



'''simple docstring'''

import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english

@require_tokenizers
class 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    Any   				=					RoCBertTokenizer
							lowerCAmelCase       :    List[str]   				=					None
							lowerCAmelCase       :    Dict   				=					False
							lowerCAmelCase       :    Optional[int]   				=					True
							lowerCAmelCase       :    int   				=					filter_non_english







							def 							__lowercase			(							self					:       Tuple		):
									super().setUp()

									_a     :      Union[str, Any]        =							['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', '你', '好', '是', '谁', 'a', 'b', 'c', 'd']
									_a     :      Optional[Any]        =							{}
									_a     :      List[str]        =							{}
									for i, value in enumerate(_UpperCAmelCase		):
											_a     :      Optional[Any]        =							i
											_a     :      Optional[int]        =							i
									_a     :      int        =							os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']		)
									_a     :      Union[str, Any]        =							os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['word_shape_file']		)
									_a     :      Any        =							os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['word_pronunciation_file']		)
									with open(self.vocab_file	,'w'	,encoding='utf-8'		) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)
									with open(self.word_shape_file	,'w'	,encoding='utf-8'		) as word_shape_writer:
											json.dump(_UpperCAmelCase	,_UpperCAmelCase	,ensure_ascii=_UpperCAmelCase		)
									with open(self.word_pronunciation_file	,'w'	,encoding='utf-8'		) as word_pronunciation_writer:
											json.dump(_UpperCAmelCase	,_UpperCAmelCase	,ensure_ascii=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Tuple		):
									_a     :      int        =							self.tokenizer_class(self.vocab_file	,self.word_shape_file	,self.word_pronunciation_file		)

									_a     :      Optional[int]        =							tokenizer.tokenize('你好[SEP]你是谁'		)
									self.assertListEqual(_UpperCAmelCase	,['你', '好', '[SEP]', '你', '是', '谁']		)
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)	,[5, 6, 2, 5, 7, 8]		)
									self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(_UpperCAmelCase		)	,[5, 6, 2, 5, 7, 8]		)
									self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(_UpperCAmelCase		)	,[5, 6, 2, 5, 7, 8]		)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      int        =							RoCBertBasicTokenizer()

									self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'		)	,['ah', '\u535A', '\u63A8', 'zz']		)







							def 							__lowercase			(							self					:       Union[str, Any]		):
									_a     :      Optional[Any]        =							RoCBertBasicTokenizer(do_lower_case=_UpperCAmelCase		)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '		)	,['hello', '!', 'how', 'are', 'you', '?']		)
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'		)	,['hello']		)







							def 							__lowercase			(							self					:       Union[str, Any]		):
									_a     :      List[str]        =							RoCBertBasicTokenizer(do_lower_case=_UpperCAmelCase	,strip_accents=_UpperCAmelCase		)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '		)	,['hällo', '!', 'how', 'are', 'you', '?']		)
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'		)	,['h\u00E9llo']		)







							def 							__lowercase			(							self					:       List[str]		):
									_a     :      Dict        =							RoCBertBasicTokenizer(do_lower_case=_UpperCAmelCase	,strip_accents=_UpperCAmelCase		)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '		)	,['hallo', '!', 'how', 'are', 'you', '?']		)
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'		)	,['hello']		)







							def 							__lowercase			(							self					:       str		):
									_a     :      Dict        =							RoCBertBasicTokenizer(do_lower_case=_UpperCAmelCase		)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '		)	,['hallo', '!', 'how', 'are', 'you', '?']		)
									self.assertListEqual(tokenizer.tokenize('H\u00E9llo'		)	,['hello']		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a     :      Optional[Any]        =							RoCBertBasicTokenizer(do_lower_case=_UpperCAmelCase		)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '		)	,['HeLLo', '!', 'how', 'Are', 'yoU', '?']		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a     :      List[str]        =							RoCBertBasicTokenizer(do_lower_case=_UpperCAmelCase	,strip_accents=_UpperCAmelCase		)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '		)	,['HäLLo', '!', 'how', 'Are', 'yoU', '?']		)







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Optional[Any]        =							RoCBertBasicTokenizer(do_lower_case=_UpperCAmelCase	,strip_accents=_UpperCAmelCase		)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '		)	,['HaLLo', '!', 'how', 'Are', 'yoU', '?']		)







							def 							__lowercase			(							self					:       Any		):
									_a     :      Optional[Any]        =							RoCBertBasicTokenizer(do_lower_case=_UpperCAmelCase	,never_split=['[UNK]']		)

									self.assertListEqual(
									    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'		)	,['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']		)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      int        =							['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

