


from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


A				:   Union[str, Any]		= logging.get_logger(__name__)  # pylint: disable=invalid-name

A				:   Dict		= "\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"




def 				lowercase_		(		_A      :   Any   , _A      :   Tuple   , _A      :   Union[str, Any]=8						):

      """simple docstring"""




      lowerCamelCase__		:    Optional[int]   =  height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      lowerCamelCase__		:    Dict   =  width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor




def 				lowercase_		(		_A      :   Optional[int]   , _A      :   Union[str, Any]=512   , _A      :   str=512						):

      """simple docstring"""




      lowerCamelCase__		:    Union[str, Any]   =  pil_image.resize((w, h)   , resample=Image.BICUBIC   , reducing_gap=1						)
      lowerCamelCase__		:    Any   =  np.array(pil_image.convert("RGB"						)						)
      lowerCamelCase__		:    List[str]   =  arr.astype(np.floataa						) / 127.5 - 1
      lowerCamelCase__		:    Dict   =  np.transpose(_A   , [2, 0, 1]						)
      lowerCamelCase__		:    Union[str, Any]   =  torch.from_numpy(_A						).unsqueeze(0						)
      return image







class       _lowercase			(							lowercase__):


       """simple docstring"""




       def __init__(       self							:      Optional[int] ,				__lowerCamelCase							:      UNetaDConditionModel ,				__lowerCamelCase							:      DDPMScheduler ,				__lowerCamelCase							:      VQModel ,				):


             '''simple docstring'''



             super().__init__()

             self.register_modules(
                 unet=__lowerCamelCase ,				scheduler=__lowerCamelCase ,				movq=__lowerCamelCase ,				)
             lowerCamelCase__		:    Any   =  2 ** (len(self.movq.config.block_out_channels	) - 1)





       def 						lowerCAmelCase					(       self							:      Any ,				__lowerCamelCase							:      Optional[Any] ,				__lowerCamelCase							:      str ,				__lowerCamelCase							:      Tuple	):


             '''simple docstring'''



             lowerCamelCase__		:    List[Any]   =  min(int(num_inference_steps * strength	) ,				__lowerCamelCase	)

             lowerCamelCase__		:    str   =  max(num_inference_steps - init_timestep ,				0	)
             lowerCamelCase__		:    int   =  self.scheduler.timesteps[t_start:]

             return timesteps, num_inference_steps - t_start





       def 						lowerCAmelCase					(       self							:      Union[str, Any] ,				__lowerCamelCase							:      List[str] ,				__lowerCamelCase							:      Tuple ,				__lowerCamelCase							:      Union[str, Any] ,				__lowerCamelCase							:      Dict ,				__lowerCamelCase							:      Dict ,				__lowerCamelCase							:      str ,				__lowerCamelCase							:      Dict=None	):


             '''simple docstring'''



             if not isinstance(__lowerCamelCase ,				(torch.Tensor, PIL.Image.Image, list)	):
                   raise ValueError(
                       f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__lowerCamelCase	)}"	)

             lowerCamelCase__		:    List[Any]   =  image.to(device=__lowerCamelCase ,				dtype=__lowerCamelCase	)

             lowerCamelCase__		:    Dict   =  batch_size * num_images_per_prompt

             if image.shape[1] == 4:
                   lowerCamelCase__		:    int   =  image

             else:
                   if isinstance(__lowerCamelCase ,				__lowerCamelCase	) and len(__lowerCamelCase	) != batch_size:
                         raise ValueError(
                             f"You have passed a list of generators of length {len(__lowerCamelCase	)}, but requested an effective batch"
                             f" size of {batch_size}. Make sure the batch size matches the length of the generators."	)

                   elif isinstance(__lowerCamelCase ,				__lowerCamelCase	):
                         lowerCamelCase__		:    Optional[Any]   =  [
                             self.movq.encode(image[i : i + 1]	).latent_dist.sample(generator[i]	) for i in range(__lowerCamelCase	)
                         ]
                         lowerCamelCase__		:    int   =  torch.cat(__lowerCamelCase ,				dim=0	)
                   else:
                         lowerCamelCase__		:    List[str]   =  self.movq.encode(__lowerCamelCase	).latent_dist.sample(__lowerCamelCase	)

                   lowerCamelCase__		:    List[str]   =  self.movq.config.scaling_factor * init_latents

             lowerCamelCase__		:    int   =  torch.cat([init_latents] ,				dim=0	)

             lowerCamelCase__		:    Optional[int]   =  init_latents.shape
             lowerCamelCase__		:    Any   =  randn_tensor(__lowerCamelCase ,				generator=__lowerCamelCase ,				device=__lowerCamelCase ,				dtype=__lowerCamelCase	)

