
"""simple docstring"""



import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


a					:	Tuple							=		logging.getLogger(__name__)
a					:	Dict							=		list(MODEL_FOR_MASKED_LM_MAPPING.keys())
a					:	List[str]							=		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class 		a_						:
    a      :				Dict     =				field(
        default=_a     ,						metadata={
            'help': (
                'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
            )
        }     ,						)
    a      :				int     =				field(
        default=_a     ,						metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(_a	)}     ,						)
    a      :				Optional[int]     =				field(
        default=_a     ,						metadata={
            'help': (
                'Override some existing default config settings when a model is trained from scratch. Example: '
                'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
            )
        }     ,						)
    a      :				Union[str, Any]     =				field(
        default=_a     ,						metadata={'help': 'Pretrained config name or path if not the same as model_name'}	)
    a      :				Tuple     =				field(
        default=_a     ,						metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}	)
    a      :				Union[str, Any]     =				field(
        default=_a     ,						metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}     ,						)
    a      :				str     =				field(
        default=_a     ,						metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}     ,						)
    a      :				List[str]     =				field(
        default='main'     ,						metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}     ,						)
    a      :				List[str]     =				field(
        default=_a     ,						metadata={
            'help': (
                'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
                'with private models).'
            )
        }     ,						)







    def      _snake_case					(			self			:       Any     )		->Any:






        '''simple docstring'''






        if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
            raise ValueError(
                """--config_overrides can\'t be used in combination with --config_name or --model_name_or_path"""     )







@dataclass
class 		a_						:
    a      :				Optional[int]     =				field(
        default=_a     ,						metadata={'help': 'The name of the dataset to use (via the datasets library).'}	)
    a      :				str     =				field(
        default=_a     ,						metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}	)
    a      :				Tuple     =				field(default=_a     ,						metadata={'help': 'The input training data file (a text file).'}	)
    a      :				Tuple     =				field(
        default=_a     ,						metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}     ,						)
    a      :				Dict     =				field(
        default=_a     ,						metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}     ,						)
    a      :				List[Any]     =				field(
        default=_a     ,						metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}     ,						)
    a      :				Any     =				field(
        default=_a     ,						metadata={'help': 'Overwrite the cached training and evaluation sets'}	)
    a      :				Optional[Any]     =				field(
        default=5     ,						metadata={
            'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
        }     ,						)
    a      :				Optional[int]     =				field(
        default=_a     ,						metadata={
            'help': (
                'The maximum total input sequence length after tokenization. Sequences longer '
                'than this will be truncated. Default to the max input length of the model.'
            )
        }     ,						)
    a      :				Tuple     =				field(
        default=_a     ,						metadata={'help': 'The number of processes to use for the preprocessing.'}     ,						)
    a      :				List[Any]     =				field(
        default=0.15     ,						metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}	)
    a      :				List[str]     =				field(
        default=_a     ,						metadata={
            'help': (
                'Whether to pad all samples to `max_seq_length`. '
                'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
            )
        }     ,						)







    def      _snake_case					(			self			:       Optional[int]     )		->Optional[Any]:






        '''simple docstring'''






        if self.train_file is not None:
            _UpperCAmelCase          =   self.train_file.split("""."""     )[-1]
            assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
        if self.validation_file is not None:
            _UpperCAmelCase          =   self.validation_file.split("""."""     )[-1]
            assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."







def  _UpperCamelCase	(						_A					,      _A      )    ->  List[Any]:





    """simple docstring"""

    with open(UpperCAmelCase__					,      """r"""					,      encoding="""utf-8"""      ) as f:
        _UpperCAmelCase          =   [json.loads(UpperCAmelCase__      ) for line in f.read().splitlines() if (len(UpperCAmelCase__      ) > 0 and not line.isspace())]
    assert len(UpperCAmelCase__      ) == len(UpperCAmelCase__      )

    _UpperCAmelCase          =   {c: dataset[c] for c in dataset.column_names}
    _UpperCAmelCase          =   refs
    return Dataset.from_dict(UpperCAmelCase__      )


def  _UpperCamelCase	(						)    ->  Any:





