





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      a__			(       lowercase__=""	):






   '''simple docstring'''






   UpperCAmelCase_           =tempfile.mkdtemp()
   return os.path.join(__lowercase	,     str(uuid.uuida()	) + suffix	)

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



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     int:
         '''simple docstring'''

         UpperCAmelCase_           =torch.rand(12		,	dtype=torch.floataa							) - 0.5
         UpperCAmelCase_           =AgentAudio(__a							)
         UpperCAmelCase_           =str(agent_type.to_string()							)

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

         del agent_type

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

         # Ensure that the file contains the same value as the original tensor
         UpperCAmelCase_           =sf.read(__a							)
         self.assertTrue(torch.allclose(__a		,	torch.tensor(__a							)		,	atol=1e-4							)							)






      def 						lowerCAmelCase__     (						self:    Optional[Any]							)    ->     Dict:
         '''simple docstring'''

         UpperCAmelCase_           =torch.rand(12		,	dtype=torch.floataa							) - 0.5
         UpperCAmelCase_           =get_new_path(suffix=".wav"							)
         sf.write(__a		,	__a		,	1_6000							)

         UpperCAmelCase_           =AgentAudio(__a							)

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

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



      def 						lowerCAmelCase__     (						self:    Dict							)    ->     Dict:
         '''simple docstring'''

         UpperCAmelCase_           =torch.randint(0		,	256		,	(64, 64, 3)							)
         UpperCAmelCase_           =AgentImage(__a							)
         UpperCAmelCase_           =str(agent_type.to_string()							)

         # Ensure that the tensor and the agent_type's tensor are the same
         self.assertTrue(torch.allclose(__a		,	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(__a							)							)



      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     Tuple:
         '''simple docstring'''

         UpperCAmelCase_           =Path(get_tests_dir("fixtures/tests_samples/COCO"							)							) / """000000039769.png"""
         UpperCAmelCase_           =Image.open(__a							)
         UpperCAmelCase_           =AgentImage(__a							)

         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(__a							)							)






      def 						lowerCAmelCase__     (						self:    Tuple							)    ->     List[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =Path(get_tests_dir("fixtures/tests_samples/COCO"							)							) / """000000039769.png"""
         UpperCAmelCase_           =Image.open(__a							)
         UpperCAmelCase_           =AgentImage(__a							)

         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(__a							)							)

class   A							(						unittest.TestCase    ):



      def 						lowerCAmelCase__     (						self:    Dict							)    ->     Any:
         '''simple docstring'''

         UpperCAmelCase_           ="""Hey!"""
         UpperCAmelCase_           =AgentText(__a							)

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










'''simple docstring'''







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


lowercase      :   Tuple      =       {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

lowercase      :   Dict      =       {
    """vocab_file""": {
        """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """unc-nlp/lxmert-base-uncased""": (
            """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

lowercase      :   List[Any]      =       {
    """unc-nlp/lxmert-base-uncased""": 512,
}

lowercase      :   Tuple      =       {
    """unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Optional[int]          =					VOCAB_FILES_NAMES
     lowerCAmelCase_  :     Optional[int]          =					PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase_  :     Any          =					PRETRAINED_INIT_CONFIGURATION
     lowerCAmelCase_  :     Optional[int]          =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase_  :     Optional[int]          =					LxmertTokenizer







     def __init__(				self							,__a=None							,__a=None							,__a=True							,__a="[UNK]"							,__a="[SEP]"							,__a="[PAD]"							,__a="[CLS]"							,__a="[MASK]"							,__a=True							,__a=None							,**__a							,)      ->				str:
          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							,)

          snake_case			:      List[Any]        =			json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
          if (
              normalizer_state.get("""lowercase"""							,__a							) != do_lower_case
              or normalizer_state.get("""strip_accents"""							,__a							) != strip_accents
              or normalizer_state.get("""handle_chinese_chars"""							,__a							) != tokenize_chinese_chars
          ):
               snake_case			:      Union[str, Any]        =			getattr(__a							,normalizer_state.pop("""type"""							)							)
               snake_case			:      Optional[int]        =			do_lower_case
               snake_case			:      Optional[int]        =			strip_accents
               snake_case			:      List[Any]        =			tokenize_chinese_chars
               snake_case			:      Union[str, Any]        =			normalizer_class(**__a							)

          snake_case			:      str        =			do_lower_case







     def 			snake_case_      (				self							,__a							,__a=None							)      ->				Optional[int]:
          snake_case			:      Tuple        =			[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 			snake_case_      (				self							,__a							,__a = None							)      ->				List[int]:
          snake_case			:      int        =			[self.sep_token_id]
          snake_case			:      Any        =			[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 			snake_case_      (				self							,__a							,__a = None							)      ->				Tuple[str]:
          snake_case			:      Optional[int]        =			self._tokenizer.model.save(__a							,name=__a							)
          return tuple(__a							)



