o ZhN @szdZddlmZddlmZmZmZddlmZe e Z dZ Gdd d eZ Gd d d eZGd d d eZgdZdS)zTensorflow mT5 model.)logging)TFT5EncoderModelTFT5ForConditionalGeneration TFT5Model) MT5ConfigZT5Configc@eZdZdZdZeZdS) TFMT5Modela This class overrides [`TFT5Model`]. Please check the superclass for the appropriate documentation alongside usage examples. Examples: ```python >>> from transformers import TFMT5Model, AutoTokenizer >>> model = TFMT5Model.from_pretrained("google/mt5-small") >>> tokenizer = AutoTokenizer.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") >>> labels = tokenizer(text_target=summary, return_tensors="tf") >>> outputs = model(input_ids=inputs["input_ids"], decoder_input_ids=labels["input_ids"]) >>> hidden_states = outputs.last_hidden_state ```mt5N__name__ __module__ __qualname____doc__Z model_typerZ config_classrrV/var/www/auris/lib/python3.10/site-packages/transformers/models/mt5/modeling_tf_mt5.pyr sr c@r )TFMT5ForConditionalGenerationa This class overrides [`TFT5ForConditionalGeneration`]. Please check the superclass for the appropriate documentation alongside usage examples. Examples: ```python >>> from transformers import TFMT5ForConditionalGeneration, AutoTokenizer >>> model = TFMT5ForConditionalGeneration.from_pretrained("google/mt5-small") >>> tokenizer = AutoTokenizer.from_pretrained("google/mt5-small") >>> article = "UN Offizier sagt, dass weiter verhandelt werden muss in Syrien." >>> summary = "Weiter Verhandlung in Syrien." >>> inputs = tokenizer(article, text_target=summary, return_tensors="tf") >>> outputs = model(**inputs) >>> loss = outputs.loss ```r Nr rrrrr4src@r )TFMT5EncoderModelan This class overrides [`TFT5EncoderModel`]. Please check the superclass for the appropriate documentation alongside usage examples. Examples: ```python >>> from transformers import TFMT5EncoderModel, AutoTokenizer >>> model = TFMT5EncoderModel.from_pretrained("google/mt5-small") >>> tokenizer = AutoTokenizer.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 ```r Nr rrrrrLsr)rrr N)rutilsrZt5.modeling_tf_t5rrrZconfiguration_mt5rZ get_loggerr loggerZ_CONFIG_FOR_DOCr rr__all__rrrrs