


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


__snake_case   							=						logging.get_logger(__name__)


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

__snake_case   							=						{
    '''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'''
        ),
    },
}

__snake_case   							=						{
    '''allenai/longformer-base-4096''': 4_0_9_6,
    '''allenai/longformer-large-4096''': 4_0_9_6,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 4_0_9_6,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 4_0_9_6,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 4_0_9_6,
}






@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 		_A		(		)					-> List[Any]:


     """simple docstring"""


     __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(_lowercase )
               cs.append(2**8 + n )
               n += 1
     __UpperCamelCase =			[chr(_lowercase ) for n in cs]
     return dict(zip(_lowercase					,  _lowercase ) )






def 		_A		(		_lowercase )					-> Dict:


     """simple docstring"""


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



class 	__lowerCamelCase			(_a						):
       _lowercase											=     VOCAB_FILES_NAMES
       _lowercase											=     PRETRAINED_VOCAB_FILES_MAP
       _lowercase											=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _lowercase											=     ["""input_ids""", """attention_mask"""]







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






            '''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: Tuple					):






            '''simple docstring'''




            return len(self.encoder					)







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Dict,A_: List[Any]					):






            '''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: Tuple,A_: List[Any]					):






            '''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: Dict,A_: Union[str, Any]					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Union[str, Any],A_: int					):






            '''simple docstring'''




            return self.decoder.get(A_					)







       def 		snake_case_	(			self: Tuple,A_: List[str]					):






            '''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: List[str],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
            __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: Union[str, 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]
            __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: 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 		snake_case_	(			self: Optional[Any],A_: List[int],A_: Optional[List[int]] = None					):






            '''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: List[str],A_: Optional[int],A_: Optional[int]=False,**A_: int					):






            '''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)










import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
							import jax.numpy as jnp

							from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
							    FlaxRobertaPreLayerNormForCausalLM,
							    FlaxRobertaPreLayerNormForMaskedLM,
							    FlaxRobertaPreLayerNormForMultipleChoice,
							    FlaxRobertaPreLayerNormForQuestionAnswering,
							    FlaxRobertaPreLayerNormForSequenceClassification,
							    FlaxRobertaPreLayerNormForTokenClassification,
							    FlaxRobertaPreLayerNormModel,
							)
class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				def __init__(	self		:       List[Any],    lowerCamelCase		:       Dict,    lowerCamelCase		:       Dict=13,    lowerCamelCase		:       Optional[int]=7,    lowerCamelCase		:       Optional[Any]=True,    lowerCamelCase		:       Union[str, Any]=True,    lowerCamelCase		:       Optional[Any]=True,    lowerCamelCase		:       Optional[Any]=True,    lowerCamelCase		:       List[Any]=99,    lowerCamelCase		:       Any=32,    lowerCamelCase		:       List[str]=5,    lowerCamelCase		:       Union[str, Any]=4,    lowerCamelCase		:       Union[str, Any]=37,    lowerCamelCase		:       Optional[int]="gelu",    lowerCamelCase		:       Tuple=0.1,    lowerCamelCase		:       List[str]=0.1,    lowerCamelCase		:       Optional[Any]=512,    lowerCamelCase		:       int=16,    lowerCamelCase		:       str=2,    lowerCamelCase		:       int=0.02,    lowerCamelCase		:       int=4,    ):

								'''simple docstring'''
								lowercase__  					=     parent
								lowercase__  					=     batch_size
								lowercase__  					=     seq_length
								lowercase__  					=     is_training
								lowercase__  					=     use_attention_mask
								lowercase__  					=     use_token_type_ids
								lowercase__  					=     use_labels
								lowercase__  					=     vocab_size
								lowercase__  					=     hidden_size
								lowercase__  					=     num_hidden_layers
								lowercase__  					=     num_attention_heads
								lowercase__  					=     intermediate_size
								lowercase__  					=     hidden_act
								lowercase__  					=     hidden_dropout_prob
								lowercase__  					=     attention_probs_dropout_prob
								lowercase__  					=     max_position_embeddings
								lowercase__  					=     type_vocab_size
								lowercase__  					=     type_sequence_label_size
								lowercase__  					=     initializer_range
								lowercase__  					=     num_choices




