




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


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


UpperCamelCase__						=		'\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = \"cuda\"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = \"openai/diffusers-cd_imagenet64_l2\"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample.png\")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample_penguin.png\")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_multistep_sample_penguin.png\")\n        ```\n'


class 							__SCREAMING_SNAKE_CASE				(						_UpperCAmelCase    ):

					def __init__(							self					,			__lowercase					,			__lowercase   ):







						'''simple docstring'''




						super().__init__()

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

						lowerCamelCase_				:						Any				  =				None

					def       lowercase_				(							self					,			__lowercase=0   ):







						'''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_				:						Any				  =				torch.device(f"cuda:{gpu_id}"   )

						if self.device.type != "cpu":
							self.to('cpu'					,			silence_dtype_warnings=lowercase_   )
							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(lowercase_					,			lowercase_   )

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

					def       lowercase_				(							self					,			__lowercase=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_				:						List[str]				  =				torch.device(f"cuda:{gpu_id}"   )

						if self.device.type != "cpu":
							self.to('cpu'					,			silence_dtype_warnings=lowercase_   )
							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]:
							lowerCamelCase_  ,     lowerCamelCase_				:						Union[str, Any]				  =				cpu_offload_with_hook(lowercase_					,			lowercase_					,			prev_module_hook=lowercase_   )

						if self.safety_checker is not None:
							lowerCamelCase_  ,     lowerCamelCase_				:						Union[str, Any]				  =				cpu_offload_with_hook(self.safety_checker					,			lowercase_					,			prev_module_hook=lowercase_   )

						# We'll offload the last model manually.
						lowerCamelCase_				:						Optional[int]				  =				hook

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







						'''simple docstring'''




						if not hasattr(self.unet					,			'_hf_hook'   ):
							return self.device
						for module in self.unet.modules():
							if (
							    hasattr(lowercase_					,			'_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       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase=None   ):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[Any]				  =				(batch_size, num_channels, height, width)
						if isinstance(lowercase_					,			lowercase_   ) and len(lowercase_   ) != batch_size:
							raise ValueError(
							    f"You have passed a list of generators of length {len(lowercase_   )}, 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_				:						str				  =				randn_tensor(lowercase_					,			generator=lowercase_					,			device=lowercase_					,			dtype=lowercase_   )
						else:
							lowerCamelCase_				:						Tuple				  =				latents.to(device=lowercase_					,			dtype=lowercase_   )

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

					def       lowercase_				(							self					,			__lowercase					,			__lowercase = "pil"   ):







						'''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_				:						Tuple				  =				(sample / 2 + 0.5).clamp(0					,			1   )
						if output_type == "pt":
							return sample

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

						# Output_type must be 'pil'
						lowerCamelCase_				:						Optional[Any]				  =				self.numpy_to_pil(lowercase_   )
						return sample

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase=None   ):







						'''simple docstring'''




						if self.unet.config.num_class_embeds is not None:
							if isinstance(lowercase_					,			lowercase_   ):
								lowerCamelCase_				:						Union[str, Any]				  =				torch.tensor(lowercase_					,			dtype=torch.int   )
							elif isinstance(lowercase_					,			lowercase_   ):
								assert batch_size == 1, "Batch size must be 1 if classes is an int"
								lowerCamelCase_				:						List[Any]				  =				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_				:						List[str]				  =				torch.randint(0					,			self.unet.config.num_class_embeds					,			size=(batch_size,)   )
							lowerCamelCase_				:						Optional[int]				  =				class_labels.to(lowercase_   )
						else:
							lowerCamelCase_				:						Optional[Any]				  =				None
						return class_labels

					def       lowercase_				(							self					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase					,			__lowercase   ):







						'''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_				:						str				  =				(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(lowercase_					,			lowercase_   ) or callback_steps <= 0)
						):
							raise ValueError(
							    f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
							    f" {type(lowercase_   )}."   )





