

'''simple docstring'''




import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

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


if is_flax_available():
  import jax
  import jax.numpy as jnp
  import numpy as np

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
  import torch



@require_flax
class     lowercase_		:

  '''simple docstring'''





  def __init__(       self				,	a_				,	a_=1_4				,	a_=7				,	a_=True				,	a_=True				,	a_=True				,	a_=9_9				,	a_=3_2				,	a_=5				,	a_=4				,	a_=3_7				,	a_="gelu"				,	a_=0.1				,	a_=0.1				,	a_=5_1_2				,	a_=0.02				,	a_=None				,	)							->			int:


    """simple docstring"""
    UpperCAmelCase  							=		parent
    UpperCAmelCase  							=		batch_size
    UpperCAmelCase  							=		seq_length
    UpperCAmelCase  							=		is_training
    UpperCAmelCase  							=		use_input_mask
    UpperCAmelCase  							=		use_labels
    UpperCAmelCase  							=		vocab_size
    UpperCAmelCase  							=		d_model
    UpperCAmelCase  							=		num_hidden_layers
    UpperCAmelCase  							=		num_attention_heads
    UpperCAmelCase  							=		ffn_dim
    UpperCAmelCase  							=		activation_function
    UpperCAmelCase  							=		activation_dropout
    UpperCAmelCase  							=		attention_dropout
    UpperCAmelCase  							=		max_position_embeddings
    UpperCAmelCase  							=		initializer_range
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		0
    UpperCAmelCase  							=		2
    UpperCAmelCase  							=		1
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		np.clip(ids_tensor([self.batch_size, self.seq_length]				,	self.vocab_size       )				,	3				,	self.vocab_size       )

    UpperCAmelCase  							=		None
    if self.use_input_mask:
      UpperCAmelCase  							=		random_attention_mask([self.batch_size, self.seq_length]       )

    UpperCAmelCase  							=		XGLMConfig(
        vocab_size=self.vocab_size				,	d_model=self.hidden_size				,	num_layers=self.num_hidden_layers				,	attention_heads=self.num_attention_heads				,	ffn_dim=self.ffn_dim				,	activation_function=self.activation_function				,	activation_dropout=self.activation_dropout				,	attention_dropout=self.attention_dropout				,	max_position_embeddings=self.max_position_embeddings				,	initializer_range=self.initializer_range				,	use_cache=__lowerCamelCase				,	bos_token_id=self.bos_token_id				,	eos_token_id=self.eos_token_id				,	pad_token_id=self.pad_token_id				,	)

    return (config, input_ids, input_mask)
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		self.prepare_config_and_inputs()
    UpperCAmelCase  							=		config_and_inputs
    UpperCAmelCase  							=		{"input_ids": input_ids, "attention_mask": attention_mask}
    return config, inputs_dict
  def   snake_case_     (       self       )							->			int:


    """simple docstring"""
    UpperCAmelCase  							=		self.prepare_config_and_inputs()

    UpperCAmelCase  							=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
    UpperCAmelCase  							=		ids_tensor([self.batch_size, self.seq_length]				,	vocab_size=2       )

    return (
        config,
        input_ids,
        attention_mask,
        encoder_hidden_states,
        encoder_attention_mask,
    )
  def   snake_case_     (       self				,	a_				,	a_				,	a_				,	a_       )							->			int:


    """simple docstring"""
    UpperCAmelCase  							=		2_0
    UpperCAmelCase  							=		model_class_name(__lowerCamelCase       )

    UpperCAmelCase  							=		model.init_cache(input_ids.shape[0]				,	__lowerCamelCase       )
    UpperCAmelCase  							=		jnp.ones((input_ids.shape[0], max_decoder_length)				,	dtype='i4'       )

    UpperCAmelCase  							=		jnp.broadcast_to(
        jnp.arange(input_ids.shape[-1] - 1       )[None, :]				,	(input_ids.shape[0], input_ids.shape[-1] - 1)       )
    UpperCAmelCase  							=		model(
        input_ids[:, :-1]				,	attention_mask=__lowerCamelCase				,	past_key_values=__lowerCamelCase				,	position_ids=__lowerCamelCase				,	)

