


import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
		from transformers import (
		    FlaxBertModel,
		    FlaxCLIPVisionModel,
		    FlaxVisionTextDualEncoderModel,
		    FlaxViTModel,
		    VisionTextDualEncoderConfig,
		    VisionTextDualEncoderProcessor,
		)
		from transformers.modeling_flax_pytorch_utils import (
		    convert_pytorch_state_dict_to_flax,
		    load_flax_weights_in_pytorch_model,
		)


if is_torch_available():
		import torch

		from transformers import VisionTextDualEncoderModel

if is_vision_available():
		from PIL import Image


def     lowercase	(    a ):



		'''simple docstring'''




		if isinstance(UpperCAmelCase_     , collections.abc.Iterable ):
				return x
		return (x, x)



@require_flax
class 						_UpperCAmelCase      :






		def _snake_case		(						self				:     Dict   ,  UpperCAmelCase				:     Tuple   ,  UpperCAmelCase				:     Any):
				pass






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






		def _snake_case		(						self				:     Optional[Any]):
				pass






		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     np.ndarray   ,  UpperCAmelCase				:     np.ndarray   ,  UpperCAmelCase				:     float):
				SCREAMING_SNAKE_CASE_				:str				     =						np.abs((a - b)).max()
				self.assertLessEqual(lowerCamelCase__   ,  lowerCamelCase__   ,  F"Difference between torch and flax is {diff} (>= {tol}).")






		def _snake_case		(						self				:     str   ,  UpperCAmelCase				:     Optional[int]   ,  UpperCAmelCase				:     Optional[Any]   ,  UpperCAmelCase				:     Any   ,  UpperCAmelCase				:     Any   ,  UpperCAmelCase				:     List[str]=None   ,  **UpperCAmelCase				:     List[str]):
				SCREAMING_SNAKE_CASE_				:Dict				     =						VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase__   ,  lowerCamelCase__)

				SCREAMING_SNAKE_CASE_				:int				     =						FlaxVisionTextDualEncoderModel(lowerCamelCase__)

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						model(input_ids=lowerCamelCase__   ,  pixel_values=lowerCamelCase__   ,  attention_mask=lowerCamelCase__)

				self.assertEqual(output["text_embeds"].shape   ,  (input_ids.shape[0], config.projection_dim))
				self.assertEqual(output["image_embeds"].shape   ,  (pixel_values.shape[0], config.projection_dim))






		def _snake_case		(						self				:     str   ,  UpperCAmelCase				:     Union[str, Any]   ,  UpperCAmelCase				:     List[str]   ,  UpperCAmelCase				:     str   ,  UpperCAmelCase				:     Any   ,  UpperCAmelCase				:     Optional[int]=None   ,  **UpperCAmelCase				:     Optional[int]):
				SCREAMING_SNAKE_CASE_				:Dict				     =						self.get_vision_text_model(lowerCamelCase__   ,  lowerCamelCase__)
				SCREAMING_SNAKE_CASE_				:int				     =						{'vision_model': vision_model, 'text_model': text_model}
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase__)

				SCREAMING_SNAKE_CASE_				:str				     =						model(input_ids=lowerCamelCase__   ,  pixel_values=lowerCamelCase__   ,  attention_mask=lowerCamelCase__)

				self.assertEqual(output["text_embeds"].shape   ,  (input_ids.shape[0], model.config.projection_dim))
				self.assertEqual(output["image_embeds"].shape   ,  (pixel_values.shape[0], model.config.projection_dim))






		def _snake_case		(						self				:     Any   ,  UpperCAmelCase				:     str   ,  UpperCAmelCase				:     Dict   ,  UpperCAmelCase				:     Optional[int]   ,  UpperCAmelCase				:     List[str]   ,  UpperCAmelCase				:     str=None   ,  **UpperCAmelCase				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:str				     =						self.get_vision_text_model(lowerCamelCase__   ,  lowerCamelCase__)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						{'vision_model': vision_model, 'text_model': text_model}
				SCREAMING_SNAKE_CASE_				:Tuple				     =						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase__)

				SCREAMING_SNAKE_CASE_				:List[str]				     =						model(input_ids=lowerCamelCase__   ,  pixel_values=lowerCamelCase__   ,  attention_mask=lowerCamelCase__)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						output[0]

				with tempfile.TemporaryDirectory() as tmpdirname:
						model.save_pretrained(lowerCamelCase__)
						SCREAMING_SNAKE_CASE_				:Any				     =						FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase__)

