import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class      lowerCAmelCase						(					unittest.TestCase			):



     @property
     def 			a__  (					self       ):
            torch.manual_seed(0       )
            _A=	UNetaDModel(
                block_out_channels=(32, 64)				,      layers_per_block=2				,      sample_size=32				,      in_channels=3				,      out_channels=3				,      down_block_types=('DownBlock2D', 'AttnDownBlock2D')				,      up_block_types=('AttnUpBlock2D', 'UpBlock2D')				,      )
            return model



     @property
     def 			a__  (					self       ):
            torch.manual_seed(0       )
            _A=	VQModel(
                block_out_channels=[32, 64]				,      in_channels=3				,      out_channels=3				,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']				,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']				,      latent_channels=3				,      )
            return model



     @property
     def 			a__  (					self       ):
            torch.manual_seed(0       )
            _A=	CLIPTextConfig(
                bos_token_id=0				,      eos_token_id=2				,      hidden_size=32				,      intermediate_size=37				,      layer_norm_eps=1E-05				,      num_attention_heads=4				,      num_hidden_layers=5				,      pad_token_id=1				,      vocab_size=1000				,      )
            return CLIPTextModel(lowerCAmelCase__       )





     def 			a__  (					self       ):
            _A=	self.dummy_uncond_unet
            _A=	DDIMScheduler()
            _A=	self.dummy_vq_model

            _A=	LDMPipeline(unet=lowerCAmelCase__				,      vqvae=lowerCAmelCase__				,      scheduler=lowerCAmelCase__       )
            ldm.to(lowerCAmelCase__       )
            ldm.set_progress_bar_config(disable=lowerCAmelCase__       )

            _A=	torch.manual_seed(0       )
            _A=	ldm(generator=lowerCAmelCase__				,      num_inference_steps=2				,      output_type='numpy'       ).images

            _A=	torch.manual_seed(0       )
            _A=	ldm(generator=lowerCAmelCase__				,      num_inference_steps=2				,      output_type='numpy'				,      return_dict=lowerCAmelCase__       )[0]

            _A=	image[0, -3:, -3:, -1]
            _A=	image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 64, 64, 3)
            _A=	np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]       )
            _A=	1E-2 if torch_device != 'mps' else 3E-2

            assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance
            assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < tolerance





@slow
@require_torch
class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'       )
            ldm.to(lowerCAmelCase__       )
            ldm.set_progress_bar_config(disable=lowerCAmelCase__       )

            _A=	torch.manual_seed(0       )
            _A=	ldm(generator=lowerCAmelCase__				,      num_inference_steps=5				,      output_type='numpy'       ).images

            _A=	image[0, -3:, -3:, -1]

            assert image.shape == (1, 256, 256, 3)
            _A=	np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]       )
            _A=	1E-2 if torch_device != 'mps' else 3E-2

            assert np.abs(image_slice.flatten() - expected_slice       ).max() < tolerance
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 							UpperCamelCase				(    lowerCAmelCase_=""   )		->						str:




       '''simple docstring'''






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





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



     def 			a__  (					self       ):
            _A=	torch.rand(12				,      dtype=torch.floataa       ) - 0.5
            _A=	AgentAudio(lowerCAmelCase__       )
            _A=	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
            _A,			_A=	sf.read(lowerCAmelCase__       )
            self.assertTrue(torch.allclose(lowerCAmelCase__				,      torch.tensor(lowerCAmelCase__       )				,      atol=1E-4       )       )





     def 			a__  (					self       ):
            _A=	torch.rand(12				,      dtype=torch.floataa       ) - 0.5
            _A=	get_new_path(suffix='.wav'       )
            sf.write(lowerCAmelCase__				,      lowerCAmelCase__				,      16000       )

            _A=	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      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	torch.randint(0				,      256				,      (64, 64, 3)       )
            _A=	AgentImage(lowerCAmelCase__       )
            _A=	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 			a__  (					self       ):
            _A=	Path(get_tests_dir('fixtures/tests_samples/COCO'       )       ) / '000000039769.png'
            _A=	Image.open(lowerCAmelCase__       )
            _A=	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 			a__  (					self       ):
            _A=	Path(get_tests_dir('fixtures/tests_samples/COCO'       )       ) / '000000039769.png'
            _A=	Image.open(lowerCAmelCase__       )
            _A=	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      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	'Hey!'
            _A=	AgentText(lowerCAmelCase__       )

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