




"""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
     A_						=				'''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 					UpperCAmelCase__       (snake_case__							:					int		,   snake_case__							:					List[str]		,   snake_case__							:					str=None		,   snake_case__							:					Optional[int]=None		,   snake_case__							:					List[Any]=None		,   snake_case__							:					int=None		,   snake_case__							:					Union[str, Any]=None		,   snake_case__							:					Tuple=None		,   ):
  """simple docstring"""

  if attention_mask is None:
    _snake_case							:		Dict								=  np.where(input_ids != config.pad_token_id		,   1		,   0    )
  if decoder_attention_mask is None:
    _snake_case							:		Tuple								=  np.where(decoder_input_ids != config.pad_token_id		,   1		,   0    )
  if head_mask is None:
    _snake_case							:		Tuple								=  np.ones((config.encoder_layers, config.encoder_attention_heads)    )
  if decoder_head_mask is None:
    _snake_case							:		Dict								=  np.ones((config.decoder_layers, config.decoder_attention_heads)    )
  if cross_attn_head_mask is None:
    _snake_case							:		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 					lowercase:







 '''simple docstring'''





 def __init__(				self:		Dict,      a_:		Any,      a_:		str=13,      a_:		int=7,      a_:		Any=True,      a_:		Union[str, Any]=False,      a_:		str=99,      a_:		List[Any]=16,      a_:		Optional[Any]=2,      a_:		int=4,      a_:		int=4,      a_:		Dict="gelu",      a_:		int=0.1,      a_:		Dict=0.1,      a_:		Tuple=32,      a_:		Optional[int]=2,      a_:		Any=1,      a_:		List[str]=0,      a_:		Union[str, Any]=0.02,      ):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  parent
   _snake_case							:		Tuple								=  batch_size
   _snake_case							:		int								=  seq_length
   _snake_case							:		Optional[int]								=  is_training
   _snake_case							:		Tuple								=  use_labels
   _snake_case							:		List[str]								=  vocab_size
   _snake_case							:		Dict								=  hidden_size
   _snake_case							:		int								=  num_hidden_layers
   _snake_case							:		Tuple								=  num_attention_heads
   _snake_case							:		Optional[int]								=  intermediate_size
   _snake_case							:		Any								=  hidden_act
   _snake_case							:		Optional[int]								=  hidden_dropout_prob
   _snake_case							:		List[Any]								=  attention_probs_dropout_prob
   _snake_case							:		Tuple								=  max_position_embeddings
   _snake_case							:		Any								=  eos_token_id
   _snake_case							:		List[str]								=  pad_token_id
   _snake_case							:		Dict								=  bos_token_id
   _snake_case							:		Tuple								=  initializer_range


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



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

   _snake_case							:		List[str]								=  shift_tokens_right(a_,      1,      2		)

   _snake_case							:		List[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=a_,      )
   _snake_case							:		Tuple								=  prepare_blenderbot_inputs_dict(a_,      a_,      a_		)
   return config, inputs_dict


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case		,					_snake_case							:		Tuple								=  self.prepare_config_and_inputs()
   return config, inputs_dict


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		Optional[Any],      a_:		Any,      a_:		Union[str, Any]		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  20
   _snake_case							:		List[str]								=  model_class_name(a_		)

   _snake_case							:		Optional[Any]								=  model.encode(inputs_dict["""input_ids"""]		)

   _snake_case		,					_snake_case							:		Optional[Any]								=  (
       inputs_dict["""decoder_input_ids"""],
       inputs_dict["""decoder_attention_mask"""],
   )

   _snake_case							:		Tuple								=  model.init_cache(decoder_input_ids.shape[0],      a_,      a_		)
   _snake_case							:		Optional[Any]								=  jnp.ones((decoder_input_ids.shape[0], max_decoder_length),      dtype="""i4"""		)

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

   _snake_case							:		Union[str, Any]								=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],      dtype="""i4"""		)
   _snake_case							:		Tuple								=  model.decode(
       decoder_input_ids[:, -1:],      a_,      decoder_attention_mask=a_,      past_key_values=outputs_cache.past_key_values,      decoder_position_ids=a_,      )

   _snake_case							:		List[str]								=  model.decode(a_,      a_		)

   _snake_case							:		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  UpperCamelCase_   (				self:		Union[str, Any],      a_:		Optional[Any],      a_:		List[Any],      a_:		Any		):




   '''simple docstring'''



   _snake_case							:		Dict								=  20
   _snake_case							:		Optional[int]								=  model_class_name(a_		)

   _snake_case							:		Optional[Any]								=  model.encode(inputs_dict["""input_ids"""]		)

   _snake_case		,					_snake_case							:		Dict								=  (
       inputs_dict["""decoder_input_ids"""],
       inputs_dict["""decoder_attention_mask"""],
   )

