import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
      import jieba

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


SCREAMING_SNAKE_CASE__		:       int       						=       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:       int       						=       {"vocab_file": "vocab.txt"}

SCREAMING_SNAKE_CASE__		:       int       						=       {
    "vocab_file": {
        "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt",
    },
}

SCREAMING_SNAKE_CASE__		:       Dict       						=       {
    "openbmb/cpm-ant-10b": 1024,
}







def      _a					(   lowercase__	:      Optional[int]			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					List[str]      =		collections.OrderedDict()
     with open(lowercase__  ,	'r'  ,	encoding='utf-8'			) as reader:
          SCREAMING_SNAKE_CASE__					:					Dict      =		reader.readlines()
     for index, token in enumerate(lowercase__			):
          SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		token.rstrip('\n'			)
          SCREAMING_SNAKE_CASE__					:					Dict      =		index
     return vocab

class    snake_case       (   UpperCamelCase_    ):




  def __init__(   self  :						List[Any]  ,			a_  :						List[str]  ,			a_  :						Union[str, Any]="<unk>"  ,			a_  :						int=200					)->							Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Dict      =		vocab
       SCREAMING_SNAKE_CASE__					:					Tuple      =		unk_token
       SCREAMING_SNAKE_CASE__					:					int      =		max_input_chars_per_word




  def 						__lowercase(   self  :						Union[str, Any]  ,			a_  :						Optional[int]					)->							List[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		list(a_					)
       if len(a_					) > self.max_input_chars_per_word:
            return [self.unk_token]

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		0
       SCREAMING_SNAKE_CASE__					:					List[str]      =		[]
       while start < len(a_					):
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		len(a_					)
            SCREAMING_SNAKE_CASE__					:					str      =		None
            while start < end:
                 SCREAMING_SNAKE_CASE__					:					Any      =		''.join(chars[start:end]					)
                 if substr in self.vocab:
                      SCREAMING_SNAKE_CASE__					:					str      =		substr
                      break
                 end -= 1
            if cur_substr is None:
                 sub_tokens.append(self.unk_token					)
                 start += 1
            else:
                 sub_tokens.append(a_					)
                 SCREAMING_SNAKE_CASE__					:					Dict      =		end

       return sub_tokens




class    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  VOCAB_FILES_NAMES
  lowercase_								=  PRETRAINED_VOCAB_FILES_MAP
  lowercase_								=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase_								=  ['input_ids', 'attention_mask']
  lowercase_								=  False




  def __init__(   self  :						Any  ,			a_  :						Union[str, Any]  ,			a_  :						Dict="<d>"  ,			a_  :						List[Any]="</d>"  ,			a_  :						Tuple="<s>"  ,			a_  :						Tuple="</s>"  ,			a_  :						Optional[Any]="<pad>"  ,			a_  :						Optional[Any]="<unk>"  ,			a_  :						Union[str, Any]="</n>"  ,			a_  :						int="</_>"  ,			a_  :						Any="left"  ,			**a_  :						Optional[int]  ,			)->							str:




       """simple docstring"""
       requires_backends(self  ,			['jieba']					)
       super().__init__(
           bod_token=a_  ,			eod_token=a_  ,			bos_token=a_  ,			eos_token=a_  ,			pad_token=a_  ,			unk_token=a_  ,			line_token=a_  ,			space_token=a_  ,			padding_side=a_  ,			**a_  ,			)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		bod_token
       SCREAMING_SNAKE_CASE__					:					List[str]      =		eod_token
       SCREAMING_SNAKE_CASE__					:					List[str]      =		load_vocab(a_					)
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.encoder[space_token]
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.encoder[line_token]

       del self.encoder[space_token]
       del self.encoder[line_token]

       SCREAMING_SNAKE_CASE__					:					List[str]      =		collections.OrderedDict(sorted(self.encoder.items()  ,			key=lambda a_					: x[1]					)					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		{v: k for k, v in self.encoder.items()}

