"""simple docstring"""






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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_nllb import NllbTokenizer
else:
       __lowerCAmelCase					: Any        =None


__lowerCAmelCase					: List[str]        =logging.get_logger(__name__)


__lowerCAmelCase					: List[Any]        ={'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase					: List[str]        ={
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'
        ),
    },
}

__lowerCAmelCase					: int        ={
    'facebook/nllb-large-en-ro': 1_0_2_4,
    'facebook/nllb-200-distilled-600M': 1_0_2_4,
}

# fmt: off
__lowerCAmelCase					: Optional[int]        =['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']





class 				_A		( a__			):
      snake_case__    :     Dict													=			VOCAB_FILES_NAMES
      snake_case__    :     List[str]													=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__    :     List[str]													=			PRETRAINED_VOCAB_FILES_MAP
      snake_case__    :     Optional[Any]													=			["""input_ids""", """attention_mask"""]
      snake_case__    :     List[Any]													=			NllbTokenizer

      snake_case__    :     List[int]													=			[]
      snake_case__    :     List[int]													=			[]

      def __init__(		self	, __lowerCAmelCase=None	, __lowerCAmelCase=None	, __lowerCAmelCase="<s>"	, __lowerCAmelCase="</s>"	, __lowerCAmelCase="</s>"	, __lowerCAmelCase="<s>"	, __lowerCAmelCase="<unk>"	, __lowerCAmelCase="<pad>"	, __lowerCAmelCase="<mask>"	, __lowerCAmelCase=None	, __lowerCAmelCase=None	, __lowerCAmelCase=None	, __lowerCAmelCase=False	, **__lowerCAmelCase	, ):







            """simple docstring"""



            lowercase														=			AddedToken(lowercase__	, lstrip=lowercase__	, rstrip=lowercase__		) if isinstance(lowercase__	, lowercase__		) else mask_token
            lowercase														=			legacy_behaviour
            super().__init__(
                vocab_file=lowercase__	, tokenizer_file=lowercase__	, bos_token=lowercase__	, eos_token=lowercase__	, sep_token=lowercase__	, cls_token=lowercase__	, unk_token=lowercase__	, pad_token=lowercase__	, mask_token=lowercase__	, src_lang=lowercase__	, tgt_lang=lowercase__	, additional_special_tokens=lowercase__	, legacy_behaviour=lowercase__	, **lowercase__	, )

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

            lowercase														=			FAIRSEQ_LANGUAGE_CODES.copy()

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

            self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens}		)
            lowercase														=			{
                lang_code: self.convert_tokens_to_ids(lowercase__		) for lang_code in FAIRSEQ_LANGUAGE_CODES
            }

            lowercase														=			src_lang if src_lang is not None else '''eng_Latn'''
            lowercase														=			self.convert_tokens_to_ids(self._src_lang		)
            lowercase														=			tgt_lang
            self.set_src_lang_special_tokens(self._src_lang		)

      @property
      def        A__ (		self		):







            """simple docstring"""



            return self._src_lang

      @src_lang.setter
      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			new_src_lang
            self.set_src_lang_special_tokens(self._src_lang		)

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = None		):







            """simple docstring"""



            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        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = None		):







            """simple docstring"""



            lowercase														=			[self.sep_token_id]
            lowercase														=			[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        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		):







            """simple docstring"""



            if src_lang is None or tgt_lang is None:
                  raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""		)
            lowercase														=			src_lang
            lowercase														=			self(lowercase__	, add_special_tokens=lowercase__	, return_tensors=lowercase__	, **lowercase__		)
            lowercase														=			self.convert_tokens_to_ids(lowercase__		)
            lowercase														=			tgt_lang_id
            return inputs

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = "eng_Latn"	, __lowerCAmelCase = None	, __lowerCAmelCase = "fra_Latn"	, **__lowerCAmelCase	, ):







            """simple docstring"""



            lowercase														=			src_lang
            lowercase														=			tgt_lang
            return super().prepare_seqaseq_batch(lowercase__	, lowercase__	, **lowercase__		)

      def        A__ (		self		):







            """simple docstring"""



            return self.set_src_lang_special_tokens(self.src_lang		)

      def        A__ (		self		):







            """simple docstring"""



            return self.set_tgt_lang_special_tokens(self.tgt_lang		)

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.convert_tokens_to_ids(lowercase__		)

            if self.legacy_behaviour:
                  lowercase														=			[]
                  lowercase														=			[self.eos_token_id, self.cur_lang_code]
            else:
                  lowercase														=			[self.cur_lang_code]
                  lowercase														=			[self.eos_token_id]

            lowercase														=			self.convert_ids_to_tokens(self.prefix_tokens		)
            lowercase														=			self.convert_ids_to_tokens(self.suffix_tokens		)

            lowercase														=			processors.TemplateProcessing(
                single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str	, pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str	, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	, self.prefix_tokens + self.suffix_tokens		)		)	, )

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.convert_tokens_to_ids(lowercase__		)
            if self.legacy_behaviour:
                  lowercase														=			[]
                  lowercase														=			[self.eos_token_id, self.cur_lang_code]
            else:
                  lowercase														=			[self.cur_lang_code]
                  lowercase														=			[self.eos_token_id]

            lowercase														=			self.convert_ids_to_tokens(self.prefix_tokens		)
            lowercase														=			self.convert_ids_to_tokens(self.suffix_tokens		)

            lowercase														=			processors.TemplateProcessing(
                single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str	, pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str	, special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	, self.prefix_tokens + self.suffix_tokens		)		)	, )







