
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      :							Tuple     	=    None


_snake_case      :							Dict     	=    logging.get_logger(__name__)

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

_snake_case      :							int     	=    {
    "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      :							str     	=    {
    "t5-small": 5_12,
    "t5-base": 5_12,
    "t5-large": 5_12,
    "t5-3b": 5_12,
    "t5-11b": 5_12,
}





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  List[str]				   =					VOCAB_FILES_NAMES
						UpperCamelCase      :  Any				   =					PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase      :  List[str]				   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase      :  List[Any]				   =					['''input_ids''', '''attention_mask''']
						UpperCamelCase      :  Optional[Any]				   =					TaTokenizer

						UpperCamelCase      :  List[int]				   =					[]





						def __init__(			self							:Union[str, Any]		,    lowerCAmelCase__							:List[Any]=None		,    lowerCAmelCase__							:Any=None		,    lowerCAmelCase__							:Union[str, Any]="</s>"		,    lowerCAmelCase__							:int="<unk>"		,    lowerCAmelCase__							:Tuple="<pad>"		,    lowerCAmelCase__							:Tuple=100		,    lowerCAmelCase__							:int=None		,    **lowerCAmelCase__							:List[Any]		,    )  ->Optional[Any]:
										# Add extra_ids to the special token list
										if extra_ids > 0 and additional_special_tokens is None:
														lowercase         =      [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 special tokens
														lowercase         =      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"     )

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

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





						@staticmethod
						def        SCREAMING_SNAKE_CASE(			lowerCAmelCase__							:Tuple		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:int     )  ->str:
										if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
														lowercase         =      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."		,    lowerCAmelCase__		,    )

										return max_model_length





						def        SCREAMING_SNAKE_CASE(			self							:str		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[str] = 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(lowerCAmelCase__     ):
														logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
														return
										lowercase         =      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__     ):
														copyfile(self.vocab_file		,    lowerCAmelCase__     )
														logger.info(F'''Copy vocab file to {out_vocab_file}'''     )

										return (out_vocab_file,)





						def        SCREAMING_SNAKE_CASE(			self							:List[str]		,    lowerCAmelCase__							:List[int]		,    lowerCAmelCase__							:Optional[List[int]] = None     )  ->List[int]:
										lowercase         =      token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
														return self.prefix_tokens + token_ids_a
										else:
														lowercase         =      token_ids_a + [self.eos_token_id]
														return self.prefix_tokens + token_ids_a + token_ids_a





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:List[int]		,    lowerCAmelCase__							:Optional[List[int]] = None     )  ->List[int]:
										lowercase         =      [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        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->List[Any]:
										return list(
										    set(filter(lambda lowerCAmelCase__     : bool(re.search(r"<extra_id_\d+>"		,    lowerCAmelCase__     )     ) is not None		,    self.additional_special_tokens     )     )     )





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->str:
										return [self.convert_tokens_to_ids(lowerCAmelCase__     ) for token in self.get_sentinel_tokens()]


import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    Adafactor,
						    AdamW,
						    get_constant_schedule,
						    get_constant_schedule_with_warmup,
						    get_cosine_schedule_with_warmup,
						    get_cosine_with_hard_restarts_schedule_with_warmup,
						    get_inverse_sqrt_schedule,
						    get_linear_schedule_with_warmup,
						    get_polynomial_decay_schedule_with_warmup,
						)

def  __snake_case							(	__magic_name__		,    __magic_name__=10						):







				'''simple docstring'''


				lowercase         =      []
				for _ in range(__magic_name__						):
								lrs.append(scheduler.get_lr()[0]						)
								scheduler.step()
				return lrs

def  __snake_case							(	__magic_name__		,    __magic_name__=10						):







				'''simple docstring'''


				lowercase         =      []
				for step in range(__magic_name__						):
								lrs.append(scheduler.get_lr()[0]						)
								scheduler.step()
								if step == num_steps // 2:
												with tempfile.TemporaryDirectory() as tmpdirname:
																lowercase         =      os.path.join(__magic_name__		,    "schedule.bin"						)
																torch.save(scheduler.state_dict()		,    __magic_name__						)

																lowercase         =      torch.load(__magic_name__						)
																scheduler.load_state_dict(__magic_name__						)
				return lrs





@require_torch
class   UpperCamelCase_      (      unittest.TestCase		):







						'''simple docstring'''



						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:List[str]		,    lowerCAmelCase__							:Tuple		,    lowerCAmelCase__							:Dict     )  ->Tuple:
										self.assertEqual(len(lowerCAmelCase__     )		,    len(lowerCAmelCase__     )     )
										for a, b in zip(lowerCAmelCase__		,    lowerCAmelCase__     ):
														self.assertAlmostEqual(lowerCAmelCase__		,    lowerCAmelCase__		,    delta=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Dict:
										lowercase         =      torch.tensor([0.1, -0.2, -0.1]		,    requires_grad=lowerCAmelCase__     )
										lowercase         =      torch.tensor([0.4, 0.2, -0.5]     )
										lowercase         =      nn.MSELoss()
										# No warmup, constant schedule, no gradient clipping
										lowercase         =      AdamW(params=[w]		,    lr=2E-1		,    weight_decay=0.0     )
										for _ in range(100     ):
														lowercase         =      criterion(lowerCAmelCase__		,    lowerCAmelCase__     )
														loss.backward()
														optimizer.step()
														w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
														w.grad.zero_()
										self.assertListAlmostEqual(w.tolist()		,    [0.4, 0.2, -0.5]		,    tol=1E-2     )





