import unittest

from transformers import is_torch_available, load_tool

from .test_tools_common import ToolTesterMixin


if is_torch_available():
   import torch







class __snake_case							(		unittest.TestCase  ,						lowerCamelCase_						):




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= load_tool("""speech-to-text""" )
     self.tool.setup()




   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.tool(torch.ones(30_00 ) )
     self.assertEqual(_lowercase			,      """ you""" )




   def       __a		(       self     :							Any ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.tool(audio=torch.ones(30_00 ) )
     self.assertEqual(_lowercase			,      """ you""" )

import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







@skip_mps
class __snake_case							(		lowerCamelCase_  ,						lowerCamelCase_  ,						lowerCamelCase_  ,						unittest.TestCase						):
   lowerCAmelCase_								=				StableDiffusionModelEditingPipeline
   lowerCAmelCase_								=				TEXT_TO_IMAGE_PARAMS
   lowerCAmelCase_								=				TEXT_TO_IMAGE_BATCH_PARAMS
   lowerCAmelCase_								=				TEXT_TO_IMAGE_IMAGE_PARAMS
   lowerCAmelCase_								=				TEXT_TO_IMAGE_IMAGE_PARAMS




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__     				= UNetaDConditionModel(
         block_out_channels=(32, 64)			,      layers_per_block=2			,      sample_size=32			,      in_channels=4			,      out_channels=4			,      down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")			,      up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")			,      cross_attention_dim=32			,      )
     SCREAMING_SNAKE_CASE__     				= DDIMScheduler()
     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__     				= AutoencoderKL(
         block_out_channels=[32, 64]			,      in_channels=3			,      out_channels=3			,      down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]			,      up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]			,      latent_channels=4			,      )
     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__     				= 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=10_00			,      )
     SCREAMING_SNAKE_CASE__     				= CLIPTextModel(_lowercase )
     SCREAMING_SNAKE_CASE__     				= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

     SCREAMING_SNAKE_CASE__     				= {
         """unet""": unet,
         """scheduler""": scheduler,
         """vae""": vae,
         """text_encoder""": text_encoder,
         """tokenizer""": tokenizer,
         """safety_checker""": None,
         """feature_extractor""": None,
     }
     return components




   def       __a		(       self     :							Optional[int]			,      _lowercase     :							Optional[int]			,      _lowercase     :							Union[str, Any]=0 ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= torch.manual_seed(_lowercase )
     SCREAMING_SNAKE_CASE__     				= {
         """prompt""": """A field of roses""",
         """generator""": generator,
         # Setting height and width to None to prevent OOMs on CPU.
         """height""": None,
         """width""": None,
         """num_inference_steps""": 2,
         """guidance_scale""": 6.0,
         """output_type""": """numpy""",
     }
     return inputs




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_components()
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline(**_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     SCREAMING_SNAKE_CASE__     				= self.get_dummy_inputs(_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe(**_lowercase ).images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1]
     assert image.shape == (1, 64, 64, 3)

     SCREAMING_SNAKE_CASE__     				= np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_components()
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline(**_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     SCREAMING_SNAKE_CASE__     				= self.get_dummy_inputs(_lowercase )
     SCREAMING_SNAKE_CASE__     				= """french fries"""
     SCREAMING_SNAKE_CASE__     				= sd_pipe(**_lowercase			,      negative_prompt=_lowercase )
     SCREAMING_SNAKE_CASE__     				= output.images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     SCREAMING_SNAKE_CASE__     				= np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2




   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_components()
     SCREAMING_SNAKE_CASE__     				= EulerAncestralDiscreteScheduler(
         beta_start=0.0_00_85			,      beta_end=0.0_12			,      beta_schedule="""scaled_linear""" )
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline(**_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     SCREAMING_SNAKE_CASE__     				= self.get_dummy_inputs(_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe(**_lowercase ).images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     SCREAMING_SNAKE_CASE__     				= np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_components()
     SCREAMING_SNAKE_CASE__     				= PNDMScheduler()
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline(**_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     SCREAMING_SNAKE_CASE__     				= self.get_dummy_inputs(_lowercase )
     # the pipeline does not expect pndm so test if it raises error.
     with self.assertRaises(_lowercase ):
       SCREAMING_SNAKE_CASE__     				= sd_pipe(**_lowercase ).images




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     super().test_inference_batch_single_identical(expected_max_diff=5E-3 )




   def       __a		(       self     :							Any ):



     """simple docstring"""






     super().test_attention_slicing_forward_pass(expected_max_diff=5E-3 )







@slow
@require_torch_gpu
class __snake_case							(		unittest.TestCase						):




   def       __a		(       self     :							str ):



     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




   def       __a		(       self     :							List[Any]			,      _lowercase     :							Dict=0 ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= torch.manual_seed(_lowercase )
     SCREAMING_SNAKE_CASE__     				= {
         """prompt""": """A field of roses""",
         """generator""": generator,
         """num_inference_steps""": 3,
         """guidance_scale""": 7.5,
         """output_type""": """numpy""",
     }
     return inputs




   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """CompVis/stable-diffusion-v1-4"""
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline.from_pretrained(_lowercase			,      safety_checker=_lowercase )
     pipe.to(_lowercase )
     pipe.set_progress_bar_config(disable=_lowercase )
     pipe.enable_attention_slicing()

     SCREAMING_SNAKE_CASE__     				= self.get_inputs()
     SCREAMING_SNAKE_CASE__     				= pipe(**_lowercase ).images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)

     SCREAMING_SNAKE_CASE__     				= np.array(
         [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58] )

     assert np.abs(expected_slice - image_slice ).max() < 1E-2

     # make sure image changes after editing
     pipe.edit_model("""A pack of roses"""			,      """A pack of blue roses""" )

     SCREAMING_SNAKE_CASE__     				= pipe(**_lowercase ).images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)

     assert np.abs(expected_slice - image_slice ).max() > 1E-1




   def       __a		(       self     :							Any ):



     """simple docstring"""






     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     SCREAMING_SNAKE_CASE__     				= """CompVis/stable-diffusion-v1-4"""
     SCREAMING_SNAKE_CASE__     				= DDIMScheduler.from_pretrained(_lowercase			,      subfolder="""scheduler""" )
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline.from_pretrained(
         _lowercase			,      scheduler=_lowercase			,      safety_checker=_lowercase )
     SCREAMING_SNAKE_CASE__     				= pipe.to(_lowercase )
     pipe.set_progress_bar_config(disable=_lowercase )
     pipe.enable_attention_slicing(1 )
     pipe.enable_sequential_cpu_offload()

     SCREAMING_SNAKE_CASE__     				= self.get_inputs()
     SCREAMING_SNAKE_CASE__     				= pipe(**_lowercase )

     SCREAMING_SNAKE_CASE__     				= torch.cuda.max_memory_allocated()
     # make sure that less than 4.4 GB is allocated
     assert mem_bytes < 4.4 * 10**9
