




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										=       logging.get_logger(__name__)

_snake_case										=       'Hello world! cécé herlolip'







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     int			=						FairseqRobertaModel.from_pretrained(_snake_case     )
      roberta.eval()  # disable dropout
      _A							:     Any			=						roberta.model.encoder.sentence_encoder
      _A							:     Any			=						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:
            _A							:     Dict			=						roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

      print("""Our RoBERTa config:""",_snake_case     )

      _A							:     Union[str, Any]			=						XLMRobertaXLForSequenceClassification(_snake_case     ) if classification_head else XLMRobertaXLForMaskedLM(_snake_case     )
      model.eval()

      # Now let's copy all the weights.
      # Embeddings
      _A							:     Optional[int]			=						roberta_sent_encoder.embed_tokens.weight
      _A							:     int			=						roberta_sent_encoder.embed_positions.weight
      _A							:     Union[str, Any]			=						torch.zeros_like(
          model.roberta.embeddings.token_type_embeddings.weight     )  # just zero them out b/c RoBERTa doesn't use them.

      _A							:     Optional[int]			=						roberta_sent_encoder.layer_norm.weight
      _A							:     Dict			=						roberta_sent_encoder.layer_norm.bias

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

            _A							:     RobertaAttention			=						layer.attention
            _A							:     List[str]			=						roberta_layer.self_attn_layer_norm.weight
            _A							:     List[Any]			=						roberta_layer.self_attn_layer_norm.bias

            # self attention
            _A							:     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)     )
            )

            _A							:     List[str]			=						roberta_layer.self_attn.q_proj.weight
            _A							:     Tuple			=						roberta_layer.self_attn.q_proj.bias
            _A							:     Tuple			=						roberta_layer.self_attn.k_proj.weight
            _A							:     int			=						roberta_layer.self_attn.k_proj.bias
            _A							:     List[str]			=						roberta_layer.self_attn.v_proj.weight
            _A							:     Union[str, Any]			=						roberta_layer.self_attn.v_proj.bias

            # self-attention output
            _A							:     BertSelfOutput			=						layer.attention.output
            assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
            _A							:     Union[str, Any]			=						roberta_layer.self_attn.out_proj.weight
            _A							:     List[str]			=						roberta_layer.self_attn.out_proj.bias

            # this one is final layer norm
            _A							:     Tuple			=						roberta_layer.final_layer_norm.weight
            _A							:     Optional[int]			=						roberta_layer.final_layer_norm.bias

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

            # output
            _A							:     BertOutput			=						layer.output
            assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
            _A							:     Tuple			=						roberta_layer.fca.weight
            _A							:     Optional[int]			=						roberta_layer.fca.bias
            # end of layer

      if classification_head:
            _A							:     List[Any]			=						roberta.model.classification_heads["""mnli"""].dense.weight
            _A							:     Optional[Any]			=						roberta.model.classification_heads["""mnli"""].dense.bias
            _A							:     Optional[Any]			=						roberta.model.classification_heads["""mnli"""].out_proj.weight
            _A							:     Union[str, Any]			=						roberta.model.classification_heads["""mnli"""].out_proj.bias
      else:
            # LM Head
            _A							:     str			=						roberta.model.encoder.lm_head.dense.weight
            _A							:     List[Any]			=						roberta.model.encoder.lm_head.dense.bias
            _A							:     Union[str, Any]			=						roberta.model.encoder.lm_head.layer_norm.weight
            _A							:     Dict			=						roberta.model.encoder.lm_head.layer_norm.bias
            _A							:     Optional[int]			=						roberta.model.encoder.lm_head.weight
            _A							:     List[str]			=						roberta.model.encoder.lm_head.bias

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

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

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


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



"""simple docstring"""




import mpmath  # for roots of unity
import numpy as np



class 	lowerCAmelCase_						:


				"""simple docstring"""



				def __init__(self   ,       SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=None    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   list(poly_a or [0]    )[:]
											SCREAMING_SNAKE_CASE__						:       Tuple           =   list(poly_b or [0]    )[:]

