Source code for transformers.modeling_xlm_prophetnet

# coding=utf-8
# Copyright 2020 The Microsoft Authors and The HuggingFace Inc. team.
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" PyTorch XLM-ProphetNet model."""

from .configuration_xlm_prophetnet import XLMProphetNetConfig
from .modeling_prophetnet import (
    ProphetNetDecoder,
    ProphetNetEncoder,
    ProphetNetForCausalLM,
    ProphetNetForConditionalGeneration,
    ProphetNetModel,
)
from .utils import logging


logger = logging.get_logger(__name__)

_TOKENIZER_FOR_DOC = "XLMProphetNetTokenizer"

XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "microsoft/xprophetnet-large-wiki100-cased",
    # See all ProphetNet models at https://huggingface.co/models?filter=xprophetnet
]


class XLMProphetNetEncoder(ProphetNetEncoder):
    r"""
    This class overrides :class:`~transformers.ProphetNetEncoder`. Please check the
    superclass for the appropriate documentation alongside usage examples.

    Example::

        >>> from transformers import XLMProphetNetTokenizer, XLMProphetNetEncoder
        >>> import torch

        >>> tokenizer = XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')
        >>> model = XLMProphetNetEncoder.from_pretrained('patrickvonplaten/xprophetnet-large-uncased-standalone', return_dict=True)
        >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
        >>> outputs = model(**inputs)

        >>> last_hidden_states = outputs.last_hidden_state
    """

    config_class = XLMProphetNetConfig


class XLMProphetNetDecoder(ProphetNetDecoder):
    r"""
    This class overrides :class:`~transformers.ProphetNetDecoder`. Please check the
    superclass for the appropriate documentation alongside usage examples.

    Example::

        >>> from transformers import XLMProphetNetTokenizer, XLMProphetNetDecoder
        >>> import torch

        >>> tokenizer = XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')
        >>> model = XLMProphetNetDecoder.from_pretrained('patrickvonplaten/xprophetnet-large-uncased-standalone', add_cross_attention=False, return_dict=True)
        >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
        >>> outputs = model(**inputs)

        >>> last_hidden_states = outputs.last_hidden_state
    """

    config_class = XLMProphetNetConfig


class XLMProphetNetModel(ProphetNetModel):
    r"""
    This class overrides :class:`~transformers.ProphetNetModel`. Please check the
    superclass for the appropriate documentation alongside usage examples.

    Example::

        >>> from transformers import XLMProphetNetTokenizer, XLMProphetNetModel

        >>> tokenizer = XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')
        >>> model = XLMProphetNetModel.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')

        >>> input_ids = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt").input_ids  # Batch size 1
        >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1
        >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids, return_dict=True)

        >>> last_hidden_states = outputs.last_hidden_state  # main stream hidden states
            >>> last_hidden_states_ngram = outputs.last_hidden_state_ngram  # predict hidden states
    """

    config_class = XLMProphetNetConfig


class XLMProphetNetForConditionalGeneration(ProphetNetForConditionalGeneration):
    r"""
    This class overrides :class:`~transformers.ProphetNetForConditionalGeneration`. Please check the
    superclass for the appropriate documentation alongside usage examples.

    Example::

        >>> from transformers import XLMProphetNetTokenizer, XLMProphetNetForConditionalGeneration

        >>> tokenizer = XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')
        >>> model =  XLMProphetNetForConditionalGeneration.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')

        >>> input_ids = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt").input_ids  # Batch size 1
        >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids  # Batch size 1
        >>> outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids, return_dict=True)

        >>> logits_next_token = outputs.logits  # logits to predict next token as usual
        >>> logits_ngram_next_tokens = outputs.logits_ngram  # logits to predict 2nd, 3rd, ... next tokens
    """

    config_class = XLMProphetNetConfig


class XLMProphetNetForCausalLM(ProphetNetForCausalLM):
    r"""
    This class overrides :class:`~transformers.ProphetNetForCausalLM`. Please check the
    superclass for the appropriate documentation alongside usage examples.

    Example::

        >>> from transformers import XLMProphetNetTokenizer, XLMProphetNetForCausalLM
        >>> import torch

        >>> tokenizer = XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')
        >>> model = XLMProphetNetForCausalLM.from_pretrained('patrickvonplaten/xprophetnet-decoder-clm-large-uncased', return_dict=True)
        >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
        >>> outputs = model(**inputs)

        >>> logits = outputs.logits

        >>> # Model can also be used with EncoderDecoder framework
        >>> from transformers import BertTokenizer, EncoderDecoderModel
        >>> import torch

        >>> tokenizer = BertTokenizer.from_pretrained('bert-uncased-large')
        >>> model = EncoderDecoderModel.from_encoder_decoder_pretrained("bert-uncased-large", "patrickvonplaten/xprophetnet-decoder-clm-large-uncased")

        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
        >>> outputs = model(input_ids=inputs["input_ids"], labels=inputs["input_ids"])

        >>> loss = outputs.loss
    """

    config_class = XLMProphetNetConfig