						SCREAMING_SNAKE_CASE_				:int				     =						model(input_ids=lowerCamelCase__   ,  pixel_values=lowerCamelCase__   ,  attention_mask=lowerCamelCase__)
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						after_output[0]
						SCREAMING_SNAKE_CASE_				:int				     =						np.amax(np.abs(out_a - out_a))
						self.assertLessEqual(lowerCamelCase__   ,  1E-3)






		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     Optional[Any]   ,  UpperCAmelCase				:     Dict   ,  UpperCAmelCase				:     List[Any]   ,  UpperCAmelCase				:     List[str]   ,  UpperCAmelCase				:     Any=None   ,  **UpperCAmelCase				:     Optional[Any]):
				SCREAMING_SNAKE_CASE_				:str				     =						self.get_vision_text_model(lowerCamelCase__   ,  lowerCamelCase__)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						{'vision_model': vision_model, 'text_model': text_model}
				SCREAMING_SNAKE_CASE_				:List[str]				     =						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**lowerCamelCase__)

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						model(
				    input_ids=lowerCamelCase__   ,  pixel_values=lowerCamelCase__   ,  attention_mask=lowerCamelCase__   ,  output_attentions=lowerCamelCase__)

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						output.vision_model_output.attentions
				self.assertEqual(len(lowerCamelCase__)   ,  vision_config.num_hidden_layers)

				# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						to_atuple(vision_model.config.image_size)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						to_atuple(vision_model.config.patch_size)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
				SCREAMING_SNAKE_CASE_				:Any				     =						num_patches + 1
				self.assertEqual(vision_attentions[0].shape[-3:]   ,  (vision_config.num_attention_heads, seq_len, seq_len))

				SCREAMING_SNAKE_CASE_				:Any				     =						output.text_model_output.attentions
				self.assertEqual(len(lowerCamelCase__)   ,  text_config.num_hidden_layers)

				self.assertEqual(
				    text_attentions[0].shape[-3:]   ,  (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])   ,  )






		def _snake_case		(						self				:     Any   ,  UpperCAmelCase				:     Union[str, Any]   ,  UpperCAmelCase				:     Optional[Any]   ,  UpperCAmelCase				:     Dict):
				pt_model.to(lowerCamelCase__)
				pt_model.eval()

				# prepare inputs
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						inputs_dict
				SCREAMING_SNAKE_CASE_				:List[str]				     =						{k: torch.tensor(v.tolist()) for k, v in flax_inputs.items()}

				with torch.no_grad():
						SCREAMING_SNAKE_CASE_				:Dict				     =						pt_model(**lowerCamelCase__).to_tuple()

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						fx_model(**lowerCamelCase__).to_tuple()
				self.assertEqual(len(lowerCamelCase__)   ,  len(lowerCamelCase__)   ,  "Output lengths differ between Flax and PyTorch")
				for fx_output, pt_output in zip(fx_outputs[:4]   ,  pt_outputs[:4]):
						self.assert_almost_equals(lowerCamelCase__   ,  pt_output.numpy()   ,  4E-2)

				# PT -> Flax
				with tempfile.TemporaryDirectory() as tmpdirname:
						pt_model.save_pretrained(lowerCamelCase__)
						SCREAMING_SNAKE_CASE_				:Any				     =						FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase__   ,  from_pt=lowerCamelCase__)

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						fx_model_loaded(**lowerCamelCase__).to_tuple()
				self.assertEqual(len(lowerCamelCase__)   ,  len(lowerCamelCase__)   ,  "Output lengths differ between Flax and PyTorch")
				for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]   ,  pt_outputs[:4]):
						self.assert_almost_equals(lowerCamelCase__   ,  pt_output.numpy()   ,  4E-2)

				# Flax -> PT
				with tempfile.TemporaryDirectory() as tmpdirname:
						fx_model.save_pretrained(lowerCamelCase__)
						SCREAMING_SNAKE_CASE_				:Tuple				     =						VisionTextDualEncoderModel.from_pretrained(lowerCamelCase__   ,  from_flax=lowerCamelCase__)

				pt_model_loaded.to(lowerCamelCase__)
				pt_model_loaded.eval()

				with torch.no_grad():
						SCREAMING_SNAKE_CASE_				:Tuple				     =						pt_model_loaded(**lowerCamelCase__).to_tuple()

				self.assertEqual(len(lowerCamelCase__)   ,  len(lowerCamelCase__)   ,  "Output lengths differ between Flax and PyTorch")
				for fx_output, pt_output_loaded in zip(fx_outputs[:4]   ,  pt_outputs_loaded[:4]):
						self.assert_almost_equals(lowerCamelCase__   ,  pt_output_loaded.numpy()   ,  4E-2)






