






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					:						List[Any]         =     logging.get_logger(__name__)

_UpperCAmelCase					:						Optional[Any]         =     '''Hello world! cécé herlolip'''






def  UpperCAmelCase__						(    lowerCamelCase	:			Any			,lowerCamelCase	:			Union[str, Any]			,lowerCamelCase	:			str    ):
   lowerCamelCase									=FairseqRobertaModel.from_pretrained(lowerCamelCase    )
   roberta.eval()  # disable dropout
   lowerCamelCase									=roberta.model.encoder.sentence_encoder
   lowerCamelCase									=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:
      lowerCamelCase									=roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
   print("""Our BERT config:"""			,lowerCamelCase    )

   lowerCamelCase									=RobertaForSequenceClassification(lowerCamelCase    ) if classification_head else RobertaForMaskedLM(lowerCamelCase    )
   model.eval()

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

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

      # self attention
      lowerCamelCase									=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)    )
      )

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

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

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

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

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

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

   lowerCamelCase									=model(lowerCamelCase    )[0]
   if classification_head:
      lowerCamelCase									=roberta.model.classification_heads["""mnli"""](roberta.extract_features(lowerCamelCase    )    )
   else:
      lowerCamelCase									=roberta.model(lowerCamelCase    )[0]
   print(our_output.shape			,their_output.shape    )
   lowerCamelCase									=torch.max(torch.abs(our_output - their_output    )    ).item()
   print(f'''max_absolute_diff = {max_absolute_diff}'''    )  # ~ 1e-7
   lowerCamelCase									=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__":
   _UpperCAmelCase					:						int         =     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					:						Dict         =     parser.parse_args()
   convert_roberta_checkpoint_to_pytorch(
       args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )

"""simple docstring"""




def __snake_case (      UpperCamelCase = 10**6    )       ->    int:


		"""simple docstring"""





		if n <= 0:
				raise ValueError('''Please enter an integer greater than 0'''    )

		a__   		=     list(range(n + 1    )    )
		for number in range(2						,					n + 1    ):
				if phi[number] == number:
						phi[number] -= 1
						for multiple in range(number * 2						,					n + 1						,					UpperCamelCase    ):
								a__   		=     (phi[multiple] // number) * (number - 1)

		a__   		=     1
		for number in range(1						,					n + 1    ):
				if (answer / phi[answer]) < (number / phi[number]):
						a__   		=     number

		return answer


if __name__ == "__main__":
					print(solution())




