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Update lxmert/src/lxmert_lrp.py
Browse files- lxmert/src/lxmert_lrp.py +4 -3
lxmert/src/lxmert_lrp.py
CHANGED
@@ -34,7 +34,8 @@ from transformers.file_utils import (
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)
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from transformers.modeling_utils import PreTrainedModel
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from transformers.utils import logging
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-
from transformers.configuration_lxmert import LxmertConfig
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logger = logging.get_logger(__name__)
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@@ -1135,7 +1136,7 @@ class LxmertModel(LxmertPreTrainedModel):
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@add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
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@add_code_sample_docstrings(
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-
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checkpoint="unc-nlp/lxmert-base-uncased",
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output_type=LxmertModelOutput,
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config_class=_CONFIG_FOR_DOC,
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@@ -1624,7 +1625,7 @@ class LxmertForQuestionAnswering(LxmertPreTrainedModel):
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@add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
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@add_code_sample_docstrings(
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-
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checkpoint="unc-nlp/lxmert-base-uncased",
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output_type=LxmertForQuestionAnsweringOutput,
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config_class=_CONFIG_FOR_DOC,
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)
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from transformers.modeling_utils import PreTrainedModel
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from transformers.utils import logging
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# from transformers.configuration_lxmert import LxmertConfig
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from transformers.models.lxmert.configuration_lxmert import LxmertConfig
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logger = logging.get_logger(__name__)
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@add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
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@add_code_sample_docstrings(
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processor_class=_TOKENIZER_FOR_DOC,
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checkpoint="unc-nlp/lxmert-base-uncased",
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output_type=LxmertModelOutput,
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config_class=_CONFIG_FOR_DOC,
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@add_start_docstrings_to_model_forward(LXMERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
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@add_code_sample_docstrings(
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+
processor_class=_TOKENIZER_FOR_DOC,
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checkpoint="unc-nlp/lxmert-base-uncased",
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output_type=LxmertForQuestionAnsweringOutput,
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config_class=_CONFIG_FOR_DOC,
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