





"""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_			=							logging.get_logger(__name__)

lowerCAmelCase_			=							'''Hello world! cécé herlolip'''

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Any:






       """simple docstring"""




       _UpperCAmelCase     =     FairseqRobertaModel.from_pretrained(A__							)
       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:',A__							)

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

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

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


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_xlm_roberta_xl_checkpoint_to_pytorch(
        args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )













"""simple docstring"""






import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


lowerCAmelCase_			=							logging.get_logger(__name__)

lowerCAmelCase_			=							{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase_			=							{
    '''vocab_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-ctx_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-ctx_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
lowerCAmelCase_			=							{
    '''vocab_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-question_encoder-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-question_encoder-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}
lowerCAmelCase_			=							{
    '''vocab_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/dpr-reader-single-nq-base''': (
            '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'''
        ),
        '''facebook/dpr-reader-multiset-base''': (
            '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'''
        ),
    },
}

lowerCAmelCase_			=							{
    '''facebook/dpr-ctx_encoder-single-nq-base''': 512,
    '''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
lowerCAmelCase_			=							{
    '''facebook/dpr-question_encoder-single-nq-base''': 512,
    '''facebook/dpr-question_encoder-multiset-base''': 512,
}
lowerCAmelCase_			=							{
    '''facebook/dpr-reader-single-nq-base''': 512,
    '''facebook/dpr-reader-multiset-base''': 512,
}


lowerCAmelCase_			=							{
    '''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowerCAmelCase_			=							{
    '''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowerCAmelCase_			=							{
    '''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
    '''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  VOCAB_FILES_NAMES
       lowerCAmelCase__						 =  CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__						 =  CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__						 =  CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
       lowerCAmelCase__						 =  DPRContextEncoderTokenizer



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  VOCAB_FILES_NAMES
       lowerCAmelCase__						 =  QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__						 =  QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__						 =  QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
       lowerCAmelCase__						 =  DPRQuestionEncoderTokenizer


lowerCAmelCase_			=							collections.namedtuple(
    '''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)

lowerCAmelCase_			=							collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])


lowerCAmelCase_			=							r'''
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `\'tf\'`: Return TensorFlow `tf.constant` objects.
                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.
                - `\'np\'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer\'s default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Return:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    '''



@add_start_docstrings(snake_case							)
class 	lowerCAmelCase					:






       def __call__(   self    ,  a__    ,  a__ = None    ,  a__ = None    ,  a__ = False    ,  a__ = False    ,  a__ = None    ,  a__ = None    ,  a__ = None    ,  **a__    ,  ):
              if titles is None and texts is None:
                     return super().__call__(
                         a__    ,  padding=a__    ,  truncation=a__    ,  max_length=a__    ,  return_tensors=a__    ,  return_attention_mask=a__    ,  **a__    ,  )
              elif titles is None or texts is None:
                     _UpperCAmelCase     =     titles if texts is None else texts
                     return super().__call__(
                         a__    ,  a__    ,  padding=a__    ,  truncation=a__    ,  max_length=a__    ,  return_tensors=a__    ,  return_attention_mask=a__    ,  **a__    ,  )
              _UpperCAmelCase     =     titles if not isinstance(a__    ,  a__     ) else [titles]
              _UpperCAmelCase     =     texts if not isinstance(a__    ,  a__     ) else [texts]
              _UpperCAmelCase     =     len(a__     )
              _UpperCAmelCase     =     questions if not isinstance(a__    ,  a__     ) else [questions] * n_passages
              assert len(a__     ) == len(
                  a__     ), f"""There should be as many titles than texts but got {len(a__     )} titles and {len(a__     )} texts."""
              _UpperCAmelCase     =     super().__call__(a__    ,  a__    ,  padding=a__    ,  truncation=a__     )['input_ids']
              _UpperCAmelCase     =     super().__call__(a__    ,  add_special_tokens=a__    ,  padding=a__    ,  truncation=a__     )['input_ids']
              _UpperCAmelCase     =     {
                  'input_ids': [
                      (encoded_question_and_title + encoded_text)[:max_length]
                      if max_length is not None and truncation
                      else encoded_question_and_title + encoded_text
                      for encoded_question_and_title, encoded_text in zip(a__    ,  a__     )
                  ]
              }
              if return_attention_mask is not False:
                     _UpperCAmelCase     =     []
                     for input_ids in encoded_inputs["input_ids"]:
                            attention_mask.append([int(input_id != self.pad_token_id     ) for input_id in input_ids]     )
                     _UpperCAmelCase     =     attention_mask
              return self.pad(a__    ,  padding=a__    ,  max_length=a__    ,  return_tensors=a__     )