    UpperCAmelCase  							=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,	dtype='i4'       )
    UpperCAmelCase  							=		model(
        input_ids[:, -1:]				,	attention_mask=__lowerCamelCase				,	past_key_values=outputs_cache.past_key_values				,	position_ids=__lowerCamelCase				,	)

    UpperCAmelCase  							=		model(__lowerCamelCase       )

    UpperCAmelCase  							=		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   snake_case_     (       self				,	a_				,	a_				,	a_				,	a_       )							->			int:


    """simple docstring"""
    UpperCAmelCase  							=		2_0
    UpperCAmelCase  							=		model_class_name(__lowerCamelCase       )

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

    UpperCAmelCase  							=		model.init_cache(input_ids.shape[0]				,	__lowerCamelCase       )
    UpperCAmelCase  							=		jnp.broadcast_to(
        jnp.arange(input_ids.shape[-1] - 1       )[None, :]				,	(input_ids.shape[0], input_ids.shape[-1] - 1)       )

    UpperCAmelCase  							=		model(
        input_ids[:, :-1]				,	attention_mask=__lowerCamelCase				,	past_key_values=__lowerCamelCase				,	position_ids=__lowerCamelCase				,	)
    UpperCAmelCase  							=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,	dtype='i4'       )
    UpperCAmelCase  							=		model(
        input_ids[:, -1:]				,	past_key_values=outputs_cache.past_key_values				,	attention_mask=__lowerCamelCase				,	position_ids=__lowerCamelCase				,	)

    UpperCAmelCase  							=		model(__lowerCamelCase				,	attention_mask=__lowerCamelCase       )
    UpperCAmelCase  							=		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_sentencepiece
@require_flax
class     lowercase_		(						UpperCamelCase__	,    UpperCamelCase__	,    unittest.TestCase	):

  '''simple docstring'''





  __lowerCAmelCase   :	str      					=				(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
  __lowerCAmelCase   :	Any      					=				(FlaxXGLMForCausalLM,) if is_flax_available() else ()
  def   snake_case_     (       self       )							->			str:


    """simple docstring"""
    UpperCAmelCase  							=		FlaxXGLMModelTester(self       )
  def   snake_case_     (       self       )							->			List[Any]:


    """simple docstring"""


    for model_class_name in self.all_model_classes:
      UpperCAmelCase  							=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.check_use_cache_forward(__lowerCamelCase				,	__lowerCamelCase				,	__lowerCamelCase				,	__lowerCamelCase       )
  def   snake_case_     (       self       )							->			Union[str, Any]:


    """simple docstring"""


    for model_class_name in self.all_model_classes:
      UpperCAmelCase  							=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.check_use_cache_forward_with_attn_mask(
          __lowerCamelCase				,	__lowerCamelCase				,	__lowerCamelCase				,	__lowerCamelCase       )
  @slow
  def   snake_case_     (       self       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		XGLMTokenizer.from_pretrained('XGLM'				,	padding_side='left'       )
    UpperCAmelCase  							=		tokenizer(['Hello this is a long string', 'Hey']				,	return_tensors='np'				,	padding=__lowerCamelCase				,	truncation=__lowerCamelCase       )

    UpperCAmelCase  							=		FlaxXGLMForCausalLM.from_pretrained('facebook/xglm-564M'       )
    UpperCAmelCase  							=		1
    UpperCAmelCase  							=		False

    UpperCAmelCase  							=		jax.jit(model.generate       )

    UpperCAmelCase  							=		jit_generate(inputs['input_ids']				,	attention_mask=inputs['attention_mask']       ).sequences

    UpperCAmelCase  							=		tokenizer.batch_decode(__lowerCamelCase				,	skip_special_tokens=__lowerCamelCase       )

