
'''simple docstring'''





# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()






class        UpperCAmelCase_       (						A				,							A				,							A				,							unittest.TestCase							):

 '''simple docstring'''
 lowercase_				:						int				=       StableDiffusionControlNetInpaintPipeline
 lowercase_				:						Union[str, Any]				=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 lowercase_				:						Union[str, Any]				=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 lowercase_				:						str				=       frozenset({"control_image"}							)  # skip `image` and `mask` for now, only test for control_image
 lowercase_				:						List[str]				=       TEXT_TO_IMAGE_IMAGE_PARAMS







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



   '''simple docstring'''




   torch.manual_seed(0   )
   UpperCAmelCase__			:				int												=       UNetaDConditionModel(
       block_out_channels=(32, 64) ,							layers_per_block=2 ,							sample_size=32 ,							in_channels=9 ,							out_channels=4 ,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,							up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,							cross_attention_dim=32 ,							)
   torch.manual_seed(0   )
   UpperCAmelCase__			:				List[str]												=       ControlNetModel(
       block_out_channels=(32, 64) ,							layers_per_block=2 ,							in_channels=4 ,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,							cross_attention_dim=32 ,							conditioning_embedding_out_channels=(16, 32) ,							)
   torch.manual_seed(0   )
   UpperCAmelCase__			:				Union[str, Any]												=       DDIMScheduler(
       beta_start=0.00085 ,							beta_end=0.012 ,							beta_schedule="scaled_linear" ,							clip_sample=snake_case__ ,							set_alpha_to_one=snake_case__ ,							)
   torch.manual_seed(0   )
   UpperCAmelCase__			:				Dict												=       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   )
   UpperCAmelCase__			:				int												=       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 ,							)
   UpperCAmelCase__			:				List[str]												=       CLIPTextModel(snake_case__   )
   UpperCAmelCase__			:				Tuple												=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

   UpperCAmelCase__			:				Optional[Any]												=       {
       "unet": unet,
       "controlnet": controlnet,
       "scheduler": scheduler,
       "vae": vae,
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "safety_checker": None,
       "feature_extractor": None,
   }
   return components







 def        UpperCamelCase     (				self		:     Any ,							snake_case__		:     str ,							snake_case__		:     List[Any]=0   ):



   '''simple docstring'''




   if str(snake_case__   ).startswith("mps"   ):
     UpperCAmelCase__			:				Optional[int]												=       torch.manual_seed(snake_case__   )
   else:
     UpperCAmelCase__			:				Union[str, Any]												=       torch.Generator(device=snake_case__   ).manual_seed(snake_case__   )

   UpperCAmelCase__			:				Any												=       2
   UpperCAmelCase__			:				Tuple												=       randn_tensor(
       (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,							generator=snake_case__ ,							device=torch.device(snake_case__   ) ,							)
   UpperCAmelCase__			:				List[str]												=       floats_tensor((1, 3, 32, 32) ,							rng=random.Random(snake_case__   )   ).to(snake_case__   )
   UpperCAmelCase__			:				Union[str, Any]												=       init_image.cpu().permute(0 ,							2 ,							3 ,							1   )[0]

   UpperCAmelCase__			:				List[str]												=       Image.fromarray(np.uinta(snake_case__   )   ).convert("RGB"   ).resize((64, 64)   )
   UpperCAmelCase__			:				int												=       Image.fromarray(np.uinta(init_image + 4   )   ).convert("RGB"   ).resize((64, 64)   )

   UpperCAmelCase__			:				Any												=       {
       "prompt": "A painting of a squirrel eating a burger",
       "generator": generator,
       "num_inference_steps": 2,
       "guidance_scale": 6.0,
       "output_type": "numpy",
       "image": image,
       "mask_image": mask_image,
       "control_image": control_image,
   }

   return inputs







 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3   )







 @unittest.skipIf(
     torch_device != "cuda" or not is_xformers_available() ,							reason="XFormers attention is only available with CUDA and `xformers` installed" ,							)
 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3   )







