





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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
       raise Exception('requires fairseq >= 1.0.0a')

logging.set_verbosity_info()
snake_case__					:							List[str]													=     logging.get_logger(__name__)

snake_case__					:							Any													=     'Hello world! cécé herlolip'



def _a				(	lowerCamelCase:   str					,				lowerCamelCase:   str					,				lowerCamelCase:   bool )	->						Optional[Any]:







      '''simple docstring'''






      __A          =				FairseqRobertaModel.from_pretrained(lowerCamelCase )
      roberta.eval()  # disable dropout
      __A          =				roberta.model.encoder.sentence_encoder
      __A          =				XLMRobertaConfig(
          vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings					,				hidden_size=roberta.cfg.model.encoder_embed_dim					,				num_hidden_layers=roberta.cfg.model.encoder_layers					,				num_attention_heads=roberta.cfg.model.encoder_attention_heads					,				intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim					,				max_position_embeddings=5_14					,				type_vocab_size=1					,				layer_norm_eps=1e-5					,				)
      if classification_head:
            __A          =				roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

      print('''Our RoBERTa config:'''					,				lowerCamelCase )

      __A          =				XLMRobertaXLForSequenceClassification(lowerCamelCase ) if classification_head else XLMRobertaXLForMaskedLM(lowerCamelCase )
      model.eval()

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

      __A          =				roberta_sent_encoder.layer_norm.weight
      __A          =				roberta_sent_encoder.layer_norm.bias

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

            __A          =				layer.attention
            __A          =				roberta_layer.self_attn_layer_norm.weight
            __A          =				roberta_layer.self_attn_layer_norm.bias

            # self attention
            __A          =				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) )
            )

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

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

            # this one is final layer norm
            __A          =				roberta_layer.final_layer_norm.weight
            __A          =				roberta_layer.final_layer_norm.bias

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

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

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

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

      __A          =				model(lowerCamelCase )[0]
      if classification_head:
            __A          =				roberta.model.classification_heads['''mnli'''](roberta.extract_features(lowerCamelCase ) )
      else:
            __A          =				roberta.model(lowerCamelCase )[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''' )

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


if __name__ == "__main__":
       snake_case__					:							Union[str, Any]													=     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.'
       )
       snake_case__					:							Optional[int]													=     parser.parse_args()
       convert_xlm_roberta_xl_checkpoint_to_pytorch(
           args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
       )












import os
from datetime import datetime as dt

from github import Github


snake_case__					:							Union[str, Any]													=     [
    'good first issue',
    'feature request',
    'wip',
]



def _a				(	)	->						List[Any]:







      '''simple docstring'''






      __A          =				Github(os.environ['''GITHUB_TOKEN'''] )
      __A          =				g.get_repo('''huggingface/accelerate''' )
      __A          =				repo.get_issues(state='''open''' )

      for issue in open_issues:
            __A          =				sorted([comment for comment in issue.get_comments()]					,				key=lambda lowerCamelCase : i.created_at					,				reverse=lowerCamelCase )
            __A          =				comments[0] if len(lowerCamelCase ) > 0 else None
            __A          =				dt.utcnow()
            __A          =				(current_time - issue.updated_at).days
            __A          =				(current_time - issue.created_at).days
            if (
                last_comment is not None
                and last_comment.user.login == "github-actions[bot]"
                and days_since_updated > 7
                and days_since_creation >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
            ):
                  # Close issue since it has been 7 days of inactivity since bot mention.
                  issue.edit(state='''closed''' )
            elif (
                days_since_updated > 23
                and days_since_creation >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
            ):
                  # Add stale comment
                  issue.create_comment(
                      '''This issue has been automatically marked as stale because it has not had '''
                      '''recent activity. If you think this still needs to be addressed '''
                      '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
                      '''[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '''
                      '''are likely to be ignored.''' )


if __name__ == "__main__":
       main()





