

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()
lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  "Hello world! cécé herlolip"
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	bool      )							->      str:





    '''simple docstring'''






    A__          =   FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE__      )
    roberta.eval()  # disable dropout
    A__          =   roberta.model.encoder.sentence_encoder
    A__          =   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__          =   roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

    print('Our RoBERTa config:'    ,			SCREAMING_SNAKE_CASE__      )

    A__          =   XLMRobertaXLForSequenceClassification(SCREAMING_SNAKE_CASE__      ) if classification_head else XLMRobertaXLForMaskedLM(SCREAMING_SNAKE_CASE__      )
    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.layer_norm.weight
    A__          =   roberta_sent_encoder.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]

        A__          =   layer.attention
        A__          =   roberta_layer.self_attn_layer_norm.weight
        A__          =   roberta_layer.self_attn_layer_norm.bias

        # 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

        # this one is final layer norm
        A__          =   roberta_layer.final_layer_norm.weight
        A__          =   roberta_layer.final_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
        # 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(SCREAMING_SNAKE_CASE__      ).unsqueeze(0      )  # batch of size 1

    A__          =   model(SCREAMING_SNAKE_CASE__      )[0]
    if classification_head:
        A__          =   roberta.model.classification_heads['mnli'](roberta.extract_features(SCREAMING_SNAKE_CASE__      )      )
    else:
        A__          =   roberta.model(SCREAMING_SNAKE_CASE__      )[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(SCREAMING_SNAKE_CASE__    ,			SCREAMING_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(SCREAMING_SNAKE_CASE__      ).mkdir(parents=SCREAMING_SNAKE_CASE__    ,			exist_ok=SCREAMING_SNAKE_CASE__      )
    print(f'Saving model to {pytorch_dump_folder_path}'      )
    model.save_pretrained(SCREAMING_SNAKE_CASE__      )


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




import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Union[str, Any]:





    '''simple docstring'''






    return EnvironmentCommand()


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @staticmethod
    def 						snake_case__     (      lowercase_ :       ArgumentParser		)->    Dict:





        '''simple docstring'''







        A__          =   parser.add_parser('env'		)
        download_parser.set_defaults(func=lowercase_		)


    def 						snake_case__     (      self :       List[Any]		)->    List[str]:





        '''simple docstring'''







        A__          =   huggingface_hub.__version__

        A__          =   'not installed'
        A__          =   'NA'
        if is_torch_available():
            import torch

            A__          =   torch.__version__
            A__          =   torch.cuda.is_available()

        A__          =   'not installed'
        if is_transformers_available():
            import transformers

            A__          =   transformers.__version__

        A__          =   'not installed'
        if is_accelerate_available():
            import accelerate

            A__          =   accelerate.__version__

        A__          =   'not installed'
        if is_xformers_available():
            import xformers

            A__          =   xformers.__version__

        A__          =   {
            '`diffusers` version': version,
            'Platform': platform.platform(),
            'Python version': platform.python_version(),
            'PyTorch version (GPU?)': F'{pt_version} ({pt_cuda_available})',
            'Huggingface_hub version': hub_version,
            'Transformers version': transformers_version,
            'Accelerate version': accelerate_version,
            'xFormers version': xformers_version,
            'Using GPU in script?': '<fill in>',
            'Using distributed or parallel set-up in script?': '<fill in>',
        }

        print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'		)
        print(self.format_dict(lowercase_		)		)

        return info


    @staticmethod
    def 						snake_case__     (      lowercase_ :       int		)->    Optional[Any]:





        '''simple docstring'''







        return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]		) + "\n"

