




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()
__a          =			logging.get_logger(__name__)

__a          =			'Hello world! cécé herlolip'







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Dict       =      FairseqRobertaModel.from_pretrained(_lowercase				)
     roberta.eval()  # disable dropout
     UpperCAmelCase_      :							str       =      roberta.model.encoder.sentence_encoder
     UpperCAmelCase_      :							int       =      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=514			,						type_vocab_size=1			,						layer_norm_eps=1E-5			,						)
     if classification_head:
          UpperCAmelCase_      :							Dict       =      roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

     UpperCAmelCase_      :							List[Any]       =      XLMRobertaXLForSequenceClassification(_lowercase				) if classification_head else XLMRobertaXLForMaskedLM(_lowercase				)
     model.eval()

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

     UpperCAmelCase_      :							Union[str, Any]       =      roberta_sent_encoder.layer_norm.weight
     UpperCAmelCase_      :							List[str]       =      roberta_sent_encoder.layer_norm.bias

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

          UpperCAmelCase_      :							RobertaAttention       =      layer.attention
          UpperCAmelCase_      :							Union[str, Any]       =      roberta_layer.self_attn_layer_norm.weight
          UpperCAmelCase_      :							Optional[Any]       =      roberta_layer.self_attn_layer_norm.bias

          # self attention
          UpperCAmelCase_      :							BertSelfAttention       =      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)				)
          )

          UpperCAmelCase_      :							str       =      roberta_layer.self_attn.q_proj.weight
          UpperCAmelCase_      :							Dict       =      roberta_layer.self_attn.q_proj.bias
          UpperCAmelCase_      :							Optional[int]       =      roberta_layer.self_attn.k_proj.weight
          UpperCAmelCase_      :							str       =      roberta_layer.self_attn.k_proj.bias
          UpperCAmelCase_      :							Any       =      roberta_layer.self_attn.v_proj.weight
          UpperCAmelCase_      :							int       =      roberta_layer.self_attn.v_proj.bias

          # self-attention output
          UpperCAmelCase_      :							BertSelfOutput       =      layer.attention.output
          assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
          UpperCAmelCase_      :							Tuple       =      roberta_layer.self_attn.out_proj.weight
          UpperCAmelCase_      :							Any       =      roberta_layer.self_attn.out_proj.bias

          # this one is final layer norm
          UpperCAmelCase_      :							List[Any]       =      roberta_layer.final_layer_norm.weight
          UpperCAmelCase_      :							Optional[int]       =      roberta_layer.final_layer_norm.bias

          # intermediate
          UpperCAmelCase_      :							BertIntermediate       =      layer.intermediate
          assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
          UpperCAmelCase_      :							Optional[Any]       =      roberta_layer.fca.weight
          UpperCAmelCase_      :							List[Any]       =      roberta_layer.fca.bias

          # output
          UpperCAmelCase_      :							BertOutput       =      layer.output
          assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
          UpperCAmelCase_      :							List[str]       =      roberta_layer.fca.weight
          UpperCAmelCase_      :							Dict       =      roberta_layer.fca.bias
          # end of layer

     if classification_head:
          UpperCAmelCase_      :							List[Any]       =      roberta.model.classification_heads['''mnli'''].dense.weight
          UpperCAmelCase_      :							Any       =      roberta.model.classification_heads['''mnli'''].dense.bias
          UpperCAmelCase_      :							List[str]       =      roberta.model.classification_heads['''mnli'''].out_proj.weight
          UpperCAmelCase_      :							str       =      roberta.model.classification_heads['''mnli'''].out_proj.bias
     else:
          # LM Head
          UpperCAmelCase_      :							Optional[Any]       =      roberta.model.encoder.lm_head.dense.weight
          UpperCAmelCase_      :							Optional[int]       =      roberta.model.encoder.lm_head.dense.bias
          UpperCAmelCase_      :							List[Any]       =      roberta.model.encoder.lm_head.layer_norm.weight
          UpperCAmelCase_      :							Optional[int]       =      roberta.model.encoder.lm_head.layer_norm.bias
          UpperCAmelCase_      :							str       =      roberta.model.encoder.lm_head.weight
          UpperCAmelCase_      :							Any       =      roberta.model.encoder.lm_head.bias

     # Let's check that we get the same results.
     UpperCAmelCase_      :							torch.Tensor       =      roberta.encode(_lowercase				).unsqueeze(0				)  # batch of size 1

     UpperCAmelCase_      :							str       =      model(_lowercase				)[0]
     if classification_head:
          UpperCAmelCase_      :							Union[str, Any]       =      roberta.model.classification_heads['''mnli'''](roberta.extract_features(_lowercase				)				)
     else:
          UpperCAmelCase_      :							Dict       =      roberta.model(_lowercase				)[0]
     print(our_output.shape			,						their_output.shape				)
     UpperCAmelCase_      :							int       =      torch.max(torch.abs(our_output - their_output				)				).item()
     print(f'''max_absolute_diff = {max_absolute_diff}'''				)  # ~ 1e-7
     UpperCAmelCase_      :							Optional[int]       =      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'''				)

     pathlib.Path(_lowercase				).mkdir(parents=_lowercase			,						exist_ok=_lowercase				)
     print(f'''Saving model to {pytorch_dump_folder_path}'''				)
     model.save_pretrained(_lowercase				)


if __name__ == "__main__":
     __a          =			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.'
     )
     __a          =			parser.parse_args()
     convert_xlm_roberta_xl_checkpoint_to_pytorch(
         args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )





from collections import defaultdict







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[int]       =      first_str.lower().strip()
     UpperCAmelCase_      :							Any       =      second_str.lower().strip()

     # Remove whitespace
     UpperCAmelCase_      :							Any       =      first_str.replace(''' '''			,						''''''				)
     UpperCAmelCase_      :							int       =      second_str.replace(''' '''			,						''''''				)

     # Strings of different lengths are not anagrams
     if len(_lowercase				) != len(_lowercase				):
          return False

     # Default values for count should be 0
     UpperCAmelCase_      :							defaultdict[str, int]       =      defaultdict(_lowercase				)

     # For each character in input strings,
     # increment count in the corresponding
     for i in range(len(_lowercase				)				):
          count[first_str[i]] += 1
          count[second_str[i]] -= 1

     return all(_count == 0 for _count in count.values()				)


if __name__ == "__main__":
     from doctest import testmod

     testmod()
     __a          =			input('Enter the first string ').strip()
     __a          =			input('Enter the second string ').strip()

     __a          =			check_anagrams(input_a, input_b)
     print(F"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")