






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   :							Union[str, Any]						= logging.get_logger(__name__)

__a   :							Tuple						= "Hello world! cécé herlolip"



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      str      ,					__lowercase :      bool     )    ->   List[Any]:





    """simple docstring"""

    __A       =						FairseqRobertaModel.from_pretrained(__lowercase     )
    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_1_4      ,					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:"""      ,					__lowercase     )

    __A       =						XLMRobertaXLForSequenceClassification(__lowercase     ) if classification_head else XLMRobertaXLForMaskedLM(__lowercase     )
    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(__lowercase     ).unsqueeze(0     )  # batch of size 1

    __A       =						model(__lowercase     )[0]
    if classification_head:
        __A       =						roberta.model.classification_heads["""mnli"""](roberta.extract_features(__lowercase     )     )
    else:
        __A       =						roberta.model(__lowercase     )[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(__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   :							List[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."
  )
  __a   :							Dict						= parser.parse_args()
  convert_xlm_roberta_xl_checkpoint_to_pytorch(
      args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )












from __future__ import annotations

from typing import Any


class    __lowercase   :







     '''simple docstring'''




     def __init__(					self							:      Dict			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      float = 0					):


         """simple docstring"""



         __A     ,						__A       =						row, column
         __A       =						[[default_value for c in range(UpperCamelCase_					)] for r in range(UpperCamelCase_					)]




     def __str__(					self							:      Any					):


         """simple docstring"""



         __A       =						F"Matrix consist of {self.row} rows and {self.column} columns\n"

         # Make string identifier
         __A       =						0
         for row_vector in self.array:
             for obj in row_vector:
                 __A       =						max(UpperCamelCase_			,						len(str(UpperCamelCase_					)					)					)
         __A       =						F"%{max_element_length}s"

         # Make string and return
         def single_line(UpperCamelCase_							:      list[float]					) -> str:
             nonlocal string_format_identifier
             __A       =						"""["""
             line += ", ".join(string_format_identifier % (obj,) for obj in row_vector					)
             line += "]"
             return line

         s += "\n".join(single_line(UpperCamelCase_					) for row_vector in self.array					)
         return s




     def __repr__(					self							:      Optional[Any]					):


         """simple docstring"""



         return str(self					)




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      tuple[int, int]					):


         """simple docstring"""



         if not (isinstance(UpperCamelCase_			,						(list, tuple)					) and len(UpperCamelCase_					) == 2):
             return False
         elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
             return False
         else:
             return True




     def __getitem__(					self							:      Optional[int]			,						UpperCamelCase_							:      tuple[int, int]					):


         """simple docstring"""



         assert self.validate_indicies(UpperCamelCase_					)
         return self.array[loc[0]][loc[1]]




     def __setitem__(					self							:      str			,						UpperCamelCase_							:      tuple[int, int]			,						UpperCamelCase_							:      float					):


         """simple docstring"""



         assert self.validate_indicies(UpperCamelCase_					)
         __A       =						value




     def __add__(					self							:      Optional[int]			,						UpperCamelCase_							:      Matrix					):


         """simple docstring"""



         assert isinstance(UpperCamelCase_			,						UpperCamelCase_					)
         assert self.row == another.row and self.column == another.column

         # Add
         __A       =						Matrix(self.row			,						self.column					)
         for r in range(self.row					):
             for c in range(self.column					):
                 __A       =						self[r, c] + another[r, c]
         return result




     def __neg__(					self							:      Any					):


         """simple docstring"""



         __A       =						Matrix(self.row			,						self.column					)
         for r in range(self.row					):
             for c in range(self.column					):
                 __A       =						-self[r, c]
         return result




     def __sub__(					self							:      List[Any]			,						UpperCamelCase_							:      Matrix					):


         """simple docstring"""



         return self + (-another)




     def __mul__(					self							:      Union[str, Any]			,						UpperCamelCase_							:      int | float | Matrix					):


         """simple docstring"""



         if isinstance(UpperCamelCase_			,						(int, float)					):  # Scalar multiplication
             __A       =						Matrix(self.row			,						self.column					)
             for r in range(self.row					):
                 for c in range(self.column					):
                     __A       =						self[r, c] * another
             return result
         elif isinstance(UpperCamelCase_			,						UpperCamelCase_					):  # Matrix multiplication
             assert self.column == another.row
             __A       =						Matrix(self.row			,						another.column					)
             for r in range(self.row					):
                 for c in range(another.column					):
                     for i in range(self.column					):
                         result[r, c] += self[r, i] * another[i, c]
             return result
         else:
             __A       =						F"Unsupported type given for another ({type(UpperCamelCase_					)})"
             raise TypeError(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         __A       =						Matrix(self.column			,						self.row					)
         for r in range(self.row					):
             for c in range(self.column					):
                 __A       =						self[r, c]
         return result





     def 		lowerCAmelCase_  (					self							:      List[str]			,						UpperCamelCase_							:      Matrix			,						UpperCamelCase_							:      Matrix					):


         """simple docstring"""



         assert isinstance(UpperCamelCase_			,						UpperCamelCase_					) and isinstance(UpperCamelCase_			,						UpperCamelCase_					)
         assert self.row == self.column == u.row == v.row  # u, v should be column vector
         assert u.column == v.column == 1  # u, v should be column vector

         # Calculate
         __A       =						v.transpose()
         __A       =						(v_t * self * u)[0, 0] + 1
         if numerator_factor == 0:
             return None  # It's not invertable
         return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":



  def       _SCREAMING_SNAKE_CASE    (			)    ->   None:





      """simple docstring"""

      __A       =						Matrix(3      ,					3      ,					0     )
      for i in range(3     ):
          __A       =						1
      print(f"a^(-1) is {ainv}"     )
      # u, v
      __A       =						Matrix(3      ,					1      ,					0     )
      __A     ,						__A     ,						__A       =						1, 2, -3
      __A       =						Matrix(3      ,					1      ,					0     )
      __A     ,						__A     ,						__A       =						4, -2, 5
      print(f"u is {u}"     )
      print(f"v is {v}"     )
      print(f"uv^T is {u * v.transpose()}"     )
      # Sherman Morrison
      print(f"(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowercase      ,					__lowercase     )}"     )



  def       _SCREAMING_SNAKE_CASE    (			)    ->   None:





      """simple docstring"""

      import doctest

      doctest.testmod()






  testa()




