


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     UpperCamelCase(  unittest.TestCase   ):
     snake_case_		:				Any										=	MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
     snake_case_		:				Tuple										=	TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING


     def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Dict						)			->	Dict:




           '''simple docstring'''

           __snake_case				=		AudioClassificationPipeline(model=lowercase_     ,    feature_extractor=lowercase_						)

           # test with a raw waveform
           __snake_case				=		np.zeros((3_4_0_0_0,)						)
           __snake_case				=		np.zeros((1_4_0_0_0,)						)
           return audio_classifier, [audioa, audio]


     def 							SCREAMING_SNAKE_CASE_  (						self	:		str     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Any						)			->	Union[str, Any]:




           '''simple docstring'''

           __snake_case				=		examples
           __snake_case				=		audio_classifier(lowercase_						)
           # by default a model is initialized with num_labels=2
           self.assertEqual(
               lowercase_     ,    [
                   {"score": ANY(lowercase_						), "label": ANY(lowercase_						)},
                   {"score": ANY(lowercase_						), "label": ANY(lowercase_						)},
               ]     ,    )
           __snake_case				=		audio_classifier(lowercase_     ,    top_k=1						)
           self.assertEqual(
               lowercase_     ,    [
                   {"score": ANY(lowercase_						), "label": ANY(lowercase_						)},
               ]     ,    )

           self.run_torchaudio(lowercase_						)


     @require_torchaudio
     def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	str:




           '''simple docstring'''

           import datasets

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


     @require_torch
     def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Dict:




           '''simple docstring'''

           __snake_case				=		"anton-l/wav2vec2-random-tiny-classifier"

           __snake_case				=		pipeline("audio-classification"     ,    model=lowercase_						)

           __snake_case				=		np.ones((8_0_0_0,)						)
           __snake_case				=		audio_classifier(lowercase_     ,    top_k=4						)

           __snake_case				=		[
               {"score": 0.0842, "label": "no"},
               {"score": 0.0838, "label": "up"},
               {"score": 0.0837, "label": "go"},
               {"score": 0.0834, "label": "right"},
           ]
           __snake_case				=		[
               {"score": 0.0845, "label": "stop"},
               {"score": 0.0844, "label": "on"},
               {"score": 0.0841, "label": "right"},
               {"score": 0.0834, "label": "left"},
           ]
           self.assertIn(nested_simplify(lowercase_     ,    decimals=4						)     ,    [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]						)

           __snake_case				=		{"array": np.ones((8_0_0_0,)						), "sampling_rate": audio_classifier.feature_extractor.sampling_rate}
           __snake_case				=		audio_classifier(lowercase_     ,    top_k=4						)
           self.assertIn(nested_simplify(lowercase_     ,    decimals=4						)     ,    [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]						)


     @require_torch
     @slow
     def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	List[str]:




           '''simple docstring'''

           import datasets

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

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

           __snake_case				=		np.array(dataset[3]["speech"]     ,    dtype=np.floataa						)
           __snake_case				=		audio_classifier(lowercase_     ,    top_k=4						)
           self.assertEqual(
               nested_simplify(lowercase_     ,    decimals=3						)     ,    [
                   {"score": 0.981, "label": "go"},
                   {"score": 0.007, "label": "up"},
                   {"score": 0.006, "label": "_unknown_"},
                   {"score": 0.001, "label": "down"},
               ]     ,    )






     @require_tf
     @unittest.skip("Audio classification is not implemented for TF"						)
     def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Optional[Any]:




           '''simple docstring'''

           pass









import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
	import torch

if is_soundfile_availble():
	import soundfile as sf

if is_vision_available():
	from PIL import Image





def 	_lowerCAmelCase       (    _lowerCAmelCase="" )						->	str:


						'''simple docstring'''






						__snake_case				=		tempfile.mkdtemp()
						return os.path.join(_lowerCAmelCase      ,	str(uuid.uuida() ) + suffix )






@require_soundfile
@require_torch
class     UpperCamelCase(  unittest.TestCase   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		torch.rand(1_2     ,    dtype=torch.floataa						) - 0.5
											__snake_case				=		AgentAudio(SCREAMING_SNAKE_CASE						)
											__snake_case				=		str(agent_type.to_string()						)

											# Ensure that the tensor and the agent_type's tensor are the same
											self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    agent_type.to_raw()     ,    atol=1e-4						)						)

											del agent_type

											# Ensure the path remains even after the object deletion
											self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE						)						)

											# Ensure that the file contains the same value as the original tensor
											__snake_case		,					__snake_case				=		sf.read(SCREAMING_SNAKE_CASE						)
											self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    torch.tensor(SCREAMING_SNAKE_CASE						)     ,    atol=1e-4						)						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	int:




											'''simple docstring'''

											__snake_case				=		torch.rand(1_2     ,    dtype=torch.floataa						) - 0.5
											__snake_case				=		get_new_path(suffix=".wav"						)
											sf.write(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    1_6_0_0_0						)

											__snake_case				=		AgentAudio(SCREAMING_SNAKE_CASE						)

											self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    agent_type.to_raw()     ,    atol=1e-4						)						)
											self.assertEqual(agent_type.to_string()     ,    SCREAMING_SNAKE_CASE						)






@require_vision
@require_torch
class     UpperCamelCase(  unittest.TestCase   ):


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




											'''simple docstring'''

											__snake_case				=		torch.randint(0     ,    2_5_6     ,    (6_4, 6_4, 3)						)
											__snake_case				=		AgentImage(SCREAMING_SNAKE_CASE						)
											__snake_case				=		str(agent_type.to_string()						)

											# Ensure that the tensor and the agent_type's tensor are the same
											self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE     ,    agent_type._tensor     ,    atol=1e-4						)						)

											self.assertIsInstance(agent_type.to_raw()     ,    Image.Image						)

											# Ensure the path remains even after the object deletion
											del agent_type
											self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE						)						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	Any:




											'''simple docstring'''

											__snake_case				=		Path(get_tests_dir("fixtures/tests_samples/COCO"						)						) / "000000039769.png"
											__snake_case				=		Image.open(SCREAMING_SNAKE_CASE						)
											__snake_case				=		AgentImage(SCREAMING_SNAKE_CASE						)

											self.assertTrue(path.samefile(agent_type.to_string()						)						)
											self.assertTrue(image == agent_type.to_raw()						)

											# Ensure the path remains even after the object deletion
											del agent_type
											self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE						)						)






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




											'''simple docstring'''

											__snake_case				=		Path(get_tests_dir("fixtures/tests_samples/COCO"						)						) / "000000039769.png"
											__snake_case				=		Image.open(SCREAMING_SNAKE_CASE						)
											__snake_case				=		AgentImage(SCREAMING_SNAKE_CASE						)

											self.assertFalse(path.samefile(agent_type.to_string()						)						)
											self.assertTrue(image == agent_type.to_raw()						)

											# Ensure the path remains even after the object deletion
											del agent_type
											self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE						)						)






class     UpperCamelCase(  unittest.TestCase   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Any:




											'''simple docstring'''

											__snake_case				=		"Hey!"
											__snake_case				=		AgentText(SCREAMING_SNAKE_CASE						)

											self.assertEqual(SCREAMING_SNAKE_CASE     ,    agent_type.to_string()						)
											self.assertEqual(SCREAMING_SNAKE_CASE     ,    agent_type.to_raw()						)
											self.assertEqual(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)





