






import random
import sys

from . import cryptomath_module as cryptomath

__A  							=  (
    r" !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`"
    r"abcdefghijklmnopqrstuvwxyz{|}~"
)







def 			lowercase__  (				A_:	int     ,			A_:	int     ,			A_:	str		)  ->							None:







    """simple docstring"""






    if mode == "encrypt":
        if key_a == 1:
            sys.exit(
                """The affine cipher becomes weak when key """
                """A is set to 1. Choose different key"""		)
        if key_b == 0:
            sys.exit(
                """The affine cipher becomes weak when key """
                """B is set to 0. Choose different key"""		)
    if key_a < 0 or key_b < 0 or key_b > len(A_		) - 1:
        sys.exit(
            """Key A must be greater than 0 and key B must """
            F'''be between 0 and {len(A_		) - 1}.'''		)
    if cryptomath.gcd(A_     ,			len(A_		)		) != 1:
        sys.exit(
            F'''Key A {key_a} and the symbol set size {len(A_		)} '''
            """are not relatively prime. Choose a different key."""		)







def 			lowercase__  (				A_:	int     ,			A_:	str		)  ->							str:







    """simple docstring"""






    __UpperCAmelCase							,   __UpperCAmelCase    							=divmod(A_     ,			len(A_		)		)
    check_keys(A_     ,			A_     ,			"""encrypt"""		)
    __UpperCAmelCase    							=""""""
    for symbol in message:
        if symbol in SYMBOLS:
            __UpperCAmelCase    							=SYMBOLS.find(A_		)
            cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(A_		)]
        else:
            cipher_text += symbol
    return cipher_text







def 			lowercase__  (				A_:	int     ,			A_:	str		)  ->							str:







    """simple docstring"""






    __UpperCAmelCase							,   __UpperCAmelCase    							=divmod(A_     ,			len(A_		)		)
    check_keys(A_     ,			A_     ,			"""decrypt"""		)
    __UpperCAmelCase    							=""""""
    __UpperCAmelCase    							=cryptomath.find_mod_inverse(A_     ,			len(A_		)		)
    for symbol in message:
        if symbol in SYMBOLS:
            __UpperCAmelCase    							=SYMBOLS.find(A_		)
            plain_text += SYMBOLS[
                (sym_index - key_b) * mod_inverse_of_key_a % len(A_		)
            ]
        else:
            plain_text += symbol
    return plain_text







def 			lowercase__  (				)  ->							int:







    """simple docstring"""






    while True:
        __UpperCAmelCase    							=random.randint(2     ,			len(A_		)		)
        __UpperCAmelCase    							=random.randint(2     ,			len(A_		)		)
        if cryptomath.gcd(A_     ,			len(A_		)		) == 1 and key_b % len(A_		) != 0:
            return key_b * len(A_		) + key_b







def 			lowercase__  (				)  ->							None:







    """simple docstring"""






    __UpperCAmelCase    							=input("""Enter message: """		).strip()
    __UpperCAmelCase    							=int(input("""Enter key [2000 - 9000]: """		).strip()		)
    __UpperCAmelCase    							=input("""Encrypt/Decrypt [E/D]: """		).strip().lower()

    if mode.startswith("""e"""		):
        __UpperCAmelCase    							="""encrypt"""
        __UpperCAmelCase    							=encrypt_message(A_     ,			A_		)
    elif mode.startswith("""d"""		):
        __UpperCAmelCase    							="""decrypt"""
        __UpperCAmelCase    							=decrypt_message(A_     ,			A_		)
    print(F'''\n{mode.title()}ed text: \n{translated}'''		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   # main()












import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
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()
__A  							=  logging.get_logger(__name__)

__A  							=  "Hello world! cécé herlolip"







def 			lowercase__  (				A_:	str     ,			A_:	str     ,			A_:	bool		)  ->							str:







       """simple docstring"""






       __UpperCAmelCase							,   __UpperCAmelCase    							=os.path.split(A_		)
       __UpperCAmelCase    							=DataaVecTextModel.from_pretrained(
           A_     ,			checkpoint_file=A_		)
       dataavec.eval()  # disable dropout
       __UpperCAmelCase    							=dataavec.models[0]
       __UpperCAmelCase    							=dataavec_model.encoder.sentence_encoder
       __UpperCAmelCase    							=DataaVecTextConfig(
           vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings     ,			hidden_size=dataavec_model.args.encoder_embed_dim     ,			num_hidden_layers=dataavec_model.args.encoder_layers     ,			num_attention_heads=dataavec_model.args.encoder_attention_heads     ,			intermediate_size=dataavec_model.args.encoder_ffn_embed_dim     ,			max_position_embeddings=514     ,			type_vocab_size=1     ,			layer_norm_eps=1e-5     ,			)
       if classification_head:
              __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
       print("""Our BERT config:"""     ,			A_		)

