






from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


a_							=      logging.get_logger(__name__)  # pylint: disable=invalid-name


a_							=      '''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''


class        lowercase__       (     _UpperCAmelCase		):

		def __init__(							self						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   None:







								'''simple docstring'''





								super().__init__()

								self.register_modules(
								    unet=__UpperCAmelCase						,      scheduler=__UpperCAmelCase						,      )

								lowerCAmelCase__	 =  None

		def 						UpperCAmelCase					(							self						,      __UpperCAmelCase=0    )->   Optional[Any]:







								'''simple docstring'''





								if is_accelerate_available() and is_accelerate_version(">="						,      "0.14.0"    ):
														from accelerate import cpu_offload
								else:
														raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher"    )

								lowerCAmelCase__	 =  torch.device(F"cuda:{gpu_id}"    )

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

								for cpu_offloaded_model in [self.unet]:
														cpu_offload(__UpperCAmelCase						,      __UpperCAmelCase    )

								if self.safety_checker is not None:
														cpu_offload(self.safety_checker						,      execution_device=__UpperCAmelCase						,      offload_buffers=__UpperCAmelCase    )

		def 						UpperCAmelCase					(							self						,      __UpperCAmelCase=0    )->   Any:







								'''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__	 =  torch.device(F"cuda:{gpu_id}"    )

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

								lowerCAmelCase__	 =  None
								for cpu_offloaded_model in [self.unet]:
														lowerCAmelCase__ ,							lowerCAmelCase__	 =  cpu_offload_with_hook(__UpperCAmelCase						,      __UpperCAmelCase						,      prev_module_hook=__UpperCAmelCase    )

								if self.safety_checker is not None:
														lowerCAmelCase__ ,							lowerCAmelCase__	 =  cpu_offload_with_hook(self.safety_checker						,      __UpperCAmelCase						,      prev_module_hook=__UpperCAmelCase    )

								# We'll offload the last model manually.
								lowerCAmelCase__	 =  hook

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







								'''simple docstring'''





								if not hasattr(self.unet						,      "_hf_hook"    ):
														return self.device
								for module in self.unet.modules():
														if (
														    hasattr(__UpperCAmelCase						,      "_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

		def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase=None    )->   Optional[Any]:







								'''simple docstring'''





								lowerCAmelCase__	 =  (batch_size, num_channels, height, width)
								if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ) and len(__UpperCAmelCase    ) != batch_size:
														raise ValueError(
														    F"You have passed a list of generators of length {len(__UpperCAmelCase    )}, but requested an effective batch"
														    F" size of {batch_size}. Make sure the batch size matches the length of the generators."    )

								if latents is None:
														lowerCAmelCase__	 =  randn_tensor(__UpperCAmelCase						,      generator=__UpperCAmelCase						,      device=__UpperCAmelCase						,      dtype=__UpperCAmelCase    )
								else:
														lowerCAmelCase__	 =  latents.to(device=__UpperCAmelCase						,      dtype=__UpperCAmelCase    )

								# scale the initial noise by the standard deviation required by the scheduler
								lowerCAmelCase__	 =  latents * self.scheduler.init_noise_sigma
								return latents

		def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase = "pil"    )->   Tuple:







								'''simple docstring'''





								if output_type not in ["pt", "np", "pil"]:
														raise ValueError(
														    F"output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"    )

								# Equivalent to diffusers.VaeImageProcessor.denormalize
								lowerCAmelCase__	 =  (sample / 2 + 0.5).clamp(0						,      1    )
								if output_type == "pt":
														return sample

								# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
								lowerCAmelCase__	 =  sample.cpu().permute(0						,      2						,      3						,      1    ).numpy()
								if output_type == "np":
														return sample

								# Output_type must be 'pil'
								lowerCAmelCase__	 =  self.numpy_to_pil(__UpperCAmelCase    )
								return sample

		def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase=None    )->   List[Any]:







								'''simple docstring'''





								if self.unet.config.num_class_embeds is not None:
														if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
																				lowerCAmelCase__	 =  torch.tensor(__UpperCAmelCase						,      dtype=torch.int    )
														elif isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
																				assert batch_size == 1, "Batch size must be 1 if classes is an int"
																				lowerCAmelCase__	 =  torch.tensor([class_labels]						,      dtype=torch.int    )
														elif class_labels is None:
																				# Randomly generate batch_size class labels
																				# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
																				lowerCAmelCase__	 =  torch.randint(0						,      self.unet.config.num_class_embeds						,      size=(batch_size,)    )
														lowerCAmelCase__	 =  class_labels.to(__UpperCAmelCase    )
								else:
														lowerCAmelCase__	 =  None
								return class_labels

		def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   List[Any]:







