




from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


__a							     =					['text', 'image', 'audio']







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				int  =     []

     for input_type in input_types:
          if input_type == "text":
               inputs.append('''Text input''' )
          elif input_type == "image":
               inputs.append(
                   Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO''' ) ) / '''000000039769.png''' ).resize((512, 512) ) )
          elif input_type == "audio":
               inputs.append(torch.ones(3000 ) )
          elif isinstance(_lowercase					,		_lowercase ):
               inputs.append(create_inputs(_lowercase ) )
          else:
               raise ValueError(f'''Invalid type requested: {input_type}''' )

     return inputs







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Dict  =     []

     for output in outputs:
          if isinstance(_lowercase					,		(str, AgentText) ):
               output_types.append('''text''' )
          elif isinstance(_lowercase					,		(Image.Image, AgentImage) ):
               output_types.append('''image''' )
          elif isinstance(_lowercase					,		(torch.Tensor, AgentAudio) ):
               output_types.append('''audio''' )
          else:
               raise ValueError(f'''Invalid output: {output}''' )

     return output_types



@is_tool_test
class     __a:






    """simple docstring"""





    def 				a__ ( self    )					->						Union[str, Any]:
         self.assertTrue(hasattr(self.tool  ,'''inputs'''    )    )
         self.assertTrue(hasattr(self.tool  ,'''outputs'''    )    )

         UpperCAmelCase_     :				str  =     self.tool.inputs
         for _input in inputs:
              if isinstance(_input  ,_SCREAMING_SNAKE_CASE    ):
                   for __input in _input:
                        self.assertTrue(__input in authorized_types    )
              else:
                   self.assertTrue(_input in authorized_types    )

         UpperCAmelCase_     :				Union[str, Any]  =     self.tool.outputs
         for _output in outputs:
              self.assertTrue(_output in authorized_types    )





    def 				a__ ( self    )					->						Any:
         UpperCAmelCase_     :				Union[str, Any]  =     create_inputs(self.tool.inputs    )
         UpperCAmelCase_     :				Any  =     self.tool(*_SCREAMING_SNAKE_CASE    )

         # There is a single output
         if len(self.tool.outputs    ) == 1:
              UpperCAmelCase_     :				Optional[int]  =     [outputs]

         self.assertListEqual(output_types(_SCREAMING_SNAKE_CASE    )  ,self.tool.outputs    )





    def 				a__ ( self    )					->						Optional[int]:
         self.assertTrue(hasattr(self.tool  ,'''description'''    )    )
         self.assertTrue(hasattr(self.tool  ,'''default_checkpoint'''    )    )
         self.assertTrue(self.tool.description.startswith('''This is a tool that'''    )    )





    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				Optional[Any]  =     create_inputs(self.tool.inputs    )
         UpperCAmelCase_     :				str  =     self.tool(*_SCREAMING_SNAKE_CASE    )

         if not isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
              UpperCAmelCase_     :				Union[str, Any]  =     [outputs]

         self.assertEqual(len(_SCREAMING_SNAKE_CASE    )  ,len(self.tool.outputs    )    )

         for output, output_type in zip(_SCREAMING_SNAKE_CASE  ,self.tool.outputs    ):
              UpperCAmelCase_     :				Union[str, Any]  =     AGENT_TYPE_MAPPING[output_type]
              self.assertTrue(isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )    )





    def 				a__ ( self    )					->						Any:
         UpperCAmelCase_     :				Union[str, Any]  =     create_inputs(self.tool.inputs    )

         UpperCAmelCase_     :				Tuple  =     []

         for _input, input_type in zip(_SCREAMING_SNAKE_CASE  ,self.tool.inputs    ):
              if isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
                   _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input    ) for _input_type in input_type]    )
              else:
                   _inputs.append(AGENT_TYPE_MAPPING[input_type](_input    )    )

        # Should not raise an error
         UpperCAmelCase_     :				List[Any]  =     self.tool(*_SCREAMING_SNAKE_CASE    )

         if not isinstance(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ):
              UpperCAmelCase_     :				Optional[Any]  =     [outputs]

         self.assertEqual(len(_SCREAMING_SNAKE_CASE    )  ,len(self.tool.outputs    )    )







"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


__lowerCamelCase							=  logging.get_logger(__name__)

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

__lowerCamelCase							=  {
    'vocab_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
        ),
        'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli': (
            'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
        ),
    },
}

__lowerCamelCase							=  {
    'squeezebert/squeezebert-uncased': 5_12,
    'squeezebert/squeezebert-mnli': 5_12,
    'squeezebert/squeezebert-mnli-headless': 5_12,
}


__lowerCamelCase							=  {
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				VOCAB_FILES_NAMES
					UpperCAmelCase__								=				PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__								=				PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__								=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__								=				SqueezeBertTokenizer

					def __init__(			self  :      Tuple     ,		__snake_case  :      str=None     ,		__snake_case  :      Optional[Any]=None     ,		__snake_case  :      str=True     ,		__snake_case  :      str="[UNK]"     ,		__snake_case  :      int="[SEP]"     ,		__snake_case  :      int="[PAD]"     ,		__snake_case  :      Any="[CLS]"     ,		__snake_case  :      List[Any]="[MASK]"     ,		__snake_case  :      Union[str, Any]=True     ,		__snake_case  :      Dict=None     ,		**__snake_case  :      str     ,		)			->							List[Any]:
											super().__init__(
											    __snake_case     ,		tokenizer_file=__snake_case     ,		do_lower_case=__snake_case     ,		unk_token=__snake_case     ,		sep_token=__snake_case     ,		pad_token=__snake_case     ,		cls_token=__snake_case     ,		mask_token=__snake_case     ,		tokenize_chinese_chars=__snake_case     ,		strip_accents=__snake_case     ,		**__snake_case     ,		)

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

											__magic_name__:	Optional[Any]							=       do_lower_case

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      List[Any]     ,		__snake_case  :      int=None					)			->							Optional[int]:
											__magic_name__:	int							=       [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   lowerCamelCase__   (			self  :      Any     ,		__snake_case  :      List[int]     ,		__snake_case  :      Optional[List[int]] = None					)			->							List[int]:
											__magic_name__:	Dict							=       [self.sep_token_id]
											__magic_name__:	List[str]							=       [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   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      str     ,		__snake_case  :      Optional[str] = None					)			->							Tuple[str]:
											__magic_name__:	Any							=       self._tokenizer.model.save(__snake_case     ,		name=__snake_case					)
											return tuple(__snake_case					)