									_a     :      Optional[int]        =							{}
									for i, token in enumerate(_UpperCAmelCase		):
											_a     :      List[str]        =							i
									_a     :      Tuple        =							RoCBertWordpieceTokenizer(vocab=_UpperCAmelCase	,unk_token='[UNK]'		)

									self.assertListEqual(tokenizer.tokenize(''		)	,[]		)

									self.assertListEqual(tokenizer.tokenize('unwanted running'		)	,['un', '##want', '##ed', 'runn', '##ing']		)

									self.assertListEqual(tokenizer.tokenize('unwantedX running'		)	,['[UNK]', 'runn', '##ing']		)







							def 							__lowercase			(							self					:       Tuple		):
									self.assertTrue(_is_whitespace(' '		)		)
									self.assertTrue(_is_whitespace('\t'		)		)
									self.assertTrue(_is_whitespace('\r'		)		)
									self.assertTrue(_is_whitespace('\n'		)		)
									self.assertTrue(_is_whitespace('\u00A0'		)		)

									self.assertFalse(_is_whitespace('A'		)		)
									self.assertFalse(_is_whitespace('-'		)		)







							def 							__lowercase			(							self					:       List[str]		):
									self.assertTrue(_is_control('\u0005'		)		)

									self.assertFalse(_is_control('A'		)		)
									self.assertFalse(_is_control(' '		)		)
									self.assertFalse(_is_control('\t'		)		)
									self.assertFalse(_is_control('\r'		)		)







							def 							__lowercase			(							self					:       Optional[int]		):
									self.assertTrue(_is_punctuation('-'		)		)
									self.assertTrue(_is_punctuation('$'		)		)
									self.assertTrue(_is_punctuation('`'		)		)
									self.assertTrue(_is_punctuation('.'		)		)

									self.assertFalse(_is_punctuation('A'		)		)
									self.assertFalse(_is_punctuation(' '		)		)







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      List[str]        =							self.get_tokenizer()

									# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
									self.assertListEqual([tokenizer.tokenize(_UpperCAmelCase		) for t in ['Test', '\xad', 'test']]	,[['[UNK]'], [], ['[UNK]']]		)

									if self.test_rust_tokenizer:
											_a     :      List[Any]        =							self.get_rust_tokenizer()
											self.assertListEqual(
											    [rust_tokenizer.tokenize(_UpperCAmelCase		) for t in ['Test', '\xad', 'test']]	,[['[UNK]'], [], ['[UNK]']]		)







							def 							__lowercase			(							self					:       int		):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
													_a     :      Any        =							self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)

													_a     :      Optional[Any]        =							F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
													_a     :      str        =							tokenizer_r.encode_plus(
													    _UpperCAmelCase	,return_attention_mask=_UpperCAmelCase	,return_token_type_ids=_UpperCAmelCase	,return_offsets_mapping=_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase	,)

													_a     :      int        =							tokenizer_r.do_lower_case if hasattr(_UpperCAmelCase	,'do_lower_case'		) else False
													_a     :      Tuple        =							(
													    [
													        ((0, 0), tokenizer_r.cls_token),
													        ((0, 1), 'A'),
													        ((1, 2), ','),
													        ((3, 5), 'na'),
													        ((5, 6), '##ï'),
													        ((6, 8), '##ve'),
													        ((9, 15), tokenizer_r.mask_token),
													        ((16, 21), 'Allen'),
													        ((21, 23), '##NL'),
													        ((23, 24), '##P'),
													        ((25, 33), 'sentence'),
													        ((33, 34), '.'),
													        ((0, 0), tokenizer_r.sep_token),
													    ]
													    if not do_lower_case
													    else [
													        ((0, 0), tokenizer_r.cls_token),
													        ((0, 1), 'a'),
													        ((1, 2), ','),
													        ((3, 8), 'naive'),
													        ((9, 15), tokenizer_r.mask_token),
													        ((16, 21), 'allen'),
													        ((21, 23), '##nl'),
													        ((23, 24), '##p'),
													        ((25, 33), 'sentence'),
													        ((33, 34), '.'),
													        ((0, 0), tokenizer_r.sep_token),
													    ]
													)

													self.assertEqual(
													    [e[1] for e in expected_results]	,tokenizer_r.convert_ids_to_tokens(tokens['input_ids']		)		)
													self.assertEqual([e[0] for e in expected_results]	,tokens['offset_mapping']		)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      Tuple        =							['的', '人', '有']
									_a     :      List[Any]        =							''.join(_UpperCAmelCase		)
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
													_a     :      Dict        =							True
													_a     :      Optional[int]        =							self.tokenizer_class.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)
													_a     :      List[Any]        =							self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)

