






'''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


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


def        _a  (     UpperCamelCase_					:				List[str]   ) ->   Optional[int]:
						"""simple docstring"""







						lowerCAmelCase__       =						[]

						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(3_000   )   )
												elif isinstance(UpperCamelCase_    ,   UpperCamelCase_   ):
																		inputs.append(create_inputs(UpperCamelCase_   )   )
												else:
																		raise ValueError(F"Invalid type requested: {input_type}"   )

						return inputs


def        _a  (     UpperCamelCase_					:				List   ) ->   Tuple:
						"""simple docstring"""







						lowerCAmelCase__       =						[]

						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      lowercase__     :

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





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

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

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

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						create_inputs(self.tool.inputs       )
							lowerCAmelCase__       =						self.tool(*__UpperCAmelCase       )

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

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

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''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 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						create_inputs(self.tool.inputs       )
							lowerCAmelCase__       =						self.tool(*__UpperCAmelCase       )

							if not isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ):
													lowerCAmelCase__       =						[outputs]

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

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


	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						create_inputs(self.tool.inputs       )

							lowerCAmelCase__       =						[]

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

							if not isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ):
													lowerCAmelCase__       =						[outputs]

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














import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class      lowercase__     (			_UpperCAmelCase,					unittest.TestCase	):
	a_         =None
	a_         =BloomTokenizerFast
	a_         =BloomTokenizerFast
	a_         =True
	a_         =False
	a_         ="""tokenizer_file"""
	a_         ={"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							super().setUp()
							lowerCAmelCase__       =						BloomTokenizerFast.from_pretrained("bigscience/tokenizer"       )
							tokenizer.save_pretrained(self.tmpdirname       )

	def 		UpperCAmelCase  (							self		,    **__UpperCAmelCase       )->					int:







							'''simple docstring'''





							kwargs.update(self.special_tokens_map       )
							return BloomTokenizerFast.from_pretrained(self.tmpdirname		,    **__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						self.get_rust_tokenizer()

							lowerCAmelCase__       =						["The quick brown fox</s>", "jumps over the lazy dog</s>"]
							lowerCAmelCase__       =						[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

							lowerCAmelCase__       =						tokenizer.batch_encode_plus(__UpperCAmelCase       )["input_ids"]
							self.assertListEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

							lowerCAmelCase__       =						tokenizer.batch_decode(__UpperCAmelCase       )
							self.assertListEqual(__UpperCAmelCase		,    __UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase=6       )->					str:







							'''simple docstring'''





							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"       ):
																			lowerCAmelCase__       =						self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase		,    **__UpperCAmelCase       )
																			# tokenizer_r.pad_token = None # Hotfixing padding = None
																			# Simple input
																			lowerCAmelCase__       =						"This is a simple input"
																			lowerCAmelCase__       =						["This is a simple input 1", "This is a simple input 2"]
																			lowerCAmelCase__       =						("This is a simple input", "This is a pair")
																			lowerCAmelCase__       =						[
																			    ("This is a simple input 1", "This is a simple input 2"),
																			    ("This is a simple pair 1", "This is a simple pair 2"),
																			]

																			# Simple input tests
																			try:
																									tokenizer_r.encode(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )
																									tokenizer_r.encode_plus(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )

																									tokenizer_r.batch_encode_plus(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )
																									tokenizer_r.encode(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )
																									tokenizer_r.batch_encode_plus(__UpperCAmelCase		,    max_length=__UpperCAmelCase       )
																			except ValueError:
																									self.fail("Bloom Tokenizer should be able to deal with padding"       )

																			lowerCAmelCase__       =						None  # Hotfixing padding = None
																			self.assertRaises(__UpperCAmelCase		,    tokenizer_r.encode		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"       )

																			# Simple input
																			self.assertRaises(__UpperCAmelCase		,    tokenizer_r.encode_plus		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"       )

																			# Simple input
																			self.assertRaises(
																			    __UpperCAmelCase		,    tokenizer_r.batch_encode_plus		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"		,    )

																			# Pair input
																			self.assertRaises(__UpperCAmelCase		,    tokenizer_r.encode		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"       )

																			# Pair input
																			self.assertRaises(__UpperCAmelCase		,    tokenizer_r.encode_plus		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"       )

																			# Pair input
																			self.assertRaises(
																			    __UpperCAmelCase		,    tokenizer_r.batch_encode_plus		,    __UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding="max_length"		,    )

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						self.get_rust_tokenizer()
							lowerCAmelCase__       =						load_dataset("xnli"		,    "all_languages"		,    split="test"		,    streaming=__UpperCAmelCase       )

							lowerCAmelCase__       =						next(iter(__UpperCAmelCase       )       )["premise"]  # pick up one data
							lowerCAmelCase__       =						list(sample_data.values()       )

							lowerCAmelCase__       =						list(map(tokenizer.encode		,    __UpperCAmelCase       )       )
							lowerCAmelCase__       =						[tokenizer.decode(__UpperCAmelCase		,    clean_up_tokenization_spaces=__UpperCAmelCase       ) for x in output_tokens]
							self.assertListEqual(__UpperCAmelCase		,    __UpperCAmelCase       )


	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





							self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map       )		,    1       )
							self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()       )[0]       )		,    1       )