		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     Dict   ,  UpperCAmelCase				:     Optional[Any]   ,  UpperCAmelCase				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:str				     =						VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase__   ,  lowerCamelCase__)

				SCREAMING_SNAKE_CASE_				:Dict				     =						VisionTextDualEncoderModel(lowerCamelCase__)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						FlaxVisionTextDualEncoderModel(lowerCamelCase__)

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,  lowerCamelCase__)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						fx_state

				self.check_pt_flax_equivalence(lowerCamelCase__   ,  lowerCamelCase__   ,  lowerCamelCase__)






		def _snake_case		(						self				:     Any   ,  UpperCAmelCase				:     Dict   ,  UpperCAmelCase				:     Union[str, Any]   ,  UpperCAmelCase				:     Dict):
				SCREAMING_SNAKE_CASE_				:Any				     =						VisionTextDualEncoderConfig.from_vision_text_configs(lowerCamelCase__   ,  lowerCamelCase__)

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						VisionTextDualEncoderModel(lowerCamelCase__)
				SCREAMING_SNAKE_CASE_				:Any				     =						FlaxVisionTextDualEncoderModel(lowerCamelCase__)

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						load_flax_weights_in_pytorch_model(lowerCamelCase__   ,  fx_model.params)

				self.check_pt_flax_equivalence(lowerCamelCase__   ,  lowerCamelCase__   ,  lowerCamelCase__)






		def _snake_case		(						self				:     Any):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						self.prepare_config_and_inputs()
				self.check_model_from_pretrained_configs(**lowerCamelCase__)






		def _snake_case		(						self				:     List[Any]):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self.prepare_config_and_inputs()
				self.check_vision_text_dual_encoder_from_pretrained(**lowerCamelCase__)






		def _snake_case		(						self				:     List[Any]):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.prepare_config_and_inputs()
				self.check_save_load(**lowerCamelCase__)






		def _snake_case		(						self				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:str				     =						self.prepare_config_and_inputs()
				self.check_vision_text_output_attention(**lowerCamelCase__)






		@is_pt_flax_cross_test
		def _snake_case		(						self				:     Optional[int]):
				SCREAMING_SNAKE_CASE_				:str				     =						self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE_				:int				     =						config_inputs_dict.pop("vision_config")
				SCREAMING_SNAKE_CASE_				:List[str]				     =						config_inputs_dict.pop("text_config")

				SCREAMING_SNAKE_CASE_				:int				     =						config_inputs_dict

				self.check_equivalence_pt_to_flax(lowerCamelCase__   ,  lowerCamelCase__   ,  lowerCamelCase__)
				self.check_equivalence_flax_to_pt(lowerCamelCase__   ,  lowerCamelCase__   ,  lowerCamelCase__)







		@slow
		def _snake_case		(						self				:     str):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.get_pretrained_model_and_inputs()

				SCREAMING_SNAKE_CASE_				:Dict				     =						model_a(**lowerCamelCase__)
				SCREAMING_SNAKE_CASE_				:str				     =						outputs[0]

				with tempfile.TemporaryDirectory() as tmp_dirname:
						model_a.save_pretrained(lowerCamelCase__)
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						FlaxVisionTextDualEncoderModel.from_pretrained(lowerCamelCase__)

						SCREAMING_SNAKE_CASE_				:str				     =						model_a(**lowerCamelCase__)
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						after_outputs[0]
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						np.amax(np.abs(out_a - out_a))
						self.assertLessEqual(lowerCamelCase__   ,  1E-5)




@require_flax
class 						_UpperCAmelCase      (     lowercase		,		unittest.TestCase							):






		def _snake_case		(						self				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:str				     =						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
				    "hf-internal-testing/tiny-random-vit"   ,  "hf-internal-testing/tiny-bert"   ,  vision_from_pt=lowerCamelCase__   ,  text_from_pt=lowerCamelCase__   ,  )
				SCREAMING_SNAKE_CASE_				:Tuple				     =						13
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						floats_tensor(
				    [
				        batch_size,
				        model.config.vision_config.num_channels,
				        model.config.vision_config.image_size,
				        model.config.vision_config.image_size,
				    ])
				SCREAMING_SNAKE_CASE_				:str				     =						ids_tensor([batch_size, 4]   ,  model.config.text_config.vocab_size)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						random_attention_mask([batch_size, 4])
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

