
"""simple docstring"""
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()
__magic_name__		       =				logging.get_logger(__name__)

__magic_name__		       =				'''Hello world! cécé herlolip'''




def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      ):


 """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__":
   __magic_name__		       =				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.'''
   )
   __magic_name__		       =				parser.parse_args()
   convert_roberta_checkpoint_to_pytorch(
       args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


__magic_name__		       =				{}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__magic_name__		       =				['''CpmTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__magic_name__		       =				['''CpmTokenizerFast''']


if TYPE_CHECKING:
			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_cpm import CpmTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_cpm_fast import CpmTokenizerFast

else:
			import sys

			__magic_name__		       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