       __UpperCAmelCase    							=DataaVecTextForSequenceClassification(A_		) if classification_head else DataaVecTextForMaskedLM(A_		)
       model.eval()

       # Now let's copy all the weights.
       # Embeddings
       __UpperCAmelCase    							=dataavec_sent_encoder.embed_tokens.weight
       __UpperCAmelCase    							=dataavec_sent_encoder.embed_positions.weight
       __UpperCAmelCase    							=torch.zeros_like(
           model.dataavec_text.embeddings.token_type_embeddings.weight		)  # just zero them out b/c data2vec doesn't use them.
       __UpperCAmelCase    							=dataavec_sent_encoder.layernorm_embedding.weight
       __UpperCAmelCase    							=dataavec_sent_encoder.layernorm_embedding.bias

       for i in range(config.num_hidden_layers		):
              # Encoder: start of layer
              __UpperCAmelCase    							=model.dataavec_text.encoder.layer[i]
              __UpperCAmelCase    							=dataavec_sent_encoder.layers[i]

              # self attention
              __UpperCAmelCase    							=layer.attention.self
              assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
                  (config.hidden_size, config.hidden_size)		), (
                  "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
                  F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
              )
              assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
                  (config.hidden_size, config.hidden_size)		), (
                  "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
                  F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
              )
              assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
                  (config.hidden_size, config.hidden_size)		), (
                  "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
                  F''' {torch.Size((config.hidden_size, config.hidden_size)		)}'''
              )

              __UpperCAmelCase    							=dataavec_layer.self_attn.q_proj.weight
              __UpperCAmelCase    							=dataavec_layer.self_attn.q_proj.bias
              __UpperCAmelCase    							=dataavec_layer.self_attn.k_proj.weight
              __UpperCAmelCase    							=dataavec_layer.self_attn.k_proj.bias
              __UpperCAmelCase    							=dataavec_layer.self_attn.v_proj.weight
              __UpperCAmelCase    							=dataavec_layer.self_attn.v_proj.bias

              # self-attention output
              __UpperCAmelCase    							=layer.attention.output
              assert (
                  self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
              ), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
              __UpperCAmelCase    							=dataavec_layer.self_attn.out_proj.weight
              __UpperCAmelCase    							=dataavec_layer.self_attn.out_proj.bias
              __UpperCAmelCase    							=dataavec_layer.self_attn_layer_norm.weight
              __UpperCAmelCase    							=dataavec_layer.self_attn_layer_norm.bias

              # intermediate
              __UpperCAmelCase    							=layer.intermediate
              assert (
                  intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
              ), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
              __UpperCAmelCase    							=dataavec_layer.fca.weight
              __UpperCAmelCase    							=dataavec_layer.fca.bias

              # output
              __UpperCAmelCase    							=layer.output
              assert (
                  bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
              ), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
              __UpperCAmelCase    							=dataavec_layer.fca.weight
              __UpperCAmelCase    							=dataavec_layer.fca.bias
              __UpperCAmelCase    							=dataavec_layer.final_layer_norm.weight
              __UpperCAmelCase    							=dataavec_layer.final_layer_norm.bias
              # end of layer

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

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

       __UpperCAmelCase    							=model(A_		)[0]
       if classification_head:
              __UpperCAmelCase    							=dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(A_		)		)
       else:
              __UpperCAmelCase    							=dataavec_model(A_		)[0]
       print(our_output.shape     ,			their_output.shape		)
       __UpperCAmelCase    							=torch.max(torch.abs(our_output - their_output		)		).item()
       print(F'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
       __UpperCAmelCase    							=torch.allclose(A_     ,			A_     ,			atol=1e-3		)
       print("""Do both models output the same tensors?"""     ,			"""🔥""" if success else """💩"""		)
       if not success:
              raise Exception("""Something went wRoNg"""		)

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


if __name__ == "__main__":
   __A  							=  argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--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_dataavec_checkpoint_to_pytorch(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )




