


'''simple docstring'''



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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
	from ...tokenization_utils_base import TextInput
from ...utils import logging


_lowercase      :      Any            =					logging.get_logger(__name__)

_lowercase      :      Dict            =					{"""vocab_file""": """spiece.model"""}

_lowercase      :      Union[str, 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""",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
_lowercase      :      Optional[int]            =					{
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}

_lowercase      :      List[Any]            =					"""▁"""






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				Optional[int]  		=	VOCAB_FILES_NAMES
				__magic_name__    :				Optional[Any]  		=	PRETRAINED_VOCAB_FILES_MAP
				__magic_name__    :				List[str]  		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__    :				int  		=	["input_ids", "attention_mask"]




				def __init__(		self      :			Tuple				,   lowerCAmelCase      :			Any				,   lowerCAmelCase      :			Optional[int]="</s>"				,   lowerCAmelCase      :			Tuple="<unk>"				,   lowerCAmelCase      :			Optional[int]="<pad>"				,   lowerCAmelCase      :			Dict=100				,   lowerCAmelCase      :			Dict=None				,   lowerCAmelCase      :			Optional[Dict[str, Any]] = None				,   lowerCAmelCase      :			List[Any]=True				,   **lowerCAmelCase      :			int				,   )->					None:

										"""simple docstring"""




										if extra_ids > 0 and additional_special_tokens is None:
																UpperCAmelCase					    =     [F"""<extra_id_{i}>""" for i in range(lowerCAmelCase				)]
										elif extra_ids > 0 and additional_special_tokens is not None:
																# Check that we have the right number of extra_id special tokens
																UpperCAmelCase					    =     len(set(filter(lambda lowerCAmelCase				: bool('''extra_id''' in str(lowerCAmelCase				)				)				,   lowerCAmelCase				)				)				)
																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'''				)
										if legacy:
																logger.warning_once(
																    F"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
																    ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'''				)

										UpperCAmelCase					    =     legacy
										UpperCAmelCase					    =     {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    eos_token=lowerCAmelCase				,   unk_token=lowerCAmelCase				,   pad_token=lowerCAmelCase				,   extra_ids=lowerCAmelCase				,   additional_special_tokens=lowerCAmelCase				,   sp_model_kwargs=self.sp_model_kwargs				,   legacy=lowerCAmelCase				,   **lowerCAmelCase				,   )

										UpperCAmelCase					    =     vocab_file
										UpperCAmelCase					    =     extra_ids

										UpperCAmelCase					    =     spm.SentencePieceProcessor(**self.sp_model_kwargs				)
										self.sp_model.Load(lowerCAmelCase				)




				@staticmethod
				def 					a__(		lowerCAmelCase      :			int				,   lowerCAmelCase      :			Optional[Any]				,   lowerCAmelCase      :			Any				)->					Union[str, Any]:

										"""simple docstring"""




										if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
																UpperCAmelCase					    =     TaTokenizer.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.'''				,   lowerCAmelCase				,   )

										return max_model_length




				@property
				def 					a__(		self      :			Any				)->					Optional[Any]:

										"""simple docstring"""




										return self.sp_model.get_piece_size() + self._extra_ids




				def 					a__(		self      :			List[str]				)->					List[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     {self.convert_ids_to_tokens(lowerCAmelCase				): i for i in range(self.vocab_size				)}
										vocab.update(self.added_tokens_encoder				)
										return vocab




				def 					a__(		self      :			int				,   lowerCAmelCase      :			List[int]				,   lowerCAmelCase      :			Optional[List[int]] = None				,   lowerCAmelCase      :			bool = False				)->					List[int]:

										"""simple docstring"""




										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=lowerCAmelCase				,   token_ids_a=lowerCAmelCase				,   already_has_special_tokens=lowerCAmelCase				)

										# normal case: some special tokens
										if token_ids_a is None:
																return ([0] * len(lowerCAmelCase				)) + [1]
										return ([0] * len(lowerCAmelCase				)) + [1] + ([0] * len(lowerCAmelCase				)) + [1]




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

										"""simple docstring"""




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




				def 					a__(		self      :			Any				)->					Any:

										"""simple docstring"""




										return [self._convert_token_to_id(lowerCAmelCase				) for token in self.get_sentinel_tokens()]




				def 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			List[int]				)->					List[int]:

										"""simple docstring"""




										if len(lowerCAmelCase				) > 0 and token_ids[-1] == self.eos_token_id:
																warnings.warn(
																    F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
																    ''' eos tokens being added.'''				)
																return token_ids
										else:
																return token_ids + [self.eos_token_id]




				def 					a__(		self      :			Optional[Any]				,   lowerCAmelCase      :			List[int]				,   lowerCAmelCase      :			Optional[List[int]] = None				)->					List[int]:

										"""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 					a__(		self      :			Dict				,   lowerCAmelCase      :			List[int]				,   lowerCAmelCase      :			Optional[List[int]] = None				)->					List[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     self._add_eos_if_not_present(lowerCAmelCase				)
										if token_ids_a is None:
																return token_ids_a
										else:
																UpperCAmelCase					    =     self._add_eos_if_not_present(lowerCAmelCase				)
																return token_ids_a + token_ids_a




				def __getstate__(		self      :			str				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     self.__dict__.copy()
										UpperCAmelCase					    =     None
										return state




				def __setstate__(		self      :			Any				,   lowerCAmelCase      :			Dict				)->					Any:

										"""simple docstring"""




										UpperCAmelCase					    =     d

										# for backward compatibility
										if not hasattr(self				,   '''sp_model_kwargs'''				):
																UpperCAmelCase					    =     {}

										UpperCAmelCase					    =     spm.SentencePieceProcessor(**self.sp_model_kwargs				)
										self.sp_model.Load(self.vocab_file				)




				def 					a__(		self      :			Tuple				,   lowerCAmelCase      :			"TextInput"				,   **lowerCAmelCase      :			Optional[int]				)->					List[str]:

										"""simple docstring"""




										if not self.legacy:
																UpperCAmelCase					    =     SPIECE_UNDERLINE + text.replace(lowerCAmelCase				,   ''' '''				)
										return super().tokenize(lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			Optional[Any]				,   lowerCAmelCase      :			List[Any]				,   **lowerCAmelCase      :			Tuple				)->					Tuple:

										"""simple docstring"""




										if not self.legacy:
																UpperCAmelCase					    =     text.startswith(lowerCAmelCase				)
																if is_first:
																						UpperCAmelCase					    =     text[1:]

										UpperCAmelCase					    =     self.sp_model.encode(lowerCAmelCase				,   out_type=lowerCAmelCase				)

										if not self.legacy and not is_first and not text.startswith(''' '''				) and tokens[0].startswith(lowerCAmelCase				):
																UpperCAmelCase					    =     ([tokens[0][1:]] if len(tokens[0]				) > 1 else []) + tokens[1:]
										return tokens




				def 					a__(		self      :			str				,   lowerCAmelCase      :			Optional[int]				)->					Dict:

										"""simple docstring"""




										if token.startswith('''<extra_id_'''				):
																UpperCAmelCase					    =     re.match(R'''<extra_id_(\d+)>'''				,   lowerCAmelCase				)
																UpperCAmelCase					    =     int(match.group(1				)				)
																return self.vocab_size - num - 1
										return self.sp_model.piece_to_id(lowerCAmelCase				)




				def 					a__(		self      :			Dict				,   lowerCAmelCase      :			Tuple				)->					Optional[int]:

										"""simple docstring"""




										if index < self.sp_model.get_piece_size():
																UpperCAmelCase					    =     self.sp_model.IdToPiece(lowerCAmelCase				)
										else:
																UpperCAmelCase					    =     F"""<extra_id_{self.vocab_size - 1 - index}>"""
										return token




				def 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			Union[str, Any]				)->					List[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     []
										UpperCAmelCase					    =     ''''''
										UpperCAmelCase					    =     False
										for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																						if not prev_is_special:
																												out_string += " "
																						out_string += self.sp_model.decode(lowerCAmelCase				) + token
																						UpperCAmelCase					    =     True
																						UpperCAmelCase					    =     []
																else:
																						current_sub_tokens.append(lowerCAmelCase				)
																						UpperCAmelCase					    =     False
										out_string += self.sp_model.decode(lowerCAmelCase				)
										return out_string.strip()





				def 					a__(		self      :			Optional[Any]				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Optional[str] = None				)->					Tuple[str]:

										"""simple docstring"""




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

										if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCAmelCase				) and os.path.isfile(self.vocab_file				):
																copyfile(self.vocab_file				,   lowerCAmelCase				)
										elif not os.path.isfile(self.vocab_file				):
																with open(lowerCAmelCase				,   '''wb'''				) as fi:
																						UpperCAmelCase					    =     self.sp_model.serialized_model_proto()
																						fi.write(lowerCAmelCase				)

										return (out_vocab_file,)