    """simple docstring"""

    _UpperCAmelCase          =   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
    if len(sys.argv      ) == 2 and sys.argv[1].endswith(""".json"""      ):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase          =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
    else:
        _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase          =   parser.parse_args_into_dataclasses()

    # Detecting last checkpoint.
    _UpperCAmelCase          =   None
    if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
        _UpperCAmelCase          =   get_last_checkpoint(training_args.output_dir      )
        if last_checkpoint is None and len(os.listdir(training_args.output_dir      )      ) > 0:
            raise ValueError(
                F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
                """Use --overwrite_output_dir to overcome."""      )
        elif last_checkpoint is not None:
            logger.info(
                F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
                """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""      )

    # Setup logging
    logging.basicConfig(
        format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""					,      datefmt="""%m/%d/%Y %H:%M:%S"""					,      handlers=[logging.StreamHandler(sys.stdout      )]					,      )
    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank      ) else logging.WARN      )

    # Log on each process the small summary:
    logger.warning(
        F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
        + F"""distributed training: {bool(training_args.local_rank != -1      )}, 16-bits training: {training_args.fpaa}"""      )
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank      ):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info("""Training/evaluation parameters %s"""					,      UpperCAmelCase__      )

    # Set seed before initializing model.
    set_seed(training_args.seed      )

    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
    # 'text' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        _UpperCAmelCase          =   load_dataset(data_args.dataset_name					,      data_args.dataset_config_name      )
        if "validation" not in datasets.keys():
            _UpperCAmelCase          =   load_dataset(
                data_args.dataset_name					,      data_args.dataset_config_name					,      split=F"""train[:{data_args.validation_split_percentage}%]"""					,      )
            _UpperCAmelCase          =   load_dataset(
                data_args.dataset_name					,      data_args.dataset_config_name					,      split=F"""train[{data_args.validation_split_percentage}%:]"""					,      )
    else:
        _UpperCAmelCase          =   {}
        if data_args.train_file is not None:
            _UpperCAmelCase          =   data_args.train_file
        if data_args.validation_file is not None:
            _UpperCAmelCase          =   data_args.validation_file
        _UpperCAmelCase          =   data_args.train_file.split("""."""      )[-1]
        if extension == "txt":
            _UpperCAmelCase          =   """text"""
        _UpperCAmelCase          =   load_dataset(UpperCAmelCase__					,      data_files=UpperCAmelCase__      )
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    _UpperCAmelCase          =   {
        """cache_dir""": model_args.cache_dir,
        """revision""": model_args.model_revision,
        """use_auth_token""": True if model_args.use_auth_token else None,
    }
    if model_args.config_name:
        _UpperCAmelCase          =   AutoConfig.from_pretrained(model_args.config_name					,      **UpperCAmelCase__      )
    elif model_args.model_name_or_path:
        _UpperCAmelCase          =   AutoConfig.from_pretrained(model_args.model_name_or_path					,      **UpperCAmelCase__      )
    else:
        _UpperCAmelCase          =   CONFIG_MAPPING[model_args.model_type]()
        logger.warning("""You are instantiating a new config instance from scratch."""      )
        if model_args.config_overrides is not None:
            logger.info(F"""Overriding config: {model_args.config_overrides}"""      )
            config.update_from_string(model_args.config_overrides      )
            logger.info(F"""New config: {config}"""      )

    _UpperCAmelCase          =   {
        """cache_dir""": model_args.cache_dir,
        """use_fast""": model_args.use_fast_tokenizer,
        """revision""": model_args.model_revision,
        """use_auth_token""": True if model_args.use_auth_token else None,
    }
    if model_args.tokenizer_name:
        _UpperCAmelCase          =   AutoTokenizer.from_pretrained(model_args.tokenizer_name					,      **UpperCAmelCase__      )
    elif model_args.model_name_or_path:
        _UpperCAmelCase          =   AutoTokenizer.from_pretrained(model_args.model_name_or_path					,      **UpperCAmelCase__      )
    else:
        raise ValueError(
            """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
            """You can do it from another script, save it, and load it from here, using --tokenizer_name."""      )

