


"""simple docstring"""





import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
 import torch

if is_soundfile_availble():
 import soundfile as sf

if is_vision_available():
 from PIL import Image


def    _snake_case  (  _snake_case :	List[Any]=""	):
      lowerCAmelCase    :		List[str]				=      tempfile.mkdtemp()
      return os.path.join(_snake_case       ,      str(uuid.uuida()	) + suffix	)



@require_soundfile
@require_torch
class 						snake_case_(				unittest.TestCase	):






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase    :		Any				=      torch.rand(1_2    , dtype=torch.floataa ) - 0.5
           lowerCAmelCase    :		Tuple				=      AgentAudio(UpperCamelCase_ )
           lowerCAmelCase    :		str				=      str(agent_type.to_string() )

           # Ensure that the tensor and the agent_type's tensor are the same
           self.assertTrue(torch.allclose(UpperCamelCase_    , agent_type.to_raw()    , atol=1E-4 ) )

           del agent_type

           # Ensure the path remains even after the object deletion
           self.assertTrue(os.path.exists(UpperCamelCase_ ) )

           # Ensure that the file contains the same value as the original tensor
           lowerCAmelCase,					lowerCAmelCase    :		str				=      sf.read(UpperCamelCase_ )
           self.assertTrue(torch.allclose(UpperCamelCase_    , torch.tensor(UpperCamelCase_ )    , atol=1E-4 ) )






     def    lowerCamelCase__   (		self     :    Any ):
           lowerCAmelCase    :		Union[str, Any]				=      torch.rand(1_2    , dtype=torch.floataa ) - 0.5
           lowerCAmelCase    :		int				=      get_new_path(suffix='''.wav''' )
           sf.write(UpperCamelCase_    , UpperCamelCase_    , 1_6_0_0_0 )

           lowerCAmelCase    :		Optional[int]				=      AgentAudio(UpperCamelCase_ )

           self.assertTrue(torch.allclose(UpperCamelCase_    , agent_type.to_raw()    , atol=1E-4 ) )
           self.assertEqual(agent_type.to_string()    , UpperCamelCase_ )



@require_vision
@require_torch
class 						snake_case_(				unittest.TestCase	):






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase    :		List[Any]				=      torch.randint(0    , 2_5_6    , (6_4, 6_4, 3) )
           lowerCAmelCase    :		List[str]				=      AgentImage(UpperCamelCase_ )
           lowerCAmelCase    :		int				=      str(agent_type.to_string() )

           # Ensure that the tensor and the agent_type's tensor are the same
           self.assertTrue(torch.allclose(UpperCamelCase_    , agent_type._tensor    , atol=1E-4 ) )

           self.assertIsInstance(agent_type.to_raw()    , Image.Image )

           # Ensure the path remains even after the object deletion
           del agent_type
           self.assertTrue(os.path.exists(UpperCamelCase_ ) )






     def    lowerCamelCase__   (		self     :    Dict ):
           lowerCAmelCase    :		Any				=      Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png'''
           lowerCAmelCase    :		Optional[int]				=      Image.open(UpperCamelCase_ )
           lowerCAmelCase    :		int				=      AgentImage(UpperCamelCase_ )

           self.assertTrue(path.samefile(agent_type.to_string() ) )
           self.assertTrue(image == agent_type.to_raw() )

           # Ensure the path remains even after the object deletion
           del agent_type
           self.assertTrue(os.path.exists(UpperCamelCase_ ) )






     def    lowerCamelCase__   (		self     :    Tuple ):
           lowerCAmelCase    :		Any				=      Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png'''
           lowerCAmelCase    :		Tuple				=      Image.open(UpperCamelCase_ )
           lowerCAmelCase    :		Optional[Any]				=      AgentImage(UpperCamelCase_ )

           self.assertFalse(path.samefile(agent_type.to_string() ) )
           self.assertTrue(image == agent_type.to_raw() )

           # Ensure the path remains even after the object deletion
           del agent_type
           self.assertTrue(os.path.exists(UpperCamelCase_ ) )



class 						snake_case_(				unittest.TestCase	):






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           lowerCAmelCase    :		Optional[int]				=      '''Hey!'''
           lowerCAmelCase    :		Dict				=      AgentText(UpperCamelCase_ )

           self.assertEqual(UpperCamelCase_    , agent_type.to_string() )
           self.assertEqual(UpperCamelCase_    , agent_type.to_raw() )
           self.assertEqual(UpperCamelCase_    , UpperCamelCase_ )





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


A_       : List[str]         =			logging.get_logger(__name__)

A_       : Tuple         =			{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A_       : List[Any]         =			{
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

A_       : Tuple         =			{
    'junnyu/roformer_chinese_small': 1536,
    'junnyu/roformer_chinese_base': 1536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


A_       : Union[str, Any]         =			{
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   Dict  							=       VOCAB_FILES_NAMES
					UpperCAmelCase__:   Tuple  							=       PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:   List[str]  							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:   List[Any]  							=       PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__:   Optional[int]  							=       RoFormerTokenizer






					def __init__( self    ,       A__=None    ,       A__=None    ,       A__=True    ,       A__="[UNK]"    ,       A__="[SEP]"    ,       A__="[PAD]"    ,       A__="[CLS]"    ,       A__="[MASK]"    ,       A__=True    ,       A__=None    ,       **A__    ,       ):
						super().__init__(
						    A__    ,       tokenizer_file=A__    ,       do_lower_case=A__    ,       unk_token=A__    ,       sep_token=A__    ,       pad_token=A__    ,       cls_token=A__    ,       mask_token=A__    ,       tokenize_chinese_chars=A__    ,       strip_accents=A__    ,       **A__    ,       )

						A__							:    Tuple        =		json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
						if (
						    pre_tok_state.get("""lowercase"""    ,       A__			) != do_lower_case
						    or pre_tok_state.get("""strip_accents"""    ,       A__			) != strip_accents
						):
							A__							:    List[Any]        =		getattr(A__    ,       pre_tok_state.pop("""type"""			)			)
							A__							:    Optional[int]        =		do_lower_case
							A__							:    int        =		strip_accents
							A__							:    str        =		pre_tok_class(**A__			)

						A__							:    Any        =		do_lower_case






					def __getstate__( self			):
						A__							:    int        =		self.__dict__.copy()
						A__							:    Union[str, Any]        =		BertPreTokenizer()
						return state






					def __setstate__( self    ,       A__			):
						A__							:    Union[str, Any]        =		d
						A__							:    Union[str, Any]        =		self.__dict__["""_tokenizer"""].get_vocab()
						A__							:    Dict        =		PreTokenizer.custom(JiebaPreTokenizer(A__			)			)






					def   __A			( self    ,       A__    ,       A__=None			):
						A__							:    Optional[Any]        =		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

						return output






					def   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Tuple        =		[self.sep_token_id]
						A__							:    Tuple        =		[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			) * [0] + len(token_ids_a + sep			) * [1]






					def   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Any        =		self._tokenizer.model.save(A__    ,       name=A__			)
						return tuple(A__			)






					def   __A			( self    ,       A__    ,       A__=None    ,       A__=None    ,       A__=False    ,       **A__    ,       ):
						A__							:    str        =		BertPreTokenizer()
						return super().save_pretrained(A__    ,       A__    ,       A__    ,       A__    ,       **A__			)



