





'''simple docstring'''


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
		import os

		# The slow tests are often failing with OOM error on GPU
		# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
		# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
		lowerCAmelCase_   :   Union[str, Any]       =					"platform"

		import jax
		import jax.numpy as jnp

		from transformers import BlenderbotTokenizer
		from transformers.models.blenderbot.modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    shift_tokens_right,
		)







def    _lowerCamelCase  (__lowerCamelCase  :     str					,  __lowerCamelCase  :     Any					,  __lowerCamelCase  :     int=None					,  __lowerCamelCase  :     List[str]=None					,  __lowerCamelCase  :     Optional[int]=None					,  __lowerCamelCase  :     Dict=None					,  __lowerCamelCase  :     Optional[Any]=None					,  __lowerCamelCase  :     List[str]=None					,  )   ->    Tuple:
			if attention_mask is None:
						a__     = np.where(input_ids != config.pad_token_id					,  1					,  0     )
			if decoder_attention_mask is None:
						a__     = np.where(decoder_input_ids != config.pad_token_id					,  1					,  0     )
			if head_mask is None:
						a__     = np.ones((config.encoder_layers, config.encoder_attention_heads)     )
			if decoder_head_mask is None:
						a__     = np.ones((config.decoder_layers, config.decoder_attention_heads)     )
			if cross_attn_head_mask is None:
						a__     = np.ones((config.decoder_layers, config.decoder_attention_heads)     )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": attention_mask,
			}






class       UpperCamelCase__  :







	def __init__(				self			:   Any , lowerCamelCase			:   List[str] , lowerCamelCase			:   Tuple=1_3 , lowerCamelCase			:   Tuple=7 , lowerCamelCase			:   Dict=True , lowerCamelCase			:   Tuple=False , lowerCamelCase			:   Optional[Any]=9_9 , lowerCamelCase			:   Optional[Any]=1_6 , lowerCamelCase			:   str=2 , lowerCamelCase			:   Dict=4 , lowerCamelCase			:   List[Any]=4 , lowerCamelCase			:   Union[str, Any]="gelu" , lowerCamelCase			:   Any=0.1 , lowerCamelCase			:   Optional[Any]=0.1 , lowerCamelCase			:   Optional[Any]=3_2 , lowerCamelCase			:   Union[str, Any]=2 , lowerCamelCase			:   Tuple=1 , lowerCamelCase			:   Dict=0 , lowerCamelCase			:   List[str]=0.02 , ):




				'''simple docstring'''
				a__     = parent
				a__     = batch_size
				a__     = seq_length
				a__     = is_training
				a__     = use_labels
				a__     = vocab_size
				a__     = hidden_size
				a__     = num_hidden_layers
				a__     = num_attention_heads
				a__     = intermediate_size
				a__     = hidden_act
				a__     = hidden_dropout_prob
				a__     = attention_probs_dropout_prob
				a__     = max_position_embeddings
				a__     = eos_token_id
				a__     = pad_token_id
				a__     = bos_token_id
				a__     = initializer_range







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				a__     = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size		) , 3 , self.vocab_size		)
				a__     = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa		)) , -1		)

				a__     = shift_tokens_right(lowerCamelCase , 1 , 2		)

				a__     = BlenderbotConfig(
				    vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=lowerCamelCase , )
				a__     = prepare_blenderbot_inputs_dict(lowerCamelCase , lowerCamelCase , lowerCamelCase		)
				return config, inputs_dict







	def 						__a   (				self			:   str		):




				'''simple docstring'''
				a__   ,				a__     = self.prepare_config_and_inputs()
				return config, inputs_dict







	def 						__a   (				self			:   Any , lowerCamelCase			:   Any , lowerCamelCase			:   Any , lowerCamelCase			:   Union[str, Any]		):




				'''simple docstring'''
				a__     = 2_0
				a__     = model_class_name(lowerCamelCase		)

				a__     = model.encode(inputs_dict["input_ids"]		)

				a__   ,				a__     = (
				    inputs_dict["decoder_input_ids"],
				    inputs_dict["decoder_attention_mask"],
				)

				a__     = model.init_cache(decoder_input_ids.shape[0] , lowerCamelCase , lowerCamelCase		)
				a__     = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="i4"		)

				a__     = jnp.broadcast_to(
				    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
				a__     = model.decode(
				    decoder_input_ids[:, :-1] , lowerCamelCase , decoder_attention_mask=lowerCamelCase , past_key_values=lowerCamelCase , decoder_position_ids=lowerCamelCase , )

				a__     = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4"		)
				a__     = model.decode(
				    decoder_input_ids[:, -1:] , lowerCamelCase , decoder_attention_mask=lowerCamelCase , past_key_values=outputs_cache.past_key_values , decoder_position_ids=lowerCamelCase , )

				a__     = model.decode(lowerCamelCase , lowerCamelCase		)

				a__     = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
				self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}'''		)







	def 						__a   (				self			:   Union[str, Any] , lowerCamelCase			:   Dict , lowerCamelCase			:   List[Any] , lowerCamelCase			:   Optional[int]		):




				'''simple docstring'''
				a__     = 2_0
				a__     = model_class_name(lowerCamelCase		)

				a__     = model.encode(inputs_dict["input_ids"]		)

				a__   ,				a__     = (
				    inputs_dict["decoder_input_ids"],
				    inputs_dict["decoder_attention_mask"],
				)

				a__     = jnp.concatenate(
				    [
				        decoder_attention_mask,
				        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
				    ] , axis=-1 , )

				a__     = model.init_cache(decoder_input_ids.shape[0] , lowerCamelCase , lowerCamelCase		)
				a__     = jnp.broadcast_to(
				    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )

				a__     = model.decode(
				    decoder_input_ids[:, :-1] , lowerCamelCase , decoder_attention_mask=lowerCamelCase , past_key_values=lowerCamelCase , decoder_position_ids=lowerCamelCase , )
				a__     = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4"		)
				a__     = model.decode(
				    decoder_input_ids[:, -1:] , lowerCamelCase , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=lowerCamelCase , decoder_position_ids=lowerCamelCase , )

				a__     = model.decode(lowerCamelCase , lowerCamelCase , decoder_attention_mask=lowerCamelCase		)

				a__     = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
				self.parent.assertTrue(diff < 1e-3 , msg=F'''Max diff is {diff}'''		)






@require_flax
class       UpperCamelCase__  (		unittest.TestCase     ):
	lowerCAmelCase__					:			Dict								=						99







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				a__     = np.array(
				    [
				        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
				        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
				        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
				        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
				        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
				        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
				        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
				        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
				        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
				        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
				        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
				        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
				        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
				    ] , dtype=np.intaa , )

				a__     = input_ids.shape[0]
				a__     = BlenderbotConfig(
				    vocab_size=self.vocab_size , d_model=2_4 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=3_2 , decoder_ffn_dim=3_2 , max_position_embeddings=4_8 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
				return config, input_ids, batch_size







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				a__   ,				a__   ,				a__     = self._get_config_and_data()
				a__     = FlaxBlenderbotForConditionalGeneration(lowerCamelCase		)
				a__     = lm_model(input_ids=lowerCamelCase		)
				a__     = (batch_size, input_ids.shape[1], config.vocab_size)
				self.assertEqual(outputs["logits"].shape , lowerCamelCase		)







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				a__     = BlenderbotConfig(
				    vocab_size=self.vocab_size , d_model=1_4 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=4_8 , )
				a__     = FlaxBlenderbotForConditionalGeneration(lowerCamelCase		)
				a__     = np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]] , dtype=np.intaa		)
				a__     = np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]] , dtype=np.intaa		)
				a__     = lm_model(input_ids=lowerCamelCase , decoder_input_ids=lowerCamelCase		)
				a__     = (*summary.shape, config.vocab_size)
				self.assertEqual(outputs["logits"].shape , lowerCamelCase		)







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				a__     = np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]] , dtype=np.intaa		)
				a__     = shift_tokens_right(lowerCamelCase , 1 , 2		)
				a__     = np.equal(lowerCamelCase , 1		).astype(np.floataa		).sum()
				a__     = np.equal(lowerCamelCase , 1		).astype(np.floataa		).sum()
				self.assertEqual(shifted.shape , input_ids.shape		)
				self.assertEqual(lowerCamelCase , n_pad_before - 1		)
				self.assertTrue(np.equal(shifted[:, 0] , 2		).all()		)






