


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor



class 						_UpperCAmelCase      (     unittest.TestCase							):






		def __init__(						self				:     int   ,  UpperCAmelCase				:     List[str]   ,  UpperCAmelCase				:     Optional[Any]=13   ,  UpperCAmelCase				:     List[Any]=3   ,  UpperCAmelCase				:     Optional[Any]=2_24   ,  UpperCAmelCase				:     int=30   ,  UpperCAmelCase				:     str=4_00   ,  UpperCAmelCase				:     str=True   ,  UpperCAmelCase				:     Any=None   ,  UpperCAmelCase				:     Tuple=True   ,  UpperCAmelCase				:     Tuple=[0.5, 0.5, 0.5]   ,  UpperCAmelCase				:     Optional[int]=[0.5, 0.5, 0.5]   ,  ):
				SCREAMING_SNAKE_CASE_				:Dict				     =						size if size is not None else {'height': 18, 'width': 18}
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						parent
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						batch_size
				SCREAMING_SNAKE_CASE_				:Dict				     =						num_channels
				SCREAMING_SNAKE_CASE_				:List[str]				     =						image_size
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						min_resolution
				SCREAMING_SNAKE_CASE_				:Dict				     =						max_resolution
				SCREAMING_SNAKE_CASE_				:Tuple				     =						do_resize
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						size
				SCREAMING_SNAKE_CASE_				:Tuple				     =						do_normalize
				SCREAMING_SNAKE_CASE_				:str				     =						image_mean
				SCREAMING_SNAKE_CASE_				:int				     =						image_std







		def _snake_case		(						self				:     List[Any]):
				return {
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_normalize": self.do_normalize,
				    "do_resize": self.do_resize,
				    "size": self.size,
				}




@require_torch
@require_vision
class 						_UpperCAmelCase      (     lowercase		,		unittest.TestCase							):
		lowerCamelCase_	:			Any       			=  ViTImageProcessor if is_vision_available() else None






		def _snake_case		(						self				:     Optional[Any]):
				SCREAMING_SNAKE_CASE_				:Dict				     =						EfficientFormerImageProcessorTester(self)






		@property
		def _snake_case		(						self				:     Optional[int]):
				return self.image_proc_tester.prepare_image_processor_dict()






		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.image_processing_class(**self.image_processor_dict)
				self.assertTrue(hasattr(lowerCamelCase__   ,  "image_mean"))
				self.assertTrue(hasattr(lowerCamelCase__   ,  "image_std"))
				self.assertTrue(hasattr(lowerCamelCase__   ,  "do_normalize"))
				self.assertTrue(hasattr(lowerCamelCase__   ,  "do_resize"))
				self.assertTrue(hasattr(lowerCamelCase__   ,  "size"))






		def _snake_case		(						self				:     List[str]):
				pass






		def _snake_case		(						self				:     int):
				SCREAMING_SNAKE_CASE_				:str				     =						self.image_processing_class(**self.image_processor_dict)
				# create random PIL images
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						prepare_image_inputs(self.image_proc_tester   ,  equal_resolution=lowerCamelCase__)
				for image in image_inputs:
						self.assertIsInstance(lowerCamelCase__   ,  Image.Image)

				# Test not batched input
				SCREAMING_SNAKE_CASE_				:int				     =						image_processor(image_inputs[0]   ,  return_tensors="pt").pixel_values
				self.assertEqual(
				    encoded_images.shape   ,  (
				        1,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )   ,  )

				# Test batched
				SCREAMING_SNAKE_CASE_				:int				     =						image_processor(lowerCamelCase__   ,  return_tensors="pt").pixel_values
				self.assertEqual(
				    encoded_images.shape   ,  (
				        self.image_proc_tester.batch_size,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )   ,  )






		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:Any				     =						self.image_processing_class(**self.image_processor_dict)
				# create random numpy tensors
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						prepare_image_inputs(self.image_proc_tester   ,  equal_resolution=lowerCamelCase__   ,  numpify=lowerCamelCase__)
				for image in image_inputs:
						self.assertIsInstance(lowerCamelCase__   ,  np.ndarray)