								'''simple docstring'''





								if num_inference_steps is None and timesteps is None:
														raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."    )

								if num_inference_steps is not None and timesteps is not None:
														logger.warning(
														    F"Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"
														    " `timesteps` will be used over `num_inference_steps`."    )

								if latents is not None:
														lowerCAmelCase__	 =  (batch_size, 3, img_size, img_size)
														if latents.shape != expected_shape:
																				raise ValueError(F"The shape of latents is {latents.shape} but is expected to be {expected_shape}."    )

								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ) or callback_steps <= 0)
								):
														raise ValueError(
														    F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
														    F" {type(__UpperCAmelCase    )}."    )


		@torch.no_grad()
		@replace_example_docstring(__UpperCAmelCase    )
		def __call__(							self						,      __UpperCAmelCase = 1						,      __UpperCAmelCase = None						,      __UpperCAmelCase = 1						,      __UpperCAmelCase = None						,      __UpperCAmelCase = None						,      __UpperCAmelCase = None						,      __UpperCAmelCase = "pil"						,      __UpperCAmelCase = True						,      __UpperCAmelCase = None						,      __UpperCAmelCase = 1						,      )->   Optional[int]:







								'''simple docstring'''





								lowerCAmelCase__	 =  self.unet.config.sample_size
								lowerCAmelCase__	 =  self._execution_device

								# 1. Check inputs
								self.check_inputs(__UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )

								# 2. Prepare image latents
								# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
								lowerCAmelCase__	 =  self.prepare_latents(
								    batch_size=__UpperCAmelCase						,      num_channels=self.unet.config.in_channels						,      height=__UpperCAmelCase						,      width=__UpperCAmelCase						,      dtype=self.unet.dtype						,      device=__UpperCAmelCase						,      generator=__UpperCAmelCase						,      latents=__UpperCAmelCase						,      )

								# 3. Handle class_labels for class-conditional models
								lowerCAmelCase__	 =  self.prepare_class_labels(__UpperCAmelCase						,      __UpperCAmelCase						,      class_labels=__UpperCAmelCase    )

								# 4. Prepare timesteps
								if timesteps is not None:
														self.scheduler.set_timesteps(timesteps=__UpperCAmelCase						,      device=__UpperCAmelCase    )
														lowerCAmelCase__	 =  self.scheduler.timesteps
														lowerCAmelCase__	 =  len(__UpperCAmelCase    )
								else:
														self.scheduler.set_timesteps(__UpperCAmelCase    )
														lowerCAmelCase__	 =  self.scheduler.timesteps

								# 5. Denoising loop
								# Multistep sampling: implements Algorithm 1 in the paper
								with self.progress_bar(total=__UpperCAmelCase    ) as progress_bar:
														for i, t in enumerate(__UpperCAmelCase    ):
																				lowerCAmelCase__	 =  self.scheduler.scale_model_input(__UpperCAmelCase						,      __UpperCAmelCase    )
																				lowerCAmelCase__	 =  self.unet(__UpperCAmelCase						,      __UpperCAmelCase						,      class_labels=__UpperCAmelCase						,      return_dict=__UpperCAmelCase    )[0]

																				lowerCAmelCase__	 =  self.scheduler.step(__UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      generator=__UpperCAmelCase    )[0]

																				# call the callback, if provided
																				progress_bar.update()
																				if callback is not None and i % callback_steps == 0:
																										callback(__UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )

        # 6. Post-process image sample
								lowerCAmelCase__	 =  self.postprocess_image(__UpperCAmelCase						,      output_type=__UpperCAmelCase    )

								# Offload last model to CPU
								if hasattr(self						,      "final_offload_hook"    ) and self.final_offload_hook is not None:
														self.final_offload_hook.offload()

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=__UpperCAmelCase    )














import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
	from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

a_							=      logging.get_logger(__name__)


def     _a					(       UpperCamelCase_   :			bytes      ,				UpperCamelCase_   :			int				)	->				np.array:
						"""simple docstring"""







						lowerCAmelCase__	 =  F"{sampling_rate}"
						lowerCAmelCase__	 =  "1"
						lowerCAmelCase__	 =  "f32le"
						lowerCAmelCase__	 =  [
						    "ffmpeg",
						    "-i",
						    "pipe:0",
						    "-ac",
						    ac,
						    "-ar",
						    ar,
						    "-f",
						    format_for_conversion,
						    "-hide_banner",
						    "-loglevel",
						    "quiet",
						    "pipe:1",
						]

						try:
												lowerCAmelCase__	 =  subprocess.Popen(UpperCamelCase_      ,				stdin=subprocess.PIPE      ,				stdout=subprocess.PIPE				)
						except FileNotFoundError:
												raise ValueError("ffmpeg was not found but is required to load audio files from filename"				)
						lowerCAmelCase__	 =  ffmpeg_process.communicate(UpperCamelCase_				)
						lowerCAmelCase__	 =  output_stream[0]

						lowerCAmelCase__	 =  np.frombuffer(UpperCamelCase_      ,				np.floataa				)
						if audio.shape[0] == 0:
												raise ValueError("Malformed soundfile"				)
						return audio