             # get latents
             lowerCamelCase__		:    Optional[int]   =  self.scheduler.add_noise(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase	)

             lowerCamelCase__		:    Tuple   =  init_latents

             return latents





       def 						lowerCAmelCase					(       self							:      List[Any] ,				__lowerCamelCase							:      str=0	):


             '''simple docstring'''



             if is_accelerate_available():
                   from accelerate import cpu_offload
             else:
                   raise ImportError("Please install accelerate via `pip install accelerate`"	)

             lowerCamelCase__		:    Tuple   =  torch.device(f"cuda:{gpu_id}"	)

             lowerCamelCase__		:    List[Any]   =  [
                 self.unet,
                 self.movq,
             ]
             for cpu_offloaded_model in models:
                   if cpu_offloaded_model is not None:
                         cpu_offload(__lowerCamelCase ,				__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      int ,				__lowerCamelCase							:      str=0	):


             '''simple docstring'''



             if is_accelerate_available() and is_accelerate_version(">=" ,				"0.17.0.dev0"	):
                   from accelerate import cpu_offload_with_hook
             else:
                   raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."	)

             lowerCamelCase__		:    Union[str, Any]   =  torch.device(f"cuda:{gpu_id}"	)

             if self.device.type != "cpu":
                   self.to("cpu" ,				silence_dtype_warnings=__lowerCamelCase	)
                   torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

             lowerCamelCase__		:    Union[str, Any]   =  None
             for cpu_offloaded_model in [self.unet, self.movq]:
                   lowerCamelCase__		:    Union[str, Any]   =  cpu_offload_with_hook(__lowerCamelCase ,				__lowerCamelCase ,				prev_module_hook=__lowerCamelCase	)

             # We'll offload the last model manually.
             lowerCamelCase__		:    Any   =  hook





       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 						lowerCAmelCase					(       self							:      int	):


             '''simple docstring'''



             if not hasattr(self.unet ,				"_hf_hook"	):
                   return self.device
             for module in self.unet.modules():
                   if (
                       hasattr(__lowerCamelCase ,				"_hf_hook"	)
                       and hasattr(module._hf_hook ,				"execution_device"	)
                       and module._hf_hook.execution_device is not None
                   ):
                         return torch.device(module._hf_hook.execution_device	)
             return self.device





       @torch.no_grad()
       @replace_example_docstring(__lowerCamelCase	)
       def __call__(       self							:      Union[str, Any] ,				__lowerCamelCase							:      Union[torch.FloatTensor, List[torch.FloatTensor]] ,				__lowerCamelCase							:      Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] ,				__lowerCamelCase							:      Union[torch.FloatTensor, List[torch.FloatTensor]] ,				__lowerCamelCase							:      torch.FloatTensor ,				__lowerCamelCase							:      int = 512 ,				__lowerCamelCase							:      int = 512 ,				__lowerCamelCase							:      int = 100 ,				__lowerCamelCase							:      float = 4.0 ,				__lowerCamelCase							:      float = 0.3 ,				__lowerCamelCase							:      int = 1 ,				__lowerCamelCase							:      Optional[Union[torch.Generator, List[torch.Generator]]] = None ,				__lowerCamelCase							:      Optional[str] = "pil" ,				__lowerCamelCase							:      bool = True ,				):


             '''simple docstring'''



             lowerCamelCase__		:    List[str]   =  self._execution_device

             lowerCamelCase__		:    Dict   =  guidance_scale > 1.0

             if isinstance(__lowerCamelCase ,				__lowerCamelCase	):
                   lowerCamelCase__		:    Any   =  torch.cat(__lowerCamelCase ,				dim=0	)
             if isinstance(__lowerCamelCase ,				__lowerCamelCase	):
                   lowerCamelCase__		:    str   =  torch.cat(__lowerCamelCase ,				dim=0	)
             if isinstance(__lowerCamelCase ,				__lowerCamelCase	):
                   lowerCamelCase__		:    int   =  torch.cat(__lowerCamelCase ,				dim=0	)

             lowerCamelCase__		:    Any   =  image_embeds.shape[0]

             if do_classifier_free_guidance:
                   lowerCamelCase__		:    Dict   =  image_embeds.repeat_interleave(__lowerCamelCase ,				dim=0	)
                   lowerCamelCase__		:    Union[str, Any]   =  negative_image_embeds.repeat_interleave(__lowerCamelCase ,				dim=0	)
                   lowerCamelCase__		:    Union[str, Any]   =  hint.repeat_interleave(__lowerCamelCase ,				dim=0	)

             lowerCamelCase__		:    Optional[int]   =  torch.cat([negative_image_embeds, image_embeds] ,				dim=0	).to(dtype=self.unet.dtype ,				device=__lowerCamelCase	)
             lowerCamelCase__		:    Dict   =  torch.cat([hint, hint] ,				dim=0	).to(dtype=self.unet.dtype ,				device=__lowerCamelCase	)

             if not isinstance(__lowerCamelCase ,				__lowerCamelCase	):
                   lowerCamelCase__		:    Union[str, Any]   =  [image]
             if not all(isinstance(__lowerCamelCase ,				(PIL.Image.Image, torch.Tensor)	) for i in image	):
                   raise ValueError(
                       f"Input is in incorrect format: {[type(__lowerCamelCase	) for i in image]}. Currently, we only support  PIL image and pytorch tensor"	)

             lowerCamelCase__		:    List[Any]   =  torch.cat([prepare_image(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase	) for i in image] ,				dim=0	)
             lowerCamelCase__		:    List[Any]   =  image.to(dtype=image_embeds.dtype ,				device=__lowerCamelCase	)

             lowerCamelCase__		:    Dict   =  self.movq.encode(__lowerCamelCase	)["latents"]
             lowerCamelCase__		:    str   =  latents.repeat_interleave(__lowerCamelCase ,				dim=0	)
             self.scheduler.set_timesteps(__lowerCamelCase ,				device=__lowerCamelCase	)
             lowerCamelCase__		:    Dict   =  self.get_timesteps(__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase	)
             lowerCamelCase__		:    Optional[Any]   =  timesteps[:1].repeat(batch_size * num_images_per_prompt	)
             lowerCamelCase__		:    List[str]   =  downscale_height_and_width(__lowerCamelCase ,				__lowerCamelCase ,				self.movq_scale_factor	)
             lowerCamelCase__		:    Union[str, Any]   =  self.prepare_latents(
                 __lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				image_embeds.dtype ,				__lowerCamelCase ,				__lowerCamelCase	)
             for i, t in enumerate(self.progress_bar(__lowerCamelCase	)	):
                   # expand the latents if we are doing classifier free guidance
                   lowerCamelCase__		:    Dict   =  torch.cat([latents] * 2	) if do_classifier_free_guidance else latents

                   lowerCamelCase__		:    List[Any]   =  {"image_embeds": image_embeds, "hint": hint}
                   lowerCamelCase__		:    Union[str, Any]   =  self.unet(
                       sample=__lowerCamelCase ,				timestep=__lowerCamelCase ,				encoder_hidden_states=__lowerCamelCase ,				added_cond_kwargs=__lowerCamelCase ,				return_dict=__lowerCamelCase ,				)[0]

                   if do_classifier_free_guidance:
                         lowerCamelCase__		:    Optional[int]   =  noise_pred.split(latents.shape[1] ,				dim=1	)
                         lowerCamelCase__		:    Union[str, Any]   =  noise_pred.chunk(2	)
                         lowerCamelCase__		:    Union[str, Any]   =  variance_pred.chunk(2	)
                         lowerCamelCase__		:    Optional[Any]   =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                         lowerCamelCase__		:    List[Any]   =  torch.cat([noise_pred, variance_pred_text] ,				dim=1	)

                   if not (
                       hasattr(self.scheduler.config ,				"variance_type"	)
                       and self.scheduler.config.variance_type in ["learned", "learned_range"]
                   ):
                         lowerCamelCase__		:    Tuple   =  noise_pred.split(latents.shape[1] ,				dim=1	)

                   # compute the previous noisy sample x_t -> x_t-1

                   lowerCamelCase__		:    Optional[int]   =  self.scheduler.step(
                       __lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				generator=__lowerCamelCase ,				)[0]

