'''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   :	int 							=   ["text", "image", "audio"]

def _lowercase	(      lowerCamelCase__						)					->       List[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				Any       				=		[]

       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(3000						)						)
              elif isinstance(UpperCAmelCase__							,    UpperCAmelCase__						):
                     inputs.append(create_inputs(UpperCAmelCase__						)						)
              else:
                     raise ValueError(f"""Invalid type requested: {input_type}"""						)

       return inputs

def _lowercase	(      lowerCamelCase__						)					->       List[Any]:





       """simple docstring"""


       __UpperCAmelCase  :				Union[str, Any]       				=		[]

       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 		__A       :

       '''simple docstring'''





       def 			_snake_case						(      self      ):
              self.assertTrue(hasattr(self.tool				,     "inputs"      )      )
              self.assertTrue(hasattr(self.tool				,     "outputs"      )      )

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

              __UpperCAmelCase  :				Optional[Any]       				=		self.tool.outputs
              for _output in outputs:
                     self.assertTrue(_output in authorized_types      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[int]       				=		create_inputs(self.tool.inputs      )
              __UpperCAmelCase  :				Optional[int]       				=		self.tool(*_A      )

              # There is a single output
              if len(self.tool.outputs      ) == 1:
                     __UpperCAmelCase  :				Union[str, Any]       				=		[outputs]

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






       def 			_snake_case						(      self      ):
              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 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Any       				=		create_inputs(self.tool.inputs      )
              __UpperCAmelCase  :				List[str]       				=		self.tool(*_A      )

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

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

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







       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Dict       				=		create_inputs(self.tool.inputs      )

              __UpperCAmelCase  :				Dict       				=		[]

              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
              __UpperCAmelCase  :				Union[str, Any]       				=		self.tool(*_A      )

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

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







'''simple docstring'''




from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    )					->       list[float]:





       """simple docstring"""


       __UpperCAmelCase				, __UpperCAmelCase  :				Union[str, Any]       				=		coefficient_matrix.shape
       __UpperCAmelCase				, __UpperCAmelCase  :				Any       				=		constant_matrix.shape

       if rowsa != colsa:
              __UpperCAmelCase  :				str       				=		f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
              raise ValueError(lowerCamelCase__						)

       if colsa != 1:
              __UpperCAmelCase  :				Optional[Any]       				=		f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
              raise ValueError(lowerCamelCase__						)

       if rowsa != rowsa:
              __UpperCAmelCase  :				Optional[int]       				=		(
                  "Coefficient and constant matrices dimensions must be nxn and nx1 but "
                  f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
              )
              raise ValueError(lowerCamelCase__						)

       if len(lowerCamelCase__						) != rowsa:
              __UpperCAmelCase  :				List[str]       				=		(
                  "Number of initial values must be equal to number of rows in coefficient "
                  f"""matrix but received {len(lowerCamelCase__						)} and {rowsa}"""
              )
              raise ValueError(lowerCamelCase__						)

       if iterations <= 0:
              raise ValueError("Iterations must be at least 1"						)

       __UpperCAmelCase  :				NDArray[floataa]       				=		np.concatenate(
           (coefficient_matrix, constant_matrix)							,    axis=1						)

       __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		table.shape

       strictly_diagonally_dominant(lowerCamelCase__						)

       # Iterates the whole matrix for given number of times
       for _ in range(lowerCamelCase__						):
              __UpperCAmelCase  :				int       				=		[]
              for row in range(lowerCamelCase__						):
                     __UpperCAmelCase  :				List[str]       				=		0
                     for col in range(lowerCamelCase__						):
                            if col == row:
                                   __UpperCAmelCase  :				int       				=		table[row][col]
                            elif col == cols - 1:
                                   __UpperCAmelCase  :				Any       				=		table[row][col]
                            else:
                                   temp += (-1) * table[row][col] * init_val[col]
                     __UpperCAmelCase  :				List[Any]       				=		(temp + val) / denom
                     new_val.append(lowerCamelCase__						)
              __UpperCAmelCase  :				str       				=		new_val

       return [float(lowerCamelCase__						) for i in new_val]

def _lowercase	(      lowerCamelCase__						)					->       bool:





       """simple docstring"""


       __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		table.shape

       __UpperCAmelCase  :				str       				=		True

       for i in range(0							,    lowerCamelCase__						):
              __UpperCAmelCase  :				Union[str, Any]       				=		0
              for j in range(0							,    cols - 1						):
                     if i == j:
                            continue
                     else:
                            total += table[i][j]

              if table[i][i] <= total:
                     raise ValueError("Coefficient matrix is not strictly diagonally dominant"						)

       return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
   import doctest

   doctest.testmod()






