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   snake_case_       (		__A  ,unittest.TestCase       ):
   __A	:		int         =				RoCBertTokenizer
   __A	:		List[str]         =				None
   __A	:		Dict         =				False
   __A	:		Optional[int]         =				True
   __A	:		List[Any]         =				filter_non_english






   def       __UpperCamelCase     (      self				:     Dict  )					->						Any:
       super().setUp()

       lowercase__ :   Dict    					=				["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "你", "好", "是", "谁", "a", "b", "c", "d"]
       lowercase__ :   List[str]    					=				{}
       lowercase__ :   List[str]    					=				{}
       for i, value in enumerate(lowercase_  ):
           lowercase__ :   Union[str, Any]    					=				i
           lowercase__ :   Tuple    					=				i
       lowercase__ :   str    					=				os.path.join(self.tmpdirname	,						VOCAB_FILES_NAMES["vocab_file"]  )
       lowercase__ :   Dict    					=				os.path.join(self.tmpdirname	,						VOCAB_FILES_NAMES["word_shape_file"]  )
       lowercase__ :   str    					=				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(lowercase_	,						lowercase_	,						ensure_ascii=lowercase_  )
       with open(self.word_pronunciation_file	,						"w"	,						encoding="utf-8"  ) as word_pronunciation_writer:
           json.dump(lowercase_	,						lowercase_	,						ensure_ascii=lowercase_  )






   def       __UpperCamelCase     (      self				:     Dict  )					->						List[str]:
       lowercase__ :   str    					=				self.tokenizer_class(self.vocab_file	,						self.word_shape_file	,						self.word_pronunciation_file  )

       lowercase__ :   Any    					=				tokenizer.tokenize("你好[SEP]你是谁"  )
       self.assertListEqual(lowercase_	,						["你", "好", "[SEP]", "你", "是", "谁"]  )
       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_  )	,						[5, 6, 2, 5, 7, 8]  )
       self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(lowercase_  )	,						[5, 6, 2, 5, 7, 8]  )
       self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(lowercase_  )	,						[5, 6, 2, 5, 7, 8]  )






   def       __UpperCamelCase     (      self				:     List[str]  )					->						Optional[Any]:
       lowercase__ :   List[str]    					=				RoCBertBasicTokenizer()

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






   def       __UpperCamelCase     (      self				:     List[str]  )					->						Dict:
       lowercase__ :   Optional[int]    					=				RoCBertBasicTokenizer(do_lower_case=lowercase_  )

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






   def       __UpperCamelCase     (      self				:     List[str]  )					->						Tuple:
       lowercase__ :   int    					=				RoCBertBasicTokenizer(do_lower_case=lowercase_	,						strip_accents=lowercase_  )

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






   def       __UpperCamelCase     (      self				:     Dict  )					->						List[str]:
       lowercase__ :   Any    					=				RoCBertBasicTokenizer(do_lower_case=lowercase_	,						strip_accents=lowercase_  )

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






   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						Union[str, Any]:
       lowercase__ :   Any    					=				RoCBertBasicTokenizer(do_lower_case=lowercase_  )

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






   def       __UpperCamelCase     (      self				:     Optional[Any]  )					->						List[Any]:
       lowercase__ :   str    					=				RoCBertBasicTokenizer(do_lower_case=lowercase_  )

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






   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						List[str]:
       lowercase__ :   Optional[int]    					=				RoCBertBasicTokenizer(do_lower_case=lowercase_	,						strip_accents=lowercase_  )

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






   def       __UpperCamelCase     (      self				:     Tuple  )					->						Optional[Any]:
       lowercase__ :   Optional[Any]    					=				RoCBertBasicTokenizer(do_lower_case=lowercase_	,						strip_accents=lowercase_  )

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






   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						Tuple:
       lowercase__ :   Optional[Any]    					=				RoCBertBasicTokenizer(do_lower_case=lowercase_	,						never_split=["[UNK]"]  )

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






   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						Dict:
       lowercase__ :   Optional[int]    					=				["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

       lowercase__ :   Union[str, Any]    					=				{}
       for i, token in enumerate(lowercase_  ):
           lowercase__ :   Optional[Any]    					=				i
       lowercase__ :   Dict    					=				RoCBertWordpieceTokenizer(vocab=lowercase_	,						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       __UpperCamelCase     (      self				:     str  )					->						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       __UpperCamelCase     (      self				:     Dict  )					->						Union[str, Any]:
       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       __UpperCamelCase     (      self				:     Dict  )					->						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       __UpperCamelCase     (      self				:     Dict  )					->						Any:
       lowercase__ :   int    					=				self.get_tokenizer()

       # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
       self.assertListEqual([tokenizer.tokenize(lowercase_  ) for t in ["Test", "\xad", "test"]]	,						[["[UNK]"], [], ["[UNK]"]]  )

       if self.test_rust_tokenizer:
           lowercase__ :   Optional[Any]    					=				self.get_rust_tokenizer()
           self.assertListEqual(
               [rust_tokenizer.tokenize(lowercase_  ) for t in ["Test", "\xad", "test"]]	,						[["[UNK]"], [], ["[UNK]"]]  )






   def       __UpperCamelCase     (      self				:     List[str]  )					->						int:
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
               lowercase__ :   Dict    					=				self.rust_tokenizer_class.from_pretrained(lowercase_	,						**lowercase_  )

               lowercase__ :   List[Any]    					=				F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
               lowercase__ :   str    					=				tokenizer_r.encode_plus(
                   lowercase_	,						return_attention_mask=lowercase_	,						return_token_type_ids=lowercase_	,						return_offsets_mapping=lowercase_	,						add_special_tokens=lowercase_	,						)

               lowercase__ :   Any    					=				tokenizer_r.do_lower_case if hasattr(lowercase_	,						"do_lower_case"  ) else False
               lowercase__ :   Any    					=				(
                   [
                       ((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       __UpperCamelCase     (      self				:     Optional[Any]  )					->						Optional[int]:
       lowercase__ :   Optional[Any]    					=				["的", "人", "有"]
       lowercase__ :   Optional[Any]    					=				"".join(lowercase_  )
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
               lowercase__ :   Optional[Any]    					=				True
               lowercase__ :   str    					=				self.tokenizer_class.from_pretrained(lowercase_	,						**lowercase_  )
               lowercase__ :   Any    					=				self.rust_tokenizer_class.from_pretrained(lowercase_	,						**lowercase_  )

               lowercase__ :   Optional[int]    					=				tokenizer_p.encode(lowercase_	,						add_special_tokens=lowercase_  )
               lowercase__ :   Optional[int]    					=				tokenizer_r.encode(lowercase_	,						add_special_tokens=lowercase_  )

               lowercase__ :   Union[str, Any]    					=				tokenizer_r.convert_ids_to_tokens(lowercase_  )
               lowercase__ :   List[Any]    					=				tokenizer_p.convert_ids_to_tokens(lowercase_  )

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

               lowercase__ :   int    					=				False
               lowercase__ :   Any    					=				self.rust_tokenizer_class.from_pretrained(lowercase_	,						**lowercase_  )
               lowercase__ :   Union[str, Any]    					=				self.tokenizer_class.from_pretrained(lowercase_	,						**lowercase_  )

               lowercase__ :   Dict    					=				tokenizer_r.encode(lowercase_	,						add_special_tokens=lowercase_  )
               lowercase__ :   List[str]    					=				tokenizer_p.encode(lowercase_	,						add_special_tokens=lowercase_  )

               lowercase__ :   List[Any]    					=				tokenizer_r.convert_ids_to_tokens(lowercase_  )
               lowercase__ :   str    					=				tokenizer_p.convert_ids_to_tokens(lowercase_  )

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






   @slow
   def       __UpperCamelCase     (      self				:     Tuple  )					->						int:
       lowercase__ :   Optional[int]    					=				self.tokenizer_class(self.vocab_file	,						self.word_shape_file	,						self.word_pronunciation_file  )

       lowercase__ :   Any    					=				tokenizer.encode("你好"	,						add_special_tokens=lowercase_  )
       lowercase__ :   Dict    					=				tokenizer.encode("你是谁"	,						add_special_tokens=lowercase_  )

       lowercase__ :   str    					=				tokenizer.build_inputs_with_special_tokens(lowercase_  )
       lowercase__ :   Optional[int]    					=				tokenizer.build_inputs_with_special_tokens(lowercase_	,						lowercase_  )

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







   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						List[str]:
       lowercase__ :   List[str]    					=				self.get_tokenizers(do_lower_case=lowercase_  )
       for tokenizer in tokenizers:
           with self.subTest(F'''{tokenizer.__class__.__name__}'''  ):
               lowercase__ :   int    					=				"你好，你是谁"
               lowercase__ :   int    					=				tokenizer.tokenize(lowercase_  )
               lowercase__ :   str    					=				tokenizer.convert_tokens_to_ids(lowercase_  )
               lowercase__ :   Tuple    					=				tokenizer.convert_tokens_to_shape_ids(lowercase_  )
               lowercase__ :   int    					=				tokenizer.convert_tokens_to_pronunciation_ids(lowercase_  )
               lowercase__ :   int    					=				tokenizer.prepare_for_model(
                   lowercase_	,						lowercase_	,						lowercase_	,						add_special_tokens=lowercase_  )

               lowercase__ :   Any    					=				tokenizer.encode_plus(lowercase_	,						add_special_tokens=lowercase_  )

               self.assertEqual(lowercase_	,						lowercase_  )







'''simple docstring'''







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer







@dataclass
class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  torch.FloatTensor







class A__       (						UpperCAmelCase__ ,     UpperCAmelCase__      ):






