
"""simple docstring"""







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


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)

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

_SCREAMING_SNAKE_CASE	       =      {
    """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"""
        ),
    }
}

_SCREAMING_SNAKE_CASE	       =      {
    """junnyu/roformer_chinese_small""": 1_536,
    """junnyu/roformer_chinese_base""": 1_536,
    """junnyu/roformer_chinese_char_small""": 512,
    """junnyu/roformer_chinese_char_base""": 512,
    """junnyu/roformer_small_discriminator""": 128,
    """junnyu/roformer_small_generator""": 128,
}


_SCREAMING_SNAKE_CASE	       =      {
    """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 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					str     							=   VOCAB_FILES_NAMES
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   PRETRAINED_VOCAB_FILES_MAP
					_SCREAMING_SNAKE_CASE       :					Any     							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_SCREAMING_SNAKE_CASE       :					List[str]     							=   PRETRAINED_INIT_CONFIGURATION
					_SCREAMING_SNAKE_CASE       :					int     							=   RoFormerTokenizer





					def __init__(				self   :							Optional[int]							,						snake_case_   :							List[Any]=None							,						snake_case_   :							Dict=None							,						snake_case_   :							List[str]=True							,						snake_case_   :							Tuple="[UNK]"							,						snake_case_   :							List[Any]="[SEP]"							,						snake_case_   :							Optional[int]="[PAD]"							,						snake_case_   :							Union[str, Any]="[CLS]"							,						snake_case_   :							List[Any]="[MASK]"							,						snake_case_   :							Dict=True							,						snake_case_   :							List[str]=None							,						**snake_case_   :							Optional[int]							,						):
												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_							,						)

												__snake_case     			=       json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
												if (
												    pre_tok_state.get("lowercase"							,						snake_case_    ) != do_lower_case
												    or pre_tok_state.get("strip_accents"							,						snake_case_    ) != strip_accents
												):
																			__snake_case     			=       getattr(snake_case_							,						pre_tok_state.pop("type"    )    )
																			__snake_case     			=       do_lower_case
																			__snake_case     			=       strip_accents
																			__snake_case     			=       pre_tok_class(**snake_case_    )

												__snake_case     			=       do_lower_case





					def __getstate__(				self   :							List[Any]    ):
												__snake_case     			=       self.__dict__.copy()
												__snake_case     			=       BertPreTokenizer()
												return state





					def __setstate__(				self   :							Optional[Any]							,						snake_case_   :							str    ):
												__snake_case     			=       d
												__snake_case     			=       self.__dict__["_tokenizer"].get_vocab()
												__snake_case     			=       PreTokenizer.custom(JiebaPreTokenizer(snake_case_    )    )





					def 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							Any							,						snake_case_   :							Tuple=None    ):
												__snake_case     			=       [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   :							int							,						snake_case_   :							List[int]							,						snake_case_   :							Optional[List[int]] = None    ):
												__snake_case     			=       [self.sep_token_id]
												__snake_case     			=       [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   :							Union[str, Any]							,						snake_case_   :							str							,						snake_case_   :							Optional[str] = None    ):
												__snake_case     			=       self._tokenizer.model.save(snake_case_							,						name=snake_case_    )
												return tuple(snake_case_    )





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							Tuple							,						snake_case_   :							Optional[Any]=None							,						snake_case_   :							List[Any]=None							,						snake_case_   :							int=False							,						**snake_case_   :							Optional[int]							,						):
												__snake_case     			=       BertPreTokenizer()
												return super().save_pretrained(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_							,						**snake_case_    )


"""simple docstring"""







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


_SCREAMING_SNAKE_CASE	       =      ["""text""", """image""", """audio"""]
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     List[Any]:







							"""simple docstring"""


							__snake_case     			=       []

							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((5_12, 5_12) ) )
														elif input_type == "audio":
																					inputs.append(torch.ones(30_00 ) )
														elif isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ):
																					inputs.append(create_inputs(SCREAMING_SNAKE_CASE ) )
														else:
																					raise ValueError(F'''Invalid type requested: {input_type}''' )

							return inputs
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Optional[Any]:







							"""simple docstring"""


							__snake_case     			=       []

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

							return output_types







@is_tool_test
class 							__magic_name__     :





					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												self.assertTrue(hasattr(self.tool							,						"inputs"    )    )
												self.assertTrue(hasattr(self.tool							,						"outputs"    )    )

												__snake_case     			=       self.tool.inputs
												for _input in inputs:
																			if isinstance(_input							,						snake_case_    ):
																										for __input in _input:
																																	self.assertTrue(__input in authorized_types    )
																			else:
																										self.assertTrue(_input in authorized_types    )

												__snake_case     			=       self.tool.outputs
												for _output in outputs:
																			self.assertTrue(_output in authorized_types    )





					def 							lowerCAmelCase      (				self   :							int    ):
												__snake_case     			=       create_inputs(self.tool.inputs    )
												__snake_case     			=       self.tool(*snake_case_    )

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

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





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												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 							lowerCAmelCase      (				self   :							Any    ):
												__snake_case     			=       create_inputs(self.tool.inputs    )
												__snake_case     			=       self.tool(*snake_case_    )

												if not isinstance(snake_case_							,						snake_case_    ):
																			__snake_case     			=       [outputs]

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

												for output, output_type in zip(snake_case_							,						self.tool.outputs    ):
																			__snake_case     			=       AGENT_TYPE_MAPPING[output_type]
																			self.assertTrue(isinstance(snake_case_							,						snake_case_    )    )





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       create_inputs(self.tool.inputs    )

												__snake_case     			=       []

												for _input, input_type in zip(snake_case_							,						self.tool.inputs    ):
																			if isinstance(snake_case_							,						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
												__snake_case     			=       self.tool(*snake_case_    )

												if not isinstance(snake_case_							,						snake_case_    ):
																			__snake_case     			=       [outputs]

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