    UpperCAmelCase  							=		[
        "Hello this is a long string of questions, but I'm not sure if I'm",
        "Hey, I'm a newbie to the forum and I'",
    ]

    self.assertListEqual(__lowerCamelCase				,	__lowerCamelCase       )
  @is_pt_flax_cross_test
  def   snake_case_     (       self       )							->			Dict:


    """simple docstring"""
    UpperCAmelCase  							=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      with self.subTest(model_class.__name__       ):
        # prepare inputs
        UpperCAmelCase  							=		self._prepare_for_class(__lowerCamelCase				,	__lowerCamelCase       )
        UpperCAmelCase  							=		{k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

        # load corresponding PyTorch class
        UpperCAmelCase  							=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
        UpperCAmelCase  							=		getattr(__lowerCamelCase				,	__lowerCamelCase       )

        UpperCAmelCase  							=		pt_inputs["input_ids"].shape
        UpperCAmelCase  							=		np.random.randint(0				,	seq_length - 1				,	size=(batch_size,)       )
        for batch_idx, start_index in enumerate(__lowerCamelCase       ):
          UpperCAmelCase  							=		0
          UpperCAmelCase  							=		1
          UpperCAmelCase  							=		0
          UpperCAmelCase  							=		1
        UpperCAmelCase  							=		pt_model_class(__lowerCamelCase       ).eval()
        # Flax models don't use the `use_cache` option and cache is not returned as a default.
        # So we disable `use_cache` here for PyTorch model.
        UpperCAmelCase  							=		False
        UpperCAmelCase  							=		model_class(__lowerCamelCase				,	dtype=jnp.floataa       )
        UpperCAmelCase  							=		convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,	__lowerCamelCase       )
        UpperCAmelCase  							=		fx_state

        with torch.no_grad():
          UpperCAmelCase  							=		pt_model(**__lowerCamelCase       ).to_tuple()

        UpperCAmelCase  							=		fx_model(**__lowerCamelCase       ).to_tuple()
        self.assertEqual(len(__lowerCamelCase       )				,	len(__lowerCamelCase       )				,	'Output lengths differ between Flax and PyTorch'       )
        for fx_output, pt_output in zip(__lowerCamelCase				,	__lowerCamelCase       ):
          self.assert_almost_equals(fx_output[:, -1]				,	pt_output[:, -1].numpy()				,	4E-2       )

        with tempfile.TemporaryDirectory() as tmpdirname:
          pt_model.save_pretrained(__lowerCamelCase       )
          UpperCAmelCase  							=		model_class.from_pretrained(__lowerCamelCase				,	from_pt=__lowerCamelCase       )

        UpperCAmelCase  							=		fx_model_loaded(**__lowerCamelCase       ).to_tuple()
        self.assertEqual(
            len(__lowerCamelCase       )				,	len(__lowerCamelCase       )				,	'Output lengths differ between Flax and PyTorch'       )
        for fx_output_loaded, pt_output in zip(__lowerCamelCase				,	__lowerCamelCase       ):
          self.assert_almost_equals(fx_output_loaded[:, -1]				,	pt_output[:, -1].numpy()				,	4E-2       )
  @is_pt_flax_cross_test
  def   snake_case_     (       self       )							->			List[str]:


    """simple docstring"""
    UpperCAmelCase  							=		self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes:
      with self.subTest(model_class.__name__       ):
        # prepare inputs
        UpperCAmelCase  							=		self._prepare_for_class(__lowerCamelCase				,	__lowerCamelCase       )
        UpperCAmelCase  							=		{k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

        # load corresponding PyTorch class
        UpperCAmelCase  							=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
        UpperCAmelCase  							=		getattr(__lowerCamelCase				,	__lowerCamelCase       )

        UpperCAmelCase  							=		pt_model_class(__lowerCamelCase       ).eval()
        UpperCAmelCase  							=		False
        UpperCAmelCase  							=		model_class(__lowerCamelCase				,	dtype=jnp.floataa       )

        UpperCAmelCase  							=		load_flax_weights_in_pytorch_model(__lowerCamelCase				,	fx_model.params       )
        UpperCAmelCase  							=		pt_inputs["input_ids"].shape
        UpperCAmelCase  							=		np.random.randint(0				,	seq_length - 1				,	size=(batch_size,)       )
        for batch_idx, start_index in enumerate(__lowerCamelCase       ):
          UpperCAmelCase  							=		0
          UpperCAmelCase  							=		1
          UpperCAmelCase  							=		0
          UpperCAmelCase  							=		1

        # make sure weights are tied in PyTorch
        pt_model.tie_weights()

        with torch.no_grad():
          UpperCAmelCase  							=		pt_model(**__lowerCamelCase       ).to_tuple()

        UpperCAmelCase  							=		fx_model(**__lowerCamelCase       ).to_tuple()
        self.assertEqual(len(__lowerCamelCase       )				,	len(__lowerCamelCase       )				,	'Output lengths differ between Flax and PyTorch'       )
        for fx_output, pt_output in zip(__lowerCamelCase				,	__lowerCamelCase       ):
          self.assert_almost_equals(fx_output[:, -1]				,	pt_output[:, -1].numpy()				,	4E-2       )

        with tempfile.TemporaryDirectory() as tmpdirname:
          fx_model.save_pretrained(__lowerCamelCase       )
          UpperCAmelCase  							=		pt_model_class.from_pretrained(__lowerCamelCase				,	from_flax=__lowerCamelCase       )

        with torch.no_grad():
          UpperCAmelCase  							=		pt_model_loaded(**__lowerCamelCase       ).to_tuple()

        self.assertEqual(
            len(__lowerCamelCase       )				,	len(__lowerCamelCase       )				,	'Output lengths differ between Flax and PyTorch'       )
        for fx_output, pt_output in zip(__lowerCamelCase				,	__lowerCamelCase       ):
          self.assert_almost_equals(fx_output[:, -1]				,	pt_output[:, -1].numpy()				,	4E-2       )





  @slow
  def   snake_case_     (       self       )							->			Dict:


    """simple docstring"""


    for model_class_name in self.all_model_classes:
      UpperCAmelCase  							=		model_class_name.from_pretrained('facebook/xglm-564M'       )
      UpperCAmelCase  							=		model(np.ones((1, 1)       )       )
      self.assertIsNotNone(__lowerCamelCase       )





'''simple docstring'''




import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


_a			:	Optional[int]								=  logging.get_logger(__name__)


_a			:	List[str]								=  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all MVP models at https://huggingface.co/models?filter=mvp
_a			:	Tuple								=  {
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
}

_a			:	str								=  {
    'RUCAIBox/mvp': 1_024,
}


@lru_cache()
def  lowerCamelCase__						(  ):
  UpperCAmelCase  							=		(
      list(range(ord('!'  )    ,				ord('~'  ) + 1  )  ) + list(range(ord('¡'  )    ,				ord('¬'  ) + 1  )  ) + list(range(ord('®'  )    ,				ord('ÿ'  ) + 1  )  )
  )
  UpperCAmelCase  							=		bs[:]
  UpperCAmelCase  							=		0
  for b in range(2**8  ):
    if b not in bs:
      bs.append(SCREAMING_SNAKE_CASE  )
      cs.append(2**8 + n  )
      n += 1
  UpperCAmelCase  							=		[chr(SCREAMING_SNAKE_CASE  ) for n in cs]
  return dict(zip(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  )  )


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				List[Any]  ):
  UpperCAmelCase  							=		set()
  UpperCAmelCase  							=		word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)  )
    UpperCAmelCase  							=		char
  return pairs



class     lowercase_		(						a	):

  '''simple docstring'''





  __lowerCAmelCase   :	str      					=				VOCAB_FILES_NAMES
  __lowerCAmelCase   :	List[Any]      					=				PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase   :	Dict      					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase   :	Optional[Any]      					=				["input_ids", "attention_mask"]
  def __init__(       self				,	a_				,	a_				,	a_="replace"				,	a_="<s>"				,	a_="</s>"				,	a_="</s>"				,	a_="<s>"				,	a_="<unk>"				,	a_="<pad>"				,	a_="<mask>"				,	a_=False				,	**a_				,	)							->			Optional[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		AddedToken(a_				,	lstrip=a_				,	rstrip=a_       ) if isinstance(a_				,	a_       ) else bos_token
    UpperCAmelCase  							=		AddedToken(a_				,	lstrip=a_				,	rstrip=a_       ) if isinstance(a_				,	a_       ) else eos_token
    UpperCAmelCase  							=		AddedToken(a_				,	lstrip=a_				,	rstrip=a_       ) if isinstance(a_				,	a_       ) else sep_token
    UpperCAmelCase  							=		AddedToken(a_				,	lstrip=a_				,	rstrip=a_       ) if isinstance(a_				,	a_       ) else cls_token
    UpperCAmelCase  							=		AddedToken(a_				,	lstrip=a_				,	rstrip=a_       ) if isinstance(a_				,	a_       ) else unk_token
    UpperCAmelCase  							=		AddedToken(a_				,	lstrip=a_				,	rstrip=a_       ) if isinstance(a_				,	a_       ) else pad_token

    # Mask token behave like a normal word, i.e. include the space before it
    UpperCAmelCase  							=		AddedToken(a_				,	lstrip=a_				,	rstrip=a_       ) if isinstance(a_				,	a_       ) else mask_token

    super().__init__(
        errors=a_				,	bos_token=a_				,	eos_token=a_				,	unk_token=a_				,	sep_token=a_				,	cls_token=a_				,	pad_token=a_				,	mask_token=a_				,	add_prefix_space=a_				,	**a_				,	)
    with open(a_				,	encoding='utf-8'       ) as vocab_handle:
      UpperCAmelCase  							=		json.load(a_       )
    UpperCAmelCase  							=		{v: k for k, v in self.encoder.items()}
    UpperCAmelCase  							=		errors  # how to handle errors in decoding
    UpperCAmelCase  							=		bytes_to_unicode()
    UpperCAmelCase  							=		{v: k for k, v in self.byte_encoder.items()}
    with open(a_				,	encoding='utf-8'       ) as merges_handle:
      UpperCAmelCase  							=		merges_handle.read().split('\n'       )[1:-1]
    UpperCAmelCase  							=		[tuple(merge.split()       ) for merge in bpe_merges]
    UpperCAmelCase  							=		dict(zip(a_				,	range(len(a_       )       )       )       )
    UpperCAmelCase  							=		{}
    UpperCAmelCase  							=		add_prefix_space

    # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
    UpperCAmelCase  							=		re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'       )
  @property
  def   snake_case_     (       self       )							->			Any:


    """simple docstring"""
    return len(self.encoder       )
  def   snake_case_     (       self       )							->			Union[str, Any]:


    """simple docstring"""
    return dict(self.encoder				,	**self.added_tokens_encoder       )
  def   snake_case_     (       self				,	a_       )							->			int:


    """simple docstring"""
    if token in self.cache:
      return self.cache[token]
    UpperCAmelCase  							=		tuple(a_       )
    UpperCAmelCase  							=		get_pairs(a_       )

    if not pairs:
      return token

    while True:
      UpperCAmelCase  							=		min(a_				,	key=lambda a_       : self.bpe_ranks.get(a_				,	float('inf'       )       )       )
      if bigram not in self.bpe_ranks:
        break
      UpperCAmelCase							, UpperCAmelCase  							=		bigram
      UpperCAmelCase  							=		[]
      UpperCAmelCase  							=		0
      while i < len(a_       ):
        try:
          UpperCAmelCase  							=		word.index(a_				,	a_       )
        except ValueError:
          new_word.extend(word[i:]       )
          break
        else:
          new_word.extend(word[i:j]       )
          UpperCAmelCase  							=		j

        if word[i] == first and i < len(a_       ) - 1 and word[i + 1] == second:
          new_word.append(first + second       )
          i += 2
        else:
          new_word.append(word[i]       )
          i += 1
      UpperCAmelCase  							=		tuple(a_       )
      UpperCAmelCase  							=		new_word
      if len(a_       ) == 1:
        break
      else:
        UpperCAmelCase  							=		get_pairs(a_       )
    UpperCAmelCase  							=		' '.join(a_       )
    UpperCAmelCase  							=		word
    return word
  def   snake_case_     (       self				,	a_       )							->			int:


    """simple docstring"""
    UpperCAmelCase  							=		[]
    for token in re.findall(self.pat				,	a_       ):
      UpperCAmelCase  							=		''.join(
          self.byte_encoder[b] for b in token.encode('utf-8'       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
      bpe_tokens.extend(bpe_token for bpe_token in self.bpe(a_       ).split(' '       )       )
    return bpe_tokens
  def   snake_case_     (       self				,	a_       )							->			Optional[int]:


    """simple docstring"""
    return self.encoder.get(a_				,	self.encoder.get(self.unk_token       )       )
  def   snake_case_     (       self				,	a_       )							->			Tuple:


    """simple docstring"""
    return self.decoder.get(a_       )
  def   snake_case_     (       self				,	a_       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		''.join(a_       )
    UpperCAmelCase  							=		bytearray([self.byte_decoder[c] for c in text]       ).decode('utf-8'				,	errors=self.errors       )
    return text
  def   snake_case_     (       self				,	a_				,	a_ = None       )							->			Tuple[str]:


    """simple docstring"""
    if not os.path.isdir(a_       ):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
      return
    UpperCAmelCase  							=		os.path.join(
        a_				,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )
    UpperCAmelCase  							=		os.path.join(
        a_				,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']       )

    with open(a_				,	'w'				,	encoding='utf-8'       ) as f:
      f.write(json.dumps(self.encoder				,	indent=2				,	sort_keys=a_				,	ensure_ascii=a_       ) + '\n'       )

    UpperCAmelCase  							=		0
    with open(a_				,	'w'				,	encoding='utf-8'       ) as writer:
      writer.write('#version: 0.2\n'       )
      for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,	key=lambda a_       : kv[1]       ):
        if index != token_index:
          logger.warning(
              F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
              ' Please check that the tokenizer is not corrupted!'       )
          UpperCAmelCase  							=		token_index
        writer.write(' '.join(a_       ) + '\n'       )
        index += 1

    return vocab_file, merge_file
  def   snake_case_     (       self				,	a_				,	a_ = None       )							->			List[int]:


    """simple docstring"""
    if token_ids_a is None:
      return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    UpperCAmelCase  							=		[self.cls_token_id]
    UpperCAmelCase  							=		[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep
  def   snake_case_     (       self				,	a_				,	a_ = None				,	a_ = False       )							->			List[int]:


    """simple docstring"""
    if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=a_				,	token_ids_a=a_				,	already_has_special_tokens=a_       )

    if token_ids_a is None:
      return [1] + ([0] * len(a_       )) + [1]
    return [1] + ([0] * len(a_       )) + [1, 1] + ([0] * len(a_       )) + [1]
  def   snake_case_     (       self				,	a_				,	a_ = None       )							->			List[int]:


    """simple docstring"""
    UpperCAmelCase  							=		[self.sep_token_id]
    UpperCAmelCase  							=		[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   snake_case_     (       self				,	a_				,	a_=False				,	**a_       )							->			Optional[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		kwargs.pop('add_prefix_space'				,	self.add_prefix_space       )
    if (is_split_into_words or add_prefix_space) and (len(a_       ) > 0 and not text[0].isspace()):
      UpperCAmelCase  							=		' ' + text
    return (text, kwargs)