             # post-processing
             lowerCamelCase__		:    Tuple   =  self.movq.decode(__lowerCamelCase ,				force_not_quantize=__lowerCamelCase	)["sample"]

             if output_type not in ["pt", "np", "pil"]:
                   raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"	)

             if output_type in ["np", "pil"]:
                   lowerCamelCase__		:    str   =  image * 0.5 + 0.5
                   lowerCamelCase__		:    str   =  image.clamp(0 ,				1	)
                   lowerCamelCase__		:    List[str]   =  image.cpu().permute(0 ,				2 ,				3 ,				1	).float().numpy()

             if output_type == "pil":
                   lowerCamelCase__		:    Dict   =  self.numpy_to_pil(__lowerCamelCase	)

             if not return_dict:
                   return (image,)

             return ImagePipelineOutput(images=__lowerCamelCase	)






import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
    import torch


A				:   Union[str, Any]		= random.Random()

def 				lowercase_		(		_A      :   Any   , _A      :   int=1.0   , _A      :   Optional[int]=None   , _A      :   Union[str, Any]=None						):

      """simple docstring"""




      if rng is None:
            lowerCamelCase__		:    Any   =  global_rng

      lowerCamelCase__		:    Optional[Any]   =  []
      for batch_idx in range(shape[0]						):
            values.append([]						)
            for _ in range(shape[1]						):
                  values[-1].append(rng.random() * scale						)

      return values







@require_torch
class       _lowercase			(							unittest.TestCase):


       """simple docstring"""




       def __init__(       self							:      Tuple ,				__lowerCamelCase							:      str ,				__lowerCamelCase							:      Union[str, Any]=7 ,				__lowerCamelCase							:      int=400 ,				__lowerCamelCase							:      Tuple=2000 ,				__lowerCamelCase							:      int=1 ,				__lowerCamelCase							:      int=0.0 ,				__lowerCamelCase							:      int=24000 ,				__lowerCamelCase							:      Union[str, Any]=True ,				):


             '''simple docstring'''



             lowerCamelCase__		:    str   =  parent
             lowerCamelCase__		:    Union[str, Any]   =  batch_size
             lowerCamelCase__		:    str   =  min_seq_length
             lowerCamelCase__		:    int   =  max_seq_length
             lowerCamelCase__		:    List[Any]   =  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
             lowerCamelCase__		:    List[str]   =  feature_size
             lowerCamelCase__		:    List[Any]   =  padding_value
             lowerCamelCase__		:    Union[str, Any]   =  sampling_rate
             lowerCamelCase__		:    Tuple   =  return_attention_mask





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             return {
                 "feature_size": self.feature_size,
                 "padding_value": self.padding_value,
                 "sampling_rate": self.sampling_rate,
                 "return_attention_mask": self.return_attention_mask,
             }





       def 						lowerCAmelCase					(       self							:      List[str] ,				__lowerCamelCase							:      Tuple=False ,				__lowerCamelCase							:      Optional[int]=False	):


             '''simple docstring'''



             def _flatten(__lowerCamelCase							:      Dict	):
                   return list(itertools.chain(*__lowerCamelCase	)	)

             if equal_length:
                   lowerCamelCase__		:    List[Any]   =  floats_list((self.batch_size, self.max_seq_length)	)
             else:
                   # make sure that inputs increase in size
                   lowerCamelCase__		:    Dict   =  [
                       _flatten(floats_list((x, self.feature_size)	)	)
                       for x in range(self.min_seq_length ,				self.max_seq_length ,				self.seq_length_diff	)
                   ]

             if numpify:
                   lowerCamelCase__		:    Optional[int]   =  [np.asarray(__lowerCamelCase	) for x in audio_inputs]

             return audio_inputs







@require_torch
class       _lowercase			(							lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__ 						=      EncodecFeatureExtractor





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


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  EnCodecFeatureExtractionTester(self	)





       def 						lowerCAmelCase					(       self							:      int	):


             '''simple docstring'''



             lowerCamelCase__		:    Tuple   =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
             # create three inputs of length 800, 1000, and 1200
             lowerCamelCase__		:    Union[str, Any]   =  [floats_list((1, x)	)[0] for x in range(800 ,				1400 ,				200	)]
             lowerCamelCase__		:    Dict   =  [np.asarray(__lowerCamelCase	) for audio_input in audio_inputs]

             # Test not batched input
             lowerCamelCase__		:    Dict   =  feat_extract(audio_inputs[0] ,				return_tensors="np"	).input_values
             lowerCamelCase__		:    Optional[int]   =  feat_extract(np_audio_inputs[0] ,				return_tensors="np"	).input_values
             self.assertTrue(np.allclose(__lowerCamelCase ,				__lowerCamelCase ,				atol=1E-3	)	)