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




   self._test_inference_batch_single_identical(expected_max_diff=2e-3   )






class        UpperCAmelCase_       (						A							):

 '''simple docstring'''
 lowercase_				:						Union[str, Any]				=       StableDiffusionControlNetInpaintPipeline
 lowercase_				:						int				=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 lowercase_				:						Optional[Any]				=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 lowercase_				:						List[Any]				=       frozenset([]							)







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



   '''simple docstring'''




   torch.manual_seed(0   )
   UpperCAmelCase__			:				Any												=       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 ,							)
   torch.manual_seed(0   )
   UpperCAmelCase__			:				Optional[Any]												=       ControlNetModel(
       block_out_channels=(32, 64) ,							layers_per_block=2 ,							in_channels=4 ,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,							cross_attention_dim=32 ,							conditioning_embedding_out_channels=(16, 32) ,							)
   torch.manual_seed(0   )
   UpperCAmelCase__			:				Dict												=       DDIMScheduler(
       beta_start=0.00085 ,							beta_end=0.012 ,							beta_schedule="scaled_linear" ,							clip_sample=snake_case__ ,							set_alpha_to_one=snake_case__ ,							)
   torch.manual_seed(0   )
   UpperCAmelCase__			:				List[str]												=       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   )
   UpperCAmelCase__			:				Union[str, Any]												=       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 ,							)
   UpperCAmelCase__			:				int												=       CLIPTextModel(snake_case__   )
   UpperCAmelCase__			:				Optional[Any]												=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

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






class        UpperCAmelCase_       (						A				,							A				,							unittest.TestCase							):

 '''simple docstring'''
 lowercase_				:						Dict				=       StableDiffusionControlNetInpaintPipeline
 lowercase_				:						str				=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 lowercase_				:						Dict				=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS







 def        UpperCamelCase     (				self		:     str   ):



   '''simple docstring'''




   torch.manual_seed(0   )
   UpperCAmelCase__			:				Tuple												=       UNetaDConditionModel(
       block_out_channels=(32, 64) ,							layers_per_block=2 ,							sample_size=32 ,							in_channels=9 ,							out_channels=4 ,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,							up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,							cross_attention_dim=32 ,							)
   torch.manual_seed(0   )

   def init_weights(snake_case__		:     Dict   ):
     if isinstance(snake_case__ ,							torch.nn.Convad   ):
       torch.nn.init.normal(m.weight   )
       m.bias.data.fill_(1.0   )

   UpperCAmelCase__			:				Tuple												=       ControlNetModel(
       block_out_channels=(32, 64) ,							layers_per_block=2 ,							in_channels=4 ,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,							cross_attention_dim=32 ,							conditioning_embedding_out_channels=(16, 32) ,							)
   controlneta.controlnet_down_blocks.apply(snake_case__   )

   torch.manual_seed(0   )
   UpperCAmelCase__			:				int												=       ControlNetModel(
       block_out_channels=(32, 64) ,							layers_per_block=2 ,							in_channels=4 ,							down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,							cross_attention_dim=32 ,							conditioning_embedding_out_channels=(16, 32) ,							)
   controlneta.controlnet_down_blocks.apply(snake_case__   )

   torch.manual_seed(0   )
   UpperCAmelCase__			:				Dict												=       DDIMScheduler(
       beta_start=0.00085 ,							beta_end=0.012 ,							beta_schedule="scaled_linear" ,							clip_sample=snake_case__ ,							set_alpha_to_one=snake_case__ ,							)
   torch.manual_seed(0   )
   UpperCAmelCase__			:				Optional[Any]												=       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   )
   UpperCAmelCase__			:				List[str]												=       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 ,							)
   UpperCAmelCase__			:				str												=       CLIPTextModel(snake_case__   )
   UpperCAmelCase__			:				Union[str, Any]												=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

   UpperCAmelCase__			:				Any												=       MultiControlNetModel([controlneta, controlneta]   )

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







 def        UpperCamelCase     (				self		:     Optional[Any] ,							snake_case__		:     Any ,							snake_case__		:     Any=0   ):



