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()
_SCREAMING_SNAKE_CASE   		=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   		=			"""Hello world! cécé herlolip"""





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)		->  List[Any]:







 '''simple docstring'''
 UpperCamelCase      =			FairseqRobertaModel.from_pretrained(UpperCamelCase_					)
 roberta.eval()  # disable dropout
 UpperCamelCase      =			roberta.model.encoder.sentence_encoder
 UpperCamelCase      =			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=514       ,				type_vocab_size=1       ,				layer_norm_eps=1E-5       ,				)
 if classification_head:
  UpperCamelCase      =			roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
 print("""Our BERT config:"""       ,				UpperCamelCase_					)

 UpperCamelCase      =			RobertaForSequenceClassification(UpperCamelCase_					) if classification_head else RobertaForMaskedLM(UpperCamelCase_					)
 model.eval()

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

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

  # self attention
  UpperCamelCase      =			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      =			roberta_layer.self_attn.q_proj.weight
  UpperCamelCase      =			roberta_layer.self_attn.q_proj.bias
  UpperCamelCase      =			roberta_layer.self_attn.k_proj.weight
  UpperCamelCase      =			roberta_layer.self_attn.k_proj.bias
  UpperCamelCase      =			roberta_layer.self_attn.v_proj.weight
  UpperCamelCase      =			roberta_layer.self_attn.v_proj.bias

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

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

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

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

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

 UpperCamelCase      =			model(UpperCamelCase_					)[0]
 if classification_head:
  UpperCamelCase      =			roberta.model.classification_heads["""mnli"""](roberta.extract_features(UpperCamelCase_					)					)
 else:
  UpperCamelCase      =			roberta.model(UpperCamelCase_					)[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(UpperCamelCase_       ,				UpperCamelCase_       ,				atol=1E-3					)
 print("""Do both models output the same tensors?"""       ,				"""🔥""" if success else """💩"""					)
 if not success:
  raise Exception("""Something went wRoNg"""					)

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


if __name__ == "__main__":
     _SCREAMING_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."""
     )
     _SCREAMING_SNAKE_CASE   		=			parser.parse_args()
     convert_roberta_checkpoint_to_pytorch(
         args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )

import numpy as np





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)		->  List[str]:







 '''simple docstring'''
 UpperCamelCase      =			n_draws * class_counts / class_counts.sum()
 # floored means we don't overshoot n_samples, but probably undershoot
 UpperCamelCase      =			np.floor(UpperCamelCase_					)
 # we add samples according to how much "left over" probability
 # they had, until we arrive at n_samples
 UpperCamelCase      =			int(n_draws - floored.sum()					)
 if need_to_add > 0:
  UpperCamelCase      =			continuous - floored
  UpperCamelCase      =			np.sort(np.unique(UpperCamelCase_					)					)[::-1]
  # add according to remainder, but break ties
  # randomly to avoid biases
  for value in values:
   ((UpperCamelCase)     ,    )      =			np.where(remainder == value					)
   # if we need_to_add less than what's in inds
   # we draw randomly from them.
   # if we need to add more, we add them all and
   # go to the next value
   UpperCamelCase      =			min(len(UpperCamelCase_					)       ,				UpperCamelCase_					)
   UpperCamelCase      =			rng.choice(UpperCamelCase_       ,				size=UpperCamelCase_       ,				replace=UpperCamelCase_					)
   floored[inds] += 1
   need_to_add -= add_now
   if need_to_add == 0:
    break
 return floored.astype(np.intaa					)





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_=10					)		->  Dict:







 '''simple docstring'''
 UpperCamelCase     ,    UpperCamelCase      =			np.unique(UpperCamelCase_       ,				return_inverse=UpperCamelCase_					)
 UpperCamelCase      =			classes.shape[0]
 UpperCamelCase      =			np.bincount(UpperCamelCase_					)
 if np.min(UpperCamelCase_					) < 2:
  raise ValueError("""Minimum class count error"""					)
 if n_train < n_classes:
  raise ValueError(
      """The train_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_train, n_classes)					)
 if n_test < n_classes:
  raise ValueError(
      """The test_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_test, n_classes)					)
 UpperCamelCase      =			np.split(np.argsort(UpperCamelCase_       ,				kind="""mergesort"""					)       ,				np.cumsum(UpperCamelCase_					)[:-1]					)
 for _ in range(UpperCamelCase_					):
  UpperCamelCase      =			approximate_mode(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)
  UpperCamelCase      =			class_counts - n_i
  UpperCamelCase      =			approximate_mode(UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_					)

  UpperCamelCase      =			[]
  UpperCamelCase      =			[]

  for i in range(UpperCamelCase_					):
   UpperCamelCase      =			rng.permutation(class_counts[i]					)
   UpperCamelCase      =			class_indices[i].take(UpperCamelCase_       ,				mode="""clip"""					)
   train.extend(perm_indices_class_i[: n_i[i]]					)
   test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]					)
  UpperCamelCase      =			rng.permutation(UpperCamelCase_					)
  UpperCamelCase      =			rng.permutation(UpperCamelCase_					)

  yield train, test
