import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY
@is_pipeline_test
class  A_			(							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
  _SCREAMING_SNAKE_CASE     					=      TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]							):
   __a							=	AudioClassificationPipeline(model=__SCREAMING_SNAKE_CASE  ,				feature_extractor=__SCREAMING_SNAKE_CASE							)

   # test with a raw waveform
   __a							=	np.zeros((3_40_00,)							)
   __a							=	np.zeros((1_40_00,)							)
   return audio_classifier, [audioa, audio]

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Tuple							):
   __a							=	examples
   __a							=	audio_classifier(__SCREAMING_SNAKE_CASE							)
   # by default a model is initialized with num_labels=2
   self.assertEqual(
       __SCREAMING_SNAKE_CASE  ,				[
           {"score": ANY(__SCREAMING_SNAKE_CASE							), "label": ANY(__SCREAMING_SNAKE_CASE							)},
           {"score": ANY(__SCREAMING_SNAKE_CASE							), "label": ANY(__SCREAMING_SNAKE_CASE							)},
       ]  ,				)
   __a							=	audio_classifier(__SCREAMING_SNAKE_CASE  ,				top_k=1							)
   self.assertEqual(
       __SCREAMING_SNAKE_CASE  ,				[
           {"score": ANY(__SCREAMING_SNAKE_CASE							), "label": ANY(__SCREAMING_SNAKE_CASE							)},
       ]  ,				)

   self.run_torchaudio(__SCREAMING_SNAKE_CASE							)

  @require_torchaudio
  def 		_UpperCAmelCase (		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   List[Any]							):
   import datasets

   # test with a local file
   __a							=	datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy"  ,				"clean"  ,				split="validation"							)
   __a							=	dataset[0]["audio"]["array"]
   __a							=	audio_classifier(__SCREAMING_SNAKE_CASE							)
   self.assertEqual(
       __SCREAMING_SNAKE_CASE  ,				[
           {"score": ANY(__SCREAMING_SNAKE_CASE							), "label": ANY(__SCREAMING_SNAKE_CASE							)},
           {"score": ANY(__SCREAMING_SNAKE_CASE							), "label": ANY(__SCREAMING_SNAKE_CASE							)},
       ]  ,				)

  @require_torch
  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   __a							=	"anton-l/wav2vec2-random-tiny-classifier"

   __a							=	pipeline("audio-classification"  ,				model=__SCREAMING_SNAKE_CASE							)

   __a							=	np.ones((80_00,)							)
   __a							=	audio_classifier(__SCREAMING_SNAKE_CASE  ,				top_k=4							)

   __a							=	[
       {"score": 0.08_42, "label": "no"},
       {"score": 0.08_38, "label": "up"},
       {"score": 0.08_37, "label": "go"},
       {"score": 0.08_34, "label": "right"},
   ]
   __a							=	[
       {"score": 0.08_45, "label": "stop"},
       {"score": 0.08_44, "label": "on"},
       {"score": 0.08_41, "label": "right"},
       {"score": 0.08_34, "label": "left"},
   ]
   self.assertIn(nested_simplify(__SCREAMING_SNAKE_CASE  ,				decimals=4							)  ,				[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]							)

   __a							=	{"array": np.ones((80_00,)							), "sampling_rate": audio_classifier.feature_extractor.sampling_rate}
   __a							=	audio_classifier(__SCREAMING_SNAKE_CASE  ,				top_k=4							)
   self.assertIn(nested_simplify(__SCREAMING_SNAKE_CASE  ,				decimals=4							)  ,				[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]							)

  @require_torch
  @slow
  def 		_UpperCAmelCase (		self			:   str							):
   import datasets

   __a							=	"superb/wav2vec2-base-superb-ks"

   __a							=	pipeline("audio-classification"  ,				model=__SCREAMING_SNAKE_CASE							)
   __a							=	datasets.load_dataset("anton-l/superb_dummy"  ,				"ks"  ,				split="test"							)