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase = 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(lowercase__		):
                  logger.error(f'Vocabulary path ({save_directory}) should be a directory.'		)
                  return
            lowercase														=			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__		)

            return (out_vocab_file,)




"""simple docstring"""






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 				_A		( lowerCAmelCase			):
      snake_case__    :     Union[str, Any]													=			(IPNDMScheduler,)
      snake_case__    :     List[str]													=			(('num_inference_steps', 50),)

      def        A__ (		self	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			{"""num_train_timesteps""": 1000}
            config.update(**__lowerCAmelCase		)
            return config

      def        A__ (		self	, __lowerCAmelCase=0	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)
            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)
            lowercase														=			self.dummy_sample
            lowercase														=			0.1 * sample
            lowercase														=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

            for scheduler_class in self.scheduler_classes:
                  lowercase														=			self.get_scheduler_config(**__lowerCAmelCase		)
                  lowercase														=			scheduler_class(**__lowerCAmelCase		)
                  scheduler.set_timesteps(__lowerCAmelCase		)
                  # copy over dummy past residuals
                  lowercase														=			dummy_past_residuals[:]

                  if time_step is None:
                        lowercase														=			scheduler.timesteps[len(scheduler.timesteps		) // 2]

                  with tempfile.TemporaryDirectory() as tmpdirname:
                        scheduler.save_config(__lowerCAmelCase		)
                        lowercase														=			scheduler_class.from_pretrained(__lowerCAmelCase		)
                        new_scheduler.set_timesteps(__lowerCAmelCase		)
                        # copy over dummy past residuals
                        lowercase														=			dummy_past_residuals[:]

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

                  assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

                  assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

      def        A__ (		self		):







            """simple docstring"""



            pass

      def        A__ (		self	, __lowerCAmelCase=0	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)
            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)
            lowercase														=			self.dummy_sample
            lowercase														=			0.1 * sample
            lowercase														=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

            for scheduler_class in self.scheduler_classes:
                  lowercase														=			self.get_scheduler_config()
                  lowercase														=			scheduler_class(**__lowerCAmelCase		)
                  scheduler.set_timesteps(__lowerCAmelCase		)

                  # copy over dummy past residuals (must be after setting timesteps)
                  lowercase														=			dummy_past_residuals[:]

                  if time_step is None:
                        lowercase														=			scheduler.timesteps[len(scheduler.timesteps		) // 2]

                  with tempfile.TemporaryDirectory() as tmpdirname:
                        scheduler.save_config(__lowerCAmelCase		)
                        lowercase														=			scheduler_class.from_pretrained(__lowerCAmelCase		)
                        # copy over dummy past residuals
                        new_scheduler.set_timesteps(__lowerCAmelCase		)

                        # copy over dummy past residual (must be after setting timesteps)
                        lowercase														=			dummy_past_residuals[:]

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

                  assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			new_scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

                  assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

      def        A__ (		self	, **__lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.scheduler_classes[0]
            lowercase														=			self.get_scheduler_config(**__lowerCAmelCase		)
            lowercase														=			scheduler_class(**__lowerCAmelCase		)

            lowercase														=			10
            lowercase														=			self.dummy_model()
            lowercase														=			self.dummy_sample_deter
            scheduler.set_timesteps(__lowerCAmelCase		)

            for i, t in enumerate(scheduler.timesteps		):
                  lowercase														=			model(__lowerCAmelCase	, __lowerCAmelCase		)
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		).prev_sample

            for i, t in enumerate(scheduler.timesteps		):
                  lowercase														=			model(__lowerCAmelCase	, __lowerCAmelCase		)
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		).prev_sample

            return sample

      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			dict(self.forward_default_kwargs		)

            lowercase														=			kwargs.pop("""num_inference_steps"""	, __lowerCAmelCase		)

            for scheduler_class in self.scheduler_classes:
                  lowercase														=			self.get_scheduler_config()
                  lowercase														=			scheduler_class(**__lowerCAmelCase		)

                  lowercase														=			self.dummy_sample
                  lowercase														=			0.1 * sample

                  if num_inference_steps is not None and hasattr(__lowerCAmelCase	, """set_timesteps"""		):
                        scheduler.set_timesteps(__lowerCAmelCase		)
                  elif num_inference_steps is not None and not hasattr(__lowerCAmelCase	, """set_timesteps"""		):
                        lowercase														=			num_inference_steps

                  # copy over dummy past residuals (must be done after set_timesteps)
                  lowercase														=			[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
                  lowercase														=			dummy_past_residuals[:]

                  lowercase														=			scheduler.timesteps[5]
                  lowercase														=			scheduler.timesteps[6]

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

                  self.assertEqual(output_a.shape	, sample.shape		)
                  self.assertEqual(output_a.shape	, output_a.shape		)

                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample
                  lowercase														=			scheduler.step(__lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, **__lowerCAmelCase		).prev_sample

                  self.assertEqual(output_a.shape	, sample.shape		)
                  self.assertEqual(output_a.shape	, output_a.shape		)

      def        A__ (		self		):







            """simple docstring"""



            for timesteps in [100, 1000]:
                  self.check_over_configs(num_train_timesteps=__lowerCAmelCase	, time_step=__lowerCAmelCase		)

      def        A__ (		self		):







            """simple docstring"""



            for t, num_inference_steps in zip([1, 5, 10]	, [10, 50, 100]		):
                  self.check_over_forward(num_inference_steps=__lowerCAmelCase	, time_step=__lowerCAmelCase		)







      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			self.full_loop()
            lowercase														=			torch.mean(torch.abs(__lowerCAmelCase		)		)

            assert abs(result_mean.item() - 254_0529		) < 10