             # Test batched
             lowerCamelCase__		:    Dict   =  feat_extract(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="np"	).input_values
             lowerCamelCase__		:    Any   =  feat_extract(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="np"	).input_values
             for enc_seq_a, enc_seq_a in zip(__lowerCamelCase ,				__lowerCamelCase	):
                   self.assertTrue(np.allclose(__lowerCamelCase ,				__lowerCamelCase ,				atol=1E-3	)	)





       def 						lowerCAmelCase					(       self							:      Tuple	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[Any]   =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
             lowerCamelCase__		:    Tuple   =  np.random.rand(100	).astype(np.floataa	)
             lowerCamelCase__		:    str   =  np_audio_inputs.tolist()

             for inputs in [py_audio_inputs, np_audio_inputs]:
                   lowerCamelCase__		:    List[Any]   =  feature_extractor.pad([{"input_values": inputs}] ,				return_tensors="np"	)
                   self.assertTrue(np_processed.input_values.dtype == np.floataa	)
                   lowerCamelCase__		:    int   =  feature_extractor.pad([{"input_values": inputs}] ,				return_tensors="pt"	)
                   self.assertTrue(pt_processed.input_values.dtype == torch.floataa	)





       def 						lowerCAmelCase					(       self							:      str ,				__lowerCamelCase							:      Any	):


             '''simple docstring'''



             from datasets import load_dataset

             lowerCamelCase__		:    List[Any]   =  load_dataset("hf-internal-testing/librispeech_asr_dummy" ,				"clean" ,				split="validation"	)
             # automatic decoding with librispeech
             lowerCamelCase__		:    Union[str, Any]   =  ds.sort("id"	).select(range(__lowerCamelCase	)	)[:num_samples]["audio"]

             return [x["array"] for x in audio_samples]





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    List[Any]   =  torch.tensor(
                 [2.3_8_0_4E-0_3, 2.0_7_5_2E-0_3, 1.9_8_3_6E-0_3, 2.1_0_5_7E-0_3, 1.6_1_7_4E-0_3,
                  3.0_5_1_8E-0_4, 9.1_5_5_3E-0_5, 3.3_5_6_9E-0_4, 9.7_6_5_6E-0_4, 1.8_3_1_1E-0_3,
                  2.0_1_4_2E-0_3, 2.1_0_5_7E-0_3, 1.7_3_9_5E-0_3, 4.5_7_7_6E-0_4, -3.9_6_7_3E-0_4,
                  4.5_7_7_6E-0_4, 1.0_0_7_1E-0_3, 9.1_5_5_3E-0_5, 4.8_8_2_8E-0_4, 1.1_5_9_7E-0_3,
                  7.3_2_4_2E-0_4, 9.4_6_0_4E-0_4, 1.8_0_0_5E-0_3, 1.8_3_1_1E-0_3, 8.8_5_0_1E-0_4,
                  4.2_7_2_5E-0_4, 4.8_8_2_8E-0_4, 7.3_2_4_2E-0_4, 1.0_9_8_6E-0_3, 2.1_0_5_7E-0_3]	)
             # fmt: on
             lowerCamelCase__		:    str   =  self._load_datasamples(1	)
             lowerCamelCase__		:    Any   =  EncodecFeatureExtractor()
             lowerCamelCase__		:    Optional[int]   =  feature_extractor(__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(input_values.shape ,				(1, 1, 93680)	)
             self.assertTrue(torch.allclose(input_values[0, 0, :30] ,				__lowerCamelCase ,				atol=1E-6	)	)





       def 						lowerCAmelCase					(       self							:      Optional[int]	):


             '''simple docstring'''



