


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
     snake_case							:       str										=	'''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		(					UpperCAmelCase_		:						Optional[int]					,    UpperCAmelCase_		:						Union[str, Any]					,    UpperCAmelCase_		:						List[str]=None					,    UpperCAmelCase_		:						Union[str, Any]=None					,    UpperCAmelCase_		:						Union[str, Any]=None					,    UpperCAmelCase_		:						str=None					,    UpperCAmelCase_		:						List[Any]=None					,    UpperCAmelCase_		:						Any=None					,    ):




      """simple docstring"""





      if attention_mask is None:
            a		:List[str]									=			np.where(input_ids != config.pad_token_id					,    1					,    0		)
      if decoder_attention_mask is None:
            a		:Optional[Any]									=			np.where(decoder_input_ids != config.pad_token_id					,    1					,    0		)
      if head_mask is None:
            a		:Dict									=			np.ones((config.encoder_layers, config.encoder_attention_heads)		)
      if decoder_head_mask is None:
            a		:Union[str, Any]									=			np.ones((config.decoder_layers, config.decoder_attention_heads)		)
      if cross_attn_head_mask is None:
            a		:Optional[Any]									=			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 						_snake_case    :




 def __init__(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=13   ,    _lowerCamelCase=7   ,    _lowerCamelCase=True   ,    _lowerCamelCase=False   ,    _lowerCamelCase=99   ,    _lowerCamelCase=16   ,    _lowerCamelCase=2   ,    _lowerCamelCase=4   ,    _lowerCamelCase=4   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=32   ,    _lowerCamelCase=2   ,    _lowerCamelCase=1   ,    _lowerCamelCase=0   ,    _lowerCamelCase=0.02   ,    ):
       a		:Optional[int]									=			parent
       a		:Dict									=			batch_size
       a		:Optional[Any]									=			seq_length
       a		:Dict									=			is_training
       a		:Optional[Any]									=			use_labels
       a		:int									=			vocab_size
       a		:Optional[Any]									=			hidden_size
       a		:Tuple									=			num_hidden_layers
       a		:Union[str, Any]									=			num_attention_heads
       a		:Optional[int]									=			intermediate_size
       a		:int									=			hidden_act
       a		:Union[str, Any]									=			hidden_dropout_prob
       a		:List[str]									=			attention_probs_dropout_prob
       a		:Dict									=			max_position_embeddings
       a		:str									=			eos_token_id
       a		:Any									=			pad_token_id
       a		:List[Any]									=			bos_token_id
       a		:List[Any]									=			initializer_range




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Tuple									=			np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,    self.vocab_size       )   ,    3   ,    self.vocab_size       )
       a		:str									=			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,    dtype=np.intaa       ))   ,    -1       )

       a		:str									=			shift_tokens_right(_UpperCamelCase   ,    1   ,    2       )

       a		:str									=			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=_UpperCamelCase   ,    )
       a		:Any									=			prepare_blenderbot_inputs_dict(_UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       )
       return config, inputs_dict




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a				,							a		:Optional[Any]									=			self.prepare_config_and_inputs()
       return config, inputs_dict




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       a		:Tuple									=			20
       a		:Optional[int]									=			model_class_name(_UpperCamelCase       )

       a		:List[str]									=			model.encode(inputs_dict['''input_ids''']       )

       a				,							a		:List[Any]									=			(
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       a		:Optional[Any]									=			model.init_cache(decoder_input_ids.shape[0]   ,    _UpperCamelCase   ,    _UpperCamelCase       )
       a		:List[Any]									=			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,    dtype='''i4'''       )

       a		:List[str]									=			jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]   ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,    )
       a		:int									=			model.decode(
           decoder_input_ids[:, :-1]   ,    _UpperCamelCase   ,    decoder_attention_mask=_UpperCamelCase   ,    past_key_values=_UpperCamelCase   ,    decoder_position_ids=_UpperCamelCase   ,    )

       a		:Union[str, Any]									=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,    dtype='''i4'''       )
       a		:Union[str, Any]									=			model.decode(
           decoder_input_ids[:, -1:]   ,    _UpperCamelCase   ,    decoder_attention_mask=_UpperCamelCase   ,    past_key_values=outputs_cache.past_key_values   ,    decoder_position_ids=_UpperCamelCase   ,    )

       a		:Optional[int]									=			model.decode(_UpperCamelCase   ,    _UpperCamelCase       )

       a		:Tuple									=			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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       a		:Tuple									=			20
       a		:List[Any]									=			model_class_name(_UpperCamelCase       )

       a		:Dict									=			model.encode(inputs_dict['''input_ids''']       )

       a				,							a		:Any									=			(
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       a		:List[str]									=			jnp.concatenate(
           [
               decoder_attention_mask,
               jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])       ),
           ]   ,    axis=-1   ,    )

       a		:str									=			model.init_cache(decoder_input_ids.shape[0]   ,    _UpperCamelCase   ,    _UpperCamelCase       )
       a		:int									=			jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]   ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,    )

       a		:List[Any]									=			model.decode(
           decoder_input_ids[:, :-1]   ,    _UpperCamelCase   ,    decoder_attention_mask=_UpperCamelCase   ,    past_key_values=_UpperCamelCase   ,    decoder_position_ids=_UpperCamelCase   ,    )
       a		:Dict									=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,    dtype='''i4'''       )
       a		:Optional[Any]									=			model.decode(
           decoder_input_ids[:, -1:]   ,    _UpperCamelCase   ,    past_key_values=outputs_cache.past_key_values   ,    decoder_attention_mask=_UpperCamelCase   ,    decoder_position_ids=_UpperCamelCase   ,    )

       a		:Tuple									=			model.decode(_UpperCamelCase   ,    _UpperCamelCase   ,    decoder_attention_mask=_UpperCamelCase       )

       a		:Union[str, Any]									=			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 						_snake_case    ( unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			99




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Tuple									=			np.array(
           [
               [71, 82, 18, 33, 46, 91, 2],
               [68, 34, 26, 58, 30, 82, 2],
               [5, 97, 17, 39, 94, 40, 2],
               [76, 83, 94, 25, 70, 78, 2],
               [87, 59, 41, 35, 48, 66, 2],
               [55, 13, 16, 58, 5, 2, 1],  # note padding
               [64, 27, 31, 51, 12, 75, 2],
               [52, 64, 86, 17, 83, 39, 2],
               [48, 61, 9, 24, 71, 82, 2],
               [26, 1, 60, 48, 22, 13, 2],
               [21, 5, 62, 28, 14, 76, 2],
               [45, 98, 37, 86, 59, 48, 2],
               [70, 70, 50, 9, 28, 0, 2],
           ]   ,    dtype=np.intaa   ,    )

       a		:int									=			input_ids.shape[0]
       a		:Dict									=			BlenderbotConfig(
           vocab_size=self.vocab_size   ,    d_model=24   ,    encoder_layers=2   ,    decoder_layers=2   ,    encoder_attention_heads=2   ,    decoder_attention_heads=2   ,    encoder_ffn_dim=32   ,    decoder_ffn_dim=32   ,    max_position_embeddings=48   ,    eos_token_id=2   ,    pad_token_id=1   ,    bos_token_id=0   ,    )
       return config, input_ids, batch_size




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a				,							a				,							a		:Tuple									=			self._get_config_and_data()
       a		:Optional[int]									=			FlaxBlenderbotForConditionalGeneration(_UpperCamelCase       )
       a		:Union[str, Any]									=			lm_model(input_ids=_UpperCamelCase       )
       a		:List[str]									=			(batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape   ,    _UpperCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			BlenderbotConfig(
           vocab_size=self.vocab_size   ,    d_model=14   ,    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=48   ,    )
       a		:Union[str, Any]									=			FlaxBlenderbotForConditionalGeneration(_UpperCamelCase       )
       a		:str									=			np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]   ,    dtype=np.intaa       )
       a		:List[str]									=			np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]   ,    dtype=np.intaa       )
       a		:List[str]									=			lm_model(input_ids=_UpperCamelCase   ,    decoder_input_ids=_UpperCamelCase       )
       a		:Dict									=			(*summary.shape, config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape   ,    _UpperCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[Any]									=			np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]   ,    dtype=np.intaa       )
       a		:List[str]									=			shift_tokens_right(_UpperCamelCase   ,    1   ,    2       )
       a		:int									=			np.equal(_UpperCamelCase   ,    1       ).astype(np.floataa       ).sum()
       a		:Optional[Any]									=			np.equal(_UpperCamelCase   ,    1       ).astype(np.floataa       ).sum()
       self.assertEqual(shifted.shape   ,    input_ids.shape       )
       self.assertEqual(_UpperCamelCase   ,    n_pad_before - 1       )
       self.assertTrue(np.equal(shifted[:, 0]   ,    2       ).all()       )







@require_flax
class 						_snake_case    ( snake_case__		,       unittest.TestCase		,       snake_case__				):
 SCREAMING_SNAKE_CASE__ =			True
 SCREAMING_SNAKE_CASE__ =			(
     (
         FlaxBlenderbotModel,
         FlaxBlenderbotForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 SCREAMING_SNAKE_CASE__ =			(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[int]									=			FlaxBlenderbotModelTester(self       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a				,							a		:str									=			self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward(_UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a				,							a		:List[Any]									=			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(_UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a				,							a		:str									=			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__       ):
                   a		:List[Any]									=			self._prepare_for_class(_UpperCamelCase   ,    _UpperCamelCase       )
                   a		:Any									=			model_class(_UpperCamelCase       )

                   @jax.jit
                   def encode_jitted(_lowerCamelCase   ,    _lowerCamelCase=None   ,    **_lowerCamelCase       ):
                         return model.encode(input_ids=_UpperCamelCase   ,    attention_mask=_UpperCamelCase       )

                   with self.subTest('''JIT Enabled'''       ):
                         a		:str									=			encode_jitted(**_UpperCamelCase       ).to_tuple()

                   with self.subTest('''JIT Disabled'''       ):
                         with jax.disable_jit():
                               a		:int									=			encode_jitted(**_UpperCamelCase       ).to_tuple()

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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a				,							a		:List[str]									=			self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__       ):
                   a		:Dict									=			model_class(_UpperCamelCase       )
                   a		:int									=			model.encode(inputs_dict['''input_ids''']   ,    inputs_dict['''attention_mask''']       )

                   a		:Tuple									=			{
                       '''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   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
                         return model.decode(
                             decoder_input_ids=_UpperCamelCase   ,    decoder_attention_mask=_UpperCamelCase   ,    encoder_outputs=_UpperCamelCase   ,    )

                   with self.subTest('''JIT Enabled'''       ):
                         a		:str									=			decode_jitted(**_UpperCamelCase       ).to_tuple()

                   with self.subTest('''JIT Disabled'''       ):
                         with jax.disable_jit():
                               a		:List[str]									=			decode_jitted(**_UpperCamelCase       ).to_tuple()

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




 @slow
 def 			SCREAMING_SNAKE_CASE__							(						self       ):


       for model_class_name in self.all_model_classes:
             a		:Union[str, Any]									=			model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''       )
             # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
             a		:Tuple									=			np.ones((1, 1)       ) * model.config.eos_token_id
             a		:int									=			model(_UpperCamelCase       )
             self.assertIsNotNone(_UpperCamelCase       )




 @unittest.skipUnless(jax_device != '''cpu'''   ,    '''3B test too slow on CPU.'''       )
 @slow
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[int]									=			{'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
       a		:List[str]									=			{'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

       a		:Tuple									=			FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''   ,    from_pt=_UpperCamelCase       )
       a		:Optional[int]									=			BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''       )

       a		:List[str]									=			['''Sam''']
       a		:str									=			tokenizer(_UpperCamelCase   ,    return_tensors='''jax'''       )

       a		:Optional[Any]									=			model.generate(**_UpperCamelCase   ,    **_UpperCamelCase       )
       a		:Optional[int]									=			'''Sam is a great name. It means \"sun\" in Gaelic.'''

       a		:List[Any]									=			tokenizer.batch_decode(_UpperCamelCase   ,    **_UpperCamelCase       )
       assert generated_txt[0].strip() == tgt_text








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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


UpperCamelCase__		 =     logging.get_logger(__name__)


UpperCamelCase__		 =     {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
UpperCamelCase__		 =     {
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

UpperCamelCase__		 =     {
    '''allenai/led-base-16384''': 16384,
}







@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 						lowerCamelCase__      ( ):


       """simple docstring"""




       __snake_case       	=							(
           list(range(ord("""!"""		)	,ord("""~"""		) + 1		)		) + list(range(ord("""¡"""		)	,ord("""¬"""		) + 1		)		) + list(range(ord("""®"""		)	,ord("""ÿ"""		) + 1		)		)
       )
       __snake_case       	=							bs[:]
       __snake_case       	=							0
       for b in range(2**8		):
              if b not in bs:
                     bs.append(__A		)
                     cs.append(2**8 + n		)
                     n += 1
       __snake_case       	=							[chr(__A		) for n in cs]
       return dict(zip(__A	,__A		)		)







def 						lowerCamelCase__      ( __A    :Dict		):


       """simple docstring"""




       __snake_case       	=							set()
       __snake_case       	=							word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)		)
              __snake_case       	=							char
       return pairs




class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							VOCAB_FILES_NAMES
       __SCREAMING_SNAKE_CASE				    =							PRETRAINED_VOCAB_FILES_MAP
       __SCREAMING_SNAKE_CASE				    =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __SCREAMING_SNAKE_CASE				    =							["input_ids", "attention_mask"]


       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase="replace"      , _UpperCamelCase="<s>"      , _UpperCamelCase="</s>"      , _UpperCamelCase="</s>"      , _UpperCamelCase="<s>"      , _UpperCamelCase="<unk>"      , _UpperCamelCase="<pad>"      , _UpperCamelCase="<mask>"      , _UpperCamelCase=False      , **_UpperCamelCase      , )      ->							Optional[int]:






              """simple docstring"""


              __snake_case       	=							AddedToken(_UpperCamelCase      , lstrip=_UpperCamelCase      , rstrip=_UpperCamelCase   ) if isinstance(_UpperCamelCase      , _UpperCamelCase   ) else bos_token
              __snake_case       	=							AddedToken(_UpperCamelCase      , lstrip=_UpperCamelCase      , rstrip=_UpperCamelCase   ) if isinstance(_UpperCamelCase      , _UpperCamelCase   ) else eos_token
              __snake_case       	=							AddedToken(_UpperCamelCase      , lstrip=_UpperCamelCase      , rstrip=_UpperCamelCase   ) if isinstance(_UpperCamelCase      , _UpperCamelCase   ) else sep_token
              __snake_case       	=							AddedToken(_UpperCamelCase      , lstrip=_UpperCamelCase      , rstrip=_UpperCamelCase   ) if isinstance(_UpperCamelCase      , _UpperCamelCase   ) else cls_token
              __snake_case       	=							AddedToken(_UpperCamelCase      , lstrip=_UpperCamelCase      , rstrip=_UpperCamelCase   ) if isinstance(_UpperCamelCase      , _UpperCamelCase   ) else unk_token
              __snake_case       	=							AddedToken(_UpperCamelCase      , lstrip=_UpperCamelCase      , rstrip=_UpperCamelCase   ) if isinstance(_UpperCamelCase      , _UpperCamelCase   ) else pad_token

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

              super().__init__(
                  errors=_UpperCamelCase      , bos_token=_UpperCamelCase      , eos_token=_UpperCamelCase      , unk_token=_UpperCamelCase      , sep_token=_UpperCamelCase      , cls_token=_UpperCamelCase      , pad_token=_UpperCamelCase      , mask_token=_UpperCamelCase      , add_prefix_space=_UpperCamelCase      , **_UpperCamelCase      , )

              with open(_UpperCamelCase      , encoding="""utf-8"""   ) as vocab_handle:
                     __snake_case       	=							json.load(_UpperCamelCase   )
              __snake_case       	=							{v: k for k, v in self.encoder.items()}
              __snake_case       	=							errors  # how to handle errors in decoding
              __snake_case       	=							bytes_to_unicode()
              __snake_case       	=							{v: k for k, v in self.byte_encoder.items()}
              with open(_UpperCamelCase      , encoding="""utf-8"""   ) as merges_handle:
                     __snake_case       	=							merges_handle.read().split("""\n"""   )[1:-1]
              __snake_case       	=							[tuple(merge.split()   ) for merge in bpe_merges]
              __snake_case       	=							dict(zip(_UpperCamelCase      , range(len(_UpperCamelCase   )   )   )   )
              __snake_case       	=							{}
              __snake_case       	=							add_prefix_space

              # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
              __snake_case       	=							re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""   )


       @property
       # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
       def 			a		(		self   )      ->							List[Any]:






              """simple docstring"""


              return len(self.encoder   )


       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


              return dict(self.encoder      , **self.added_tokens_encoder   )


       def 			a		(		self      , _UpperCamelCase   )      ->							str:






              """simple docstring"""


              if token in self.cache:
                     return self.cache[token]
              __snake_case       	=							tuple(_UpperCamelCase   )
              __snake_case       	=							get_pairs(_UpperCamelCase   )

              if not pairs:
                     return token

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

                            if word[i] == first and i < len(_UpperCamelCase   ) - 1 and word[i + 1] == second:
                                   new_word.append(first + second   )
                                   i += 2
                            else:
                                   new_word.append(word[i]   )
                                   i += 1
                     __snake_case       	=							tuple(_UpperCamelCase   )
                     __snake_case       	=							new_word
                     if len(_UpperCamelCase   ) == 1:
                            break
                     else:
                            __snake_case       	=							get_pairs(_UpperCamelCase   )
              __snake_case       	=							""" """.join(_UpperCamelCase   )
              __snake_case       	=							word
              return word


       def 			a		(		self      , _UpperCamelCase   )      ->							List[Any]:






              """simple docstring"""


              __snake_case       	=							[]
              for token in re.findall(self.pat      , _UpperCamelCase   ):
                     __snake_case       	=							"""""".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(_UpperCamelCase   ).split(""" """   )   )
              return bpe_tokens


       def 			a		(		self      , _UpperCamelCase   )      ->							Optional[Any]:






              """simple docstring"""


              return self.encoder.get(_UpperCamelCase      , self.encoder.get(self.unk_token   )   )


       def 			a		(		self      , _UpperCamelCase   )      ->							Any:






              """simple docstring"""


              return self.decoder.get(_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase   )      ->							str:






              """simple docstring"""


              __snake_case       	=							"""""".join(_UpperCamelCase   )
              __snake_case       	=							bytearray([self.byte_decoder[c] for c in text]   ).decode("""utf-8"""      , errors=self.errors   )
              return text


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None   )      ->							Tuple[str]:






              """simple docstring"""


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

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

              __snake_case       	=							0
              with open(_UpperCamelCase      , """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 _UpperCamelCase   : 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!"""   )
                                   __snake_case       	=							token_index
                            writer.write(""" """.join(_UpperCamelCase   ) + """\n"""   )
                            index += 1

              return vocab_file, merge_file


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None   )      ->							List[int]:






              """simple docstring"""


              if token_ids_a is None:
                     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
              __snake_case       	=							[self.cls_token_id]
              __snake_case       	=							[self.sep_token_id]
              return cls + token_ids_a + sep + sep + token_ids_a + sep


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None      , _UpperCamelCase = False   )      ->							List[int]:






              """simple docstring"""


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

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


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None   )      ->							List[int]:






              """simple docstring"""


              __snake_case       	=							[self.sep_token_id]
              __snake_case       	=							[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      , _UpperCamelCase      , _UpperCamelCase=False      , **_UpperCamelCase   )      ->							List[str]:






              """simple docstring"""


              __snake_case       	=							kwargs.pop("""add_prefix_space"""      , self.add_prefix_space   )
              if (is_split_into_words or add_prefix_space) and (len(_UpperCamelCase   ) > 0 and not text[0].isspace()):
                     __snake_case       	=							""" """ + text
              return (text, kwargs)






       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None      , _UpperCamelCase = PaddingStrategy.DO_NOT_PAD      , _UpperCamelCase = None      , _UpperCamelCase = None      , )      ->							dict:






              """simple docstring"""


              __snake_case       	=							super()._pad(
                  encoded_inputs=_UpperCamelCase      , max_length=_UpperCamelCase      , padding_strategy=_UpperCamelCase      , pad_to_multiple_of=_UpperCamelCase      , return_attention_mask=_UpperCamelCase      , )

              # Load from model defaults
              if return_attention_mask is None:
                     __snake_case       	=							"""attention_mask""" in self.model_input_names

              if return_attention_mask and "global_attention_mask" in encoded_inputs:
                     __snake_case       	=							encoded_inputs[self.model_input_names[0]]
                     # `global_attention_mask` need to have the same length as other (sequential) inputs.
                     __snake_case       	=							len(encoded_inputs["""global_attention_mask"""]   ) != len(_UpperCamelCase   )

                     if needs_to_be_padded:
                            __snake_case       	=							len(_UpperCamelCase   ) - len(encoded_inputs["""global_attention_mask"""]   )

                            if self.padding_side == "right":
                                   # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                                   __snake_case       	=							(
                                       encoded_inputs["""global_attention_mask"""] + [-1] * difference
                                   )
                            elif self.padding_side == "left":
                                   __snake_case       	=							[-1] * difference + encoded_inputs[
                                       """global_attention_mask"""
                                   ]
                            else:
                                   raise ValueError("""Invalid padding strategy:""" + str(self.padding_side   )   )

              return encoded_inputs

