


import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


lowerCAmelCase__   	=				logging.get_logger(__name__)







@dataclass
class   lowerCAmelCase__ :




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				42
     __SCREAMING_SNAKE_CASE						    =				42
     __SCREAMING_SNAKE_CASE						    =				None
     __SCREAMING_SNAKE_CASE						    =				None


     def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
            return json.dumps(dataclasses.asdict(self)				,		indent=2) + "\n"







@dataclass(frozen=a)
class   lowerCAmelCase__ :




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				42
     __SCREAMING_SNAKE_CASE						    =				None
     __SCREAMING_SNAKE_CASE						    =				None
     __SCREAMING_SNAKE_CASE						    =				None


     def 				_lowerCamelCase				(		self)    ->       Optional[int]:
            return json.dumps(dataclasses.asdict(self)) + "\n"







class   lowerCAmelCase__ :




     '''simple docstring'''


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       Union[str, Any]:
            raise NotImplementedError()


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       Tuple:
            raise NotImplementedError()


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       Union[str, Any]:
            raise NotImplementedError()


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       int:
            raise NotImplementedError()


     def 				_lowerCamelCase				(		self)    ->       Any:
            raise NotImplementedError()


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       List[Any]:
            if len(self.get_labels()) > 1:
                   _A   : Dict       					=				self.get_labels()[int(example.label)]
            return example


     @classmethod
     def 				_lowerCamelCase				(		cls				,		__lowerCamelCase				,		__lowerCamelCase=None)    ->       Optional[Any]:
            with open(__lowerCamelCase				,		"r"				,		encoding="utf-8-sig") as f:
                   return list(csv.reader(__lowerCamelCase				,		delimiter="\t"				,		quotechar=__lowerCamelCase))







class   lowerCAmelCase__ (       a):




     '''simple docstring'''


     def __init__(		self				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		__lowerCamelCase="classification"				,		__lowerCamelCase=False)    ->       Dict:
            _A   : Optional[Any]       					=				[] if labels is None else labels
            _A   : Dict       					=				[] if examples is None else examples
            _A   : Any       					=				mode
            _A   : Optional[int]       					=				verbose


     def __len__(		self)    ->       Any:
            return len(self.examples)


     def __getitem__(		self				,		__lowerCamelCase)    ->       Optional[int]:
            if isinstance(__lowerCamelCase				,		__lowerCamelCase):
                   return SingleSentenceClassificationProcessor(labels=self.labels				,		examples=self.examples[idx])
            return self.examples[idx]


     @classmethod
     def 				_lowerCamelCase				(		cls				,		__lowerCamelCase				,		__lowerCamelCase=""				,		__lowerCamelCase=0				,		__lowerCamelCase=1				,		__lowerCamelCase=None				,		__lowerCamelCase=False				,		**__lowerCamelCase)    ->       int:
            _A   : Optional[int]       					=				cls(**__lowerCamelCase)
            processor.add_examples_from_csv(
                __lowerCamelCase				,		split_name=__lowerCamelCase				,		column_label=__lowerCamelCase				,		column_text=__lowerCamelCase				,		column_id=__lowerCamelCase				,		skip_first_row=__lowerCamelCase				,		overwrite_labels=__lowerCamelCase				,		overwrite_examples=__lowerCamelCase				,		)
            return processor


     @classmethod
     def 				_lowerCamelCase				(		cls				,		__lowerCamelCase				,		__lowerCamelCase=None				,		**__lowerCamelCase)    ->       str:
            _A   : List[Any]       					=				cls(**__lowerCamelCase)
            processor.add_examples(__lowerCamelCase				,		labels=__lowerCamelCase)
            return processor


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase=""				,		__lowerCamelCase=0				,		__lowerCamelCase=1				,		__lowerCamelCase=None				,		__lowerCamelCase=False				,		__lowerCamelCase=False				,		__lowerCamelCase=False				,		)    ->       Dict:
            _A   : int       					=				self._read_tsv(__lowerCamelCase)
            if skip_first_row:
                   _A   : int       					=				lines[1:]
            _A   : Optional[Any]       					=				[]
            _A   : List[str]       					=				[]
            _A   : Dict       					=				[]
            for i, line in enumerate(__lowerCamelCase):
                   texts.append(line[column_text])
                   labels.append(line[column_label])
                   if column_id is not None:
                          ids.append(line[column_id])
                   else:
                          _A   : str       					=				F"{split_name}-{i}" if split_name else str(__lowerCamelCase)
                          ids.append(__lowerCamelCase)

            return self.add_examples(
                __lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		overwrite_labels=__lowerCamelCase				,		overwrite_examples=__lowerCamelCase)


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase=None				,		__lowerCamelCase=None				,		__lowerCamelCase=False				,		__lowerCamelCase=False)    ->       Tuple:
            if labels is not None and len(__lowerCamelCase) != len(__lowerCamelCase):
                   raise ValueError(
                       F"Text and labels have mismatched lengths {len(__lowerCamelCase)} and {len(__lowerCamelCase)}")
            if ids is not None and len(__lowerCamelCase) != len(__lowerCamelCase):
                   raise ValueError(F"Text and ids have mismatched lengths {len(__lowerCamelCase)} and {len(__lowerCamelCase)}")
            if ids is None:
                   _A   : Optional[Any]       					=				[None] * len(__lowerCamelCase)
            if labels is None:
                   _A   : Union[str, Any]       					=				[None] * len(__lowerCamelCase)
            _A   : Optional[int]       					=				[]
            _A   : List[Any]       					=				set()
            for text_or_text_and_label, label, guid in zip(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase):
                   if isinstance(__lowerCamelCase				,		(tuple, list)) and label is None:
                          _A					,    _A   : List[str]       					=				text_or_text_and_label
                   else:
                          _A   : Union[str, Any]       					=				text_or_text_and_label
                   added_labels.add(__lowerCamelCase)
                   examples.append(InputExample(guid=__lowerCamelCase				,		text_a=__lowerCamelCase				,		text_b=__lowerCamelCase				,		label=__lowerCamelCase))