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

   _snake_case							:		List[Any]								=  model.init_cache(decoder_input_ids.shape[0],      a_,      a_		)
   _snake_case							:		Union[str, Any]								=  jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :],      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),      )

   _snake_case							:		str								=  model.decode(
       decoder_input_ids[:, :-1],      a_,      decoder_attention_mask=a_,      past_key_values=a_,      decoder_position_ids=a_,      )
   _snake_case							:		str								=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],      dtype="""i4"""		)
   _snake_case							:		Optional[int]								=  model.decode(
       decoder_input_ids[:, -1:],      a_,      past_key_values=outputs_cache.past_key_values,      decoder_attention_mask=a_,      decoder_position_ids=a_,      )

   _snake_case							:		Optional[int]								=  model.decode(a_,      a_,      decoder_attention_mask=a_		)

   _snake_case							:		Optional[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 					lowercase(     unittest.TestCase ):







 '''simple docstring'''





 lowercase__ 					=					99


 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  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,      )

   _snake_case							:		Any								=  input_ids.shape[0]
   _snake_case							:		int								=  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  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case		,					_snake_case		,					_snake_case							:		Dict								=  self._get_config_and_data()
   _snake_case							:		List[str]								=  FlaxBlenderbotForConditionalGeneration(a_		)
   _snake_case							:		str								=  lm_model(input_ids=a_		)
   _snake_case							:		Optional[int]								=  (batch_size, input_ids.shape[1], config.vocab_size)
   self.assertEqual(outputs["""logits"""].shape,      a_		)


 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  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,      )
   _snake_case							:		Union[str, Any]								=  FlaxBlenderbotForConditionalGeneration(a_		)
   _snake_case							:		str								=  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],      dtype=np.intaa		)
   _snake_case							:		Dict								=  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],      dtype=np.intaa		)
   _snake_case							:		Optional[Any]								=  lm_model(input_ids=a_,      decoder_input_ids=a_		)
   _snake_case							:		List[str]								=  (*summary.shape, config.vocab_size)
   self.assertEqual(outputs["""logits"""].shape,      a_		)


 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



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







@require_flax
class 					lowercase(     __a       ,			unittest.TestCase       ,			__a ):







 '''simple docstring'''





 lowercase__ 					=					True
 lowercase__ 					=					(
     (
         FlaxBlenderbotModel,
         FlaxBlenderbotForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 lowercase__ 					=					(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  FlaxBlenderbotModelTester(self		)


 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   _snake_case		,					_snake_case							:		List[str]								=  self.model_tester.prepare_config_and_inputs()
   for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward(a_,      a_,      a_		)


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case		,					_snake_case							:		Optional[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(a_,      a_,      a_		)


 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   _snake_case		,					_snake_case							:		Optional[Any]								=  self.model_tester.prepare_config_and_inputs_for_common()

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

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

       with self.subTest("""JIT Enabled"""		):
         _snake_case							:		Optional[Any]								=  encode_jitted(**a_		).to_tuple()

       with self.subTest("""JIT Disabled"""		):
         with jax.disable_jit():
           _snake_case							:		int								=  encode_jitted(**a_		).to_tuple()

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


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case		,					_snake_case							:		List[Any]								=  self.model_tester.prepare_config_and_inputs_for_common()

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

       _snake_case							:		Optional[Any]								=  {
           """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(a_:		Any,      a_:		Tuple,      a_:		int		):
         return model.decode(
             decoder_input_ids=a_,      decoder_attention_mask=a_,      encoder_outputs=a_,      )

       with self.subTest("""JIT Enabled"""		):
         _snake_case							:		Any								=  decode_jitted(**a_		).to_tuple()

       with self.subTest("""JIT Disabled"""		):
         with jax.disable_jit():
           _snake_case							:		List[str]								=  decode_jitted(**a_		).to_tuple()

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


 @slow
 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



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


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




   '''simple docstring'''



   _snake_case							:		Dict								=  {"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
   _snake_case							:		str								=  {"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

   _snake_case							:		List[str]								=  FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B""",      from_pt=a_		)
   _snake_case							:		Union[str, Any]								=  BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""		)

   _snake_case							:		str								=  ["""Sam"""]
   _snake_case							:		Tuple								=  tokenizer(a_,      return_tensors="""jax"""		)

   _snake_case							:		str								=  model.generate(**a_,      **a_		)
   _snake_case							:		List[Any]								=  """Sam is a great name. It means \"sun\" in Gaelic."""