'''simple docstring'''



import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
	import torch

if is_soundfile_availble():
	import soundfile as sf

if is_vision_available():
	from PIL import Image


def 				lowerCamelCase__				(	A       :      str=""					):




						'''simple docstring'''






						UpperCAmelCase					    =     tempfile.mkdtemp()
						return os.path.join(A				,       str(uuid.uuida()					) + suffix					)






@require_soundfile
@require_torch
class 	UpperCamelCase__(       unittest.TestCase	):




				def 					a__(		self      :			int				)->					int:

										"""simple docstring"""




										UpperCAmelCase					    =     torch.rand(12				,   dtype=torch.floataa				) - 0.5
										UpperCAmelCase					    =     AgentAudio(lowerCAmelCase				)
										UpperCAmelCase					    =     str(agent_type.to_string()				)

										# Ensure that the tensor and the agent_type's tensor are the same
										self.assertTrue(torch.allclose(lowerCAmelCase				,   agent_type.to_raw()				,   atol=1E-4				)				)

										del agent_type

										# Ensure the path remains even after the object deletion
										self.assertTrue(os.path.exists(lowerCAmelCase				)				)

										# Ensure that the file contains the same value as the original tensor
										UpperCAmelCase    ,    UpperCAmelCase					    =     sf.read(lowerCAmelCase				)
										self.assertTrue(torch.allclose(lowerCAmelCase				,   torch.tensor(lowerCAmelCase				)				,   atol=1E-4				)				)





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

										"""simple docstring"""




										UpperCAmelCase					    =     torch.rand(12				,   dtype=torch.floataa				) - 0.5
										UpperCAmelCase					    =     get_new_path(suffix='''.wav'''				)
										sf.write(lowerCAmelCase				,   lowerCAmelCase				,   16000				)

										UpperCAmelCase					    =     AgentAudio(lowerCAmelCase				)

										self.assertTrue(torch.allclose(lowerCAmelCase				,   agent_type.to_raw()				,   atol=1E-4				)				)
										self.assertEqual(agent_type.to_string()				,   lowerCAmelCase				)







@require_vision
@require_torch
class 	UpperCamelCase__(       unittest.TestCase	):




				def 					a__(		self      :			List[Any]				)->					Any:

										"""simple docstring"""




										UpperCAmelCase					    =     torch.randint(0				,   256				,   (64, 64, 3)				)
										UpperCAmelCase					    =     AgentImage(lowerCAmelCase				)
										UpperCAmelCase					    =     str(agent_type.to_string()				)

										# Ensure that the tensor and the agent_type's tensor are the same
										self.assertTrue(torch.allclose(lowerCAmelCase				,   agent_type._tensor				,   atol=1E-4				)				)

										self.assertIsInstance(agent_type.to_raw()				,   Image.Image				)

										# Ensure the path remains even after the object deletion
										del agent_type
										self.assertTrue(os.path.exists(lowerCAmelCase				)				)




				def 					a__(		self      :			List[Any]				)->					List[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / '''000000039769.png'''
										UpperCAmelCase					    =     Image.open(lowerCAmelCase				)
										UpperCAmelCase					    =     AgentImage(lowerCAmelCase				)

										self.assertTrue(path.samefile(agent_type.to_string()				)				)
										self.assertTrue(image == agent_type.to_raw()				)

										# Ensure the path remains even after the object deletion
										del agent_type
										self.assertTrue(os.path.exists(lowerCAmelCase				)				)





				def 					a__(		self      :			Optional[Any]				)->					List[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / '''000000039769.png'''
										UpperCAmelCase					    =     Image.open(lowerCAmelCase				)
										UpperCAmelCase					    =     AgentImage(lowerCAmelCase				)

										self.assertFalse(path.samefile(agent_type.to_string()				)				)
										self.assertTrue(image == agent_type.to_raw()				)

										# Ensure the path remains even after the object deletion
										del agent_type
										self.assertTrue(os.path.exists(lowerCAmelCase				)				)







class 	UpperCamelCase__(       unittest.TestCase	):




				def 					a__(		self      :			int				)->					Any:

										"""simple docstring"""




										UpperCAmelCase					    =     '''Hey!'''
										UpperCAmelCase					    =     AgentText(lowerCAmelCase				)

										self.assertEqual(lowerCAmelCase				,   agent_type.to_string()				)
										self.assertEqual(lowerCAmelCase				,   agent_type.to_raw()				)
										self.assertEqual(lowerCAmelCase				,   lowerCAmelCase				)