      @register_to_config
      def __init__( self			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:int = 3     ,						SCREAMING_SNAKE_CASE			:int = 3     ,						SCREAMING_SNAKE_CASE			:Tuple[str] = ("DownEncoderBlock2D",)     ,						SCREAMING_SNAKE_CASE			:Tuple[str] = ("UpDecoderBlock2D",)     ,						SCREAMING_SNAKE_CASE			:Tuple[int] = (6_4,)     ,						SCREAMING_SNAKE_CASE			:int = 1     ,						SCREAMING_SNAKE_CASE			:str = "silu"     ,						SCREAMING_SNAKE_CASE			:int = 3     ,						SCREAMING_SNAKE_CASE			:int = 3_2     ,						SCREAMING_SNAKE_CASE			:int = 2_5_6     ,						SCREAMING_SNAKE_CASE			:int = 3_2     ,						SCREAMING_SNAKE_CASE			:Optional[int] = None     ,						SCREAMING_SNAKE_CASE			:float = 0.18_215     ,						SCREAMING_SNAKE_CASE			:str = "group"     ,						)       ->					Optional[int]:




             '''simple docstring'''
             super().__init__()

             # pass init params to Encoder
             _a    : Union[str, Any]     					=Encoder(
                 in_channels=SCREAMING_SNAKE_CASE     ,						out_channels=SCREAMING_SNAKE_CASE     ,						down_block_types=SCREAMING_SNAKE_CASE     ,						block_out_channels=SCREAMING_SNAKE_CASE     ,						layers_per_block=SCREAMING_SNAKE_CASE     ,						act_fn=SCREAMING_SNAKE_CASE     ,						norm_num_groups=SCREAMING_SNAKE_CASE     ,						double_z=SCREAMING_SNAKE_CASE     ,						)

             _a    : Optional[int]     					=vq_embed_dim if vq_embed_dim is not None else latent_channels

             _a    : Optional[int]     					=nn.Convad(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						1	)
             _a    : str     					=VectorQuantizer(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						beta=0.25     ,						remap=SCREAMING_SNAKE_CASE     ,						sane_index_shape=SCREAMING_SNAKE_CASE	)
             _a    : List[str]     					=nn.Convad(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						1	)

             # pass init params to Decoder
             _a    : List[str]     					=Decoder(
                 in_channels=SCREAMING_SNAKE_CASE     ,						out_channels=SCREAMING_SNAKE_CASE     ,						up_block_types=SCREAMING_SNAKE_CASE     ,						block_out_channels=SCREAMING_SNAKE_CASE     ,						layers_per_block=SCREAMING_SNAKE_CASE     ,						act_fn=SCREAMING_SNAKE_CASE     ,						norm_num_groups=SCREAMING_SNAKE_CASE     ,						norm_type=SCREAMING_SNAKE_CASE     ,						)






      @apply_forward_hook
      def __UpperCAmelCase       ( self			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:torch.FloatTensor     ,						SCREAMING_SNAKE_CASE			:bool = True	)       ->					VQEncoderOutput:




             '''simple docstring'''
             _a    : Optional[int]     					=self.encoder(SCREAMING_SNAKE_CASE	)
             _a    : int     					=self.quant_conv(SCREAMING_SNAKE_CASE	)

             if not return_dict:
                    return (h,)

             return VQEncoderOutput(latents=SCREAMING_SNAKE_CASE	)






      @apply_forward_hook
      def __UpperCAmelCase       ( self			:List[Any]     ,						SCREAMING_SNAKE_CASE			:torch.FloatTensor     ,						SCREAMING_SNAKE_CASE			:bool = False     ,						SCREAMING_SNAKE_CASE			:bool = True	)       ->					Union[DecoderOutput, torch.FloatTensor]:




             '''simple docstring'''
             # also go through quantization layer
             if not force_not_quantize:
                    _a					,    _a					,    _a    : Tuple     					=self.quantize(SCREAMING_SNAKE_CASE	)
             else:
                    _a    : str     					=h
             _a    : Dict     					=self.post_quant_conv(SCREAMING_SNAKE_CASE	)
             _a    : Union[str, Any]     					=self.decoder(SCREAMING_SNAKE_CASE     ,						quant if self.config.norm_type == """spatial""" else None	)

             if not return_dict:
                    return (dec,)

             return DecoderOutput(sample=SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:torch.FloatTensor     ,						SCREAMING_SNAKE_CASE			:bool = True	)       ->					Union[DecoderOutput, torch.FloatTensor]:




             '''simple docstring'''
             _a    : Tuple     					=sample
             _a    : int     					=self.encode(SCREAMING_SNAKE_CASE	).latents
             _a    : List[Any]     					=self.decode(SCREAMING_SNAKE_CASE	).sample

             if not return_dict:
                    return (dec,)

             return DecoderOutput(sample=SCREAMING_SNAKE_CASE	)