   '''simple docstring'''




   if str(snake_case__   ).startswith("mps"   ):
     UpperCAmelCase__			:				List[Any]												=       torch.manual_seed(snake_case__   )
   else:
     UpperCAmelCase__			:				Union[str, Any]												=       torch.Generator(device=snake_case__   ).manual_seed(snake_case__   )

   UpperCAmelCase__			:				Optional[int]												=       2

   UpperCAmelCase__			:				str												=       [
       randn_tensor(
           (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,							generator=snake_case__ ,							device=torch.device(snake_case__   ) ,							),
       randn_tensor(
           (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) ,							generator=snake_case__ ,							device=torch.device(snake_case__   ) ,							),
   ]
   UpperCAmelCase__			:				Tuple												=       floats_tensor((1, 3, 32, 32) ,							rng=random.Random(snake_case__   )   ).to(snake_case__   )
   UpperCAmelCase__			:				Optional[int]												=       init_image.cpu().permute(0 ,							2 ,							3 ,							1   )[0]

   UpperCAmelCase__			:				Tuple												=       Image.fromarray(np.uinta(snake_case__   )   ).convert("RGB"   ).resize((64, 64)   )
   UpperCAmelCase__			:				Dict												=       Image.fromarray(np.uinta(init_image + 4   )   ).convert("RGB"   ).resize((64, 64)   )

   UpperCAmelCase__			:				int												=       {
       "prompt": "A painting of a squirrel eating a burger",
       "generator": generator,
       "num_inference_steps": 2,
       "guidance_scale": 6.0,
       "output_type": "numpy",
       "image": image,
       "mask_image": mask_image,
       "control_image": control_image,
   }

   return inputs







 def        UpperCamelCase     (				self		:     Dict   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       self.get_dummy_components()
   UpperCAmelCase__			:				List[str]												=       self.pipeline_class(**snake_case__   )
   pipe.to(snake_case__   )

   UpperCAmelCase__			:				Optional[Any]												=       10.0
   UpperCAmelCase__			:				int												=       4

   UpperCAmelCase__			:				int												=       self.get_dummy_inputs(snake_case__   )
   UpperCAmelCase__			:				str												=       steps
   UpperCAmelCase__			:				Dict												=       scale
   UpperCAmelCase__			:				Optional[int]												=       pipe(**snake_case__   )[0]

   UpperCAmelCase__			:				List[str]												=       self.get_dummy_inputs(snake_case__   )
   UpperCAmelCase__			:				Optional[int]												=       steps
   UpperCAmelCase__			:				Any												=       scale
   UpperCAmelCase__			:				List[str]												=       pipe(**snake_case__ ,							control_guidance_start=0.1 ,							control_guidance_end=0.2   )[0]

   UpperCAmelCase__			:				Tuple												=       self.get_dummy_inputs(snake_case__   )
   UpperCAmelCase__			:				Tuple												=       steps
   UpperCAmelCase__			:				Any												=       scale
   UpperCAmelCase__			:				Dict												=       pipe(**snake_case__ ,							control_guidance_start=[0.1, 0.3] ,							control_guidance_end=[0.2, 0.7]   )[0]

   UpperCAmelCase__			:				Optional[Any]												=       self.get_dummy_inputs(snake_case__   )
   UpperCAmelCase__			:				List[str]												=       steps
   UpperCAmelCase__			:				Any												=       scale
   UpperCAmelCase__			:				Optional[Any]												=       pipe(**snake_case__ ,							control_guidance_start=0.4 ,							control_guidance_end=[0.5, 0.8]   )[0]

   # make sure that all outputs are different
   assert np.sum(np.abs(output_a - output_a   )   ) > 1e-3
   assert np.sum(np.abs(output_a - output_a   )   ) > 1e-3
   assert np.sum(np.abs(output_a - output_a   )   ) > 1e-3







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



   '''simple docstring'''




   return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3   )







