


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:
			__snake_case            =   None


__snake_case            =   logging.get_logger(__name__)

__snake_case            =   {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__snake_case            =   {
    "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
__snake_case            =   {
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= VOCAB_FILES_NAMES
			lowercase						= PRETRAINED_VOCAB_FILES_MAP
			lowercase						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase						= ["""input_ids""", """attention_mask"""]
			lowercase						= TaTokenizer

			lowercase						= []




			def __init__(						self							:		str       ,       __magic_name__							:		List[Any]=None       ,       __magic_name__							:		Optional[int]=None       ,       __magic_name__							:		int="</s>"       ,       __magic_name__							:		int="<unk>"       ,       __magic_name__							:		Optional[Any]="<pad>"       ,       __magic_name__							:		Any=1_0_0       ,       __magic_name__							:		Union[str, Any]=None       ,       **__magic_name__							:		Optional[Any]       ,       ):







						"""simple docstring"""







						if extra_ids > 0 and additional_special_tokens is None:
									UpperCamelCase         =					[F'<extra_id_{i}>' for i in range(__magic_name__						)]
						elif extra_ids > 0 and additional_special_tokens is not None:
									# Check that we have the right number of extra special tokens
									UpperCamelCase         =					len(set(filter(lambda __magic_name__						: bool("""extra_id_""" in str(__magic_name__						)						)       ,       __magic_name__						)						)						)
									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__(
						    __magic_name__       ,       tokenizer_file=__magic_name__       ,       eos_token=__magic_name__       ,       unk_token=__magic_name__       ,       pad_token=__magic_name__       ,       extra_ids=__magic_name__       ,       additional_special_tokens=__magic_name__       ,       **__magic_name__       ,       )

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




			@staticmethod
			def   lowerCamelCase_					(						__magic_name__							:		Union[str, Any]       ,       __magic_name__							:		Any       ,       __magic_name__							:		Dict						):







						"""simple docstring"""







						if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
									UpperCamelCase         =					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."""       ,       __magic_name__       ,       )

						return max_model_length




			def   lowerCamelCase_					(						self							:		List[Any]       ,       __magic_name__							:		str       ,       __magic_name__							:		Optional[str] = None						):







						"""simple docstring"""







						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(__magic_name__						):
									logger.error(F'Vocabulary path ({save_directory}) should be a directory'						)
									return
						UpperCamelCase         =					os.path.join(
						    __magic_name__       ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

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

						return (out_vocab_file,)




			def   lowerCamelCase_					(						self							:		List[Any]       ,       __magic_name__							:		List[int]       ,       __magic_name__							:		Optional[List[int]] = None						):







						"""simple docstring"""







						UpperCamelCase         =					token_ids_a + [self.eos_token_id]
						if token_ids_a is None:
									return self.prefix_tokens + token_ids_a
						else:
									UpperCamelCase         =					token_ids_a + [self.eos_token_id]
									return self.prefix_tokens + token_ids_a + token_ids_a




			def   lowerCamelCase_					(						self							:		Union[str, Any]       ,       __magic_name__							:		List[int]       ,       __magic_name__							:		Optional[List[int]] = None						):







						"""simple docstring"""







						UpperCamelCase         =					[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   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						return list(
						    set(filter(lambda __magic_name__						: bool(re.search(R"""<extra_id_\d+>"""       ,       __magic_name__						)						) is not None       ,       self.additional_special_tokens						)						)						)





			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						return [self.convert_tokens_to_ids(__magic_name__						) for token in self.get_sentinel_tokens()]








import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available






def 							_lowercase    (		*SCREAMING_SNAKE_CASE_   :		List[Any]	):





			"""simple docstring"""




			if not isinstance(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	):
						UpperCamelCase         =					list(SCREAMING_SNAKE_CASE_	)
			for i in range(len(SCREAMING_SNAKE_CASE_	)	):
						UpperCamelCase         =					None
			gc.collect()
			if is_xpu_available():
						torch.xpu.empty_cache()
			elif is_npu_available():
						torch.npu.empty_cache()
			else:
						torch.cuda.empty_cache()
			return objects






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Exception	):





			"""simple docstring"""




			UpperCamelCase         =					[
			    """CUDA out of memory.""",  # CUDA OOM
			    """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
			    """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
			]
			if isinstance(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	) and len(exception.args	) == 1:
						return any(err in exception.args[0] for err in _statements	)
			return False






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		callable = None       ,  SCREAMING_SNAKE_CASE_   :		int = 128	):





			"""simple docstring"""




			if function is None:
						return functools.partial(SCREAMING_SNAKE_CASE_       ,  starting_batch_size=SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					starting_batch_size

			def decorator(*SCREAMING_SNAKE_CASE_   :		Union[str, Any]       ,  **SCREAMING_SNAKE_CASE_   :		Tuple	):
						nonlocal batch_size
						gc.collect()
						if is_xpu_available():
									torch.xpu.empty_cache()
						elif is_npu_available():
									torch.npu.empty_cache()
						else:
									torch.cuda.empty_cache()
						UpperCamelCase         =					list(inspect.signature(SCREAMING_SNAKE_CASE_	).parameters.keys()	)
						# Guard against user error
						if len(SCREAMING_SNAKE_CASE_	) < (len(SCREAMING_SNAKE_CASE_	) + 1):
									UpperCamelCase         =					""", """.join([f'{arg}={value}' for arg, value in zip(params[1:]       ,  args[1:]	)]	)
									raise TypeError(
									    f'Batch size was passed into `{function.__name__}` as the first argument when called.'
									    f'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'	)
						while True:
									if batch_size == 0:
												raise RuntimeError("""No executable batch size found, reached zero."""	)
									try:
												return function(SCREAMING_SNAKE_CASE_       ,  *SCREAMING_SNAKE_CASE_       ,  **SCREAMING_SNAKE_CASE_	)
									except Exception as e:
												if should_reduce_batch_size(SCREAMING_SNAKE_CASE_	):
															gc.collect()
															if is_xpu_available():
																		torch.xpu.empty_cache()
															elif is_npu_available():
																		torch.npu.empty_cache()
															else:
																		torch.cuda.empty_cache()
															batch_size //= 2
												else:
															raise

			return decorator




