





'''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()
__lowerCAmelCase				:      Tuple						      =logging.get_logger(__name__)

__lowerCAmelCase				:      Dict						      ="Hello world! cécé herlolip"





def 		UpperCamelCase							(				_lowerCamelCase   :   str		,			_lowerCamelCase   :   str		,			_lowerCamelCase   :   bool			):
    A__							=     FairseqRobertaModel.from_pretrained(_lowerCamelCase			)
    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=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 RoBERTa config:"		,			_lowerCamelCase			)

    A__							=     XLMRobertaXLForSequenceClassification(_lowerCamelCase			) if classification_head else XLMRobertaXLForMaskedLM(_lowerCamelCase			)
    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(_lowerCamelCase			).unsqueeze(0			)  # batch of size 1

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

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


if __name__ == "__main__":
  __lowerCAmelCase				:      str						      =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				:      Optional[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'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""image_processor""", """tokenizer"""]
 __lowercase  						=							"""CLIPImageProcessor"""
 __lowercase  						=							("""CLIPTokenizer""", """CLIPTokenizerFast""")
 def __init__(							self :List[Any]		,			lowercase_ :List[str]=None		,			lowercase_ :Any=None		,			**lowercase_ :Union[str, Any]				)->				Optional[Any]:
     A__							=     None
     if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."		,			lowercase_		,			)
         A__							=     kwargs.pop("feature_extractor"				)

     A__							=     image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."				)
     if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."				)

     super().__init__(lowercase_		,			lowercase_				)
 def __call__(							self :Optional[Any]		,			lowercase_ :Optional[int]=None		,			lowercase_ :Dict=None		,			lowercase_ :List[Any]=None		,			**lowercase_ :Optional[Any]				)->				Union[str, Any]:

     if text is None and images is None:
         raise ValueError("You have to specify either text or images. Both cannot be none."				)

     if text is not None:
         A__							=     self.tokenizer(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

     if images is not None:
         A__							=     self.image_processor(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

     if text is not None and images is not None:
         A__							=     image_features.pixel_values
         return encoding
     elif text is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**lowercase_				)		,			tensor_type=lowercase_				)
 def 	UpperCAmelCase_				(							self :int		,			*lowercase_ :Tuple		,			**lowercase_ :Any				)->				Dict:
     return self.tokenizer.batch_decode(*lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :Any		,			*lowercase_ :Any		,			**lowercase_ :Any				)->				List[str]:
     return self.tokenizer.decode(*lowercase_		,			**lowercase_				)
 @property
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Optional[int]:
     A__							=     self.tokenizer.model_input_names
     A__							=     self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)
 @property
 def 	UpperCAmelCase_				(							self :List[Any]				)->				str:
     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,			lowercase_		,			)
     return self.image_processor_class



 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				List[Any]:
     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,			lowercase_		,			)
     return self.image_processor