 @unittest.skipIf(
     torch_device != "cuda" or not is_xformers_available() ,							reason="XFormers attention is only available with CUDA and `xformers` installed" ,							)
 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3   )







 def        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




   self._test_inference_batch_single_identical(expected_max_diff=2e-3   )







 def        UpperCamelCase     (				self		:     Optional[Any]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       self.get_dummy_components()
   UpperCAmelCase__			:				List[Any]												=       self.pipeline_class(**snake_case__   )
   pipe.to(snake_case__   )
   pipe.set_progress_bar_config(disable=snake_case__   )
   with tempfile.TemporaryDirectory() as tmpdir:
     try:
       # save_pretrained is not implemented for Multi-ControlNet
       pipe.save_pretrained(snake_case__   )
     except NotImplementedError:
       pass






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

 '''simple docstring'''







 def        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




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







 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Dict												=       ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"   )

   UpperCAmelCase__			:				Optional[Any]												=       StableDiffusionControlNetInpaintPipeline.from_pretrained(
       "runwayml/stable-diffusion-inpainting" ,							safety_checker=snake_case__ ,							controlnet=snake_case__   )
   pipe.enable_model_cpu_offload()
   pipe.set_progress_bar_config(disable=snake_case__   )

   UpperCAmelCase__			:				str												=       torch.Generator(device="cpu"   ).manual_seed(0   )
   UpperCAmelCase__			:				List[Any]												=       load_image(
       "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"   ).resize((5_12, 5_12)   )

   UpperCAmelCase__			:				Optional[Any]												=       load_image(
       "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
       "/stable_diffusion_inpaint/input_bench_mask.png"   ).resize((5_12, 5_12)   )

   UpperCAmelCase__			:				Optional[int]												=       "pitch black hole"

   UpperCAmelCase__			:				List[Any]												=       load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"   ).resize((5_12, 5_12)   )

   UpperCAmelCase__			:				Optional[Any]												=       pipe(
       snake_case__ ,							image=snake_case__ ,							mask_image=snake_case__ ,							control_image=snake_case__ ,							generator=snake_case__ ,							output_type="np" ,							num_inference_steps=3 ,							)

   UpperCAmelCase__			:				List[str]												=       output.images[0]

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

   UpperCAmelCase__			:				Tuple												=       load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"   )

   assert np.abs(expected_image - image   ).max() < 9e-2







 def        UpperCamelCase     (				self		:     str   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Tuple												=       ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint"   )

   UpperCAmelCase__			:				List[str]												=       StableDiffusionControlNetInpaintPipeline.from_pretrained(
       "runwayml/stable-diffusion-v1-5" ,							safety_checker=snake_case__ ,							controlnet=snake_case__   )
   UpperCAmelCase__			:				Optional[int]												=       DDIMScheduler.from_config(pipe.scheduler.config   )
   pipe.enable_model_cpu_offload()
   pipe.set_progress_bar_config(disable=snake_case__   )

   UpperCAmelCase__			:				Union[str, Any]												=       torch.Generator(device="cpu"   ).manual_seed(33   )

   UpperCAmelCase__			:				Union[str, Any]												=       load_image(
       "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"   )
   UpperCAmelCase__			:				Tuple												=       init_image.resize((5_12, 5_12)   )

   UpperCAmelCase__			:				str												=       load_image(
       "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"   )
   UpperCAmelCase__			:				List[Any]												=       mask_image.resize((5_12, 5_12)   )

   UpperCAmelCase__			:				Optional[int]												=       "a handsome man with ray-ban sunglasses"

   def make_inpaint_condition(snake_case__		:     Optional[Any] ,							snake_case__		:     int   ):
     UpperCAmelCase__			:				Union[str, Any]												=       np.array(image.convert("RGB"   )   ).astype(np.floataa   ) / 255.0
     UpperCAmelCase__			:				List[str]												=       np.array(image_mask.convert("L"   )   ).astype(np.floataa   ) / 255.0

     assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
     UpperCAmelCase__			:				List[str]												=       -1.0  # set as masked pixel
     UpperCAmelCase__			:				List[str]												=       np.expand_dims(snake_case__ ,							0   ).transpose(0 ,							3 ,							1 ,							2   )
     UpperCAmelCase__			:				Tuple												=       torch.from_numpy(snake_case__   )
     return image

