





'''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 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()
a__							:							int =logging.get_logger(__name__)

a__							:							Union[str, Any] ='''Hello world! cécé herlolip'''
def 				lowercase__   (   __lowercase						:  str				,  __lowercase						:  str				,  __lowercase						:  bool     ) ->      Tuple:



       """simple docstring"""






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

       print('Our RoBERTa config:'				,  __lowercase     )

       __UpperCamelCase       =  XLMRobertaXLForSequenceClassification(__lowercase     ) if classification_head else XLMRobertaXLForMaskedLM(__lowercase     )
       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.layer_norm.weight
       __UpperCamelCase       =  roberta_sent_encoder.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]

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

              # 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

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

       __UpperCamelCase       =  model(__lowercase     )[0]
       if classification_head:
              __UpperCamelCase       =  roberta.model.classification_heads['mnli'](roberta.extract_features(__lowercase     )     )
       else:
              __UpperCamelCase       =  roberta.model(__lowercase     )[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(__lowercase				,  __lowercase				,  atol=1e-3     )
       print('Do both models output the same tensors?'				,  '🔥' if success else '💩'     )
       if not success:
              raise Exception('Something went wRoNg'     )

       pathlib.Path(__lowercase     ).mkdir(parents=__lowercase				,  exist_ok=__lowercase     )
       print(F'''Saving model to {pytorch_dump_folder_path}'''     )
       model.save_pretrained(__lowercase     )


if __name__ == "__main__":
    a__							:							int =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.'''
    )
    a__							:							List[Any] =parser.parse_args()
    convert_xlm_roberta_xl_checkpoint_to_pytorch(
        args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )







'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


a__							:							Any =logging.get_logger(__name__)

a__							:							Optional[Any] ={
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Dict					="gpt_neo"
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=["past_key_values"]
 SCREAMING_SNAKE_CASE_	:					List[Any]					={"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}






 def __init__(       self       :							Union[str, Any]							,	__A       :							Union[str, Any]=5_0_2_5_7							,	__A       :							Any=2_0_4_8							,	__A       :							Optional[Any]=2_0_4_8							,	__A       :							Any=2_4							,	__A       :							Union[str, Any]=[[["global", "local"], 1_2]]							,	__A       :							str=1_6							,	__A       :							Optional[int]=None							,	__A       :							Union[str, Any]=2_5_6							,	__A       :							Any="gelu_new"							,	__A       :							Dict=0.0							,	__A       :							Optional[int]=0.0							,	__A       :							int=0.0							,	__A       :							List[str]=0.1							,	__A       :							Any=1e-5							,	__A       :							int=0.02							,	__A       :							List[str]=True							,	__A       :							Tuple=5_0_2_5_6							,	__A       :							Optional[Any]=5_0_2_5_6							,	**__A       :							Optional[Any]							,	):
        __UpperCamelCase       =  vocab_size
        __UpperCamelCase       =  max_position_embeddings
        __UpperCamelCase       =  hidden_size
        __UpperCamelCase       =  num_layers
        __UpperCamelCase       =  num_heads
        __UpperCamelCase       =  intermediate_size
        __UpperCamelCase       =  window_size
        __UpperCamelCase       =  activation_function
        __UpperCamelCase       =  resid_dropout
        __UpperCamelCase       =  embed_dropout
        __UpperCamelCase       =  attention_dropout
        __UpperCamelCase       =  classifier_dropout
        __UpperCamelCase       =  layer_norm_epsilon
        __UpperCamelCase       =  initializer_range
        __UpperCamelCase       =  use_cache

        __UpperCamelCase       =  bos_token_id
        __UpperCamelCase       =  eos_token_id

        __UpperCamelCase       =  attention_types
        __UpperCamelCase       =  self.expand_attention_types_params(__A	)

        if len(self.attention_layers	) != self.num_layers:
               raise ValueError(
                   'Configuration for convolutional module is incorrect. '
                   'It is required that `len(config.attention_layers)` == `config.num_layers` '
                   f'''but is `len(config.attention_layers) = {len(self.attention_layers	)}`, '''
                   f'''`config.num_layers = {self.num_layers}`. '''
                   '`config.attention_layers` is prepared using `config.attention_types`. '
                   'Please verify the value of `config.attention_types` argument.'	)

        super().__init__(bos_token_id=__A							,	eos_token_id=__A							,	**__A	)