    if model_args.model_name_or_path:
        _UpperCAmelCase          =   AutoModelForMaskedLM.from_pretrained(
            model_args.model_name_or_path					,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path      )					,      config=UpperCAmelCase__					,      cache_dir=model_args.cache_dir					,      revision=model_args.model_revision					,      use_auth_token=True if model_args.use_auth_token else None					,      )
    else:
        logger.info("""Training new model from scratch"""      )
        _UpperCAmelCase          =   AutoModelForMaskedLM.from_config(UpperCAmelCase__      )

    model.resize_token_embeddings(len(UpperCAmelCase__      )      )

    # Preprocessing the datasets.
    # First we tokenize all the texts.
    if training_args.do_train:
        _UpperCAmelCase          =   datasets["""train"""].column_names
    else:
        _UpperCAmelCase          =   datasets["""validation"""].column_names
    _UpperCAmelCase          =   """text""" if """text""" in column_names else column_names[0]

    _UpperCAmelCase          =   """max_length""" if data_args.pad_to_max_length else False

    def tokenize_function(_A      ):
        # Remove empty lines
        _UpperCAmelCase          =   [line for line in examples["""text"""] if len(UpperCAmelCase__      ) > 0 and not line.isspace()]
        return tokenizer(examples["""text"""]					,      padding=UpperCAmelCase__					,      truncation=UpperCAmelCase__					,      max_length=data_args.max_seq_length      )

    _UpperCAmelCase          =   datasets.map(
        UpperCAmelCase__					,      batched=UpperCAmelCase__					,      num_proc=data_args.preprocessing_num_workers					,      remove_columns=[text_column_name]					,      load_from_cache_file=not data_args.overwrite_cache					,      )

    # Add the chinese references if provided
    if data_args.train_ref_file is not None:
        _UpperCAmelCase          =   add_chinese_references(tokenized_datasets["""train"""]					,      data_args.train_ref_file      )
    if data_args.validation_ref_file is not None:
        _UpperCAmelCase          =   add_chinese_references(
            tokenized_datasets["""validation"""]					,      data_args.validation_ref_file      )
    # If we have ref files, need to avoid it removed by trainer
    _UpperCAmelCase          =   data_args.train_ref_file or data_args.validation_ref_file
    if has_ref:
        _UpperCAmelCase          =   False

    # Data collator
    # This one will take care of randomly masking the tokens.
    _UpperCAmelCase          =   DataCollatorForWholeWordMask(tokenizer=UpperCAmelCase__					,      mlm_probability=data_args.mlm_probability      )

    # Initialize our Trainer
    _UpperCAmelCase          =   Trainer(
        model=UpperCAmelCase__					,      args=UpperCAmelCase__					,      train_dataset=tokenized_datasets["""train"""] if training_args.do_train else None					,      eval_dataset=tokenized_datasets["""validation"""] if training_args.do_eval else None					,      tokenizer=UpperCAmelCase__					,      data_collator=UpperCAmelCase__					,      )

    # Training
    if training_args.do_train:
        if last_checkpoint is not None:
            _UpperCAmelCase          =   last_checkpoint
        elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path      ):
            _UpperCAmelCase          =   model_args.model_name_or_path
        else:
            _UpperCAmelCase          =   None
        _UpperCAmelCase          =   trainer.train(resume_from_checkpoint=UpperCAmelCase__      )
        trainer.save_model()  # Saves the tokenizer too for easy upload

        _UpperCAmelCase          =   os.path.join(training_args.output_dir					,      """train_results.txt"""      )
        if trainer.is_world_process_zero():
            with open(UpperCAmelCase__					,      """w"""      ) as writer:
                logger.info("""***** Train results *****"""      )
                for key, value in sorted(train_result.metrics.items()      ):
                    logger.info(F"""  {key} = {value}"""      )
                    writer.write(F"""{key} = {value}\n"""      )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
            trainer.state.save_to_json(os.path.join(training_args.output_dir					,      """trainer_state.json"""      )      )

