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# coding=utf-8
# Copyright 2020 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Tensorflow mT5 model. """
from ...utils import logging
from ..t5.modeling_tf_t5 import TFT5EncoderModel, TFT5ForConditionalGeneration, TFT5Model
from .configuration_mt5 import MT5Config
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "T5Config"
_TOKENIZER_FOR_DOC = "T5Tokenizer"
class TFMT5Model(TFT5Model):
r"""
This class overrides :class:`~transformers.TFT5Model`. Please check the superclass for the appropriate
documentation alongside usage examples.
Examples::
>>> from transformers import TFMT5Model, T5Tokenizer
>>> model = TFMT5Model.from_pretrained("google/mt5-small")
>>> tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
>>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
>>> summary = "Weiter Verhandlung in Syrien."
>>> inputs = tokenizer(article, return_tensors="tf")
>>> with tokenizer.as_target_tokenizer():
... labels = tokenizer(summary, return_tensors="tf")
>>> outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"])
>>> hidden_states = outputs.last_hidden_state
"""
model_type = "mt5"
config_class = MT5Config
class TFMT5ForConditionalGeneration(TFT5ForConditionalGeneration):
r"""
This class overrides :class:`~transformers.TFT5ForConditionalGeneration`. Please check the superclass for the
appropriate documentation alongside usage examples.
Examples::
>>> from transformers import TFMT5ForConditionalGeneration, T5Tokenizer
>>> model = TFMT5ForConditionalGeneration.from_pretrained("google/mt5-small")
>>> tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
>>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
>>> summary = "Weiter Verhandlung in Syrien."
>>> inputs = tokenizer(article, return_tensors="tf")
>>> with tokenizer.as_target_tokenizer():
... labels = tokenizer(summary, return_tensors="tf")
>>> outputs = model(**inputs,labels=labels["input_ids"])
>>> loss = outputs.loss
"""
model_type = "mt5"
config_class = MT5Config
class TFMT5EncoderModel(TFT5EncoderModel):
r"""
This class overrides :class:`~transformers.TFT5EncoderModel`. Please check the superclass for the appropriate
documentation alongside usage examples.
Examples::
>>> from transformers import TFMT5EncoderModel, T5Tokenizer
>>> model = TFMT5EncoderModel.from_pretrained("google/mt5-small")
>>> tokenizer = T5Tokenizer.from_pretrained("google/mt5-small")
>>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien."
>>> input_ids = tokenizer(article, return_tensors="tf").input_ids
>>> outputs = model(input_ids)
>>> hidden_state = outputs.last_hidden_state
"""
model_type = "mt5"
config_class = MT5Config
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