 @staticmethod
 def        _lowerCamelCase					(       __A       :							Tuple	):
        __UpperCamelCase       =  []
        for item in attention_types:
               for _ in range(item[1]	):
                      attentions.extend(item[0]	)
        return attentions






def 				lowercase__   (   __lowercase						:  Tuple				,  __lowercase						:  Any				,  __lowercase						:  Union[str, Any]				,  __lowercase						:  List[str]     ) ->      Any:



       """simple docstring"""






       import torch

       __UpperCamelCase       =  input.size()
       __UpperCamelCase       =  len(__lowercase     )
       __UpperCamelCase       =  shape[dimension]

       __UpperCamelCase       =  torch.arange(0				,  __lowercase				,  __lowercase     )
       __UpperCamelCase       =  torch.div(sizedim - size				,  __lowercase				,  rounding_mode='floor'     ) + 1
       __UpperCamelCase       =  torch.arange(__lowercase     ) + low_indices[:min_length][:, None]

       __UpperCamelCase       =  [slice(__lowercase     )] * rank
       __UpperCamelCase       =  indices
       __UpperCamelCase       =  input[s]

       __UpperCamelCase       =  list(range(0				,  rank + 1     )     )
       perm.append(perm.pop(dimension + 1     )     )

       return sliced.permute(__lowercase     )
def 				lowercase__   (   __lowercase						:  Union[str, Any]				,  __lowercase						:  Optional[int]     ) ->      Optional[int]:



       """simple docstring"""






       import torch

       __UpperCamelCase       =  torch.arange(1				,  __lowercase     )
       __UpperCamelCase       =  torch.remainder(__lowercase				,  __lowercase     )
       __UpperCamelCase       =  remainders == 0
       __UpperCamelCase       =  candidates[divisor_indices]
       __UpperCamelCase       =  torch.max(__lowercase     )
       return largest_divisor, torch.div(__lowercase				,  __lowercase				,  rounding_mode='floor'     )


class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 @property
 def        _lowerCamelCase					(       self       :							Tuple	):
        __UpperCamelCase       =  OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}	)
        if self.use_past:
               self.fill_with_past_key_values_(__A							,	direction='inputs'	)
               __UpperCamelCase       =  {0: 'batch', 1: 'past_sequence + sequence'}
        else:
               __UpperCamelCase       =  {0: 'batch', 1: 'sequence'}

        return common_inputs






 @property
 def        _lowerCamelCase					(       self       :							int	):
        return self._config.num_heads






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							PreTrainedTokenizer							,	__A       :							int = -1							,	__A       :							int = -1							,	__A       :							bool = False							,	__A       :							Optional[TensorType] = None							,	):
        __UpperCamelCase       =  super(__A							,	self	).generate_dummy_inputs(
            __A							,	batch_size=__A							,	seq_length=__A							,	is_pair=__A							,	framework=__A	)

        # We need to order the input in the way they appears in the forward()
        __UpperCamelCase       =  OrderedDict({'input_ids': common_inputs['input_ids']}	)

        # Need to add the past_keys
        if self.use_past:
               if not is_torch_available():
                      raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'	)
               else:
                      import torch

                      __UpperCamelCase       ,						__UpperCamelCase       =  common_inputs['input_ids'].shape
                      # Not using the same length for past_key_values
                      __UpperCamelCase       =  seqlen + 2
                      __UpperCamelCase       =  (
                          batch,
                          self.num_attention_heads,
                          past_key_values_length,
                          self._config.hidden_size // self.num_attention_heads,
                      )
                      __UpperCamelCase       =  [
                          (torch.zeros(__A	), torch.zeros(__A	)) for _ in range(self.num_layers	)
                      ]

        __UpperCamelCase       =  common_inputs['attention_mask']
        if self.use_past:
               __UpperCamelCase       =  ordered_inputs['attention_mask'].dtype
               __UpperCamelCase       =  torch.cat(
                   [ordered_inputs['attention_mask'], torch.ones(__A							,	__A							,	dtype=__A	)]							,	dim=1	)

        return ordered_inputs






 @property
 def        _lowerCamelCase					(       self       :							Dict	):
        return 1_3