@add_end_docstrings(_UpperCAmelCase		)
class        lowercase__       (     _UpperCAmelCase		):

	def __init__(							self						,      *__UpperCAmelCase						,      **__UpperCAmelCase    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__	 =  5
							super().__init__(*__UpperCAmelCase						,      **__UpperCAmelCase    )

							if self.framework != "pt":
													raise ValueError(F"The {self.__class__} is only available in PyTorch."    )

							self.check_model_type(__UpperCAmelCase    )

	def __call__(							self						,      __UpperCAmelCase						,      **__UpperCAmelCase						,      )->   List[str]:







							'''simple docstring'''





							return super().__call__(__UpperCAmelCase						,      **__UpperCAmelCase    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase=None						,      **__UpperCAmelCase    )->   Dict:







							'''simple docstring'''





							lowerCAmelCase__	 =  {}
							if top_k is not None:
													if top_k > self.model.config.num_labels:
																			lowerCAmelCase__	 =  self.model.config.num_labels
													lowerCAmelCase__	 =  top_k
							return {}, {}, postprocess_params

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   str:







							'''simple docstring'''





							if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
													if inputs.startswith("http://"    ) or inputs.startswith("https://"    ):
																			# We need to actually check for a real protocol, otherwise it's impossible to use a local file
																			# like http_huggingface_co.png
																			lowerCAmelCase__	 =  requests.get(__UpperCAmelCase    ).content
													else:
																			with open(__UpperCAmelCase						,      "rb"    ) as f:
																									lowerCAmelCase__	 =  f.read()

							if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
													lowerCAmelCase__	 =  ffmpeg_read(__UpperCAmelCase						,      self.feature_extractor.sampling_rate    )

							if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
													# Accepting `"array"` which is the key defined in `datasets` for
													# better integration
													if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
																			raise ValueError(
																			    "When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a "
																			    "\"raw\" key containing the numpy array representing the audio and a \"sampling_rate\" key, "
																			    "containing the sampling_rate associated with that array"    )

													lowerCAmelCase__	 =  inputs.pop("raw"						,      __UpperCAmelCase    )
													if _inputs is None:
																			# Remove path which will not be used from `datasets`.
																			inputs.pop("path"						,      __UpperCAmelCase    )
																			lowerCAmelCase__	 =  inputs.pop("array"						,      __UpperCAmelCase    )
													lowerCAmelCase__	 =  inputs.pop("sampling_rate"    )
													lowerCAmelCase__	 =  _inputs
													if in_sampling_rate != self.feature_extractor.sampling_rate:
																			import torch

																			if is_torchaudio_available():
																									from torchaudio import functional as F
																			else:
																									raise ImportError(
																									    "torchaudio is required to resample audio samples in AudioClassificationPipeline. "
																									    "The torchaudio package can be installed through: `pip install torchaudio`."    )

																			lowerCAmelCase__	 =  F.resample(
																			    torch.from_numpy(__UpperCAmelCase    )						,      __UpperCAmelCase						,      self.feature_extractor.sampling_rate    ).numpy()

							if not isinstance(__UpperCAmelCase						,      np.ndarray    ):
													raise ValueError("We expect a numpy ndarray as input"    )
							if len(inputs.shape    ) != 1:
													raise ValueError("We expect a single channel audio input for AudioClassificationPipeline"    )

							lowerCAmelCase__	 =  self.feature_extractor(
							    __UpperCAmelCase						,      sampling_rate=self.feature_extractor.sampling_rate						,      return_tensors="pt"    )
							return processed

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Tuple:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.model(**__UpperCAmelCase    )
							return model_outputs


	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase=5    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  model_outputs.logits[0].softmax(-1    )
							lowerCAmelCase__ ,							lowerCAmelCase__	 =  probs.topk(__UpperCAmelCase    )

							lowerCAmelCase__	 =  scores.tolist()
							lowerCAmelCase__	 =  ids.tolist()

							lowerCAmelCase__	 =  [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__UpperCAmelCase						,      __UpperCAmelCase    )]

							return labels