             lowerCamelCase__		:    Any   =  torch.tensor(
                 [2.3_8_0_4E-0_3, 2.0_7_5_2E-0_3, 1.9_8_3_6E-0_3, 2.1_0_5_7E-0_3, 1.6_1_7_4E-0_3,
                  3.0_5_1_8E-0_4, 9.1_5_5_3E-0_5, 3.3_5_6_9E-0_4, 9.7_6_5_6E-0_4, 1.8_3_1_1E-0_3,
                  2.0_1_4_2E-0_3, 2.1_0_5_7E-0_3, 1.7_3_9_5E-0_3, 4.5_7_7_6E-0_4, -3.9_6_7_3E-0_4,
                  4.5_7_7_6E-0_4, 1.0_0_7_1E-0_3, 9.1_5_5_3E-0_5, 4.8_8_2_8E-0_4, 1.1_5_9_7E-0_3,
                  7.3_2_4_2E-0_4, 9.4_6_0_4E-0_4, 1.8_0_0_5E-0_3, 1.8_3_1_1E-0_3, 8.8_5_0_1E-0_4,
                  4.2_7_2_5E-0_4, 4.8_8_2_8E-0_4, 7.3_2_4_2E-0_4, 1.0_9_8_6E-0_3, 2.1_0_5_7E-0_3]	)
             # fmt: on
             lowerCamelCase__		:    Any   =  self._load_datasamples(1	)
             lowerCamelCase__		:    int   =  [np.tile(input_audio[0][None] ,				reps=(2, 1)	)]
             input_audio[0][1] *= 0.5
             lowerCamelCase__		:    List[str]   =  EncodecFeatureExtractor(feature_size=2	)
             lowerCamelCase__		:    Optional[Any]   =  feature_extractor(__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(input_values.shape ,				(1, 2, 93680)	)
             self.assertTrue(torch.allclose(input_values[0, 0, :30] ,				__lowerCamelCase ,				atol=1E-6	)	)
             self.assertTrue(torch.allclose(input_values[0, 1, :30] ,				EXPECTED_INPUT_VALUES * 0.5 ,				atol=1E-6	)	)





       def 						lowerCAmelCase					(       self							:      Dict	):


             '''simple docstring'''



             lowerCamelCase__		:    Tuple   =  self._load_datasamples(2	)
             # would be easier if the stride was like
             lowerCamelCase__		:    Optional[Any]   =  EncodecFeatureExtractor(feature_size=1 ,				chunk_length_s=1 ,				overlap=0.0_1	)

             # pad and trunc raise an error ?
             with self.assertRaisesRegex(
                 __lowerCamelCase ,				"^Both padding and truncation were set. Make sure you only set one.$" ,				):
                   lowerCamelCase__		:    str   =  feature_extractor(
                       __lowerCamelCase ,				padding="max_length" ,				truncation=__lowerCamelCase ,				return_tensors="pt"	).input_values

             # truncate to chunk
             lowerCamelCase__		:    Optional[int]   =  feature_extractor(__lowerCamelCase ,				truncation=__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(2, 1, 71520)	)  # 2 chunks

             # force truncate to max_length
             lowerCamelCase__		:    List[str]   =  feature_extractor(
                 __lowerCamelCase ,				truncation=__lowerCamelCase ,				max_length=48000 ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(2, 1, 48000)	)

             # pad to chunk
             lowerCamelCase__		:    int   =  feature_extractor(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(padded_outputs.shape ,				(2, 1, 95280)	)

             # pad to chunk
             lowerCamelCase__		:    Optional[int]   =  feature_extractor(__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(2, 1, 95280)	)

             # force pad to max length
             lowerCamelCase__		:    Tuple   =  feature_extractor(
                 __lowerCamelCase ,				padding="max_length" ,				max_length=100000 ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(2, 1, 100000)	)

             # force no pad
             with self.assertRaisesRegex(
                 __lowerCamelCase ,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$" ,				):
                   lowerCamelCase__		:    Optional[Any]   =  feature_extractor(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values

             lowerCamelCase__		:    str   =  feature_extractor(input_audio[0] ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(1, 1, 93680)	)

             # no pad if no chunk_length_s
             lowerCamelCase__		:    Tuple   =  None
             with self.assertRaisesRegex(
                 __lowerCamelCase ,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$" ,				):
                   lowerCamelCase__		:    List[str]   =  feature_extractor(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values

             lowerCamelCase__		:    Tuple   =  feature_extractor(input_audio[0] ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(1, 1, 93680)	)

             # no pad if no overlap
             lowerCamelCase__		:    List[Any]   =  2
             lowerCamelCase__		:    List[Any]   =  None
             with self.assertRaisesRegex(
                 __lowerCamelCase ,				"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$" ,				):
                   lowerCamelCase__		:    Optional[Any]   =  feature_extractor(__lowerCamelCase ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values

             lowerCamelCase__		:    Optional[Any]   =  feature_extractor(input_audio[0] ,				padding=__lowerCamelCase ,				return_tensors="pt"	).input_values
             self.assertEquals(truncated_outputs.shape ,				(1, 1, 93680)	)


