
import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  [
          "decoder.version",
          "decoder.output_projection.weight",
          "_float_tensor",
          "decoder.embed_positions._float_tensor",
      ]
      for k in ignore_keys:
            state_dict.pop(_lowercase     ,      _lowercase				)


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  emb.weight.shape
      SCREAMING_SNAKE_CASE_          =  nn.Linear(_lowercase     ,      _lowercase     ,      bias=_lowercase				)
      SCREAMING_SNAKE_CASE_          =  emb.weight.data
      return lin_layer


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  torch.load(_lowercase     ,      map_location="cpu"				)
      SCREAMING_SNAKE_CASE_          =  Namespace(**checkpoint["cfg"]["model"]				)
      SCREAMING_SNAKE_CASE_          =  checkpoint["model"]
      remove_ignore_keys_(_lowercase				)
      SCREAMING_SNAKE_CASE_          =  state_dict["decoder.embed_tokens.weight"].shape[0]

      SCREAMING_SNAKE_CASE_          =  {key.replace("decoder"     ,      "model"				): val for key, val in state_dict.items()}

      SCREAMING_SNAKE_CASE_          =  XGLMConfig(
          vocab_size=_lowercase     ,      max_position_embeddings=args.max_target_positions     ,      num_layers=args.decoder_layers     ,      attention_heads=args.decoder_attention_heads     ,      ffn_dim=args.decoder_ffn_embed_dim     ,      d_model=args.decoder_embed_dim     ,      layerdrop=args.decoder_layerdrop     ,      dropout=args.dropout     ,      attention_dropout=args.attention_dropout     ,      activation_dropout=args.activation_dropout     ,      activation_function="gelu"     ,      scale_embedding=not args.no_scale_embedding     ,      tie_word_embeddings=args.share_decoder_input_output_embed     ,      )

      SCREAMING_SNAKE_CASE_          =  XGLMForCausalLM(_lowercase				)
      SCREAMING_SNAKE_CASE_          =  model.load_state_dict(_lowercase     ,      strict=_lowercase				)
      print(_lowercase				)
      SCREAMING_SNAKE_CASE_          =  make_linear_from_emb(model.model.embed_tokens				)

      return model


if __name__ == "__main__":
      A     :  Tuple					     =       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
      parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
      A     :  List[str]					     =       parser.parse_args()
      A     :  Tuple					     =       convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
      model.save_pretrained(args.pytorch_dump_folder_path)



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :  str					     =       {
    "configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig", "RemBertOnnxConfig"]
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       ["RemBertTokenizer"]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       ["RemBertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       [
          "REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RemBertForCausalLM",
          "RemBertForMaskedLM",
          "RemBertForMultipleChoice",
          "RemBertForQuestionAnswering",
          "RemBertForSequenceClassification",
          "RemBertForTokenClassification",
          "RemBertLayer",
          "RemBertModel",
          "RemBertPreTrainedModel",
          "load_tf_weights_in_rembert",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  List[str]					     =       [
          "TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRemBertForCausalLM",
          "TFRemBertForMaskedLM",
          "TFRemBertForMultipleChoice",
          "TFRemBertForQuestionAnswering",
          "TFRemBertForSequenceClassification",
          "TFRemBertForTokenClassification",
          "TFRemBertLayer",
          "TFRemBertModel",
          "TFRemBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert import RemBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert_fast import RemBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_rembert import (
                REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RemBertForCausalLM,
                RemBertForMaskedLM,
                RemBertForMultipleChoice,
                RemBertForQuestionAnswering,
                RemBertForSequenceClassification,
                RemBertForTokenClassification,
                RemBertLayer,
                RemBertModel,
                RemBertPreTrainedModel,
                load_tf_weights_in_rembert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_rembert import (
                TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRemBertForCausalLM,
                TFRemBertForMaskedLM,
                TFRemBertForMultipleChoice,
                TFRemBertForQuestionAnswering,
                TFRemBertForSequenceClassification,
                TFRemBertForTokenClassification,
                TFRemBertLayer,
                TFRemBertModel,
                TFRemBertPreTrainedModel,
            )


else:
      import sys

      A     :  Tuple					     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