   UpperCAmelCase__			:				Dict												=       make_inpaint_condition(snake_case__ ,							snake_case__   )

   UpperCAmelCase__			:				List[str]												=       pipe(
       snake_case__ ,							image=snake_case__ ,							mask_image=snake_case__ ,							control_image=snake_case__ ,							guidance_scale=9.0 ,							eta=1.0 ,							generator=snake_case__ ,							num_inference_steps=20 ,							output_type="np" ,							)
   UpperCAmelCase__			:				Optional[int]												=       output.images[0]

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

   UpperCAmelCase__			:				Optional[Any]												=       load_numpy(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"   )

   assert np.abs(expected_image - image   ).max() < 9e-2





'''simple docstring'''





import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
      pass

if is_speech_available():
      from transformers import EncodecFeatureExtractor, MusicgenProcessor


SCREAMING_SNAKE_CASE    =					random.Random()







def snake_case_    (				lowercase__      ,					lowercase__=1.0      ,					lowercase__=None      ,					lowercase__=None		):
  if rng is None:
    UpperCAmelCase__			:				Optional[int]												=       global_rng

  UpperCAmelCase__			:				List[str]												=       []
  for batch_idx in range(shape[0]		):
    values.append([]		)
    for _ in range(shape[1]		):
      values[-1].append(rng.random() * scale		)

  return values






@require_torch
@require_sentencepiece
class        UpperCAmelCase_       (						unittest.TestCase							):

 '''simple docstring'''







 def        UpperCamelCase     (				self		:     Optional[int]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Tuple												=       "facebook/musicgen-small"
   UpperCAmelCase__			:				Union[str, Any]												=       tempfile.mkdtemp()







 def        UpperCamelCase     (				self		:     Union[str, Any] ,							**snake_case__		:     Dict   ):



   '''simple docstring'''




   return TaTokenizer.from_pretrained(self.checkpoint ,							**snake_case__   )







 def        UpperCamelCase     (				self		:     Optional[int] ,							**snake_case__		:     str   ):



   '''simple docstring'''




   return EncodecFeatureExtractor.from_pretrained(self.checkpoint ,							**snake_case__   )







 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   shutil.rmtree(self.tmpdirname   )







 def        UpperCamelCase     (				self		:     Any   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       self.get_tokenizer()
   UpperCAmelCase__			:				Tuple												=       self.get_feature_extractor()

   UpperCAmelCase__			:				int												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   processor.save_pretrained(self.tmpdirname   )
   UpperCAmelCase__			:				Dict												=       MusicgenProcessor.from_pretrained(self.tmpdirname   )

   self.assertEqual(processor.tokenizer.get_vocab() ,							tokenizer.get_vocab()   )
   self.assertIsInstance(processor.tokenizer ,							snake_case__   )

   self.assertEqual(processor.feature_extractor.to_json_string() ,							feature_extractor.to_json_string()   )
   self.assertIsInstance(processor.feature_extractor ,							snake_case__   )







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       MusicgenProcessor(tokenizer=self.get_tokenizer() ,							feature_extractor=self.get_feature_extractor()   )
   processor.save_pretrained(self.tmpdirname   )

   UpperCAmelCase__			:				List[Any]												=       self.get_tokenizer(bos_token="(BOS)" ,							eos_token="(EOS)"   )
   UpperCAmelCase__			:				int												=       self.get_feature_extractor(do_normalize=snake_case__ ,							padding_value=1.0   )

