
"""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 				__a    (   unittest.TestCase				,      __a					):



							'''simple docstring'''







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




										'''simple docstring'''
										__lowercase						      =					load_tool("translation"					)
										self.tool.setup()
										__lowercase						      =					load_tool("translation"     ,						remote=_lowerCamelCase					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       str:




										'''simple docstring'''
										__lowercase						      =					self.tool("Hey, what's up?"     ,						src_lang="English"     ,						tgt_lang="French"					)
										self.assertEqual(_lowerCamelCase     ,						"- Hé, comment ça va?"					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[str]:




										'''simple docstring'''
										__lowercase						      =					self.remote_tool("Hey, what's up?"     ,						src_lang="English"     ,						tgt_lang="French"					)
										self.assertEqual(_lowerCamelCase     ,						"- Hé, comment ça va?"					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Optional[int]:




										'''simple docstring'''
										__lowercase						      =					self.tool(text="Hey, what's up?"     ,						src_lang="English"     ,						tgt_lang="French"					)
										self.assertEqual(_lowerCamelCase     ,						"- Hé, comment ça va?"					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       str:




										'''simple docstring'''
										__lowercase						      =					self.remote_tool(text="Hey, what's up?"     ,						src_lang="English"     ,						tgt_lang="French"					)
										self.assertEqual(_lowerCamelCase     ,						"- Hé, comment ça va?"					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       int:




										'''simple docstring'''
										__lowercase						      =					["Hey, what's up?", "English", "Spanish"]
										__lowercase						      =					self.tool(*_lowerCamelCase					)

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

										self.assertListEqual(output_types(_lowerCamelCase					)     ,						self.tool.outputs					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       str:




										'''simple docstring'''
										__lowercase						      =					["Hey, what's up?", "English", "Spanish"]
										__lowercase						      =					self.tool(*_lowerCamelCase					)

										if not isinstance(_lowerCamelCase     ,						_lowerCamelCase					):
													__lowercase						      =					[outputs]

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

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





							def      SCREAMING_SNAKE_CASE	(    self					) ->       int:




										'''simple docstring'''
										__lowercase						      =					["Hey, what's up?", "English", "Spanish"]

										__lowercase						      =					[]

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

										if not isinstance(_lowerCamelCase     ,						_lowerCamelCase					):
													__lowercase						      =					[outputs]

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







"""simple docstring"""






import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
					from .testing_utils import (
					    floats_tensor,
					    load_hf_numpy,
					    load_image,
					    load_numpy,
					    load_pt,
					    nightly,
					    parse_flag_from_env,
					    print_tensor_test,
					    require_torch_a,
					    require_torch_gpu,
					    skip_mps,
					    slow,
					    torch_all_close,
					    torch_device,
					)
					from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


_lowercase							 =							get_logger(__name__)



def     lowerCAmelCase__ (		__magic_name__			)	->Optional[int]:
			if version.parse(__magic_name__			) < version.parse(__magic_name__			):
						if "dev" in min_version:
									__lowercase						      =					(
									    "This example requires a source install from HuggingFace diffusers (see "
									    "`https://huggingface.co/docs/diffusers/installation#install-from-source`),"
									)
						else:
									__lowercase						      =					F'''This example requires a minimum version of {min_version},'''
						error_message += F''' but the version found is {__version__}.\n'''
						raise ImportError(__magic_name__			)