   _snake_case							:		int								=  tokenizer.batch_decode(a_,      **a_		)
   assert generated_txt[0].strip() == tgt_text



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


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


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

__lowerCamelCase					:	List[Any]       =  {
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
}

__lowerCamelCase					:	int       =  {
    '''allenai/longformer-base-4096''': 40_96,
    '''allenai/longformer-large-4096''': 40_96,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 40_96,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 40_96,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 40_96,
}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def       _snake_case	(   ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    (
		    list(range(ord("!"	)   , ord("~"	) + 1	)	) + list(range(ord("¡"	)   , ord("¬"	) + 1	)	) + list(range(ord("®"	)   , ord("ÿ"	) + 1	)	)
		)
		SCREAMING_SNAKE_CASE_  :		str				      =    bs[:]
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    0
		for b in range(2**8	):
				if b not in bs:
						bs.append(lowerCAmelCase	)
						cs.append(2**8 + n	)
						n += 1
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    [chr(lowerCAmelCase	) for n in cs]
		return dict(zip(lowerCAmelCase   , lowerCAmelCase	)	)




def       _snake_case	(   lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    set()
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)	)
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    char
		return pairs







class 		a__						(					A__					):
					A	       =							VOCAB_FILES_NAMES
					A	       =							PRETRAINED_VOCAB_FILES_MAP
					A	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A	       =							['input_ids', 'attention_mask']

					def __init__(    self      :       Union[str, Any],_A      :       List[Any],_A      :       Tuple,_A      :       str="replace",_A      :       Optional[int]="<s>",_A      :       Dict="</s>",_A      :       Any="</s>",_A      :       Optional[Any]="<s>",_A      :       Union[str, Any]="<unk>",_A      :       int="<pad>",_A      :       Dict="<mask>",_A      :       int=False,**_A      :       Dict,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else bos_token
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else eos_token
							SCREAMING_SNAKE_CASE_  :		str				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else sep_token
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else cls_token
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else unk_token
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    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
							SCREAMING_SNAKE_CASE_  :		Dict				      =    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:
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    json.load(_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {v: k for k, v in self.encoder.items()}
							SCREAMING_SNAKE_CASE_  :		Any				      =    errors  # how to handle errors in decoding
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    bytes_to_unicode()
							SCREAMING_SNAKE_CASE_  :		str				      =    {v: k for k, v in self.byte_encoder.items()}
							with open(_A,encoding="utf-8"    ) as merges_handle:
									SCREAMING_SNAKE_CASE_  :		int				      =    merges_handle.read().split("\n"    )[1:-1]
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    [tuple(merge.split()    ) for merge in bpe_merges]
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    dict(zip(_A,range(len(_A    )    )    )    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    {}
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    add_prefix_space

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

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







							"""simple docstring"""






							return len(self.encoder    )

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       Any,_A      :       int    ):







							"""simple docstring"""






							if token in self.cache:
									return self.cache[token]
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tuple(_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    get_pairs(_A    )

							if not pairs:
									return token

							while True:
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    min(_A,key=lambda _A    : self.bpe_ranks.get(_A,float("inf"    )    )    )
									if bigram not in self.bpe_ranks:
											break
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Tuple				      =    bigram
									SCREAMING_SNAKE_CASE_  :		int				      =    []
									SCREAMING_SNAKE_CASE_  :		Dict				      =    0
									while i < len(_A    ):
											try:
													SCREAMING_SNAKE_CASE_  :		Tuple				      =    word.index(_A,_A    )
											except ValueError:
													new_word.extend(word[i:]    )
													break
											else:
													new_word.extend(word[i:j]    )
													SCREAMING_SNAKE_CASE_  :		str				      =    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
									SCREAMING_SNAKE_CASE_  :		Dict				      =    tuple(_A    )
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    new_word
									if len(_A    ) == 1:
											break
									else:
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    get_pairs(_A    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    " ".join(_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    word
							return word

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    []
							for token in re.findall(self.pat,_A    ):
									SCREAMING_SNAKE_CASE_  :		Any				      =    "".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 				__UpperCamelCase   (    self      :       Optional[int],_A      :       str    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       str    ):







							"""simple docstring"""






							return self.decoder.get(_A    )

					def 				__UpperCamelCase   (    self      :       List[str],_A      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    "".join(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    bytearray([self.byte_decoder[c] for c in text]    ).decode("utf-8",errors=self.errors    )
							return text

					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       str,_A      :       Optional[str] = None    ):







							"""simple docstring"""






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

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    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!"    )
													SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    token_index
											writer.write(" ".join(_A    ) + "\n"    )
											index += 1

							return vocab_file, merge_file

					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       List[int],_A      :       Optional[List[int]] = None    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[int],_A      :       Optional[List[int]] = None,_A      :       bool = False    ):







							"""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 				__UpperCamelCase   (    self      :       Any,_A      :       List[int],_A      :       Optional[List[int]] = None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    [self.sep_token_id]
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    [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 				__UpperCamelCase   (    self      :       Any,_A      :       Union[str, Any],_A      :       Any=False,**_A      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    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()):
									SCREAMING_SNAKE_CASE_  :		str				      =    " " + text
							return (text, kwargs)