@require_flax
class       UpperCamelCase__  (		__lowerCAmelCase	,unittest.TestCase	,__lowerCAmelCase     ):
	lowerCAmelCase__					:			Dict								=						True
	lowerCAmelCase__					:			Union[str, Any]								=						(
	    (
	        FlaxBlenderbotModel,
	        FlaxBlenderbotForConditionalGeneration,
	    )
	    if is_flax_available()
	    else ()
	)
	lowerCAmelCase__					:			Optional[int]								=						(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()







	def 						__a   (				self			:   int		):




				'''simple docstring'''
				a__     = FlaxBlenderbotModelTester(self		)







	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				a__   ,				a__     = self.model_tester.prepare_config_and_inputs()
				for model_class in self.all_model_classes:
							self.model_tester.check_use_cache_forward(lowerCamelCase , lowerCamelCase , lowerCamelCase		)







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				a__   ,				a__     = self.model_tester.prepare_config_and_inputs()
				for model_class in self.all_model_classes:
							self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase , lowerCamelCase , lowerCamelCase		)







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				a__   ,				a__     = self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__		):
										a__     = self._prepare_for_class(lowerCamelCase , lowerCamelCase		)
										a__     = model_class(lowerCamelCase		)

										@jax.jit
										def encode_jitted(lowerCamelCase			:   Optional[int] , lowerCamelCase			:   str=None , **lowerCamelCase			:   str		):
													return model.encode(input_ids=lowerCamelCase , attention_mask=lowerCamelCase		)

										with self.subTest("JIT Enabled"		):
													a__     = encode_jitted(**lowerCamelCase		).to_tuple()

										with self.subTest("JIT Disabled"		):
													with jax.disable_jit():
																a__     = encode_jitted(**lowerCamelCase		).to_tuple()

										self.assertEqual(len(lowerCamelCase		) , len(lowerCamelCase		)		)
										for jitted_output, output in zip(lowerCamelCase , lowerCamelCase		):
													self.assertEqual(jitted_output.shape , output.shape		)







	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				a__   ,				a__     = self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__		):
										a__     = model_class(lowerCamelCase		)
										a__     = model.encode(inputs_dict["input_ids"] , inputs_dict["attention_mask"]		)

										a__     = {
										    "decoder_input_ids": inputs_dict["decoder_input_ids"],
										    "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
										    "encoder_outputs": encoder_outputs,
										}

										@jax.jit
										def decode_jitted(lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   Optional[Any]		):
													return model.decode(
													    decoder_input_ids=lowerCamelCase , decoder_attention_mask=lowerCamelCase , encoder_outputs=lowerCamelCase , )

										with self.subTest("JIT Enabled"		):
													a__     = decode_jitted(**lowerCamelCase		).to_tuple()

										with self.subTest("JIT Disabled"		):
													with jax.disable_jit():
																a__     = decode_jitted(**lowerCamelCase		).to_tuple()

										self.assertEqual(len(lowerCamelCase		) , len(lowerCamelCase		)		)
										for jitted_output, output in zip(lowerCamelCase , lowerCamelCase		):
													self.assertEqual(jitted_output.shape , output.shape		)







	@slow
	def 						__a   (				self			:   int		):




				'''simple docstring'''
				for model_class_name in self.all_model_classes:
							a__     = model_class_name.from_pretrained("facebook/blenderbot-400M-distill"		)
							# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
							a__     = np.ones((1, 1)		) * model.config.eos_token_id
							a__     = model(lowerCamelCase		)
							self.assertIsNotNone(lowerCamelCase		)







	@unittest.skipUnless(jax_device != "cpu" , "3B test too slow on CPU."		)
	@slow
	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				a__     = {"num_beams": 1, "early_stopping": True, "min_length": 1_5, "max_length": 2_5}
				a__     = {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

				a__     = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B" , from_pt=lowerCamelCase		)
				a__     = BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"		)

				a__     = ["Sam"]
				a__     = tokenizer(lowerCamelCase , return_tensors="jax"		)

				a__     = model.generate(**lowerCamelCase , **lowerCamelCase		)
				a__     = "Sam is a great name. It means \"sun\" in Gaelic."

				a__     = tokenizer.batch_decode(lowerCamelCase , **lowerCamelCase		)
				assert generated_txt[0].strip() == tgt_text










'''simple docstring'''