    # Evaluation
    _UpperCAmelCase          =   {}
    if training_args.do_eval:
        logger.info("""*** Evaluate ***"""      )

        _UpperCAmelCase          =   trainer.evaluate()

        _UpperCAmelCase          =   math.exp(eval_output["""eval_loss"""]      )
        _UpperCAmelCase          =   perplexity

        _UpperCAmelCase          =   os.path.join(training_args.output_dir					,      """eval_results_mlm_wwm.txt"""      )
        if trainer.is_world_process_zero():
            with open(UpperCAmelCase__					,      """w"""      ) as writer:
                logger.info("""***** Eval results *****"""      )
                for key, value in sorted(results.items()      ):
                    logger.info(F"""  {key} = {value}"""      )
                    writer.write(F"""{key} = {value}\n"""      )

    return results


def  _UpperCamelCase	(						_A      )    ->  Any:





    """simple docstring"""

    main()


if __name__ == "__main__":
 main()

"""simple docstring"""



import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel




class 		a_						:







    def __init__(			self			:       Tuple     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       List[Any]=99     ,					__UpperCamelCase			:       Optional[int]=13     ,					__UpperCamelCase			:       List[str]=7     ,					__UpperCamelCase			:       List[Any]=9     ,					__UpperCamelCase			:       List[Any]=True     ,					__UpperCamelCase			:       str=True     ,					__UpperCamelCase			:       int=False     ,					__UpperCamelCase			:       int=32     ,					__UpperCamelCase			:       Dict=5     ,					__UpperCamelCase			:       Optional[int]=4     ,					__UpperCamelCase			:       Any=37     ,					__UpperCamelCase			:       List[Any]=8     ,					__UpperCamelCase			:       Optional[int]=0.1     ,					__UpperCamelCase			:       str=0.0_0_2     ,					__UpperCamelCase			:       Union[str, Any]=1     ,					__UpperCamelCase			:       List[Any]=0     ,					__UpperCamelCase			:       Tuple=0     ,					__UpperCamelCase			:       Optional[int]=None     ,					__UpperCamelCase			:       Any=None     ,					)		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   parent
        _UpperCAmelCase          =   batch_size
        _UpperCAmelCase          =   encoder_seq_length
        _UpperCAmelCase          =   decoder_seq_length
        # For common tests
        _UpperCAmelCase          =   self.decoder_seq_length
        _UpperCAmelCase          =   is_training
        _UpperCAmelCase          =   use_attention_mask
        _UpperCAmelCase          =   use_labels
        _UpperCAmelCase          =   vocab_size
        _UpperCAmelCase          =   hidden_size
        _UpperCAmelCase          =   num_hidden_layers
        _UpperCAmelCase          =   num_attention_heads
        _UpperCAmelCase          =   d_ff
        _UpperCAmelCase          =   relative_attention_num_buckets
        _UpperCAmelCase          =   dropout_rate
        _UpperCAmelCase          =   initializer_factor
        _UpperCAmelCase          =   eos_token_id
        _UpperCAmelCase          =   pad_token_id
        _UpperCAmelCase          =   decoder_start_token_id
        _UpperCAmelCase          =   None
        _UpperCAmelCase          =   decoder_layers







    def      _snake_case					(			self			:       Union[str, Any]     )		->Union[str, Any]:






        '''simple docstring'''






        return TaConfig.from_pretrained("""google/umt5-base"""     )







    def      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Tuple=None     ,					__UpperCamelCase			:       Optional[Any]=None     ,					__UpperCamelCase			:       Optional[Any]=None     ,					__UpperCamelCase			:       Any=None     ,					__UpperCamelCase			:       str=None     ,					)		->int:






