case       : List[Any]    =       from_adapter.fca.weight
            snake_case       : Dict    =       from_adapter.fca.bias

        # end of layer

    if xmod_sent_encoder.layer_norm is not None:
        snake_case       : List[Any]    =       xmod_sent_encoder.layer_norm.weight
        snake_case       : Tuple    =       xmod_sent_encoder.layer_norm.bias

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

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

    snake_case       : Optional[int]    =       model(lowercase					)[0]
    if classification_head:
        snake_case       : Optional[Any]    =       xmod.model.classification_heads["mnli"](xmod.extract_features(lowercase					)					)
    else:
        snake_case       : Optional[Any]    =       xmod.model(lowercase		,				lang_id=[SAMPLE_LANGUAGE]					)[0]
    print(our_output.shape		,				their_output.shape					)
    snake_case       : Dict    =       torch.max(torch.abs(our_output - their_output					)					).item()
    print(F'max_absolute_diff = {max_absolute_diff}'					)  # ~ 1e-7
    snake_case       : List[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__":
  __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."""
  )
  __snake_case       =       parser.parse_args()
  convert_xmod_checkpoint_to_pytorch(
      args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )









"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


__snake_case       =       logging.get_logger(__name__)







class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      *UpperCamelCase__		,      **UpperCamelCase__		)      ->						None:

       '''simple docstring'''




       warnings.warn(
           "The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
           " Please use SegformerImageProcessor instead."		,      UpperCamelCase__		,      )
       super().__init__(*UpperCamelCase__		,      **UpperCamelCase__		)





