
import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.9.0"):
   raise Exception("requires fairseq >= 0.9.0")


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

__UpperCAmelCase   						=     "Hello world! cécé herlolip"


def  A__ (       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      FairseqRobertaModel.from_pretrained(_SCREAMING_SNAKE_CASE						)
   roberta.eval()  # disable dropout
   SCREAMING_SNAKE_CASE_	=      roberta.model.encoder.sentence_encoder
   SCREAMING_SNAKE_CASE_	=      RobertaConfig(
       vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings,  hidden_size=roberta.args.encoder_embed_dim,  num_hidden_layers=roberta.args.encoder_layers,  num_attention_heads=roberta.args.encoder_attention_heads,  intermediate_size=roberta.args.encoder_ffn_embed_dim,  max_position_embeddings=5_14,  type_vocab_size=1,  layer_norm_eps=1E-5,  )
   if classification_head:
      SCREAMING_SNAKE_CASE_	=      roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
   print('''Our BERT config:''',  _SCREAMING_SNAKE_CASE						)

   SCREAMING_SNAKE_CASE_	=      RobertaForSequenceClassification(_SCREAMING_SNAKE_CASE						) if classification_head else RobertaForMaskedLM(_SCREAMING_SNAKE_CASE						)
   model.eval()

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

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

      # self attention
      SCREAMING_SNAKE_CASE_	=      layer.attention.self
      assert (
          roberta_layer.self_attn.k_proj.weight.data.shape
          == roberta_layer.self_attn.q_proj.weight.data.shape
          == roberta_layer.self_attn.v_proj.weight.data.shape
          == torch.Size((config.hidden_size, config.hidden_size)						)
      )

      SCREAMING_SNAKE_CASE_	=      roberta_layer.self_attn.q_proj.weight
      SCREAMING_SNAKE_CASE_	=      roberta_layer.self_attn.q_proj.bias
      SCREAMING_SNAKE_CASE_	=      roberta_layer.self_attn.k_proj.weight
      SCREAMING_SNAKE_CASE_	=      roberta_layer.self_attn.k_proj.bias
      SCREAMING_SNAKE_CASE_	=      roberta_layer.self_attn.v_proj.weight
      SCREAMING_SNAKE_CASE_	=      roberta_layer.self_attn.v_proj.bias

      # self-attention output
      SCREAMING_SNAKE_CASE_	=      layer.attention.output
      assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
      SCREAMING_SNAKE_CASE_	=      roberta_layer.self_attn.out_proj.weight
      SCREAMING_SNAKE_CASE_	=      roberta_layer.self_attn.out_proj.bias
      SCREAMING_SNAKE_CASE_	=      roberta_layer.self_attn_layer_norm.weight
      SCREAMING_SNAKE_CASE_	=      roberta_layer.self_attn_layer_norm.bias

      # intermediate
      SCREAMING_SNAKE_CASE_	=      layer.intermediate
      assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
      SCREAMING_SNAKE_CASE_	=      roberta_layer.fca.weight
      SCREAMING_SNAKE_CASE_	=      roberta_layer.fca.bias

      # output
      SCREAMING_SNAKE_CASE_	=      layer.output
      assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
      SCREAMING_SNAKE_CASE_	=      roberta_layer.fca.weight
      SCREAMING_SNAKE_CASE_	=      roberta_layer.fca.bias
      SCREAMING_SNAKE_CASE_	=      roberta_layer.final_layer_norm.weight
      SCREAMING_SNAKE_CASE_	=      roberta_layer.final_layer_norm.bias
      # end of layer

   if classification_head:
      SCREAMING_SNAKE_CASE_	=      roberta.model.classification_heads['''mnli'''].dense.weight
      SCREAMING_SNAKE_CASE_	=      roberta.model.classification_heads['''mnli'''].dense.bias
      SCREAMING_SNAKE_CASE_	=      roberta.model.classification_heads['''mnli'''].out_proj.weight
      SCREAMING_SNAKE_CASE_	=      roberta.model.classification_heads['''mnli'''].out_proj.bias
   else:
      # LM Head
      SCREAMING_SNAKE_CASE_	=      roberta.model.encoder.lm_head.dense.weight
      SCREAMING_SNAKE_CASE_	=      roberta.model.encoder.lm_head.dense.bias
      SCREAMING_SNAKE_CASE_	=      roberta.model.encoder.lm_head.layer_norm.weight
      SCREAMING_SNAKE_CASE_	=      roberta.model.encoder.lm_head.layer_norm.bias
      SCREAMING_SNAKE_CASE_	=      roberta.model.encoder.lm_head.weight
      SCREAMING_SNAKE_CASE_	=      roberta.model.encoder.lm_head.bias

   # Let's check that we get the same results.
   SCREAMING_SNAKE_CASE_	=      roberta.encode(_SCREAMING_SNAKE_CASE						).unsqueeze(0						)  # batch of size 1

   SCREAMING_SNAKE_CASE_	=      model(_SCREAMING_SNAKE_CASE						)[0]
   if classification_head:
      SCREAMING_SNAKE_CASE_	=      roberta.model.classification_heads['''mnli'''](roberta.extract_features(_SCREAMING_SNAKE_CASE						)						)
   else:
      SCREAMING_SNAKE_CASE_	=      roberta.model(_SCREAMING_SNAKE_CASE						)[0]
   print(our_output.shape,  their_output.shape						)
   SCREAMING_SNAKE_CASE_	=      torch.max(torch.abs(our_output - their_output						)						).item()
   print(F'''max_absolute_diff = {max_absolute_diff}'''						)  # ~ 1e-7
   SCREAMING_SNAKE_CASE_	=      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'''						)

   pathlib.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__":
   __UpperCAmelCase   						=     argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--roberta_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."
   )
   __UpperCAmelCase   						=     parser.parse_args()
   convert_roberta_checkpoint_to_pytorch(
       args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )





from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


UpperCamelCase__	:   int								=     [
    "AutomaticSpeechRecognition",
    "AudioClassification",
    "ImageClassification",
    "LanguageModeling",
    "QuestionAnsweringExtractive",
    "Summarization",
    "TaskTemplate",
    "TextClassification",
]

UpperCamelCase__	:   Any								=     get_logger(__name__)


UpperCamelCase__	:   Union[str, Any]								=     {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		dict							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    task_template_dict.get('task'							)
    if task_name is None:
        logger.warning(f"""Couldn't find template for task '{task_name}'. Available templates: {list(_SCREAMING_SNAKE_CASE							)}"""							)
        return None
    SCREAMING_SNAKE_CASE_						=    NAMEaTEMPLATE.get(_SCREAMING_SNAKE_CASE							)
    return template.from_dict(_SCREAMING_SNAKE_CASE							)


