T^(hN dZddlmZddlmZmZmZddlmZeje Z dZ Gdd eZ Gd d eZGd d eZgdZy)zTensorflow mT5 model.)logging)TFT5EncoderModelTFT5ForConditionalGeneration TFT5Model) MT5ConfigT5ConfigceZdZdZdZeZy) 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__ model_typer config_classe/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/mt5/modeling_tf_mt5.pyr r s (JLrr ceZdZdZdZeZy)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 Nrrrrrr4s &JLrrceZdZdZdZeZy)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 NrrrrrrLs "JLrr)rrr N)rutilsrt5.modeling_tf_t5rrrconfiguration_mt5r get_loggerrlogger_CONFIG_FOR_DOCr rr__all__rrrr#s]YY(   H %2$@0(, Or