       SCREAMING_SNAKE_CASE__					:					str      =		WordpieceTokenizer(vocab=self.encoder  ,			unk_token=self.unk_token					)




  @property
  def 						__lowercase(   self  :						Union[str, Any]					)->							Optional[int]:




       """simple docstring"""
       return self.encoder[self.bod_token]




  @property
  def 						__lowercase(   self  :						str					)->							Any:




       """simple docstring"""
       return self.encoder[self.eod_token]




  @property
  def 						__lowercase(   self  :						Union[str, Any]					)->							Optional[Any]:




       """simple docstring"""
       return self.encoder["\n"]




  @property
  def 						__lowercase(   self  :						List[str]					)->							int:




       """simple docstring"""
       return len(self.encoder					)




  def 						__lowercase(   self  :						Optional[int]					)->							List[Any]:




       """simple docstring"""
       return dict(self.encoder  ,			**self.added_tokens_encoder					)




  def 						__lowercase(   self  :						Any  ,			a_  :						Any					)->							List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		[]
       for x in jieba.cut(a_  ,			cut_all=a_					):
            output_tokens.extend(self.wordpiece_tokenizer.tokenize(a_					)					)
       return output_tokens




  def 						__lowercase(   self  :						str  ,			a_  :						int  ,			**a_  :						str					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		[i for i in token_ids if i >= 0]
       SCREAMING_SNAKE_CASE__					:					Any      =		[
           x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
       ]
       return super()._decode(a_  ,			**a_					)




  def 						__lowercase(   self  :						Optional[int]  ,			a_  :						List[str]					)->							Optional[Any]:




       """simple docstring"""
       return token in self.encoder




  def 						__lowercase(   self  :						str  ,			a_  :						List[str]					)->							str:




       """simple docstring"""
       return "".join(a_					)




  def 						__lowercase(   self  :						int  ,			a_  :						Tuple					)->							int:




       """simple docstring"""
       return self.encoder.get(a_  ,			self.encoder.get(self.unk_token					)					)




  def 						__lowercase(   self  :						str  ,			a_  :						List[Any]					)->							Tuple:




       """simple docstring"""
       return self.decoder.get(a_  ,			self.unk_token					)




  def 						__lowercase(   self  :						Any  ,			a_  :						str  ,			a_  :						Optional[str] = None					)->							Tuple[str]:




       """simple docstring"""
       if os.path.isdir(a_					):
            SCREAMING_SNAKE_CASE__					:					str      =		os.path.join(
                a_  ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)
       else:
            SCREAMING_SNAKE_CASE__					:					Tuple      =		(filename_prefix + '-' if filename_prefix else '') + save_directory
       SCREAMING_SNAKE_CASE__					:					str      =		0
       if " " in self.encoder:
            SCREAMING_SNAKE_CASE__					:					Dict      =		self.encoder[' ']
            del self.encoder[" "]
       if "\n" in self.encoder:
            SCREAMING_SNAKE_CASE__					:					Dict      =		self.encoder['\n']
            del self.encoder["\n"]
       SCREAMING_SNAKE_CASE__					:					int      =		collections.OrderedDict(sorted(self.encoder.items()  ,			key=lambda a_					: x[1]					)					)
       with open(a_  ,			'w'  ,			encoding='utf-8'					) as writer:
            for token, token_index in self.encoder.items():
                 if index != token_index:
                      logger.warning(
                          F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                          ' Please check that the vocabulary is not corrupted!'					)
                      SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		token_index
                 writer.write(token + '\n'					)
                 index += 1
       return (vocab_file,)




  def 						__lowercase(   self  :						Tuple  ,			a_  :						List[int]  ,			a_  :						List[int] = None					)->							List[int]:




       """simple docstring"""
       if token_ids_a is None:
            return [self.bos_token_id] + token_ids_a
       return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a




  def 						__lowercase(   self  :						int  ,			a_  :						List[int]  ,			a_  :						Optional[List[int]] = None  ,			a_  :						bool = 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 not None:
            return [1] + ([0] * len(a_					)) + [1] + ([0] * len(a_					))
       return [1] + ([0] * len(a_					))







import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.distilbert.modeling_flax_distilbert import (
         FlaxDistilBertForMaskedLM,
         FlaxDistilBertForMultipleChoice,
         FlaxDistilBertForQuestionAnswering,
         FlaxDistilBertForSequenceClassification,
         FlaxDistilBertForTokenClassification,
         FlaxDistilBertModel,
     )







class 						_snake_case    ( unittest.TestCase				):




 def __init__(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=13   ,    _lowerCamelCase=7   ,    _lowerCamelCase=True   ,    _lowerCamelCase=True   ,    _lowerCamelCase=True   ,    _lowerCamelCase=True   ,    _lowerCamelCase=99   ,    _lowerCamelCase=32   ,    _lowerCamelCase=5   ,    _lowerCamelCase=4   ,    _lowerCamelCase=37   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=512   ,    _lowerCamelCase=16   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.02   ,    _lowerCamelCase=4   ,    ):
       a		:Optional[Any]									=			parent
       a		:str									=			batch_size
       a		:Tuple									=			seq_length
       a		:List[Any]									=			is_training
       a		:Optional[int]									=			use_attention_mask
       a		:List[str]									=			use_token_type_ids
       a		:str									=			use_labels
       a		:Optional[Any]									=			vocab_size
       a		:Optional[int]									=			hidden_size
       a		:Tuple									=			num_hidden_layers
       a		:Union[str, Any]									=			num_attention_heads
       a		:int									=			intermediate_size
       a		:int									=			hidden_act
       a		:int									=			hidden_dropout_prob
       a		:Union[str, Any]									=			attention_probs_dropout_prob
       a		:str									=			max_position_embeddings
       a		:Dict									=			type_vocab_size
       a		:str									=			type_sequence_label_size
       a		:List[str]									=			initializer_range
       a		:Optional[Any]									=			num_choices




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Tuple									=			ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size       )

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

       a		:Any									=			DistilBertConfig(
           vocab_size=self.vocab_size   ,    dim=self.hidden_size   ,    n_layers=self.num_hidden_layers   ,    n_heads=self.num_attention_heads   ,    hidden_dim=self.intermediate_size   ,    hidden_act=self.hidden_act   ,    dropout=self.hidden_dropout_prob   ,    attention_dropout=self.attention_probs_dropout_prob   ,    max_position_embeddings=self.max_position_embeddings   ,    initializer_range=self.initializer_range   ,    tie_weights_=_lowerCamelCase   ,    )

       return config, input_ids, attention_mask




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			self.prepare_config_and_inputs()
       a				,							a				,							a		:str									=			config_and_inputs
       a		:List[Any]									=			{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict







@require_flax
class 						_snake_case    ( _snake_case		,       unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			(
     (
         FlaxDistilBertModel,
         FlaxDistilBertForMaskedLM,
         FlaxDistilBertForMultipleChoice,
         FlaxDistilBertForQuestionAnswering,
         FlaxDistilBertForSequenceClassification,
         FlaxDistilBertForTokenClassification,
         FlaxDistilBertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )




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




 @slow
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       for model_class_name in self.all_model_classes:
             a		:int									=			model_class_name.from_pretrained('''distilbert-base-uncased'''       )
             a		:List[str]									=			model(np.ones((1, 1)       )       )
             self.assertIsNotNone(_lowerCamelCase       )







@require_flax
class 						_snake_case    ( unittest.TestCase				):




 @slow
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased'''       )
       a		:Optional[Any]									=			np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]       )
       a		:List[Any]									=			np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
       a		:List[str]									=			model(_lowerCamelCase   ,    attention_mask=_lowerCamelCase       )[0]
       a		:Union[str, Any]									=			(1, 11, 768)
       self.assertEqual(output.shape   ,    _lowerCamelCase       )
       a		:int									=			np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]       )

       self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]   ,    _lowerCamelCase   ,    atol=1e-4       )       )