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->List[Any]:
										lowercase         =      torch.tensor([0.1, -0.2, -0.1]		,    requires_grad=lowerCAmelCase__     )
										lowercase         =      torch.tensor([0.4, 0.2, -0.5]     )
										lowercase         =      nn.MSELoss()
										# No warmup, constant schedule, no gradient clipping
										lowercase         =      Adafactor(
										    params=[w]		,    lr=1E-2		,    eps=(1E-3_0, 1E-3)		,    clip_threshold=1.0		,    decay_rate=-0.8		,    betaa=lowerCAmelCase__		,    weight_decay=0.0		,    relative_step=lowerCAmelCase__		,    scale_parameter=lowerCAmelCase__		,    warmup_init=lowerCAmelCase__		,    )
										for _ in range(1000     ):
														lowercase         =      criterion(lowerCAmelCase__		,    lowerCAmelCase__     )
														loss.backward()
														optimizer.step()
														w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
														w.grad.zero_()
										self.assertListAlmostEqual(w.tolist()		,    [0.4, 0.2, -0.5]		,    tol=1E-2     )





@require_torch
class   UpperCamelCase_      (      unittest.TestCase		):







						'''simple docstring'''



						UpperCamelCase      :  Any				   =					nn.Linear(50		,    50		) if is_torch_available() else None
						UpperCamelCase      :  Union[str, Any]				   =					AdamW(m.parameters()		,    lr=10.0		) if is_torch_available() else None
						UpperCamelCase      :  int				   =					10





						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:Any		,    lowerCAmelCase__							:Any=None     )  ->Dict:
										self.assertEqual(len(lowerCAmelCase__     )		,    len(lowerCAmelCase__     )     )
										for a, b in zip(lowerCAmelCase__		,    lowerCAmelCase__     ):
														self.assertAlmostEqual(lowerCAmelCase__		,    lowerCAmelCase__		,    delta=lowerCAmelCase__		,    msg=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->str:
										lowercase         =      {"num_warmup_steps": 2, "num_training_steps": 10}
										# schedulers doct format
										# function: (sched_args_dict, expected_learning_rates)
										lowercase         =      {
										    get_constant_schedule: ({}, [10.0] * self.num_steps),
										    get_constant_schedule_with_warmup: (
										        {"num_warmup_steps": 4},
										        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
										    ),
										    get_linear_schedule_with_warmup: (
										        {**common_kwargs},
										        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
										    ),
										    get_cosine_schedule_with_warmup: (
										        {**common_kwargs},
										        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
										    ),
										    get_cosine_with_hard_restarts_schedule_with_warmup: (
										        {**common_kwargs, "num_cycles": 2},
										        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
										    ),
										    get_polynomial_decay_schedule_with_warmup: (
										        {**common_kwargs, "power": 2.0, "lr_end": 1E-7},
										        [0.0, 5.0, 10.0, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
										    ),
										    get_inverse_sqrt_schedule: (
										        {"num_warmup_steps": 2},
										        [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
										    ),
										}

										for scheduler_func, data in scheds.items():
														lowercase     ,	lowercase         =      data

														lowercase         =      scheduler_func(self.optimizer		,    **lowerCAmelCase__     )
														self.assertEqual(len([scheduler.get_lr()[0]]     )		,    1     )
														lowercase         =      unwrap_schedule(lowerCAmelCase__		,    self.num_steps     )
														self.assertListAlmostEqual(
														    lowerCAmelCase__		,    lowerCAmelCase__		,    tol=1E-2		,    msg=F'''failed for {scheduler_func} in normal scheduler'''		,    )

														lowercase         =      scheduler_func(self.optimizer		,    **lowerCAmelCase__     )
														if scheduler_func.__name__ != "get_constant_schedule":
																		LambdaScheduleWrapper.wrap_scheduler(lowerCAmelCase__     )  # wrap to test picklability of the schedule
														lowercase         =      unwrap_and_save_reload_schedule(lowerCAmelCase__		,    self.num_steps     )
														self.assertListEqual(lowerCAmelCase__		,    lowerCAmelCase__		,    msg=F'''failed for {scheduler_func} in save and reload'''     )





class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:List[str]		,    lowerCAmelCase__							:int     )  ->Optional[int]:
										lowercase         =      fn





						def __call__(			self							:Optional[Any]		,    *lowerCAmelCase__							:int		,    **lowerCAmelCase__							:List[str]     )  ->List[str]:
										return self.fn(*lowerCAmelCase__		,    **lowerCAmelCase__     )





						@classmethod
						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:Optional[Any]     )  ->str:
										lowercase         =      list(map(self		,    scheduler.lr_lambdas     )     )
