


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__   	=				logging.get_logger(__name__)

lowerCAmelCase__   	=				{
    'uw-madison/yoso-4096': 'https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}







class   lowerCAmelCase__ (       a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				"yoso"


     def __init__(		self				,		__lowerCamelCase=5_0_2_6_5				,		__lowerCamelCase=7_6_8				,		__lowerCamelCase=1_2				,		__lowerCamelCase=1_2				,		__lowerCamelCase=3_0_7_2				,		__lowerCamelCase="gelu"				,		__lowerCamelCase=0.1				,		__lowerCamelCase=0.1				,		__lowerCamelCase=4_0_9_6				,		__lowerCamelCase=1				,		__lowerCamelCase=0.0_2				,		__lowerCamelCase=1e-12				,		__lowerCamelCase="absolute"				,		__lowerCamelCase=True				,		__lowerCamelCase=9				,		__lowerCamelCase=6_4				,		__lowerCamelCase=None				,		__lowerCamelCase=True				,		__lowerCamelCase=True				,		__lowerCamelCase=1				,		__lowerCamelCase=0				,		__lowerCamelCase=2				,		**__lowerCamelCase				,		)    ->       Any:
            super().__init__(pad_token_id=__lowerCamelCase				,		bos_token_id=__lowerCamelCase				,		eos_token_id=__lowerCamelCase				,		**__lowerCamelCase)

            _A   : Optional[Any]       					=				vocab_size
            _A   : List[Any]       					=				max_position_embeddings
            _A   : Optional[int]       					=				hidden_size
            _A   : Optional[Any]       					=				num_hidden_layers
            _A   : Dict       					=				num_attention_heads
            _A   : List[Any]       					=				intermediate_size
            _A   : Dict       					=				hidden_act
            _A   : str       					=				hidden_dropout_prob
            _A   : Optional[int]       					=				attention_probs_dropout_prob
            _A   : Union[str, Any]       					=				initializer_range
            _A   : int       					=				type_vocab_size
            _A   : Dict       					=				layer_norm_eps
            _A   : Optional[int]       					=				position_embedding_type
            _A   : str       					=				use_expectation
            _A   : List[Any]       					=				hash_code_len
            _A   : List[Any]       					=				num_hash
            _A   : Optional[Any]       					=				conv_window
            _A   : List[Any]       					=				use_fast_hash
            _A   : str       					=				lsh_backward




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()
lowerCAmelCase__   	=				logging.get_logger(__name__)

lowerCAmelCase__   	=				'Hello world! cécé herlolip'
def 							_UpperCAmelCase       (UpperCamelCase__  :						str       ,							UpperCamelCase__  :						str       ,							UpperCamelCase__  :						bool		):
       _A   : Union[str, Any]       					=				FairseqRobertaModel.from_pretrained(UpperCamelCase__		)
       roberta.eval()  # disable dropout
       _A   : Dict       					=				roberta.model.encoder.sentence_encoder
       _A   : int       					=				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:
              _A   : List[Any]       					=				roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
       print("Our BERT config:"       ,							UpperCamelCase__		)

       _A   : Dict       					=				RobertaForSequenceClassification(UpperCamelCase__		) if classification_head else RobertaForMaskedLM(UpperCamelCase__		)
       model.eval()

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

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

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

              # output
              _A   : BertOutput       					=				layer.output
              assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
              _A   : int       					=				roberta_layer.fca.weight
              _A   : List[str]       					=				roberta_layer.fca.bias
              _A   : List[str]       					=				roberta_layer.final_layer_norm.weight
              _A   : Any       					=				roberta_layer.final_layer_norm.bias
              # end of layer

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

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

       _A   : int       					=				model(UpperCamelCase__		)[0]
       if classification_head:
              _A   : str       					=				roberta.model.classification_heads["mnli"](roberta.extract_features(UpperCamelCase__		)		)
       else:
              _A   : int       					=				roberta.model(UpperCamelCase__		)[0]
       print(our_output.shape       ,							their_output.shape		)
       _A   : List[Any]       					=				torch.max(torch.abs(our_output - their_output		)		).item()
       print(f"max_absolute_diff = {max_absolute_diff}"		)  # ~ 1e-7
       _A   : List[Any]       					=				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__":
    lowerCAmelCase__   	=				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.'
    )
    lowerCAmelCase__   	=				parser.parse_args()
    convert_roberta_checkpoint_to_pytorch(
        args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )
