





"""simple docstring"""


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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__						= logging.get_logger(__name__)

lowerCamelCase__						= "▁"

lowerCamelCase__						= {"vocab_file": "sentencepiece.bpe.model"}

lowerCamelCase__						= {
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

lowerCamelCase__						= {
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
lowerCamelCase__						= ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:List[str]						  =       VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__				:Any						  =       PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__				:str						  =       ["input_ids", "attention_mask"]

  SCREAMING_SNAKE_CASE__				:List[int]						  =       []
  SCREAMING_SNAKE_CASE__				:List[int]						  =       []

  def __init__(		self :							Dict       , __a :							str       , __a :							List[Any]="<s>"       , __a :							Optional[int]="</s>"       , __a :							Union[str, Any]="</s>"       , __a :							Optional[Any]="<s>"       , __a :							Dict="<unk>"       , __a :							Optional[Any]="<pad>"       , __a :							Union[str, Any]="<mask>"       , __a :							str=None       , __a :							Tuple=None       , __a :							Any=None       , __a :							Optional[Dict[str, Any]] = None       , __a :							str=None       , **__a :							Any       , )		->					Optional[int]:
        # Mask token behave like a normal word, i.e. include the space before it
        _UpperCamelCase   :						Optional[Any]    =							AddedToken(__a       , lstrip=__a       , rstrip=__a						) if isinstance(__a       , __a						) else mask_token

        _UpperCamelCase   :						Union[str, Any]    =							{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=__a       , eos_token=__a       , unk_token=__a       , sep_token=__a       , cls_token=__a       , pad_token=__a       , mask_token=__a       , tokenizer_file=__a       , src_lang=__a       , tgt_lang=__a       , additional_special_tokens=__a       , sp_model_kwargs=self.sp_model_kwargs       , **__a       , )

        _UpperCamelCase   :						Any    =							spm.SentencePieceProcessor(**self.sp_model_kwargs						)
        self.sp_model.Load(str(__a						)						)
        _UpperCamelCase   :						List[Any]    =							vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # Mimic fairseq token-to-id alignment for the first 4 token
        _UpperCamelCase   :						Tuple    =							{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

        # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
        _UpperCamelCase   :						Union[str, Any]    =							1

        _UpperCamelCase   :						Dict    =							len(self.sp_model						)
        _UpperCamelCase   :						Optional[Any]    =							{
            code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__a						)
        }
        _UpperCamelCase   :						Optional[Any]    =							{v: k for k, v in self.lang_code_to_id.items()}
        _UpperCamelCase   :						Optional[Any]    =							len(self.sp_model						) + len(self.lang_code_to_id						) + self.fairseq_offset

        self.fairseq_tokens_to_ids.update(self.lang_code_to_id						)
        _UpperCamelCase   :						int    =							{v: k for k, v in self.fairseq_tokens_to_ids.items()}
        _UpperCamelCase   :						Union[str, Any]    =							list(self.lang_code_to_id.keys()						)

        if additional_special_tokens is not None:
              # Only add those special tokens if they are not already there.
              self._additional_special_tokens.extend(
                  [t for t in additional_special_tokens if t not in self._additional_special_tokens]						)

        _UpperCamelCase   :						List[Any]    =							src_lang if src_lang is not None else "en_XX"
        _UpperCamelCase   :						Dict    =							self.lang_code_to_id[self._src_lang]
        _UpperCamelCase   :						Any    =							tgt_lang
        self.set_src_lang_special_tokens(self._src_lang						)

  def __getstate__(		self :							str						)		->					int:
        _UpperCamelCase   :						int    =							self.__dict__.copy()
        _UpperCamelCase   :						Union[str, Any]    =							None
        _UpperCamelCase   :						Optional[Any]    =							self.sp_model.serialized_model_proto()
        return state

  def __setstate__(		self :							str       , __a :							Any						)		->					List[str]:
        _UpperCamelCase   :						Any    =							d

        # for backward compatibility
        if not hasattr(self       , "sp_model_kwargs"						):
              _UpperCamelCase   :						str    =							{}

        _UpperCamelCase   :						Optional[int]    =							spm.SentencePieceProcessor(**self.sp_model_kwargs						)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple						)		->					int:
        return len(self.sp_model						) + len(self.lang_code_to_id						) + self.fairseq_offset + 1  # Plus 1 for the mask token

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Dict						)		->					str:
        return self._src_lang

  @src_lang.setter
  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple       , __a :							str						)		->					None:
        _UpperCamelCase   :						Optional[int]    =							new_src_lang
        self.set_src_lang_special_tokens(self._src_lang						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple       , __a :							List[int]       , __a :							Optional[List[int]] = None       , __a :							bool = False						)		->					List[int]:

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

        _UpperCamelCase   :						List[str]    =							[1] * len(self.prefix_tokens						)
        _UpperCamelCase   :						Tuple    =							[1] * len(self.suffix_tokens						)
        if token_ids_a is None:
              return prefix_ones + ([0] * len(__a						)) + suffix_ones
        return prefix_ones + ([0] * len(__a						)) + ([0] * len(__a						)) + suffix_ones

  def  __SCREAMING_SNAKE_CASE					(		self :							str       , __a :							List[int]       , __a :							Optional[List[int]] = None						)		->					List[int]:
        if token_ids_a is None:
              return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

  def  __SCREAMING_SNAKE_CASE					(		self :							int       , __a :							List[int]       , __a :							Optional[List[int]] = None						)		->					List[int]:
        _UpperCamelCase   :						Tuple    =							[self.sep_token_id]
        _UpperCamelCase   :						List[str]    =							[self.cls_token_id]

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

  def  __SCREAMING_SNAKE_CASE					(		self :							Dict       , __a :							Dict       , __a :							str       , __a :							Optional[str]       , __a :							Optional[str]       , **__a :							Any						)		->					Tuple:
        if src_lang is None or tgt_lang is None:
              raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"						)
        _UpperCamelCase   :						Optional[Any]    =							src_lang
        _UpperCamelCase   :						Union[str, Any]    =							self(__a       , add_special_tokens=__a       , return_tensors=__a       , **__a						)
        _UpperCamelCase   :						str    =							self.convert_tokens_to_ids(__a						)
        _UpperCamelCase   :						str    =							tgt_lang_id
        return inputs

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					Tuple:
        _UpperCamelCase   :						Union[str, Any]    =							{self.convert_ids_to_tokens(__a						): i for i in range(self.vocab_size						)}
        vocab.update(self.added_tokens_encoder						)
        return vocab

  def  __SCREAMING_SNAKE_CASE					(		self :							Any       , __a :							str						)		->					List[str]:
        return self.sp_model.encode(__a       , out_type=__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]       , __a :							Dict						)		->					List[Any]:
        if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
        _UpperCamelCase   :						List[str]    =							self.sp_model.PieceToId(__a						)

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

  def  __SCREAMING_SNAKE_CASE					(		self :							Dict       , __a :							Any						)		->					List[str]:
        if index in self.fairseq_ids_to_tokens:
              return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset						)

  def  __SCREAMING_SNAKE_CASE					(		self :							str       , __a :							Optional[int]						)		->					Tuple:
        _UpperCamelCase   :						List[str]    =							"".join(__a						).replace(__a       , " "						).strip()
        return out_string

  def  __SCREAMING_SNAKE_CASE					(		self :							int       , __a :							str       , __a :							Optional[str] = None						)		->					Tuple[str]:
        if not os.path.isdir(__a						):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
              return
        _UpperCamelCase   :						List[Any]    =							os.path.join(
            __a       , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

        if os.path.abspath(self.vocab_file						) != os.path.abspath(__a						) and os.path.isfile(self.vocab_file						):
              copyfile(self.vocab_file       , __a						)
        elif not os.path.isfile(self.vocab_file						):
              with open(__a       , "wb"						) as fi:
                    _UpperCamelCase   :						Dict    =							self.sp_model.serialized_model_proto()
                    fi.write(__a						)

        return (out_vocab_file,)

  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]       , __a :							List[str]       , __a :							str = "en_XX"       , __a :							Optional[List[str]] = None       , __a :							str = "ro_RO"       , **__a :							str       , )		->					BatchEncoding:
        _UpperCamelCase   :						str    =							src_lang
        _UpperCamelCase   :						List[str]    =							tgt_lang
        return super().prepare_seqaseq_batch(__a       , __a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					Optional[int]:
        return self.set_src_lang_special_tokens(self.src_lang						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					Optional[int]:
        return self.set_tgt_lang_special_tokens(self.tgt_lang						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							List[Any]						)		->					None:
        _UpperCamelCase   :						str    =							self.lang_code_to_id[src_lang]
        _UpperCamelCase   :						Union[str, Any]    =							[]
        _UpperCamelCase   :						List[str]    =							[self.eos_token_id, self.cur_lang_code]


  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]       , __a :							str						)		->					None:
        _UpperCamelCase   :						List[str]    =							self.lang_code_to_id[lang]
        _UpperCamelCase   :						Optional[int]    =							[]
        _UpperCamelCase   :						Optional[Any]    =							[self.eos_token_id, self.cur_lang_code]







"""simple docstring"""


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        lowercase__     (       lowercase_		,lowercase_=10      )       ->		Tuple:




      """simple docstring"""

      _UpperCamelCase   :						str    =							[]
      for _ in range(lowercase_      ):
            lrs.append(scheduler.get_lr()[0]      )
            scheduler.step()
      return lrs




def        lowercase__     (       lowercase_		,lowercase_=10      )       ->		str:




      """simple docstring"""

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

                        _UpperCamelCase   :						Optional[int]    =							torch.load(lowercase_      )
                        scheduler.load_state_dict(lowercase_      )
      return lrs



@require_torch
class   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


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

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					str:
        _UpperCamelCase   :						Optional[int]    =							torch.tensor([0.1, -0.2, -0.1]       , requires_grad=__a						)
        _UpperCamelCase   :						List[Any]    =							torch.tensor([0.4, 0.2, -0.5]						)
        _UpperCamelCase   :						Any    =							nn.MSELoss()
        # No warmup, constant schedule, no gradient clipping
        _UpperCamelCase   :						Dict    =							AdamW(params=[w]       , lr=2e-1       , weight_decay=0.0						)
        for _ in range(100						):
              _UpperCamelCase   :						List[Any]    =							criterion(__a       , __a						)
              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 :							List[str]						)		->					Tuple:
        _UpperCamelCase   :						List[Any]    =							torch.tensor([0.1, -0.2, -0.1]       , requires_grad=__a						)
        _UpperCamelCase   :						Any    =							torch.tensor([0.4, 0.2, -0.5]						)
        _UpperCamelCase   :						List[str]    =							nn.MSELoss()
        # No warmup, constant schedule, no gradient clipping
        _UpperCamelCase   :						Dict    =							Adafactor(
            params=[w]       , lr=1e-2       , eps=(1e-3_0, 1e-3)       , clip_threshold=1.0       , decay_rate=-0.8       , betaa=__a       , weight_decay=0.0       , relative_step=__a       , scale_parameter=__a       , warmup_init=__a       , )
        for _ in range(1000						):
              _UpperCamelCase   :						Optional[Any]    =							criterion(__a       , __a						)
              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   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       nn.Linear(50		,	50 ) if is_torch_available() else None
  SCREAMING_SNAKE_CASE__				:List[Any]						  =       AdamW(m.parameters()		,	lr=10.0 ) if is_torch_available() else None
  SCREAMING_SNAKE_CASE__				:List[str]						  =       10

  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]       , __a :							Tuple       , __a :							List[Any]       , __a :							Dict       , __a :							str=None						)		->					List[Any]:
        self.assertEqual(len(__a						)       , len(__a						)						)
        for a, b in zip(__a       , __a						):
              self.assertAlmostEqual(__a       , __a       , delta=__a       , msg=__a						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]						)		->					List[Any]:
        _UpperCamelCase   :						Union[str, Any]    =							{"num_warmup_steps": 2, "num_training_steps": 10}
        # schedulers doct format
        # function: (sched_args_dict, expected_learning_rates)
        _UpperCamelCase   :						Optional[Any]    =							{
            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():
              _UpperCamelCase,	_UpperCamelCase   :						List[str]    =							data

              _UpperCamelCase   :						List[str]    =							scheduler_func(self.optimizer       , **__a						)
              self.assertEqual(len([scheduler.get_lr()[0]]						)       , 1						)
              _UpperCamelCase   :						Optional[int]    =							unwrap_schedule(__a       , self.num_steps						)
              self.assertListAlmostEqual(
                  __a       , __a       , tol=1e-2       , msg=F'''failed for {scheduler_func} in normal scheduler'''       , )

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





class   __SCREAMING_SNAKE_CASE     :







  '''simple docstring'''


  def __init__(		self :							List[Any]       , __a :							Optional[int]						)		->					Tuple:
        _UpperCamelCase   :						Any    =							fn

  def __call__(		self :							Tuple       , *__a :							Optional[Any]       , **__a :							str						)		->					Any:
        return self.fn(*__a       , **__a						)


  @classmethod
  def  __SCREAMING_SNAKE_CASE					(		self :							Any       , __a :							Any						)		->					Tuple:
        _UpperCamelCase   :						Any    =							list(map(self       , scheduler.lr_lambdas						)						)