				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     ids_tensor([self.batch_size, self.seq_length],    self.vocab_size    )

								lowercase__  					=     None
								if self.use_attention_mask:
												lowercase__  					=     random_attention_mask([self.batch_size, self.seq_length]    )

								lowercase__  					=     None
								if self.use_token_type_ids:
												lowercase__  					=     ids_tensor([self.batch_size, self.seq_length],    self.type_vocab_size    )

								lowercase__  					=     RobertaPreLayerNormConfig(
								    vocab_size=self.vocab_size,    hidden_size=self.hidden_size,    num_hidden_layers=self.num_hidden_layers,    num_attention_heads=self.num_attention_heads,    intermediate_size=self.intermediate_size,    hidden_act=self.hidden_act,    hidden_dropout_prob=self.hidden_dropout_prob,    attention_probs_dropout_prob=self.attention_probs_dropout_prob,    max_position_embeddings=self.max_position_embeddings,    type_vocab_size=self.type_vocab_size,    is_decoder=lowerCamelCase,    initializer_range=self.initializer_range,    )

								return config, input_ids, token_type_ids, attention_mask




				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     self.prepare_config_and_inputs()
								lowercase__					,		lowercase__					,		lowercase__					,		lowercase__  					=     config_and_inputs
								lowercase__  					=     {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
								return config, inputs_dict




				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     self.prepare_config_and_inputs()
								lowercase__					,		lowercase__					,		lowercase__					,		lowercase__  					=     config_and_inputs

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

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)



@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class  _UpperCAmelCase   (			A__						,unittest.TestCase      ):

				"""simple docstring"""




				lowercase__											=			True

				lowercase__											=			(
				    (
				        FlaxRobertaPreLayerNormModel,
				        FlaxRobertaPreLayerNormForCausalLM,
				        FlaxRobertaPreLayerNormForMaskedLM,
				        FlaxRobertaPreLayerNormForSequenceClassification,
				        FlaxRobertaPreLayerNormForTokenClassification,
				        FlaxRobertaPreLayerNormForMultipleChoice,
				        FlaxRobertaPreLayerNormForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)




				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								lowercase__  					=     FlaxRobertaPreLayerNormModelTester(self    )




				@slow
				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								for model_class_name in self.all_model_classes:
												lowercase__  					=     model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''',    from_pt=lowerCamelCase    )
												lowercase__  					=     model(np.ones((1, 1)    )    )
												self.assertIsNotNone(lowerCamelCase    )



@require_flax
class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				@slow
				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								lowercase__  					=     FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''',    from_pt=lowerCamelCase    )

								lowercase__  					=     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]],    dtype=jnp.intaa    )
								lowercase__  					=     model(lowerCamelCase    )[0]
								lowercase__  					=     [1, 11, 50_265]
								self.assertEqual(list(output.shape    ),    lowerCamelCase    )
								# compare the actual values for a slice.
								lowercase__  					=     np.array(
								    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]],    dtype=np.floataa    )
								self.assertTrue(np.allclose(output[:, :3, :3],    lowerCamelCase,    atol=1E-4    )    )




				@slow
				def 							lowercase__  (	self		:       Any    ):

								'''simple docstring'''
								lowercase__  					=     FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''',    from_pt=lowerCamelCase    )

								lowercase__  					=     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]],    dtype=jnp.intaa    )
								lowercase__  					=     model(lowerCamelCase    )[0]
								# compare the actual values for a slice.
								lowercase__  					=     np.array(
								    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]],    dtype=np.floataa    )
								self.assertTrue(np.allclose(output[:, :3, :3],    lowerCamelCase,    atol=1E-4    )    )




