




from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
  import torch
class 							__lowercase   (			lowerCamelCase__    ):
     _lowerCAmelCase		       =						["""image_processor""", """tokenizer"""]
     _lowerCAmelCase		       =						"""OneFormerImageProcessor"""
     _lowerCAmelCase		       =						("""CLIPTokenizer""", """CLIPTokenizerFast""")



     def __init__(  self   :							List[Any]      ,		lowercase__   :							str=None      ,		lowercase__   :							Any=None      ,		lowercase__   :							Union[str, Any] = 7_7      ,		lowercase__   :							Optional[int] = 7_7      ,		**lowercase__   :							str       ):





       if image_processor is None:
         raise ValueError('''You need to specify an `image_processor`.'''       )
       if tokenizer is None:
         raise ValueError('''You need to specify a `tokenizer`.'''       )

       a_		    =  max_seq_length
       a_		    =  task_seq_length

       super().__init__(snake_case__      ,		snake_case__       )



     def 				__magic_name__		(  self   :							Any      ,		lowercase__   :							Dict=None      ,		lowercase__   :							Any=7_7       ):





       if text_list is None:
         raise ValueError('''tokens cannot be None.'''       )

       a_		    =  self.tokenizer(snake_case__      ,		padding='''max_length'''      ,		max_length=snake_case__      ,		truncation=snake_case__       )

       a_		    =  tokens['''attention_mask'''], tokens['''input_ids''']

       a_		    =  []
       for attn_mask, input_id in zip(snake_case__      ,		snake_case__       ):
         a_		    =  torch.tensor(snake_case__       ) * torch.tensor(snake_case__       )
         token_inputs.append(token.unsqueeze(0       )       )

       a_		    =  torch.cat(snake_case__      ,		dim=0       )
       return token_inputs



     def __call__(  self   :							Optional[int]      ,		lowercase__   :							Optional[int]=None      ,		lowercase__   :							Optional[Any]=None      ,		lowercase__   :							List[str]=None      ,		**lowercase__   :							Optional[Any]       ):





       if task_inputs is None:
         raise ValueError('''You have to specify the task_input. Found None.'''       )
       elif images is None:
         raise ValueError('''You have to specify the image. Found None.'''       )

       if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs       ):
         raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''       )

       a_		    =  self.image_processor(snake_case__      ,		snake_case__      ,		snake_case__      ,		**snake_case__       )

       if isinstance(snake_case__      ,		snake_case__       ):
         a_		    =  [task_inputs]

       if isinstance(snake_case__      ,		snake_case__       ) and all(isinstance(snake_case__      ,		snake_case__       ) for task_input in task_inputs       ):
         a_		    =  []
         for task in task_inputs:
           a_		    =  f"the task is {task}"
           task_token_inputs.append(snake_case__       )
         a_		    =  self._preprocess_text(snake_case__      ,		max_length=self.task_seq_length       )
       else:
         raise TypeError('''Task Inputs should be a string or a list of strings.'''       )

       if hasattr(snake_case__      ,		'''text_inputs'''       ):
         a_		    =  encoded_inputs.text_inputs

         a_		    =  []
         for texts in texts_list:
           a_		    =  self._preprocess_text(snake_case__      ,		max_length=self.max_seq_length       )
           text_inputs.append(text_input_list.unsqueeze(0       )       )

         a_		    =  torch.cat(snake_case__      ,		dim=0       )

       return encoded_inputs



     def 				__magic_name__		(  self   :							Union[str, Any]      ,		lowercase__   :							int=None      ,		lowercase__   :							str=None      ,		lowercase__   :							Dict=None      ,		**lowercase__   :							str       ):





       if task_inputs is None:
         raise ValueError('''You have to specify the task_input. Found None.'''       )
       elif images is None:
         raise ValueError('''You have to specify the image. Found None.'''       )

       if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs       ):
         raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''       )

       a_		    =  self.image_processor.encode_inputs(snake_case__      ,		snake_case__      ,		snake_case__      ,		**snake_case__       )

       if isinstance(snake_case__      ,		snake_case__       ):
         a_		    =  [task_inputs]

       if isinstance(snake_case__      ,		snake_case__       ) and all(isinstance(snake_case__      ,		snake_case__       ) for task_input in task_inputs       ):
         a_		    =  []
         for task in task_inputs:
           a_		    =  f"the task is {task}"
           task_token_inputs.append(snake_case__       )
         a_		    =  self._preprocess_text(snake_case__      ,		max_length=self.task_seq_length       )
       else:
         raise TypeError('''Task Inputs should be a string or a list of strings.'''       )