				return model, inputs






		def _snake_case		(						self				:     Optional[Any]   ,  UpperCAmelCase				:     List[str]   ,  UpperCAmelCase				:     List[Any]):
				SCREAMING_SNAKE_CASE_				:Any				     =						FlaxViTModel(lowerCamelCase__)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						FlaxBertModel(lowerCamelCase__)
				return vision_model, text_model







		def _snake_case		(						self				:     Any):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						FlaxViTModelTester(self)
				SCREAMING_SNAKE_CASE_				:Tuple				     =						FlaxBertModelTester(self)
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						vit_model_tester.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE_				:Dict				     =						bert_model_tester.prepare_config_and_inputs()

				SCREAMING_SNAKE_CASE_				:int				     =						vision_config_and_inputs

				SCREAMING_SNAKE_CASE_				:int				     =						text_config_and_inputs

				# make sure that cross attention layers are added
				return {
				    "text_config": text_config,
				    "vision_config": vision_config,
				    "pixel_values": pixel_values,
				    "attention_mask": attention_mask,
				    "input_ids": input_ids,
				    "token_type_ids": token_type_ids,
				}




@require_torch
class 						_UpperCAmelCase      (     lowercase		,		unittest.TestCase							):






		def _snake_case		(						self				:     List[Any]):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
				    "hf-internal-testing/tiny-random-clip"   ,  "hf-internal-testing/tiny-bert"   ,  vision_from_pt=lowerCamelCase__   ,  text_from_pt=lowerCamelCase__   ,  )
				SCREAMING_SNAKE_CASE_				:str				     =						13
				SCREAMING_SNAKE_CASE_				:int				     =						floats_tensor(
				    [
				        batch_size,
				        model.config.vision_config.num_channels,
				        model.config.vision_config.image_size,
				        model.config.vision_config.image_size,
				    ])
				SCREAMING_SNAKE_CASE_				:Dict				     =						ids_tensor([batch_size, 4]   ,  model.config.text_config.vocab_size)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						random_attention_mask([batch_size, 4])
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

				return model, inputs






		def _snake_case		(						self				:     Any   ,  UpperCAmelCase				:     int   ,  UpperCAmelCase				:     Dict):
				SCREAMING_SNAKE_CASE_				:str				     =						FlaxCLIPVisionModel(lowerCamelCase__)
				SCREAMING_SNAKE_CASE_				:Dict				     =						FlaxBertModel(lowerCamelCase__)
				return vision_model, text_model







		def _snake_case		(						self				:     Any):
				SCREAMING_SNAKE_CASE_				:Dict				     =						FlaxCLIPVisionModelTester(self)
				SCREAMING_SNAKE_CASE_				:Tuple				     =						FlaxBertModelTester(self)
				SCREAMING_SNAKE_CASE_				:Tuple				     =						clip_model_tester.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						bert_model_tester.prepare_config_and_inputs()

				SCREAMING_SNAKE_CASE_				:Any				     =						vision_config_and_inputs

				SCREAMING_SNAKE_CASE_				:Any				     =						text_config_and_inputs

				# make sure that cross attention layers are added
				return {
				    "text_config": text_config,
				    "vision_config": vision_config,
				    "pixel_values": pixel_values,
				    "attention_mask": attention_mask,
				    "input_ids": input_ids,
				    "token_type_ids": token_type_ids,
				}




@require_flax
@require_vision
class 						_UpperCAmelCase      (     unittest.TestCase							):






		@slow
		def _snake_case		(						self				:     Dict):
				SCREAMING_SNAKE_CASE_				:str				     =						FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian"   ,  logit_scale_init_value=1.0)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian")

				SCREAMING_SNAKE_CASE_				:List[str]				     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
				SCREAMING_SNAKE_CASE_				:List[str]				     =						processor(
				    text=["una foto di un gatto", "una foto di un cane"]   ,  images=lowerCamelCase__   ,  padding=lowerCamelCase__   ,  return_tensors="np")

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						model(**lowerCamelCase__)

				# verify the logits
				self.assertEqual(outputs.logits_per_image.shape   ,  (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]))
				self.assertEqual(
				    outputs.logits_per_text.shape   ,  (inputs.input_ids.shape[0], inputs.pixel_values.shape[0])   ,  )

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						np.array([[1.2284727, 0.3104122]])

				self.assertTrue(np.allclose(outputs.logits_per_image   ,  lowerCamelCase__   ,  atol=1E-3))











'''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__					)


