


"""simple docstring"""






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 					A_      (      _lowerCAmelCase   :    List[Any]=""				):






     """simple docstring"""

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


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

     '''simple docstring'''


     def 							_UpperCAmelCase					(					self							)				->							Union[str, Any]:
          _a					= torch.rand(12			, dtype=torch.floataa							) - 0.5
          _a					= AgentAudio(lowercase_							)
          _a					= str(agent_type.to_string()							)

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

          del agent_type

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

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





     def 							_UpperCAmelCase					(					self							)				->							Tuple:
          _a					= torch.rand(12			, dtype=torch.floataa							) - 0.5
          _a					= get_new_path(suffix='''.wav'''							)
          sf.write(lowercase_			, lowercase_			, 16000							)

          _a					= AgentAudio(lowercase_							)

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







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

     '''simple docstring'''


     def 							_UpperCAmelCase					(					self							)				->							Tuple:
          _a					= torch.randint(0			, 256			, (64, 64, 3)							)
          _a					= AgentImage(lowercase_							)
          _a					= str(agent_type.to_string()							)

          # Ensure that the tensor and the agent_type's tensor are the same
          self.assertTrue(torch.allclose(lowercase_			, 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(lowercase_							)							)


     def 							_UpperCAmelCase					(					self							)				->							List[Any]:
          _a					= Path(get_tests_dir('''fixtures/tests_samples/COCO'''							)							) / '''000000039769.png'''
          _a					= Image.open(lowercase_							)
          _a					= AgentImage(lowercase_							)

          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(lowercase_							)							)





     def 							_UpperCAmelCase					(					self							)				->							int:
          _a					= Path(get_tests_dir('''fixtures/tests_samples/COCO'''							)							) / '''000000039769.png'''
          _a					= Image.open(lowercase_							)
          _a					= AgentImage(lowercase_							)

          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(lowercase_							)							)







class 			__lowerCamelCase     (    unittest.TestCase ):

     '''simple docstring'''


     def 							_UpperCAmelCase					(					self							)				->							Optional[Any]:
          _a					= '''Hey!'''
          _a					= AgentText(lowercase_							)

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


"""simple docstring"""





import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY







@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING







					def        __lowerCAmelCase (       self			:				Optional[Any]   ,lowercase_			:				Optional[Any]   ,lowercase_			:				int   ,lowercase_			:				str		):
						lowerCAmelCase__	:						Optional[int]  				=	hf_hub_download(
						    repo_id='''nateraw/video-demo'''   ,filename='''archery.mp4'''   ,repo_type='''dataset'''		)
						lowerCAmelCase__	:						Tuple  				=	VideoClassificationPipeline(model=lowercase_   ,image_processor=lowercase_   ,top_k=2		)
						lowerCAmelCase__	:						Optional[int]  				=	[
						    example_video_filepath,
						    '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
						]
						return video_classifier, examples







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				int   ,lowercase_			:				Dict		):
						for example in examples:
							lowerCAmelCase__	:						Dict  				=	video_classifier(lowercase_		)

							self.assertEqual(
							    lowercase_   ,[
							        {'''score''': ANY(lowercase_		), '''label''': ANY(lowercase_		)},
							        {'''score''': ANY(lowercase_		), '''label''': ANY(lowercase_		)},
							    ]   ,)







					@require_torch
					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						Optional[int]  				=	'''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
						lowerCAmelCase__	:						List[str]  				=	VideoMAEFeatureExtractor(
						    size={'''shortest_edge''': 1_0}   ,crop_size={'''height''': 1_0, '''width''': 1_0}		)
						lowerCAmelCase__	:						Optional[Any]  				=	pipeline(
						    '''video-classification'''   ,model=lowercase_   ,feature_extractor=lowercase_   ,frame_sampling_rate=4		)

						lowerCAmelCase__	:						Optional[int]  				=	hf_hub_download(repo_id='''nateraw/video-demo'''   ,filename='''archery.mp4'''   ,repo_type='''dataset'''		)
						lowerCAmelCase__	:						Optional[int]  				=	video_classifier(lowercase_   ,top_k=2		)
						self.assertEqual(
						    nested_simplify(lowercase_   ,decimals=4		)   ,[{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}]   ,)

						lowerCAmelCase__	:						Dict  				=	video_classifier(
						    [
						        video_file_path,
						        video_file_path,
						    ]   ,top_k=2   ,)
						self.assertEqual(
						    nested_simplify(lowercase_   ,decimals=4		)   ,[
						        [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
						        [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
						    ]   ,)







					@require_tf
					def        __lowerCAmelCase (       self			:				int		):
						pass




