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 lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE	)
    roberta.eval()  # disable dropout
    __UpperCamelCase     :Dict			  =		roberta.model.encoder.sentence_encoder
    __UpperCamelCase     :Optional[Any]			  =		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:
        __UpperCamelCase     :Optional[int]			  =		roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

    print('''Our RoBERTa config:'''  ,			SCREAMING_SNAKE_CASE	)

    __UpperCamelCase     :List[str]			  =		XLMRobertaXLForSequenceClassification(SCREAMING_SNAKE_CASE	) if classification_head else XLMRobertaXLForMaskedLM(SCREAMING_SNAKE_CASE	)
    model.eval()

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

    __UpperCamelCase     :Union[str, Any]			  =		roberta_sent_encoder.layer_norm.weight
    __UpperCamelCase     :int			  =		roberta_sent_encoder.layer_norm.bias

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

        __UpperCamelCase     :RobertaAttention			  =		layer.attention
        __UpperCamelCase     :Dict			  =		roberta_layer.self_attn_layer_norm.weight
        __UpperCamelCase     :str			  =		roberta_layer.self_attn_layer_norm.bias

        # self attention
        __UpperCamelCase     :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)	)
        )

        __UpperCamelCase     :str			  =		roberta_layer.self_attn.q_proj.weight
        __UpperCamelCase     :List[str]			  =		roberta_layer.self_attn.q_proj.bias
        __UpperCamelCase     :int			  =		roberta_layer.self_attn.k_proj.weight
        __UpperCamelCase     :List[Any]			  =		roberta_layer.self_attn.k_proj.bias
        __UpperCamelCase     :Dict			  =		roberta_layer.self_attn.v_proj.weight
        __UpperCamelCase     :int			  =		roberta_layer.self_attn.v_proj.bias

        # self-attention output
        __UpperCamelCase     :BertSelfOutput			  =		layer.attention.output
        assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
        __UpperCamelCase     :Any			  =		roberta_layer.self_attn.out_proj.weight
        __UpperCamelCase     :Optional[Any]			  =		roberta_layer.self_attn.out_proj.bias

        # this one is final layer norm
        __UpperCamelCase     :Any			  =		roberta_layer.final_layer_norm.weight
        __UpperCamelCase     :int			  =		roberta_layer.final_layer_norm.bias

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

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

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

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

    __UpperCamelCase     :Union[str, Any]			  =		model(SCREAMING_SNAKE_CASE	)[0]
    if classification_head:
        __UpperCamelCase     :str			  =		roberta.model.classification_heads['''mnli'''](roberta.extract_features(SCREAMING_SNAKE_CASE	)	)
    else:
        __UpperCamelCase     :int			  =		roberta.model(SCREAMING_SNAKE_CASE	)[0]
    print(our_output.shape  ,			their_output.shape	)
    __UpperCamelCase     :Any			  =		torch.max(torch.abs(our_output - their_output	)	).item()
    print(f"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
    __UpperCamelCase     :Tuple			  =		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 inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version


def   _SCREAMING_SNAKE_CASE	( *SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE = None ,			SCREAMING_SNAKE_CASE=True ,			SCREAMING_SNAKE_CASE=2       ):
  from .. import __version__

  A_      :				Union[str, Any]		     =					take_from
  A_      :				Optional[Any]		     =					()
  if not isinstance(args[0] ,			SCREAMING_SNAKE_CASE       ):
    A_      :				List[str]		     =					(args,)

  for attribute, version_name, message in args:
    if version.parse(version.parse(SCREAMING_SNAKE_CASE       ).base_version       ) >= version.parse(SCREAMING_SNAKE_CASE       ):
      raise ValueError(
          f'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
          f''' version {__version__} is >= {version_name}'''       )

    A_      :				List[Any]		     =					None
    if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) and attribute in deprecated_kwargs:
      values += (deprecated_kwargs.pop(SCREAMING_SNAKE_CASE       ),)
      A_      :				Optional[Any]		     =					f'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
    elif hasattr(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
      values += (getattr(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ),)
      A_      :				int		     =					f'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
    elif deprecated_kwargs is None:
      A_      :				List[Any]		     =					f'''`{attribute}` is deprecated and will be removed in version {version_name}.'''

    if warning is not None:
      A_      :				Union[str, Any]		     =					warning + ''' ''' if standard_warn else ''''''
      warnings.warn(warning + message ,			SCREAMING_SNAKE_CASE ,			stacklevel=SCREAMING_SNAKE_CASE       )

  if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) and len(SCREAMING_SNAKE_CASE       ) > 0:
    A_      :				Dict		     =					inspect.getouterframes(inspect.currentframe()       )[1]
    A_      :				Optional[int]		     =					call_frame.filename
    A_      :				Optional[int]		     =					call_frame.lineno
    A_      :				str		     =					call_frame.function
    A_     ,       A_      :				List[str]		     =					next(iter(deprecated_kwargs.items()       )       )
    raise TypeError(f'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'''       )

  if len(SCREAMING_SNAKE_CASE       ) == 0:
    return
  elif len(SCREAMING_SNAKE_CASE       ) == 1:
    return values[0]
  return values




