import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class        snake_case__							(					_UpperCamelCase       ,    unittest.TestCase				):
   _SCREAMING_SNAKE_CASE						:			int         =   DebertaTokenizer
   _SCREAMING_SNAKE_CASE						:			Optional[Any]         =   True
   _SCREAMING_SNAKE_CASE						:			Union[str, Any]         =   DebertaTokenizerFast



   def        UpperCAmelCase__							(					self : Optional[int] )				->  Union[str, Any]:




         '''simple docstring'''




         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         snake_case_  :      Dict  	=							[
             "l",
             "o",
             "w",
             "e",
             "r",
             "s",
             "t",
             "i",
             "d",
             "n",
             "\u0120",
             "\u0120l",
             "\u0120n",
             "\u0120lo",
             "\u0120low",
             "er",
             "\u0120lowest",
             "\u0120newer",
             "\u0120wider",
             "[UNK]",
         ]
         snake_case_  :      Optional[int]  	=							dict(zip(a_      ,						range(len(a_ ) ) ) )
         snake_case_  :      str  	=							["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
         snake_case_  :      Union[str, Any]  	=							{"unk_token": "[UNK]"}

         snake_case_  :      Any  	=							os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES["vocab_file"] )
         snake_case_  :      Optional[Any]  	=							os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES["merges_file"] )
         with open(self.vocab_file      ,						"w"      ,						encoding="utf-8" ) as fp:
               fp.write(json.dumps(a_ ) + "\n" )
         with open(self.merges_file      ,						"w"      ,						encoding="utf-8" ) as fp:
               fp.write("\n".join(a_ ) )



   def        UpperCAmelCase__							(					self : int      ,						**A__ : Optional[int] )				->  str:




         '''simple docstring'''




         kwargs.update(self.special_tokens_map )
         return self.tokenizer_class.from_pretrained(self.tmpdirname      ,						**a_ )



   def        UpperCAmelCase__							(					self : Union[str, Any]      ,						A__ : Optional[int] )				->  Optional[Any]:




         '''simple docstring'''




         snake_case_  :      Dict  	=							"lower newer"
         snake_case_  :      int  	=							"lower newer"
         return input_text, output_text



   def        UpperCAmelCase__							(					self : Optional[Any] )				->  str:




         '''simple docstring'''




         snake_case_  :      Tuple  	=							self.get_tokenizer()
         snake_case_  :      Union[str, Any]  	=							"lower newer"
         snake_case_  :      Optional[int]  	=							["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
         snake_case_  :      Union[str, Any]  	=							tokenizer.tokenize(a_ )
         self.assertListEqual(a_      ,						a_ )

         snake_case_  :      str  	=							tokens + [tokenizer.unk_token]
         snake_case_  :      Any  	=							[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a_ )      ,						a_ )



   def        UpperCAmelCase__							(					self : Dict )				->  List[Any]:




         '''simple docstring'''




         snake_case_  :      Optional[Any]  	=							self.get_tokenizer()
         snake_case_  :      Optional[Any]  	=							tokenizer("Hello"      ,						"World" )
         snake_case_  :      int  	=							[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
         self.assertListEqual(tokd["token_type_ids"]      ,						a_ )



   @slow
   def        UpperCAmelCase__							(					self : Any )				->  int:




         '''simple docstring'''




         snake_case_  :      List[str]  	=							self.tokenizer_class.from_pretrained("microsoft/deberta-base" )

         snake_case_  :      Dict  	=							tokenizer.encode("sequence builders"      ,						add_special_tokens=a_ )
         snake_case_  :      str  	=							tokenizer.encode("multi-sequence build"      ,						add_special_tokens=a_ )

         snake_case_  :      str  	=							tokenizer.encode(
             "sequence builders"      ,						add_special_tokens=a_      ,						add_prefix_space=a_ )
         snake_case_  :      Optional[int]  	=							tokenizer.encode(
             "sequence builders"      ,						"multi-sequence build"      ,						add_special_tokens=a_      ,						add_prefix_space=a_ )

         snake_case_  :      Optional[Any]  	=							tokenizer.build_inputs_with_special_tokens(a_ )
         snake_case_  :      Tuple  	=							tokenizer.build_inputs_with_special_tokens(a_      ,						a_ )

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode



   @slow
   def        UpperCAmelCase__							(					self : Tuple )				->  int:




         '''simple docstring'''




         snake_case_  :      List[str]  	=							[self.tokenizer_class]
         if self.test_rust_tokenizer:
               tokenizer_classes.append(self.rust_tokenizer_class )

         for tokenizer_class in tokenizer_classes:
               snake_case_  :      Union[str, Any]  	=							tokenizer_class.from_pretrained("microsoft/deberta-base" )

               snake_case_  :      Dict  	=							[
                   "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
                   "ALBERT incorporates two parameter reduction techniques",
                   "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
                   " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
                   " vocabulary embedding.",
               ]

               snake_case_  :      int  	=							tokenizer(a_      ,						padding=a_ )
               snake_case_  :      Optional[Any]  	=							[tokenizer.decode(a_      ,						skip_special_tokens=a_ ) for seq in encoding["input_ids"]]

               # fmt: off
               snake_case_  :      str  	=							{
                   "input_ids": [
                       [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                       [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                       [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
                   ],
                   "token_type_ids": [
                       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                   ],
                   "attention_mask": [
                       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                   ]
               }
               # fmt: on

               snake_case_  :      str  	=							[
                   "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
                   "ALBERT incorporates two parameter reduction techniques",
                   "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
                   " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
                   " vocabulary embedding.",
               ]

               self.assertDictEqual(encoding.data      ,						a_ )

               for expected, decoded in zip(a_      ,						a_ ):
                     self.assertEqual(a_      ,						a_ )






'''simple docstring'''




import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow



@require_torch
class 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						'ylacombe/bark-small'
    UpperCAmelCase    				=						tempfile.mkdtemp()
    UpperCAmelCase    				=						'en_speaker_1'
    UpperCAmelCase    				=						'This is a test string'
    UpperCAmelCase    				=						'speaker_embeddings_path.json'
    UpperCAmelCase    				=						'speaker_embeddings'
  def     snake_case_      (  self ,			**a_  )				->					List[str]:


    """simple docstring"""
    return AutoTokenizer.from_pretrained(self.checkpoint ,			**a_  )
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    shutil.rmtree(self.tmpdirname  )
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						BarkProcessor(tokenizer=a_  )

    processor.save_pretrained(self.tmpdirname  )
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(self.tmpdirname  )

    self.assertEqual(processor.tokenizer.get_vocab() ,			tokenizer.get_vocab()  )
  @slow
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(
        pretrained_processor_name_or_path=self.checkpoint ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,			)
    processor.save_pretrained(
        self.tmpdirname ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,			speaker_embeddings_directory=self.speaker_embeddings_directory ,			)

    UpperCAmelCase    				=						self.get_tokenizer(bos_token='(BOS)' ,			eos_token='(EOS)'  )

    UpperCAmelCase    				=						BarkProcessor.from_pretrained(
        self.tmpdirname ,			self.speaker_embeddings_dict_path ,			bos_token='(BOS)' ,			eos_token='(EOS)' ,			)

    self.assertEqual(processor.tokenizer.get_vocab() ,			tokenizer_add_kwargs.get_vocab()  )
  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(
        pretrained_processor_name_or_path=self.checkpoint ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,			)

    UpperCAmelCase    				=						3_5
    UpperCAmelCase    				=						2
    UpperCAmelCase    				=						8

    UpperCAmelCase    				=						{
        'semantic_prompt': np.ones(a_  ),
        'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len)  ),
        'fine_prompt': np.ones((nb_codebooks_total, seq_len)  ),
    }

    # test providing already loaded voice_preset
    UpperCAmelCase    				=						processor(text=self.input_string ,			voice_preset=a_  )

    UpperCAmelCase    				=						inputs['history_prompt']
    for key in voice_preset:
      self.assertListEqual(voice_preset[key].tolist() ,			processed_voice_preset.get(a_ ,			np.array([]  )  ).tolist()  )

    # test loading voice preset from npz file
    UpperCAmelCase    				=						os.path.join(self.tmpdirname ,			'file.npz'  )
    np.savez(a_ ,			**a_  )
    UpperCAmelCase    				=						processor(text=self.input_string ,			voice_preset=a_  )
    UpperCAmelCase    				=						inputs['history_prompt']

    for key in voice_preset:
      self.assertListEqual(voice_preset[key].tolist() ,			processed_voice_preset.get(a_ ,			np.array([]  )  ).tolist()  )

    # test loading voice preset from the hub
    UpperCAmelCase    				=						processor(text=self.input_string ,			voice_preset=self.voice_preset  )





  def     snake_case_      (  self  )				->					List[Any]:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						BarkProcessor(tokenizer=a_  )

    UpperCAmelCase    				=						processor(text=self.input_string  )

    UpperCAmelCase    				=						tokenizer(
        self.input_string ,			padding='max_length' ,			max_length=2_5_6 ,			add_special_tokens=a_ ,			return_attention_mask=a_ ,			return_token_type_ids=a_ ,			)

    for key in encoded_tok.keys():
      self.assertListEqual(encoded_tok[key] ,			encoded_processor[key].squeeze().tolist()  )