   UpperCAmelCase__			:				Optional[Any]												=       MusicgenProcessor.from_pretrained(
       self.tmpdirname ,							bos_token="(BOS)" ,							eos_token="(EOS)" ,							do_normalize=snake_case__ ,							padding_value=1.0   )

   self.assertEqual(processor.tokenizer.get_vocab() ,							tokenizer_add_kwargs.get_vocab()   )
   self.assertIsInstance(processor.tokenizer ,							snake_case__   )

   self.assertEqual(processor.feature_extractor.to_json_string() ,							feature_extractor_add_kwargs.to_json_string()   )
   self.assertIsInstance(processor.feature_extractor ,							snake_case__   )







 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       self.get_feature_extractor()
   UpperCAmelCase__			:				int												=       self.get_tokenizer()

   UpperCAmelCase__			:				List[str]												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   UpperCAmelCase__			:				Optional[Any]												=       floats_list((3, 10_00)   )

   UpperCAmelCase__			:				List[Any]												=       feature_extractor(snake_case__ ,							return_tensors="np"   )
   UpperCAmelCase__			:				List[Any]												=       processor(snake_case__ ,							return_tensors="np"   )

   for key in input_feat_extract.keys():
     self.assertAlmostEqual(input_feat_extract[key].sum() ,							input_processor[key].sum() ,							delta=1e-2   )







 def        UpperCamelCase     (				self		:     List[str]   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Union[str, Any]												=       self.get_feature_extractor()
   UpperCAmelCase__			:				Union[str, Any]												=       self.get_tokenizer()

   UpperCAmelCase__			:				Optional[int]												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   UpperCAmelCase__			:				List[Any]												=       "This is a test string"

   UpperCAmelCase__			:				Dict												=       processor(text=snake_case__   )

   UpperCAmelCase__			:				Any												=       tokenizer(snake_case__   )

   for key in encoded_tok.keys():
     self.assertListEqual(encoded_tok[key] ,							encoded_processor[key]   )







 def        UpperCamelCase     (				self		:     int   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Union[str, Any]												=       self.get_feature_extractor()
   UpperCAmelCase__			:				str												=       self.get_tokenizer()

   UpperCAmelCase__			:				List[Any]												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   UpperCAmelCase__			:				Optional[int]												=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   UpperCAmelCase__			:				Any												=       processor.batch_decode(sequences=snake_case__   )
   UpperCAmelCase__			:				Optional[Any]												=       tokenizer.batch_decode(snake_case__   )

   self.assertListEqual(snake_case__ ,							snake_case__   )







 def        UpperCamelCase     (				self		:     Tuple   ):



   '''simple docstring'''




   UpperCAmelCase__			:				Any												=       self.get_feature_extractor()
   UpperCAmelCase__			:				List[Any]												=       self.get_tokenizer()

   UpperCAmelCase__			:				Union[str, Any]												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   self.assertListEqual(
       processor.model_input_names ,							feature_extractor.model_input_names ,							msg="`processor` and `feature_extractor` model input names do not match" ,							)







 def        UpperCamelCase     (				self		:     Dict   ):



   '''simple docstring'''




   UpperCAmelCase__			:				int												=       self.get_feature_extractor(padding_side="left"   )
   UpperCAmelCase__			:				str												=       self.get_tokenizer()

   UpperCAmelCase__			:				int												=       MusicgenProcessor(tokenizer=snake_case__ ,							feature_extractor=snake_case__   )

   UpperCAmelCase__			:				List[Any]												=       [floats_list((1, x)   )[0] for x in range(5 ,							20 ,							5   )]
   UpperCAmelCase__			:				List[Any]												=       processor(snake_case__   ).padding_mask

   UpperCAmelCase__			:				Dict												=       np.asarray(floats_list((3, 20)   )   )[:, None, :]
   UpperCAmelCase__			:				Optional[Any]												=       processor.batch_decode(snake_case__ ,							padding_mask=snake_case__   )

   self.assertIsInstance(snake_case__ ,							snake_case__   )

   for audio in decoded_audios:
     self.assertIsInstance(snake_case__ ,							np.ndarray   )

   self.assertTrue(decoded_audios[0].shape == (1, 10)   )
   self.assertTrue(decoded_audios[1].shape == (1, 15)   )
   self.assertTrue(decoded_audios[2].shape == (1, 20)   )



