





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      UpperCamelCase_				(   unittest.TestCase ,     UpperCamelCase__			):


							def    _snake_case     (							self			:Optional[int]						)      ->      Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					load_tool("""translation"""						)
									self.tool.setup()
									SCREAMING_SNAKE_CASE__								=					load_tool("""translation"""							, remote=__A						)


							def    _snake_case     (							self			:Dict						)      ->      Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.tool("""Hey, what's up?"""							, src_lang="""English"""							, tgt_lang="""French"""						)
									self.assertEqual(__A							, """- Hé, comment ça va?"""						)


							def    _snake_case     (							self			:Optional[int]						)      ->      List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.remote_tool("""Hey, what's up?"""							, src_lang="""English"""							, tgt_lang="""French"""						)
									self.assertEqual(__A							, """- Hé, comment ça va?"""						)


							def    _snake_case     (							self			:Any						)      ->      Tuple:



									"""simple docstring"""



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


							def    _snake_case     (							self			:List[Any]						)      ->      str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					self.remote_tool(text="""Hey, what's up?"""							, src_lang="""English"""							, tgt_lang="""French"""						)
									self.assertEqual(__A							, """- Hé, comment ça va?"""						)


							def    _snake_case     (							self			:Optional[Any]						)      ->      Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					["""Hey, what's up?""", """English""", """Spanish"""]
									SCREAMING_SNAKE_CASE__								=					self.tool(*__A						)

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

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


							def    _snake_case     (							self			:Optional[int]						)      ->      Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					["""Hey, what's up?""", """English""", """Spanish"""]
									SCREAMING_SNAKE_CASE__								=					self.tool(*__A						)

									if not isinstance(__A							, __A						):
											SCREAMING_SNAKE_CASE__								=					[outputs]

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

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


							def    _snake_case     (							self			:str						)      ->      Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					["""Hey, what's up?""", """English""", """Spanish"""]

									SCREAMING_SNAKE_CASE__								=					[]

									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
									SCREAMING_SNAKE_CASE__								=					self.tool(*__A						)

									if not isinstance(__A							, __A						):
											SCREAMING_SNAKE_CASE__								=					[outputs]

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



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
       import torch

       from transformers import CamembertModel


@require_torch
@require_sentencepiece
@require_tokenizers
class        A_					(       unittest.TestCase    ):




      '''simple docstring'''


      @slow
      def      _snake_case	(				self:       Any				):
       __lowerCamelCase		:							Dict							=						CamembertModel.from_pretrained('camembert-base'				)
       model.to(a				)

       __lowerCamelCase		:							List[Any]							=						torch.tensor(
           [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]						,      device=a						,      dtype=torch.long						,      )  # J'aime le camembert !
       with torch.no_grad():
        __lowerCamelCase		:							Tuple							=						model(a				)['last_hidden_state']
       __lowerCamelCase		:							Union[str, Any]							=						torch.Size((1, 10, 768)				)
       self.assertEqual(output.shape						,      a				)
       # compare the actual values for a slice.
       __lowerCamelCase		:							Tuple							=						torch.tensor(
           [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]						,      device=a						,      dtype=torch.float						,      )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(torch.allclose(output[:, :3, :3]						,      a						,      atol=1e-4				)				)