        '''simple docstring'''






        if attention_mask is None:
            _UpperCAmelCase          =   input_ids.ne(config.pad_token_id     )
        if decoder_attention_mask is None:
            _UpperCAmelCase          =   decoder_input_ids.ne(config.pad_token_id     )
        if head_mask is None:
            _UpperCAmelCase          =   torch.ones(config.num_hidden_layers     ,					config.num_attention_heads     ,					device=__UpperCamelCase     )
        if decoder_head_mask is None:
            _UpperCAmelCase          =   torch.ones(config.num_decoder_layers     ,					config.num_attention_heads     ,					device=__UpperCamelCase     )
        if cross_attn_head_mask is None:
            _UpperCAmelCase          =   torch.ones(
                config.num_decoder_layers     ,					config.num_attention_heads     ,					device=__UpperCamelCase     )
        return {
            "input_ids": input_ids,
            "decoder_input_ids": decoder_input_ids,
            "attention_mask": attention_mask,
            "decoder_attention_mask": decoder_attention_mask,
            "head_mask": head_mask,
            "decoder_head_mask": decoder_head_mask,
            "cross_attn_head_mask": cross_attn_head_mask,
        }







    def      _snake_case					(			self			:       List[Any]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   ids_tensor([self.batch_size, self.encoder_seq_length]     ,					self.vocab_size     )
        _UpperCAmelCase          =   ids_tensor([self.batch_size, self.decoder_seq_length]     ,					self.vocab_size     )

        # we need to clamp the input ids here to avoid having pad token in between
        # this is because for NllbMoe the position_ids are prepared such that
        # all pad tokens have pos id = 2 and rest are between 2..seq_length
        # and the seq_length here is seq_length - num_pad_tokens
        # but when using past, there is no way of knowing if the past input ids had
        # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
        # position_ids being off by num_pad_tokens in past input
        _UpperCAmelCase          =   input_ids.clamp(self.pad_token_id + 1     )
        _UpperCAmelCase          =   decoder_input_ids.clamp(self.pad_token_id + 1     )

        _UpperCAmelCase          =   self.get_config()
        _UpperCAmelCase          =   config.num_attention_heads
        _UpperCAmelCase          =   self.prepare_inputs_dict(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     )
        return config, input_dict







    def      _snake_case					(			self			:       Union[str, Any]     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.prepare_config_and_inputs()
        return config, inputs_dict







    def      _snake_case					(			self			:       Dict     )		->List[str]:






        '''simple docstring'''






        return TaConfig(
            vocab_size=1_66     ,					d_model=self.hidden_size     ,					d_ff=self.d_ff     ,					d_kv=self.hidden_size // self.num_attention_heads     ,					num_layers=self.num_hidden_layers     ,					num_decoder_layers=self.decoder_layers     ,					num_heads=self.num_attention_heads     ,					relative_attention_num_buckets=self.relative_attention_num_buckets     ,					dropout_rate=self.dropout_rate     ,					initializer_factor=self.initializer_factor     ,					eos_token_id=self.eos_token_id     ,					bos_token_id=self.pad_token_id     ,					pad_token_id=self.pad_token_id     ,					decoder_start_token_id=self.decoder_start_token_id     ,					)







    def      _snake_case					(			self			:       Tuple     )		->Dict:






        '''simple docstring'''






        return TaConfig(
            vocab_size=self.vocab_size     ,					d_model=self.hidden_size     ,					d_ff=self.d_ff     ,					d_kv=self.hidden_size // self.num_attention_heads     ,					num_layers=self.num_hidden_layers     ,					num_decoder_layers=self.decoder_layers     ,					num_heads=self.num_attention_heads     ,					relative_attention_num_buckets=self.relative_attention_num_buckets     ,					dropout_rate=self.dropout_rate     ,					initializer_factor=self.initializer_factor     ,					eos_token_id=self.eos_token_id     ,					bos_token_id=self.pad_token_id     ,					pad_token_id=self.pad_token_id     ,					decoder_start_token_id=self.decoder_start_token_id     ,					)