       def 							__A (   self    ,  a__    ,  a__    ,  a__ = 16    ,  a__ = 64    ,  a__ = 4    ,  ):
              _UpperCAmelCase     =     reader_input['input_ids']
              _UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase     =     reader_output[:3]
              _UpperCAmelCase     =     len(a__     )
              _UpperCAmelCase     =     sorted(range(a__     )    ,  reverse=a__    ,  key=relevance_logits.__getitem__     )
              _UpperCAmelCase     =     []
              for doc_id in sorted_docs:
                     _UpperCAmelCase     =     list(input_ids[doc_id]     )
                     # assuming question & title information is at the beginning of the sequence
                     _UpperCAmelCase     =     sequence_ids.index(self.sep_token_id    ,  2     ) + 1  # second sep id
                     if sequence_ids[-1] == self.pad_token_id:
                            _UpperCAmelCase     =     sequence_ids.index(self.pad_token_id     )
                     else:
                            _UpperCAmelCase     =     len(a__     )

                     _UpperCAmelCase     =     self._get_best_spans(
                         start_logits=start_logits[doc_id][passage_offset:sequence_len]    ,  end_logits=end_logits[doc_id][passage_offset:sequence_len]    ,  max_answer_length=a__    ,  top_spans=a__    ,  )
                     for start_index, end_index in best_spans:
                            start_index += passage_offset
                            end_index += passage_offset
                            nbest_spans_predictions.append(
                                DPRSpanPrediction(
                                    span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]    ,  relevance_score=relevance_logits[doc_id]    ,  doc_id=a__    ,  start_index=a__    ,  end_index=a__    ,  text=self.decode(sequence_ids[start_index : end_index + 1]     )    ,  )     )
                     if len(a__     ) >= num_spans:
                            break
              return nbest_spans_predictions[:num_spans]






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  a__    ,  ):
              _UpperCAmelCase     =     []
              for start_index, start_score in enumerate(a__     ):
                     for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]     ):
                            scores.append(((start_index, start_index + answer_length), start_score + end_score)     )
              _UpperCAmelCase     =     sorted(a__    ,  key=lambda a__     : x[1]    ,  reverse=a__     )
              _UpperCAmelCase     =     []
              for (start_index, end_index), score in scores:
                     assert start_index <= end_index, f"""Wrong span indices: [{start_index}:{end_index}]"""
                     _UpperCAmelCase     =     end_index - start_index + 1
                     assert length <= max_answer_length, f"""Span is too long: {length} > {max_answer_length}"""
                     if any(
                         start_index <= prev_start_index <= prev_end_index <= end_index
                         or prev_start_index <= start_index <= end_index <= prev_end_index
                         for (prev_start_index, prev_end_index) in chosen_span_intervals     ):
                            continue
                     chosen_span_intervals.append((start_index, end_index)     )

                     if len(a__     ) == top_spans:
                            break
              return chosen_span_intervals



@add_end_docstrings(snake_case							)
class 	lowerCAmelCase					(      snake_case			,	snake_case							):
       lowerCAmelCase__						 =  VOCAB_FILES_NAMES
       lowerCAmelCase__						 =  READER_PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__						 =  READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__						 =  READER_PRETRAINED_INIT_CONFIGURATION
       lowerCAmelCase__						 =  ["""input_ids""", """attention_mask"""]
       lowerCAmelCase__						 =  DPRReaderTokenizer






