





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				:    Union[str, Any]       =   ["text", "image", "audio"]





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				List[str]:







			'''simple docstring'''

			UpperCAmelCase   		=				[]

			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(UpperCamelCase__			,			UpperCamelCase__ ):
									inputs.append(create_inputs(UpperCamelCase__ ) )
						else:
									raise ValueError(F"""Invalid type requested: {input_type}""" )

			return inputs





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase   		=				[]

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

			return output_types






@is_tool_test
class 	A_       :
	def      _lowercase				(    self		):






				'''simple docstring'''







				self.assertTrue(hasattr(self.tool							,					'''inputs'''		)		)
				self.assertTrue(hasattr(self.tool							,					'''outputs'''		)		)

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

				UpperCAmelCase   		=				self.tool.outputs
				for _output in outputs:
							self.assertTrue(_output in authorized_types		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				create_inputs(self.tool.inputs		)
				UpperCAmelCase   		=				self.tool(*_A		)

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

				self.assertListEqual(output_types(_A		)							,					self.tool.outputs		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				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      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				create_inputs(self.tool.inputs		)
				UpperCAmelCase   		=				self.tool(*_A		)

				if not isinstance(_A							,					_A		):
							UpperCAmelCase   		=				[outputs]

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

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







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				create_inputs(self.tool.inputs		)

				UpperCAmelCase   		=				[]

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

				if not isinstance(_A							,					_A		):
							UpperCAmelCase   		=				[outputs]

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










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


__A				:    Dict       =   logging.get_logger(__name__)

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

__A				:    Tuple       =   {
    "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"
        ),
    },
}

__A				:    List[Any]       =   {
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


__A				:    List[Any]       =   {
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}

class 	A_       (a_			):
	UpperCAmelCase__					=			VOCAB_FILES_NAMES
	UpperCAmelCase__					=			PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase__					=			PRETRAINED_INIT_CONFIGURATION
	UpperCAmelCase__					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase__					=			SqueezeBertTokenizer
	def __init__(    self							,					_A=None							,					_A=None							,					_A=True							,					_A="[UNK]"							,					_A="[SEP]"							,					_A="[PAD]"							,					_A="[CLS]"							,					_A="[MASK]"							,					_A=True							,					_A=None							,					**_A							,					):






				'''simple docstring'''







				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							,					)

				UpperCAmelCase   		=				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
				):
							UpperCAmelCase   		=				getattr(_A							,					normalizer_state.pop('''type'''		)		)
							UpperCAmelCase   		=				do_lower_case
							UpperCAmelCase   		=				strip_accents
							UpperCAmelCase   		=				tokenize_chinese_chars
							UpperCAmelCase   		=				normalizer_class(**_A		)

				UpperCAmelCase   		=				do_lower_case
	def      _lowercase				(    self							,					_A							,					_A=None		):






				'''simple docstring'''







				UpperCAmelCase   		=				[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      _lowercase				(    self							,					_A							,					_A = None		):






				'''simple docstring'''







				UpperCAmelCase   		=				[self.sep_token_id]
				UpperCAmelCase   		=				[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      _lowercase				(    self							,					_A							,					_A = None		):






				'''simple docstring'''







				UpperCAmelCase   		=				self._tokenizer.model.save(_A							,					name=_A		)
				return tuple(_A		)