    def      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       List[str]     ,					)		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaModel(config=__UpperCamelCase     )
        model.to(__UpperCamelCase     )
        model.eval()
        _UpperCAmelCase          =   model(
            input_ids=__UpperCamelCase     ,					decoder_input_ids=__UpperCamelCase     ,					attention_mask=__UpperCamelCase     ,					decoder_attention_mask=__UpperCamelCase     ,					)
        _UpperCAmelCase          =   model(input_ids=__UpperCamelCase     ,					decoder_input_ids=__UpperCamelCase     )
        _UpperCAmelCase          =   result.last_hidden_state
        _UpperCAmelCase          =   result.past_key_values
        _UpperCAmelCase          =   result.encoder_last_hidden_state

        self.parent.assertEqual(encoder_output.size()     ,					(self.batch_size, self.encoder_seq_length, self.hidden_size)     )
        self.parent.assertEqual(decoder_output.size()     ,					(self.batch_size, self.decoder_seq_length, self.hidden_size)     )
        # There should be `num_layers` key value embeddings stored in decoder_past
        self.parent.assertEqual(len(__UpperCamelCase     )     ,					config.num_layers     )
        # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
        self.parent.assertEqual(len(decoder_past[0]     )     ,					4     )







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[int]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       List[str]     ,					)		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaModel(config=__UpperCamelCase     ).get_decoder().to(__UpperCamelCase     ).eval()
        # first forward pass
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					use_cache=__UpperCamelCase     )
        _UpperCAmelCase          =   model(__UpperCamelCase     )
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					use_cache=__UpperCamelCase     )

        self.parent.assertTrue(len(__UpperCamelCase     ) == len(__UpperCamelCase     )     )
        self.parent.assertTrue(len(__UpperCamelCase     ) == len(__UpperCamelCase     ) + 1     )

        _UpperCAmelCase    ,_UpperCAmelCase          =   outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        _UpperCAmelCase          =   ids_tensor((self.batch_size, 1)     ,					config.vocab_size     )

        # append to next input_ids and
        _UpperCAmelCase          =   torch.cat([input_ids, next_tokens]     ,					dim=-1     )

        _UpperCAmelCase          =   model(__UpperCamelCase     )["""last_hidden_state"""]
        _UpperCAmelCase          =   model(__UpperCamelCase     ,					past_key_values=__UpperCamelCase     )["""last_hidden_state"""]

        # select random slice
        _UpperCAmelCase          =   ids_tensor((1,)     ,					output_from_past.shape[-1]     ).item()
        _UpperCAmelCase          =   output_from_no_past[:, -1, random_slice_idx].detach()
        _UpperCAmelCase          =   output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(__UpperCamelCase     ,					__UpperCamelCase     ,					atol=1e-3     )     )







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       Dict     ,					)		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaModel(config=__UpperCamelCase     ).to(__UpperCamelCase     ).half().eval()
        _UpperCAmelCase          =   model(**__UpperCamelCase     )["""last_hidden_state"""]
        self.parent.assertFalse(torch.isnan(__UpperCamelCase     ).any().item()     )







@require_torch
class 		a_						( _UpperCAmelCase     ,						_UpperCAmelCase     ,						_UpperCAmelCase     ,						unittest.TestCase	):
    a      :				List[str]     =				(
        (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
    )
    a      :				Tuple     =				(UMTaForConditionalGeneration,) if is_torch_available() else ()
    a      :				Optional[Any]     =				(
        {
            'conversational': UMTaForConditionalGeneration,
            'feature-extraction': UMTaModel,
            'summarization': UMTaForConditionalGeneration,
            'text2text-generation': UMTaForConditionalGeneration,
            'translation': UMTaForConditionalGeneration,
            'question-answering': UMTaForQuestionAnswering,
        }
        if is_torch_available()
        else {}
    )
    a      :				Any     =				True
    a      :				Optional[int]     =				False
    a      :				Any     =				False
    a      :				Optional[int]     =				True
    a      :				Optional[Any]     =				True
    # The small UMT5 model needs higher percentages for CPU/MP tests
    a      :				int     =				[0.8, 0.9]







    def      _snake_case					(			self			:       Optional[Any]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaModelTester(self     )







