


'''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    ,  UpperCamelCase							):



   '''simple docstring'''






   def   __lowerCAmelCase						(	self	:					int						)->	Any:
          snake_case__					:	str          =					load_tool("""translation"""						)
          self.tool.setup()
          snake_case__					:	Dict          =					load_tool("""translation"""					, remote=lowerCamelCase						)




   def   __lowerCAmelCase						(	self	:					List[Any]						)->	List[str]:
          snake_case__					:	List[Any]          =					self.tool("""Hey, what's up?"""					, src_lang="""English"""					, tgt_lang="""French"""						)
          self.assertEqual(lowerCamelCase					, """- Hé, comment ça va?"""						)




   def   __lowerCAmelCase						(	self	:					Optional[int]						)->	Any:
          snake_case__					:	Tuple          =					self.remote_tool("""Hey, what's up?"""					, src_lang="""English"""					, tgt_lang="""French"""						)
          self.assertEqual(lowerCamelCase					, """- Hé, comment ça va?"""						)




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




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




   def   __lowerCAmelCase						(	self	:					Union[str, Any]						)->	str:
          snake_case__					:	Tuple          =					["""Hey, what's up?""", """English""", """Spanish"""]
          snake_case__					:	Any          =					self.tool(*lowerCamelCase						)

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

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




   def   __lowerCAmelCase						(	self	:					Optional[int]						)->	List[str]:
          snake_case__					:	Any          =					["""Hey, what's up?""", """English""", """Spanish"""]
          snake_case__					:	List[str]          =					self.tool(*lowerCamelCase						)

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

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

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




   def   __lowerCAmelCase						(	self	:					int						)->	Dict:
          snake_case__					:	Union[str, Any]          =					["""Hey, what's up?""", """English""", """Spanish"""]

          snake_case__					:	Optional[Any]          =					[]

          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
          snake_case__					:	Optional[int]          =					self.tool(*lowerCamelCase						)

          if not isinstance(lowerCamelCase					, lowerCamelCase						):
                 snake_case__					:	int          =					[outputs]

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








'''simple docstring'''
from typing import Dict, Optional, Tuple

import torch



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Optional[int]          =					boundaries[1] - boundaries[0]
       snake_case__					:	Optional[Any]          =					boundaries + step / 2
       snake_case__					:	str          =					torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1     )]    ,       dim=0     )
       return bin_centers



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       ):






       """simple docstring"""





       snake_case__					:	str          =					_calculate_bin_centers(UpperCAmelCase     )
       return (
           torch.sum(aligned_distance_error_probs * bin_centers    ,       dim=-1     ),
           bin_centers[-1],
       )



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase = 31    ,       UpperCAmelCase = 64    ,       **UpperCAmelCase    ,       ):






       """simple docstring"""





       snake_case__					:	Dict          =					torch.linspace(0    ,       UpperCAmelCase    ,       steps=(no_bins - 1)    ,       device=logits.device     )

       snake_case__					:	str          =					torch.nn.functional.softmax(UpperCAmelCase    ,       dim=-1     )
       snake_case__       ,   snake_case__					:	List[str]          =					_calculate_expected_aligned_error(
           alignment_confidence_breaks=UpperCAmelCase    ,       aligned_distance_error_probs=UpperCAmelCase    ,       )

       return {
           "aligned_confidence_probs": aligned_confidence_probs,
           "predicted_aligned_error": predicted_aligned_error,
           "max_predicted_aligned_error": max_predicted_aligned_error,
       }






def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase = None    ,       UpperCAmelCase = 31    ,       UpperCAmelCase = 64    ,       UpperCAmelCase = 1E-8    ,       **UpperCAmelCase    ,       ):






       """simple docstring"""





       if residue_weights is None:
              snake_case__					:	List[Any]          =					logits.new_ones(logits.shape[-2]     )

       snake_case__					:	Optional[Any]          =					torch.linspace(0    ,       UpperCAmelCase    ,       steps=(no_bins - 1)    ,       device=logits.device     )

       snake_case__					:	int          =					_calculate_bin_centers(UpperCAmelCase     )
       torch.sum(UpperCAmelCase     )
       snake_case__					:	Dict          =					logits.shape[-2]
       snake_case__					:	Optional[Any]          =					max(UpperCAmelCase    ,       19     )

       snake_case__					:	Optional[int]          =					1.2_4 * (clipped_n - 15) ** (1.0 / 3) - 1.8

       snake_case__					:	Tuple          =					torch.nn.functional.softmax(UpperCAmelCase    ,       dim=-1     )

       snake_case__					:	int          =					1.0 / (1 + (bin_centers**2) / (da**2))
       snake_case__					:	Optional[int]          =					torch.sum(probs * tm_per_bin    ,       dim=-1     )

       snake_case__					:	Optional[Any]          =					residue_weights / (eps + residue_weights.sum())
       snake_case__					:	Any          =					torch.sum(predicted_tm_term * normed_residue_mask    ,       dim=-1     )

       snake_case__					:	Dict          =					per_alignment * residue_weights

       snake_case__					:	List[str]          =					(weighted == torch.max(UpperCAmelCase     )).nonzero()[0]
       return per_alignment[tuple(UpperCAmelCase     )]




