
"""simple docstring"""
import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types




class    _snake_case  (						unittest.TestCase ,				_lowercase       ):






							def 							__lowercase  (					self						:			int    ):






												"""simple docstring"""



												__a:						Tuple		    =					load_tool("""translation"""    )
												self.tool.setup()
												__a:						Optional[int]		    =					load_tool("""translation"""						, remote=snake_case__    )






							def 							__lowercase  (					self						:			Union[str, Any]    ):






												"""simple docstring"""



												__a:						Optional[int]		    =					self.tool("""Hey, what's up?"""						, src_lang="""English"""						, tgt_lang="""French"""    )
												self.assertEqual(snake_case__						, """- Hé, comment ça va?"""    )






							def 							__lowercase  (					self						:			str    ):






												"""simple docstring"""



												__a:						Union[str, Any]		    =					self.remote_tool("""Hey, what's up?"""						, src_lang="""English"""						, tgt_lang="""French"""    )
												self.assertEqual(snake_case__						, """- Hé, comment ça va?"""    )






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



												__a:						int		    =					self.tool(text="""Hey, what's up?"""						, src_lang="""English"""						, tgt_lang="""French"""    )
												self.assertEqual(snake_case__						, """- Hé, comment ça va?"""    )






							def 							__lowercase  (					self						:			Dict    ):






												"""simple docstring"""



												__a:						List[Any]		    =					self.remote_tool(text="""Hey, what's up?"""						, src_lang="""English"""						, tgt_lang="""French"""    )
												self.assertEqual(snake_case__						, """- Hé, comment ça va?"""    )






							def 							__lowercase  (					self						:			Optional[int]    ):






												"""simple docstring"""



												__a:						List[str]		    =					["""Hey, what's up?""", """English""", """Spanish"""]
												__a:						List[Any]		    =					self.tool(*snake_case__    )

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

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






							def 							__lowercase  (					self						:			List[Any]    ):






												"""simple docstring"""



												__a:						Union[str, Any]		    =					["""Hey, what's up?""", """English""", """Spanish"""]
												__a:						Union[str, Any]		    =					self.tool(*snake_case__    )

												if not isinstance(snake_case__						, snake_case__    ):
																	__a:						Tuple		    =					[outputs]

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

												for output, output_type in zip(snake_case__						, self.tool.outputs    ):
																	__a:						Optional[Any]		    =					AGENT_TYPE_MAPPING[output_type]
																	self.assertTrue(isinstance(snake_case__						, snake_case__    )    )






							def 							__lowercase  (					self						:			List[Any]    ):






												"""simple docstring"""



												__a:						Union[str, Any]		    =					["""Hey, what's up?""", """English""", """Spanish"""]

												__a:						Any		    =					[]

												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
												__a:						List[Any]		    =					self.tool(*snake_case__    )

												if not isinstance(snake_case__						, snake_case__    ):
																	__a:						Dict		    =					[outputs]

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


"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase		:						List[str] =					{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						Tuple =					['LlamaTokenizer']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						Dict =					['LlamaTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						List[Any] =					[
			    'OpenLlamaForCausalLM',
			    'OpenLlamaModel',
			    'OpenLlamaPreTrainedModel',
			    'OpenLlamaForSequenceClassification',
			]


if TYPE_CHECKING:
			from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_open_llama import (
						    OpenLlamaForCausalLM,
						    OpenLlamaForSequenceClassification,
						    OpenLlamaModel,
						    OpenLlamaPreTrainedModel,
						)


else:
			import sys

			lowercase		:						Dict =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