    @unittest.skip("""Test has a segmentation fault on torch 1.8.0"""     )
    def      _snake_case					(			self			:       Union[str, Any]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        _UpperCAmelCase          =   UMTaModel(config_and_inputs[0]     ).to(__UpperCamelCase     )
        with tempfile.TemporaryDirectory() as tmpdirname:
            torch.onnx.export(
                __UpperCamelCase     ,					(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])     ,					f"""{tmpdirname}/t5_test.onnx"""     ,					export_params=__UpperCamelCase     ,					opset_version=9     ,					input_names=["""input_ids""", """decoder_input_ids"""]     ,					)







    @unittest.skipIf(torch_device == """cpu"""     ,					"""Cant do half precision"""     )
    def      _snake_case					(			self			:       Tuple     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model_fpaa_forward(*__UpperCamelCase     )







    def      _snake_case					(			self			:       Any     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
        _UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs()
        _UpperCAmelCase          =   config_and_inputs[0]
        _UpperCAmelCase          =   UMTaForConditionalGeneration(__UpperCamelCase     ).eval()
        model.to(__UpperCamelCase     )

        _UpperCAmelCase          =   {
            """head_mask""": torch.zeros(config.num_layers     ,					config.num_heads     ,					device=__UpperCamelCase     ),
            """decoder_head_mask""": torch.zeros(config.num_decoder_layers     ,					config.num_heads     ,					device=__UpperCamelCase     ),
            """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers     ,					config.num_heads     ,					device=__UpperCamelCase     ),
        }

        for attn_name, (name, mask) in zip(__UpperCamelCase     ,					head_masking.items()     ):
            _UpperCAmelCase          =   {name: mask}
            # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
            if name == "head_mask":
                _UpperCAmelCase          =   torch.ones(
                    config.num_decoder_layers     ,					config.num_heads     ,					device=__UpperCamelCase     )

            _UpperCAmelCase          =   model.generate(
                config_and_inputs[1]["""input_ids"""]     ,					num_beams=1     ,					max_length=3     ,					output_attentions=__UpperCamelCase     ,					return_dict_in_generate=__UpperCamelCase     ,					**__UpperCamelCase     ,					)
            # We check the state of decoder_attentions and cross_attentions just from the last step
            _UpperCAmelCase          =   out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
            self.assertEqual(sum([w.sum().item() for w in attn_weights]     )     ,					0.0     )







    @unittest.skip("""Does not work on the tiny model as we keep hitting edge cases."""     )
    def      _snake_case					(			self			:       Tuple     )		->List[Any]:






        '''simple docstring'''






        pass







@require_torch
@require_sentencepiece
@require_tokenizers
class 		a_						( unittest.TestCase	):







    @slow
    @unittest.skip(
        """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"""     )
    def      _snake_case					(			self			:       Optional[Any]     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""     ,					return_dict=__UpperCamelCase     ).to(__UpperCamelCase     )
        _UpperCAmelCase          =   AutoTokenizer.from_pretrained("""google/umt5-small"""     ,					use_fast=__UpperCamelCase     ,					legacy=__UpperCamelCase     )
        _UpperCAmelCase          =   [
            """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
            """No se como puedo <extra_id_0>.""",
            """This is the reason why we <extra_id_0> them.""",
            """The <extra_id_0> walks in <extra_id_1>, seats""",
            """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
        ]
        _UpperCAmelCase          =   tokenizer(__UpperCamelCase     ,					return_tensors="""pt"""     ,					padding=__UpperCamelCase     ).input_ids
        # fmt: off
        _UpperCAmelCase          =   torch.tensor(
            [
                [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
                [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
            ]     )
        # fmt: on
        torch.testing.assert_allclose(__UpperCamelCase     ,					__UpperCamelCase     )

        _UpperCAmelCase          =   model.generate(input_ids.to(__UpperCamelCase     )     )
        _UpperCAmelCase          =   [
            """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
            """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
            """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
            """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
            """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
        ]
        _UpperCAmelCase          =   tokenizer.batch_decode(__UpperCamelCase     )
        self.assertEqual(__UpperCamelCase     ,					__UpperCamelCase     )