o ¹ÈÏiN ã@szdZddlmZddlmZmZmZddlmZe  e ¡Z dZ Gdd „d eƒZ Gd d „d eƒZGd d „d eƒZgd¢ZdS)zTensorflow mT5 model.é)Úloggingé)ÚTFT5EncoderModelÚTFT5ForConditionalGenerationÚ TFT5Modelé)Ú MT5ConfigÚT5Configc@óeZdZdZdZeZdS)Ú 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 ```Úmt5N©Ú__name__Ú __module__Ú __qualname__Ú__doc__Ú model_typerÚ config_class©rrú[/home/ubuntu/.local/lib/python3.10/site-packages/transformers/models/mt5/modeling_tf_mt5.pyr sr c@r )ÚTFMT5ForConditionalGenerationa· 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 )ÚTFMT5EncoderModelan 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)rÚutilsrÚt5.modeling_tf_t5rrrÚconfiguration_mt5rÚ get_loggerrÚloggerÚ_CONFIG_FOR_DOCr rrÚ__all__rrrrÚs