				# Test not batched input
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						image_processor(image_inputs[0]   ,  return_tensors="pt").pixel_values
				self.assertEqual(
				    encoded_images.shape   ,  (
				        1,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )   ,  )

				# Test batched
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						image_processor(lowerCamelCase__   ,  return_tensors="pt").pixel_values
				self.assertEqual(
				    encoded_images.shape   ,  (
				        self.image_proc_tester.batch_size,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )   ,  )







		def _snake_case		(						self				:     Optional[int]):
				SCREAMING_SNAKE_CASE_				:Any				     =						self.image_processing_class(**self.image_processor_dict)
				# create random PyTorch tensors
				SCREAMING_SNAKE_CASE_				:Any				     =						prepare_image_inputs(self.image_proc_tester   ,  equal_resolution=lowerCamelCase__   ,  torchify=lowerCamelCase__)
				for image in image_inputs:
						self.assertIsInstance(lowerCamelCase__   ,  torch.Tensor)

				# Test not batched input
				SCREAMING_SNAKE_CASE_				:Dict				     =						image_processor(image_inputs[0]   ,  return_tensors="pt").pixel_values
				self.assertEqual(
				    encoded_images.shape   ,  (
				        1,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )   ,  )

				# Test batched
				SCREAMING_SNAKE_CASE_				:Any				     =						image_processor(lowerCamelCase__   ,  return_tensors="pt").pixel_values
				self.assertEqual(
				    encoded_images.shape   ,  (
				        self.image_proc_tester.batch_size,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )   ,  )











'''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__       (	UpperCAmelCase_=""				):
						_UpperCamelCase   :						Any												=  tempfile.mkdtemp()
						return os.path.join(UpperCAmelCase_    ,    str(uuid.uuida()				) + suffix				)






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





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  torch.rand(12					,dtype=torch.floataa					) - 0.5
							_UpperCamelCase   :						Optional[int]												=  AgentAudio(lowerCamelCase__					)
							_UpperCamelCase   :						List[str]												=  str(agent_type.to_string()					)

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

							del agent_type

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

							# Ensure that the file contains the same value as the original tensor
							_UpperCamelCase							,     _UpperCamelCase   :						Union[str, Any]												=  sf.read(lowerCamelCase__					)
							self.assertTrue(torch.allclose(lowerCamelCase__					,torch.tensor(lowerCamelCase__					)					,atol=1E-4					)					)





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  torch.rand(12					,dtype=torch.floataa					) - 0.5
							_UpperCamelCase   :						Any												=  get_new_path(suffix='.wav'					)
							sf.write(lowerCamelCase__					,lowerCamelCase__					,16000					)

							_UpperCamelCase   :						List[Any]												=  AgentAudio(lowerCamelCase__					)

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






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





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  torch.randint(0					,256					,(64, 64, 3)					)
							_UpperCamelCase   :						Optional[Any]												=  AgentImage(lowerCamelCase__					)
							_UpperCamelCase   :						List[Any]												=  str(agent_type.to_string()					)

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





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  Path(get_tests_dir('fixtures/tests_samples/COCO'					)					) / '000000039769.png'
							_UpperCamelCase   :						Tuple												=  Image.open(lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  AgentImage(lowerCamelCase__					)

							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(lowerCamelCase__					)					)





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  Path(get_tests_dir('fixtures/tests_samples/COCO'					)					) / '000000039769.png'
							_UpperCamelCase   :						Union[str, Any]												=  Image.open(lowerCamelCase__					)
							_UpperCamelCase   :						List[Any]												=  AgentImage(lowerCamelCase__					)

							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(lowerCamelCase__					)					)






class 				lowercase__      (				unittest.TestCase  ):





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  'Hey!'
							_UpperCamelCase   :						Optional[int]												=  AgentText(lowerCamelCase__					)

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


