"""simple docstring"""



import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__					)							->     Optional[int]:


			'''simple docstring'''


			a__     				=		CanineConfig()
			a__     				=		CanineModel(UpperCAmelCase__					)
			model.eval()

			print(f'''Building PyTorch model from configuration: {config}'''					)

			# Load weights from tf checkpoint
			load_tf_weights_in_canine(UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)

			# Save pytorch-model (weights and configuration)
			print(f'''Save PyTorch model to {pytorch_dump_path}'''					)
			model.save_pretrained(UpperCAmelCase__					)

			# Save tokenizer files
			a__     				=		CanineTokenizer()
			print(f'''Save tokenizer files to {pytorch_dump_path}'''					)
			tokenizer.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
					__magic_name__    				=					argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--tf_checkpoint_path",
					    default=None,
					    type=str,
					    required=True,
					    help="Path to the TensorFlow checkpoint. Should end with model.ckpt",
					)
					parser.add_argument(
					    "--pytorch_dump_path",
					    default=None,
					    type=str,
					    required=True,
					    help="Path to a folder where the PyTorch model will be placed.",
					)
					__magic_name__    				=					parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)



"""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__					)							->     Dict:


   '''simple docstring'''


   a__     				=		FairseqRobertaModel.from_pretrained(UpperCAmelCase__					)
   roberta.eval()  # disable dropout
   a__     				=		roberta.model.encoder.sentence_encoder
   a__     				=		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=5_14,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 BERT config:',UpperCAmelCase__					)

   a__     				=		RobertaForSequenceClassification(UpperCAmelCase__					) if classification_head else RobertaForMaskedLM(UpperCAmelCase__					)
   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.emb_layer_norm.weight
   a__     				=		roberta_sent_encoder.emb_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]

      # 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
      a__     				=		roberta_layer.self_attn_layer_norm.weight
      a__     				=		roberta_layer.self_attn_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
      a__     				=		roberta_layer.final_layer_norm.weight
      a__     				=		roberta_layer.final_layer_norm.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(UpperCAmelCase__					).unsqueeze(0					)  # batch of size 1

   a__     				=		model(UpperCAmelCase__					)[0]
   if classification_head:
      a__     				=		roberta.model.classification_heads['mnli'](roberta.extract_features(UpperCAmelCase__					)					)
   else:
      a__     				=		roberta.model(UpperCAmelCase__					)[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(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
     )


