


import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_ta import TaTokenizer
else:
	_lowerCAmelCase:						Optional[int]   				=       None


_lowerCAmelCase:						str   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Optional[Any]   				=       {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_lowerCAmelCase:						List[Any]   				=       {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
_lowerCAmelCase:						Union[str, Any]   				=       {
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}




class       lowercase_					(lowercase__   ):
				snake_case	    =VOCAB_FILES_NAMES
				snake_case	    =PRETRAINED_VOCAB_FILES_MAP
				snake_case	    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				snake_case	    =['input_ids', 'attention_mask']
				snake_case	    =TaTokenizer

				snake_case	    =[]





				def __init__(				self       ,  lowercase_=None       ,  lowercase_=None       ,  lowercase_="</s>"       ,  lowercase_="<unk>"       ,  lowercase_="<pad>"       ,  lowercase_=100       ,  lowercase_=None       ,  **lowercase_       ,  )  -> List[str]:
											# Add extra_ids to the special token list
											if extra_ids > 0 and additional_special_tokens is None:
																		a__      =[F"""<extra_id_{i}>""" for i in range(lowercase_)]
											elif extra_ids > 0 and additional_special_tokens is not None:
																		# Check that we have the right number of extra special tokens
																		a__      =len(set(filter(lambda lowercase_: bool('extra_id_' in str(lowercase_))       ,  lowercase_)))
																		if extra_tokens != extra_ids:
																									raise ValueError(
																									    F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
																									    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
																									    ' tokens')

											super().__init__(
											    lowercase_       ,  tokenizer_file=lowercase_       ,  eos_token=lowercase_       ,  unk_token=lowercase_       ,  pad_token=lowercase_       ,  extra_ids=lowercase_       ,  additional_special_tokens=lowercase_       ,  **lowercase_       ,  )

											a__      =vocab_file
											a__      =False if not self.vocab_file else True
											a__      =extra_ids





				@staticmethod
				def 						__UpperCamelCase    (				lowercase_       ,  lowercase_       ,  lowercase_)  -> Dict:
											if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
																		a__      =TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
																		if init_max_model_length is not None and init_max_model_length != max_model_length:
																									return init_max_model_length
																		elif init_max_model_length is None:
																									warnings.warn(
																									    'This tokenizer was incorrectly instantiated with a model max length of'
																									    F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
																									    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
																									    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
																									    F""" {pretrained_model_name_or_path} automatically truncating your input to"""
																									    F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
																									    F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
																									    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
																									    ' instantiate this tokenizer with `model_max_length` set to your preferred value.'       ,  lowercase_       ,  )

											return max_model_length





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_ = None)  -> Tuple[str]:
											if not self.can_save_slow_tokenizer:
																		raise ValueError(
																		    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
																		    'tokenizer.')

											if not os.path.isdir(lowercase_):
																		logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
																		return
											a__      =os.path.join(
											    lowercase_       ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

											if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase_):
																		copyfile(self.vocab_file       ,  lowercase_)
																		logger.info(F"""Copy vocab file to {out_vocab_file}""")

											return (out_vocab_file,)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_ = None)  -> List[int]:
											a__      =token_ids_a + [self.eos_token_id]
											if token_ids_a is None:
																		return self.prefix_tokens + token_ids_a
											else:
																		a__      =token_ids_a + [self.eos_token_id]
																		return self.prefix_tokens + token_ids_a + token_ids_a





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_ = None)  -> List[int]:
											a__      =[self.eos_token_id]

											if token_ids_a is None:
																		return len(token_ids_a + eos) * [0]
											return len(token_ids_a + eos + token_ids_a + eos) * [0]





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											return list(
											    set(filter(lambda lowercase_: bool(re.search(R'<extra_id_\d+>'       ,  lowercase_)) is not None       ,  self.additional_special_tokens)))





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											return [self.convert_tokens_to_ids(lowercase_) for token in self.get_sentinel_tokens()]





from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
	from .deepspeed import (
	    DeepSpeedEngineWrapper,
	    DeepSpeedOptimizerWrapper,
	    DeepSpeedSchedulerWrapper,
	    DummyOptim,
	    DummyScheduler,
	    HfDeepSpeedConfig,
	)

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers

