



import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE           =  """▁"""

__SCREAMING_SNAKE_CASE           =  {
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__SCREAMING_SNAKE_CASE           =  {
    """vocab_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json""",
    },
    """spm_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_config_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json""",
    },
}

__SCREAMING_SNAKE_CASE           =  {
    """facebook/m2m100_418M""": 1024,
}

# fmt: off
__SCREAMING_SNAKE_CASE           =  {
    """m2m100""": ["""af""", """am""", """ar""", """ast""", """az""", """ba""", """be""", """bg""", """bn""", """br""", """bs""", """ca""", """ceb""", """cs""", """cy""", """da""", """de""", """el""", """en""", """es""", """et""", """fa""", """ff""", """fi""", """fr""", """fy""", """ga""", """gd""", """gl""", """gu""", """ha""", """he""", """hi""", """hr""", """ht""", """hu""", """hy""", """id""", """ig""", """ilo""", """is""", """it""", """ja""", """jv""", """ka""", """kk""", """km""", """kn""", """ko""", """lb""", """lg""", """ln""", """lo""", """lt""", """lv""", """mg""", """mk""", """ml""", """mn""", """mr""", """ms""", """my""", """ne""", """nl""", """no""", """ns""", """oc""", """or""", """pa""", """pl""", """ps""", """pt""", """ro""", """ru""", """sd""", """si""", """sk""", """sl""", """so""", """sq""", """sr""", """ss""", """su""", """sv""", """sw""", """ta""", """th""", """tl""", """tn""", """tr""", """uk""", """ur""", """uz""", """vi""", """wo""", """xh""", """yi""", """yo""", """zh""", """zu"""],
    """wmt21""": ["""en""", """ha""", """is""", """ja""", """cs""", """ru""", """zh""", """de"""]
}

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  VOCAB_FILES_NAMES
						a__   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a__   =  PRETRAINED_VOCAB_FILES_MAP
						a__   =  ["input_ids", "attention_mask"]

						a__   =  []
						a__   =  []







						def __init__(	self      :			Dict	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			Tuple=None	,	__lowerCamelCase      :			int=None	,	__lowerCamelCase      :			Union[str, Any]="<s>"	,	__lowerCamelCase      :			int="</s>"	,	__lowerCamelCase      :			str="</s>"	,	__lowerCamelCase      :			int="<pad>"	,	__lowerCamelCase      :			List[str]="<unk>"	,	__lowerCamelCase      :			str="m2m100"	,	__lowerCamelCase      :			Optional[Dict[str, Any]] = None	,	__lowerCamelCase      :			int=8	,	**__lowerCamelCase      :			List[Any]	,	)      ->				None:
									A							:					List[Any]              =						{} if sp_model_kwargs is None else sp_model_kwargs

									A							:					Optional[Any]              =						language_codes
									A							:					Union[str, Any]              =						FAIRSEQ_LANGUAGE_CODES[language_codes]
									A							:					Optional[Any]              =						{lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}

									A							:					Union[str, Any]              =						kwargs.get("additional_special_tokens"	,	[] )
									kwargs["additional_special_tokens"] += [
									    self.get_lang_token(__lowerCamelCase )
									    for lang_code in fairseq_language_code
									    if self.get_lang_token(__lowerCamelCase ) not in kwargs["additional_special_tokens"]
									]

									super().__init__(
									    src_lang=__lowerCamelCase	,	tgt_lang=__lowerCamelCase	,	bos_token=__lowerCamelCase	,	eos_token=__lowerCamelCase	,	sep_token=__lowerCamelCase	,	unk_token=__lowerCamelCase	,	pad_token=__lowerCamelCase	,	language_codes=__lowerCamelCase	,	sp_model_kwargs=self.sp_model_kwargs	,	num_madeup_words=__lowerCamelCase	,	**__lowerCamelCase	,	)

									A							:					Dict              =						vocab_file
									A							:					List[str]              =						load_json(__lowerCamelCase )
									A							:					Optional[Any]              =						{v: k for k, v in self.encoder.items()}
									A							:					Any              =						spm_file
									A							:					List[Any]              =						load_spm(__lowerCamelCase	,	self.sp_model_kwargs )

									A							:					str              =						len(self.encoder )

									A							:					List[str]              =						{
									    self.get_lang_token(__lowerCamelCase ): self.encoder_size + i for i, lang_code in enumerate(__lowerCamelCase )
									}
									A							:					List[str]              =						{lang_code: self.encoder_size + i for i, lang_code in enumerate(__lowerCamelCase )}
									A							:					Optional[Any]              =						{v: k for k, v in self.lang_token_to_id.items()}

									A							:					List[str]              =						src_lang if src_lang is not None else "en"
									A							:					Union[str, Any]              =						tgt_lang
									A							:					Union[str, Any]              =						self.get_lang_id(self._src_lang )
									self.set_src_lang_special_tokens(self._src_lang )

									A							:					str              =						num_madeup_words







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				int:
									return len(self.encoder ) + len(self.lang_token_to_id )







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				str:
									return self._src_lang







						@src_lang.setter
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			str )      ->				None:
									A							:					str              =						new_src_lang
									self.set_src_lang_special_tokens(self._src_lang )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			str )      ->				List[str]:
									return self.sp_model.encode(__lowerCamelCase	,	out_type=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			str )      ->				Tuple:
									if token in self.lang_token_to_id:
												return self.lang_token_to_id[token]
									return self.encoder.get(__lowerCamelCase	,	self.encoder[self.unk_token] )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			int )      ->				str:
									if index in self.id_to_lang_token:
												return self.id_to_lang_token[index]
									return self.decoder.get(__lowerCamelCase	,	self.unk_token )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int]	,	__lowerCamelCase      :			Optional[Any] )      ->				List[Any]:
									A							:					Any              =						[]
									A							:					Union[str, Any]              =						""
									for token in tokens:
												# make sure that special tokens are not decoded using sentencepiece model
												if token in self.all_special_tokens:
															out_string += self.sp_model.decode(__lowerCamelCase ) + token
															A							:					Optional[int]              =						[]
												else:
															current_sub_tokens.append(__lowerCamelCase )
									out_string += self.sp_model.decode(__lowerCamelCase )
									return out_string.strip()







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

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

									A							:					str              =						[1] * len(self.prefix_tokens )
									A							:					Dict              =						[1] * len(self.suffix_tokens )
									if token_ids_a is None:
												return prefix_ones + ([0] * len(__lowerCamelCase )) + suffix_ones
									return prefix_ones + ([0] * len(__lowerCamelCase )) + ([0] * len(__lowerCamelCase )) + suffix_ones







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			List[int]	,	__lowerCamelCase      :			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      :			List[str] )      ->				Dict:
									A							:					List[Any]              =						{self.convert_ids_to_tokens(__lowerCamelCase ): i for i in range(self.vocab_size )}
									vocab.update(self.added_tokens_encoder )
									return vocab







						def __getstate__(	self      :			Optional[int] )      ->				Dict:
									A							:					Optional[Any]              =						self.__dict__.copy()
									A							:					List[Any]              =						None
									return state







						def __setstate__(	self      :			str	,	__lowerCamelCase      :			Dict )      ->				None:
									A							:					List[Any]              =						d

									# for backward compatibility
									if not hasattr(self	,	"sp_model_kwargs" ):
												A							:					List[str]              =						{}

									A							:					List[Any]              =						load_spm(self.spm_file	,	self.sp_model_kwargs )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			Optional[str] = None )      ->				Tuple[str]:
									A							:					List[str]              =						Path(__lowerCamelCase )
									if not save_dir.is_dir():
												raise OSError(F"""{save_directory} should be a directory""" )
									A							:					int              =						save_dir / (
									    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
									)
									A							:					Optional[int]              =						save_dir / (
									    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
									)

									save_json(self.encoder	,	__lowerCamelCase )

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

									return (str(__lowerCamelCase ), str(__lowerCamelCase ))







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			str = "en"	,	__lowerCamelCase      :			Optional[List[str]] = None	,	__lowerCamelCase      :			str = "ro"	,	**__lowerCamelCase      :			Tuple	,	)      ->				BatchEncoding:
									A							:					Dict              =						src_lang
									A							:					int              =						tgt_lang
									self.set_src_lang_special_tokens(self.src_lang )
									return super().prepare_seqaseq_batch(__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			Optional[str]	,	__lowerCamelCase      :			Optional[str]	,	**__lowerCamelCase      :			Dict )      ->				Optional[int]:
									if src_lang is None or tgt_lang is None:
												raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
									A							:					Any              =						src_lang
									A							:					Optional[int]              =						self(__lowerCamelCase	,	add_special_tokens=__lowerCamelCase	,	**__lowerCamelCase )
									A							:					int              =						self.get_lang_id(__lowerCamelCase )
									A							:					Any              =						tgt_lang_id
									return inputs