            # Update examples
            if overwrite_examples:
                   _A   : Optional[Any]       					=				examples
            else:
                   self.examples.extend(__lowerCamelCase)

            # Update labels
            if overwrite_labels:
                   _A   : Optional[Any]       					=				list(__lowerCamelCase)
            else:
                   _A   : Any       					=				list(set(self.labels).union(__lowerCamelCase))

            return self.examples


     def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase=None				,		__lowerCamelCase=False				,		__lowerCamelCase=0				,		__lowerCamelCase=True				,		__lowerCamelCase=None				,		)    ->       Tuple:
            if max_length is None:
                   _A   : Optional[int]       					=				tokenizer.max_len

            _A   : Any       					=				{label: i for i, label in enumerate(self.labels)}

            _A   : Optional[Any]       					=				[]
            for ex_index, example in enumerate(self.examples):
                   if ex_index % 1_0_0_0_0 == 0:
                          logger.info(F"Tokenizing example {ex_index}")

                   _A   : Any       					=				tokenizer.encode(
                       example.text_a				,		add_special_tokens=__lowerCamelCase				,		max_length=min(__lowerCamelCase				,		tokenizer.max_len)				,		)
                   all_input_ids.append(__lowerCamelCase)

            _A   : List[str]       					=				max(len(__lowerCamelCase) for input_ids in all_input_ids)

            _A   : Any       					=				[]
            for ex_index, (input_ids, example) in enumerate(zip(__lowerCamelCase				,		self.examples)):
                   if ex_index % 1_0_0_0_0 == 0:
                          logger.info(F"Writing example {ex_index}/{len(self.examples)}")
                   # The mask has 1 for real tokens and 0 for padding tokens. Only real
                   # tokens are attended to.
                   _A   : Optional[int]       					=				[1 if mask_padding_with_zero else 0] * len(__lowerCamelCase)

                   # Zero-pad up to the sequence length.
                   _A   : Optional[Any]       					=				batch_length - len(__lowerCamelCase)
                   if pad_on_left:
                          _A   : List[str]       					=				([pad_token] * padding_length) + input_ids
                          _A   : Dict       					=				([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
                   else:
                          _A   : Dict       					=				input_ids + ([pad_token] * padding_length)
                          _A   : int       					=				attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

                   if len(__lowerCamelCase) != batch_length:
                          raise ValueError(F"Error with input length {len(__lowerCamelCase)} vs {batch_length}")
                   if len(__lowerCamelCase) != batch_length:
                          raise ValueError(F"Error with input length {len(__lowerCamelCase)} vs {batch_length}")

                   if self.mode == "classification":
                          _A   : Tuple       					=				label_map[example.label]
                   elif self.mode == "regression":
                          _A   : Optional[int]       					=				float(example.label)
                   else:
                          raise ValueError(self.mode)

                   if ex_index < 5 and self.verbose:
                          logger.info("*** Example ***")
                          logger.info(F"guid: {example.guid}")
                          logger.info(F"input_ids: {' '.join([str(__lowerCamelCase) for x in input_ids])}")
                          logger.info(F"attention_mask: {' '.join([str(__lowerCamelCase) for x in attention_mask])}")
                          logger.info(F"label: {example.label} (id = {label})")

                   features.append(InputFeatures(input_ids=__lowerCamelCase				,		attention_mask=__lowerCamelCase				,		label=__lowerCamelCase))

            if return_tensors is None:
                   return features
            elif return_tensors == "tf":
                   if not is_tf_available():
                          raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported")
                   import tensorflow as tf

                   def gen():
                          for ex in features:
                                 yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

                   _A   : Tuple       					=				tf.data.Dataset.from_generator(
                       __lowerCamelCase				,		({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)				,		({"input_ids": tf.TensorShape([None]), "attention_mask": tf.TensorShape([None])}, tf.TensorShape([]))				,		)
                   return dataset
            elif return_tensors == "pt":
                   if not is_torch_available():
                          raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported")
                   import torch
                   from torch.utils.data import TensorDataset

                   _A   : Dict       					=				torch.tensor([f.input_ids for f in features]				,		dtype=torch.long)
                   _A   : Any       					=				torch.tensor([f.attention_mask for f in features]				,		dtype=torch.long)
                   if self.mode == "classification":
                          _A   : int       					=				torch.tensor([f.label for f in features]				,		dtype=torch.long)
                   elif self.mode == "regression":
                          _A   : int       					=				torch.tensor([f.label for f in features]				,		dtype=torch.float)

                   _A   : Dict       					=				TensorDataset(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase)
                   return dataset
            else:
                   raise ValueError("return_tensors should be one of 'tf' or 'pt'")




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
    )