											# Remove leading zero coefficients
											while self.polyA[-1] == 0:
																		self.polyA.pop()
											SCREAMING_SNAKE_CASE__						:       int           =   len(self.polyA    )

											while self.polyB[-1] == 0:
																		self.polyB.pop()
											SCREAMING_SNAKE_CASE__						:       List[str]           =   len(self.polyB    )

											# Add 0 to make lengths equal a power of 2
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   int(
											    2 ** np.ceil(np.loga(len(self.polyA    ) + len(self.polyB    ) - 1    )    )    )

											while len(self.polyA    ) < self.c_max_length:
																		self.polyA.append(0    )
											while len(self.polyB    ) < self.c_max_length:
																		self.polyB.append(0    )
											# A complex root used for the fourier transform
											SCREAMING_SNAKE_CASE__						:       List[str]           =   complex(mpmath.root(x=1   ,       n=self.c_max_length   ,       k=1    )    )

											# The product
											SCREAMING_SNAKE_CASE__						:       Tuple           =   self.__multiply()



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   [[x] for x in self.polyA] if which == """A""" else [[x] for x in self.polyB]
											# Corner case
											if len(SCREAMING_SNAKE_CASE__    ) <= 1:
																		return dft[0]
											#
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.c_max_length // 2
											while next_ncol > 0:
																		SCREAMING_SNAKE_CASE__						:       Any           =   [[] for i in range(SCREAMING_SNAKE_CASE__    )]
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   self.root**next_ncol

																		# First half of next step
																		SCREAMING_SNAKE_CASE__						:       str           =   1
																		for j in range(self.c_max_length // (next_ncol * 2)    ):
																									for i in range(SCREAMING_SNAKE_CASE__    ):
																																new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]    )
																									current_root *= root
																		# Second half of next step
																		SCREAMING_SNAKE_CASE__						:       int           =   1
																		for j in range(self.c_max_length // (next_ncol * 2)    ):
																									for i in range(SCREAMING_SNAKE_CASE__    ):
																																new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]    )
																									current_root *= root
																		# Update
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   new_dft
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   next_ncol // 2
											return dft[0]



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.__dft("""A"""    )
											SCREAMING_SNAKE_CASE__						:       Dict           =   self.__dft("""B"""    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   [[dft_a[i] * dft_b[i] for i in range(self.c_max_length    )]]
											del dft_a
											del dft_b

											# Corner Case
											if len(inverce_c[0]    ) <= 1:
																		return inverce_c[0]
											# Inverse DFT
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   2
											while next_ncol <= self.c_max_length:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   [[] for i in range(SCREAMING_SNAKE_CASE__    )]
																		SCREAMING_SNAKE_CASE__						:       Tuple           =   self.root ** (next_ncol // 2)
																		SCREAMING_SNAKE_CASE__						:       Any           =   1
																		# First half of next step
																		for j in range(self.c_max_length // next_ncol    ):
																									for i in range(next_ncol // 2    ):
																																# Even positions
																																new_inverse_c[i].append(
																																    (
																																        inverce_c[i][j]
																																        + inverce_c[i][j + self.c_max_length // next_ncol]
																																    )
																																    / 2    )
																																# Odd positions
																																new_inverse_c[i + next_ncol // 2].append(
																																    (
																																        inverce_c[i][j]
																																        - inverce_c[i][j + self.c_max_length // next_ncol]
																																    )
																																    / (2 * current_root)    )
																									current_root *= root
																		# Update
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   new_inverse_c
																		next_ncol *= 2
											# Unpack
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [round(x[0].real   ,       8    ) + round(x[0].imag   ,       8    ) * 1j for x in inverce_c]

											# Remove leading 0's
											while inverce_c[-1] == 0:
																		inverce_c.pop()
											return inverce_c



				def __str__(self    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       List[str]           =   """A = """ + """ + """.join(
											    F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A]    )    )
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """B = """ + """ + """.join(
											    F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B]    )    )
											SCREAMING_SNAKE_CASE__						:       int           =   """A*B = """ + """ + """.join(
											    F'''{coef}*x^{i}''' for coef, i in enumerate(self.product    )    )

											return F'''{a}\n{b}\n{c}'''


# Unit tests
if __name__ == "__main__":
					import doctest

					doctest.testmod()