													_a     :      Tuple        =							tokenizer_p.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)
													_a     :      Optional[Any]        =							tokenizer_r.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)

													_a     :      List[Any]        =							tokenizer_r.convert_ids_to_tokens(_UpperCAmelCase		)
													_a     :      Optional[int]        =							tokenizer_p.convert_ids_to_tokens(_UpperCAmelCase		)

													# it is expected that each Chinese character is not preceded by "##"
													self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)
													self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)

													_a     :      int        =							False
													_a     :      Optional[Any]        =							self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)
													_a     :      Dict        =							self.tokenizer_class.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)

													_a     :      Any        =							tokenizer_r.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)
													_a     :      str        =							tokenizer_p.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)

													_a     :      Optional[Any]        =							tokenizer_r.convert_ids_to_tokens(_UpperCAmelCase		)
													_a     :      str        =							tokenizer_p.convert_ids_to_tokens(_UpperCAmelCase		)

													# it is expected that only the first Chinese character is not preceded by "##".
													_a     :      int        =							[
													    F"""##{token}""" if idx != 0 else token for idx, token in enumerate(_UpperCAmelCase		)
													]
													self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)
													self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)







							@slow
							def 							__lowercase			(							self					:       str		):
									_a     :      int        =							self.tokenizer_class(self.vocab_file	,self.word_shape_file	,self.word_pronunciation_file		)

									_a     :      Optional[int]        =							tokenizer.encode('你好'	,add_special_tokens=_UpperCAmelCase		)
									_a     :      Any        =							tokenizer.encode('你是谁'	,add_special_tokens=_UpperCAmelCase		)

									_a     :      Tuple        =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase		)
									_a     :      str        =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase	,_UpperCAmelCase		)

									assert encoded_sentence == [1] + text + [2]
									assert encoded_pair == [1] + text + [2] + text_a + [2]







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      int        =							self.get_tokenizers(do_lower_case=_UpperCAmelCase		)
									for tokenizer in tokenizers:
											with self.subTest(F"""{tokenizer.__class__.__name__}"""		):
													_a     :      Optional[Any]        =							'你好，你是谁'
													_a     :      Optional[int]        =							tokenizer.tokenize(_UpperCAmelCase		)
													_a     :      int        =							tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)
													_a     :      List[str]        =							tokenizer.convert_tokens_to_shape_ids(_UpperCAmelCase		)
													_a     :      List[str]        =							tokenizer.convert_tokens_to_pronunciation_ids(_UpperCAmelCase		)
													_a     :      List[str]        =							tokenizer.prepare_for_model(
													    _UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)

													_a     :      Dict        =							tokenizer.encode_plus(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)

													self.assertEqual(_UpperCAmelCase	,_UpperCAmelCase		)









from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
    from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       {
    '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/resolve/main/config.json''',
    '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/config.json''',
    '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json''',
    '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json''',
    '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/config.json''',
    '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json''',
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Tuple        =  "bloom"
       UpperCAmelCase__				:					List[Any]        =  ["past_key_values"]
       UpperCAmelCase__				:					str        =  {
           "num_hidden_layers": "n_layer",
           "num_attention_heads": "n_head",
       }
       def __init__(			self,			SCREAMING_SNAKE_CASE_=25_0880,			SCREAMING_SNAKE_CASE_=64,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=8,			SCREAMING_SNAKE_CASE_=1e-5,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=True,			SCREAMING_SNAKE_CASE_=1,			SCREAMING_SNAKE_CASE_=2,			SCREAMING_SNAKE_CASE_=False,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=0.0,			SCREAMING_SNAKE_CASE_=1,			SCREAMING_SNAKE_CASE_=False,			**SCREAMING_SNAKE_CASE_,			)     ->		Tuple:
          UpperCamelCase    :  str							=	vocab_size
          # Backward compatibility with n_embed kwarg
          UpperCamelCase    :  Optional[Any]							=	kwargs.pop('n_embed',			SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Union[str, Any]							=	hidden_size if n_embed is None else n_embed
          UpperCamelCase    :  Tuple							=	n_layer
          UpperCamelCase    :  Dict							=	n_head
          UpperCamelCase    :  List[Any]							=	layer_norm_epsilon
          UpperCamelCase    :  Optional[int]							=	initializer_range
          UpperCamelCase    :  int							=	use_cache
          UpperCamelCase    :  int							=	pretraining_tp
          UpperCamelCase    :  Optional[int]							=	apply_residual_connection_post_layernorm
          UpperCamelCase    :  str							=	hidden_dropout
          UpperCamelCase    :  str							=	attention_dropout