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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_   :   str       =					logging.get_logger(__name__)

lowerCAmelCase_   :   Optional[Any]       =					"▁"

lowerCAmelCase_   :   Union[str, Any]       =					{"vocab_file": "sentencepiece.bpe.model"}

lowerCAmelCase_   :   str       =					{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    }
}

lowerCAmelCase_   :   Optional[int]       =					{
    "facebook/xglm-564M": 2048,
}






class       UpperCamelCase__  (		__lowerCAmelCase     ):
	lowerCAmelCase__					:			int								=						VOCAB_FILES_NAMES
	lowerCAmelCase__					:			Optional[int]								=						PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__					:			Tuple								=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__					:			Optional[int]								=						["input_ids", "attention_mask"]







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




				'''simple docstring'''
				a__     = {} if sp_model_kwargs is None else sp_model_kwargs

				# Compatibility with the original tokenizer
				a__     = 7
				a__     = [F'''<madeupword{i}>''' for i in range(self.num_madeup_words		)]

				a__     = kwargs.get("additional_special_tokens" , []		)
				kwargs["additional_special_tokens"] += [
				    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
				]

				super().__init__(
				    bos_token=lowerCamelCase , eos_token=lowerCamelCase , unk_token=lowerCamelCase , sep_token=lowerCamelCase , cls_token=lowerCamelCase , pad_token=lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **lowerCamelCase , )

				a__     = spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.Load(str(lowerCamelCase		)		)
				a__     = 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'

				# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
				a__     = 1

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

				a__     = len(self.sp_model		)
				a__     = {F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words		)}
				self.fairseq_tokens_to_ids.update(lowerCamelCase		)

				a__     = {v: k for k, v in self.fairseq_tokens_to_ids.items()}







	def __getstate__(				self			:   Optional[Any]		):




				'''simple docstring'''
				a__     = self.__dict__.copy()
				a__     = None
				a__     = self.sp_model.serialized_model_proto()
				return state







	def __setstate__(				self			:   Dict , lowerCamelCase			:   List[Any]		):




				'''simple docstring'''
				a__     = d

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

				a__     = spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)







	def 						__a   (				self			:   Union[str, Any] , lowerCamelCase			:   List[int] , lowerCamelCase			:   Optional[List[int]] = None		):




				'''simple docstring'''

				if token_ids_a is None:
							return [self.sep_token_id] + token_ids_a
				a__     = [self.sep_token_id]
				return sep + token_ids_a + sep + sep + token_ids_a







	def 						__a   (				self			:   int , lowerCamelCase			:   List[int] , lowerCamelCase			:   Optional[List[int]] = None , lowerCamelCase			:   bool = False		):




				'''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		)

				if token_ids_a is None:
							return [1] + ([0] * len(lowerCamelCase		))
				return [1] + ([0] * len(lowerCamelCase		)) + [1, 1] + ([0] * len(lowerCamelCase		))







	def 						__a   (				self			:   Dict , lowerCamelCase			:   List[int] , lowerCamelCase			:   Optional[List[int]] = None		):




				'''simple docstring'''
				a__     = [self.sep_token_id]

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







	@property
	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				return len(self.sp_model		) + self.fairseq_offset + self.num_madeup_words







	def 						__a   (				self			:   Dict		):




				'''simple docstring'''
				a__     = {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			:   str		):




				'''simple docstring'''
				return self.sp_model.encode(lowerCamelCase , out_type=lowerCamelCase		)







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   Union[str, Any]		):




				'''simple docstring'''
				if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
				a__     = self.sp_model.PieceToId(lowerCamelCase		)

				# 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 						__a   (				self			:   Any , lowerCamelCase			:   List[str]		):




				'''simple docstring'''
				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 						__a   (				self			:   int , lowerCamelCase			:   Optional[Any]		):




				'''simple docstring'''
				a__     = "".join(lowerCamelCase		).replace(lowerCamelCase , " "		).strip()
				return out_string







	def 						__a   (				self			:   int , lowerCamelCase			:   str , lowerCamelCase			:   Optional[str] = None		):




				'''simple docstring'''
				if not os.path.isdir(lowerCamelCase		):
							logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
							return
				a__     = 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:
										a__     = self.sp_model.serialized_model_proto()
										fi.write(lowerCamelCase		)

				return (out_vocab_file,)