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Optional[int]:
									self.set_src_lang_special_tokens(self.src_lang )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Optional[int]:
									self.set_tgt_lang_special_tokens(self.tgt_lang )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	__lowerCamelCase      :			str )      ->				None:
									A							:					Optional[int]              =						self.get_lang_token(__lowerCamelCase )
									A							:					List[str]              =						self.lang_token_to_id[lang_token]
									A							:					str              =						[self.cur_lang_id]
									A							:					Tuple              =						[self.eos_token_id]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	__lowerCamelCase      :			str )      ->				None:
									A							:					List[Any]              =						self.get_lang_token(__lowerCamelCase )
									A							:					int              =						self.lang_token_to_id[lang_token]
									A							:					List[Any]              =						[self.cur_lang_id]
									A							:					Tuple              =						[self.eos_token_id]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	__lowerCamelCase      :			str )      ->				str:
									return self.lang_code_to_token[lang]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			str )      ->				int:
									A							:					Optional[Any]              =						self.get_lang_token(__lowerCamelCase )
									return self.lang_token_to_id[lang_token]







def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Dict              =						sentencepiece.SentencePieceProcessor(**_lowerCamelCase    )
			spm.Load(str(_lowerCamelCase    )    )
			return spm




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			with open(_lowerCamelCase ,    "r"    ) as f:
						return json.load(_lowerCamelCase    )






def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			with open(_lowerCamelCase ,    "w"    ) as f:
						json.dump(_lowerCamelCase ,    _lowerCamelCase ,    indent=2    )




import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
							import tensorflow as tf
							from tensorflow.python.eager import context
							from tensorflow.python.framework import ops

							from transformers import GradientAccumulator, create_optimizer

@require_tf
class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				List[Any]:
									A							:					List[Any]              =						GradientAccumulator()
									accumulator([tf.constant([1.0, 2.0] )] )
									accumulator([tf.constant([-2.0, 1.0] )] )
									accumulator([tf.constant([-1.0, 2.0] )] )
									with self.assertRaises(__lowerCamelCase ):
												accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] )
									self.assertEqual(accumulator.step	,	3 )
									self.assertEqual(len(accumulator.gradients )	,	1 )
									self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()	,	[-2.0, 5.0]	,	tol=1e-2 )
									accumulator.reset()
									self.assertEqual(accumulator.step	,	0 )
									self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()	,	[0.0, 0.0]	,	tol=1e-2 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				List[str]:
									A							:					Union[str, Any]              =						None
									ops.enable_eager_execution_internal()
									A							:					Tuple              =						tf.config.list_physical_devices("CPU" )
									if len(__lowerCamelCase ) == 1:
												tf.config.set_logical_device_configuration(
												    physical_devices[0]	,	[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
									A							:					Dict              =						tf.config.list_logical_devices(device_type="CPU" )
									A							:					List[str]              =						tf.distribute.MirroredStrategy(devices=devices[:2] )

									with strategy.scope():
												A							:					Optional[int]              =						GradientAccumulator()
												A							:					Tuple              =						tf.Variable([4.0, 3.0] )
												A       , A							:					List[Any]              =						create_optimizer(5e-5	,	10	,	5 )
												A							:					List[str]              =						tf.Variable([0.0, 0.0]	,	trainable=__lowerCamelCase )

									def accumulate_on_replica(__lowerCamelCase      :			Tuple ):
												accumulator([gradient] )

									def apply_on_replica():
												optimizer.apply_gradients(list(zip(accumulator.gradients	,	[variable] ) ) )

									@tf.function
									def accumulate(__lowerCamelCase      :			Any	,	__lowerCamelCase      :			Optional[int] ):
												with strategy.scope():
															A							:					int              =						strategy.experimental_local_results(__lowerCamelCase )
															local_variables[0].assign(__lowerCamelCase )
															local_variables[1].assign(__lowerCamelCase )
															strategy.run(__lowerCamelCase	,	args=(gradient_placeholder,) )

									@tf.function
									def apply_grad():
												with strategy.scope():
															strategy.run(__lowerCamelCase )

									def _check_local_values(__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			List[Any] ):
												A							:					Optional[int]              =						strategy.experimental_local_results(accumulator._gradients[0] )
												self.assertListAlmostEqual(values[0].value()	,	__lowerCamelCase	,	tol=1e-2 )
												self.assertListAlmostEqual(values[1].value()	,	__lowerCamelCase	,	tol=1e-2 )

									accumulate([1.0, 2.0]	,	[-1.0, 1.0] )
									accumulate([3.0, -1.0]	,	[-1.0, -1.0] )
									accumulate([-2.0, 2.0]	,	[3.0, -2.0] )
									self.assertEqual(accumulator.step	,	3 )
									_check_local_values([2.0, 3.0]	,	[1.0, -2.0] )
									apply_grad()
									self.assertListAlmostEqual(variable.value()	,	[4.0, 3.0]	,	tol=1e-2 )
									accumulator.reset()
									self.assertEqual(accumulator.step	,	0 )
									_check_local_values([0.0, 0.0]	,	[0.0, 0.0] )