       if hasattr(snake_case__      ,		'''text_inputs'''       ):
         a_		    =  encoded_inputs.text_inputs

         a_		    =  []
         for texts in texts_list:
           a_		    =  self._preprocess_text(snake_case__      ,		max_length=self.max_seq_length       )
           text_inputs.append(text_input_list.unsqueeze(0       )       )

         a_		    =  torch.cat(snake_case__      ,		dim=0       )

       return encoded_inputs



     def 				__magic_name__		(  self   :							Dict      ,		*lowercase__   :							Tuple      ,		**lowercase__   :							int       ):
       return self.image_processor.post_process_semantic_segmentation(*snake_case__      ,		**snake_case__       )



     def 				__magic_name__		(  self   :							str      ,		*lowercase__   :							Any      ,		**lowercase__   :							Dict       ):
       return self.image_processor.post_process_instance_segmentation(*snake_case__      ,		**snake_case__       )



     def 				__magic_name__		(  self   :							Dict      ,		*lowercase__   :							List[str]      ,		**lowercase__   :							List[Any]       ):
       return self.image_processor.post_process_panoptic_segmentation(*snake_case__      ,		**snake_case__       )

import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin







class       __snake_case (  lowerCamelCase__       , unittest.TestCase ):
 __lowerCamelCase     : Any 	=   SpeechaTextaTokenizer
 __lowerCamelCase     : Tuple 	=   False
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       super().setUp()

       UpperCAmelCase     :			Union[str, Any]     	='''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' '''    )
       UpperCAmelCase     :			int     	=['''he re</w> 123''', '''here a 1456''']
       UpperCAmelCase     :			int     	=dict(zip(snake_case__		, range(len(snake_case__    )    )    )    )

       UpperCAmelCase     :			Tuple     	={'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

       UpperCAmelCase     :			Tuple     	=tempfile.mkdtemp()
       UpperCAmelCase     :			Optional[Any]     	=os.path.join(self.tmpdirname		, VOCAB_FILES_NAMES['''vocab_file''']    )
       UpperCAmelCase     :			Dict     	=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(snake_case__    ) + '''\n'''    )

       with open(self.merges_file		, '''w'''    ) as fp:
             fp.write('''\n'''.join(snake_case__    )    )
 def 						UpperCAmelCase__							(	self    )						->				Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=list(self.get_tokenizer().get_vocab().keys()    )

       self.assertEqual(vocab_keys[0]		, '''<s>'''    )
       self.assertEqual(vocab_keys[1]		, '''<pad>'''    )
       self.assertEqual(vocab_keys[-1]		, '''vocab'''    )
       self.assertEqual(len(snake_case__    )		, 14    )
 def 						UpperCAmelCase__							(	self    )						->				Union[str, Any]:
       '''simple docstring'''





       self.assertEqual(self.get_tokenizer().vocab_size		, 14    )
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=SpeechaTextaTokenizer.from_pretrained(self.tmpdirname    )

       # make sure @@ is correctly concatenated
       UpperCAmelCase     :			str     	=[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
       UpperCAmelCase     :			Optional[Any]     	=tokenizer.decode(snake_case__    )

       self.assertTrue(output_string == '''herecouple words ofthe'''    )
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[Any]     	=SpeechaTextaTokenizer.from_pretrained(self.tmpdirname    )

       with tempfile.TemporaryDirectory() as tmp_dirname:
             tokenizer.save_pretrained(snake_case__    )
             os.remove(os.path.join(snake_case__		, '''merges.txt'''    )    )

             # load tokenizer without merges file should not throw an error
             UpperCAmelCase     :			Any     	=SpeechaTextaTokenizer.from_pretrained(snake_case__    )

       with tempfile.TemporaryDirectory() as tmp_dirname:
             # save tokenizer and load again
             tokenizer.save_pretrained(snake_case__    )
             UpperCAmelCase     :			List[str]     	=SpeechaTextaTokenizer.from_pretrained(snake_case__    )

       self.assertIsNotNone(snake_case__    )






 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       if not self.test_slow_tokenizer:
             return

       UpperCAmelCase     :			List[str]     	=inspect.signature(self.tokenizer_class.__init__    )
       UpperCAmelCase     :			Union[str, Any]     	=self.get_tokenizer()

       for parameter_name, parameter in signature.parameters.items():
             if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
                   self.assertIn(snake_case__		, tokenizer.init_kwargs    )