          UpperCamelCase    :  List[Any]							=	bos_token_id
          UpperCamelCase    :  Tuple							=	eos_token_id
          UpperCamelCase    :  Union[str, Any]							=	slow_but_exact

          super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_,			eos_token_id=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )






class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Any        =  version.parse("1.12"							)
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = "default",			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False,			)     ->		Any:
          super().__init__(SCREAMING_SNAKE_CASE_,			task=SCREAMING_SNAKE_CASE_,			patching_specs=SCREAMING_SNAKE_CASE_,			use_past=SCREAMING_SNAKE_CASE_       )
          if not getattr(self._config,			'pad_token_id',			SCREAMING_SNAKE_CASE_       ):
             # TODO: how to do that better?
             UpperCamelCase    :  Tuple							=	0
       @property
       def 					snake_case_       (			self       )     ->		Mapping[str, Mapping[int, str]]:
          UpperCamelCase    :  str							=	OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}       )
          if self.use_past:
             # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
             self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_,			direction='inputs',			inverted_values_shape=SCREAMING_SNAKE_CASE_       )
             UpperCamelCase    :  Union[str, Any]							=	{0: 'batch', 1: 'past_sequence + sequence'}
          else:
             UpperCamelCase    :  Optional[int]							=	{0: 'batch', 1: 'sequence'}

          return common_inputs
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self._config.n_layer
       @property
       def 					snake_case_       (			self       )     ->		int:
          return self._config.n_head
       @property
       def 					snake_case_       (			self       )     ->		float:
          return 1e-3
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = -1,			SCREAMING_SNAKE_CASE_ = -1,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			)     ->		Mapping[str, Any]:
          UpperCamelCase    :  Dict							=	super(SCREAMING_SNAKE_CASE_,			self       ).generate_dummy_inputs(
              SCREAMING_SNAKE_CASE_,			batch_size=SCREAMING_SNAKE_CASE_,			seq_length=SCREAMING_SNAKE_CASE_,			is_pair=SCREAMING_SNAKE_CASE_,			framework=SCREAMING_SNAKE_CASE_       )

          # We need to order the input in the way they appears in the forward()
          UpperCamelCase    :  Any							=	OrderedDict({'input_ids': common_inputs['input_ids']}       )

          # Need to add the past_keys
          if self.use_past:
             if not is_torch_available():
                raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'       )
             else:
                import torch

                UpperCamelCase	,     UpperCamelCase    :  int							=	common_inputs['input_ids'].shape
                # Not using the same length for past_key_values
                UpperCamelCase    :  Any							=	seqlen + 2
                UpperCamelCase    :  Optional[int]							=	self._config.hidden_size // self.num_attention_heads
                UpperCamelCase    :  Any							=	(
                    batch * self.num_attention_heads,
                    head_dim,
                    past_key_values_length,
                )
                UpperCamelCase    :  Optional[Any]							=	(
                    batch * self.num_attention_heads,
                    past_key_values_length,
                    head_dim,
                )
                UpperCamelCase    :  List[str]							=	[
                    (torch.zeros(SCREAMING_SNAKE_CASE_       ), torch.zeros(SCREAMING_SNAKE_CASE_       )) for _ in range(self.num_layers       )
                ]

          UpperCamelCase    :  str							=	common_inputs['attention_mask']
          if self.use_past:
             UpperCamelCase    :  int							=	ordered_inputs['attention_mask'].dtype
             UpperCamelCase    :  List[Any]							=	torch.cat(
                 [ordered_inputs['attention_mask'], torch.ones(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			dtype=SCREAMING_SNAKE_CASE_       )],			dim=1       )

          return ordered_inputs






       @property
       def 					snake_case_       (			self       )     ->		int:
          return 13