   __a							=	np.array(dataset[3]["speech"]  ,				dtype=np.floataa							)
   __a							=	audio_classifier(__SCREAMING_SNAKE_CASE  ,				top_k=4							)
   self.assertEqual(
       nested_simplify(__SCREAMING_SNAKE_CASE  ,				decimals=3							)  ,				[
           {"score": 0.9_81, "label": "go"},
           {"score": 0.0_07, "label": "up"},
           {"score": 0.0_06, "label": "_unknown_"},
           {"score": 0.0_01, "label": "down"},
       ]  ,				)

  @require_tf
  @unittest.skip("Audio classification is not implemented for TF"							)
  def 		_UpperCAmelCase (		self			:   List[str]							):
   pass









import numpy as np


class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:List[str]	):





        '''simple docstring'''




        A_       :					Optional[int]        =      (0, 0)
        A_       :					Dict        =      None
        A_       :					Optional[Any]        =      0
        A_       :					Optional[Any]        =      0
        A_       :					List[Any]        =      0



     def __eq__(				self							:int      ,   snake_case							:str	):





        '''simple docstring'''




        return self.position == cell.position





     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        print(self.position	)




class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:Dict      ,   snake_case							:Optional[Any]=(5, 5)	):





        '''simple docstring'''




        A_       :					Optional[int]        =      np.zeros(snake_case	)
        A_       :					Tuple        =      world_size[0]
        A_       :					Optional[Any]        =      world_size[1]



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        print(self.w	)





     def     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   snake_case							:int	):





        '''simple docstring'''




        A_       :					Optional[int]        =      [
            (-1, -1),
            (-1, 0),
            (-1, 1),
            (0, -1),
            (0, 1),
            (1, -1),
            (1, 0),
            (1, 1),
        ]
        A_       :					Union[str, Any]        =      cell.position[0]
        A_       :					int        =      cell.position[1]
        A_       :					Optional[int]        =      []
        for n in neughbour_cord:
           A_       :					List[str]        =      current_x + n[0]
           A_       :					Optional[Any]        =      current_y + n[1]
           if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
              A_       :					List[Any]        =      Cell()
              A_       :					Tuple        =      (x, y)
              A_       :					List[Any]        =      cell
              neighbours.append(snake_case	)
        return neighbours




def   __snake_case    (       _lowerCAmelCase			:			List[str]					,					_lowerCAmelCase			:			Optional[Any]					,					_lowerCAmelCase			:			str						)			->    str:
   A_       :					Optional[Any]        =      []
   A_       :					Tuple        =      []
   _open.append(_lowerCAmelCase						)

   while _open:
      A_       :					List[Any]        =      np.argmin([n.f for n in _open]						)
      A_       :					Optional[Any]        =      _open[min_f]
      _closed.append(_open.pop(_lowerCAmelCase						)						)
      if current == goal:
         break
      for n in world.get_neigbours(_lowerCAmelCase						):
         for c in _closed:
            if c == n:
               continue
         A_       :					int        =      current.g + 1
         A_   ,    A_       :					List[str]        =      n.position
         A_   ,    A_       :					Any        =      goal.position
         A_       :					Optional[Any]        =      (ya - ya) ** 2 + (xa - xa) ** 2
         A_       :					Optional[Any]        =      n.h + n.g

         for c in _open:
            if c == n and c.f < n.f:
               continue
         _open.append(_lowerCAmelCase						)
   A_       :					Optional[int]        =      []
   while current.parent is not None:
      path.append(current.position						)
      A_       :					List[Any]        =      current.parent
   path.append(current.position						)
   return path[::-1]


if __name__ == "__main__":
 _lowerCAmelCase				:   Union[str, Any]						       =						Gridworld()
 # Start position and goal
 _lowerCAmelCase				:   Tuple						       =						Cell()
 _lowerCAmelCase				:   str						       =						(0, 0)
 _lowerCAmelCase				:   Union[str, Any]						       =						Cell()
 _lowerCAmelCase				:   List[str]						       =						(4, 4)
 print(F'''path from {start.position} to {goal.position}''')
 _lowerCAmelCase				:   Union[str, Any]						       =						astar(world, start, goal)
 #   Just for visual reasons.
 for i in s:
  _lowerCAmelCase				:   Any						       =						1
 print(world.w)