					@torch.no_grad()
					@replace_example_docstring(lowercase_   )
					def __call__(							self					,			__lowercase = 1					,			__lowercase = None					,			__lowercase = 1					,			__lowercase = None					,			__lowercase = None					,			__lowercase = None					,			__lowercase = "pil"					,			__lowercase = True					,			__lowercase = None					,			__lowercase = 1					,			):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[int]				  =				self.unet.config.sample_size
						lowerCamelCase_				:						Optional[Any]				  =				self._execution_device

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

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

						# 3. Handle class_labels for class-conditional models
						lowerCamelCase_				:						Optional[int]				  =				self.prepare_class_labels(lowercase_					,			lowercase_					,			class_labels=lowercase_   )

						# 4. Prepare timesteps
						if timesteps is not None:
							self.scheduler.set_timesteps(timesteps=lowercase_					,			device=lowercase_   )
							lowerCamelCase_				:						Optional[int]				  =				self.scheduler.timesteps
							lowerCamelCase_				:						Any				  =				len(lowercase_   )
						else:
							self.scheduler.set_timesteps(lowercase_   )
							lowerCamelCase_				:						Dict				  =				self.scheduler.timesteps

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

								lowerCamelCase_				:						Tuple				  =				self.scheduler.step(lowercase_					,			lowercase_					,			lowercase_					,			generator=lowercase_   )[0]

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

        # 6. Post-process image sample
						lowerCamelCase_				:						Optional[Any]				  =				self.postprocess_image(lowercase_					,			output_type=lowercase_   )

						# 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=lowercase_   )



from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowercase_									=    logging.get_logger(__name__)


class 					A		(				_UpperCAmelCase					):







    """simple docstring"""





    lowerCamelCase						      =				['input_values', 'attention_mask']


    def __init__( self					:			Tuple,lowercase_					:			Optional[Any]=1,lowercase_					:			Union[str, Any]=1_6_0_0_0,lowercase_					:			str=1_2_8,lowercase_					:			Optional[Any]=1_0_2_4,lowercase_					:			Union[str, Any]=0.0,lowercase_					:			Any=True,lowercase_					:			List[Any]=-4.2_677_393,lowercase_					:			Union[str, Any]=4.5_689_974,lowercase_					:			Dict=False,**lowercase_					:			Dict,)->       int:





        '''simple docstring'''







        super().__init__(feature_size=lowercase_,sampling_rate=lowercase_,padding_value=lowercase_,**lowercase_						)
        A__        =  num_mel_bins
        A__        =  max_length
        A__        =  do_normalize
        A__        =  mean
        A__        =  std
        A__        =  return_attention_mask


    def 		snake_case__	( self					:			Any,lowercase_					:			np.ndarray,lowercase_					:			int,)->       np.ndarray:





        '''simple docstring'''







        A__        =  torch.from_numpy(lowercase_						).unsqueeze(0						)
        A__        =  ta_kaldi.fbank(
            lowercase_,htk_compat=lowercase_,sample_frequency=self.sampling_rate,use_energy=lowercase_,window_type='hanning',num_mel_bins=self.num_mel_bins,dither=0.0,frame_shift=1_0,)

        A__        =  fbank.shape[0]
        A__        =  max_length - n_frames

        # pad or truncate, depending on difference
        if difference > 0:
            A__        =  torch.nn.ZeroPadad((0, 0, 0, difference)						)
            A__        =  pad_module(lowercase_						)
        elif difference < 0:
            A__        =  fbank[0:max_length, :]

        A__        =  fbank.numpy()

        return fbank


    def 		snake_case__	( self					:			int,lowercase_					:			np.ndarray						)->       np.ndarray:





        '''simple docstring'''







        return (input_values - (self.mean)) / (self.std * 2)


    def __call__( self					:			str,lowercase_					:			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],lowercase_					:			Optional[int] = None,lowercase_					:			Optional[Union[str, TensorType]] = None,**lowercase_					:			str,)->       BatchFeature:





        '''simple docstring'''







        if sampling_rate is not None:
            if sampling_rate != self.sampling_rate:
                raise ValueError(
                    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                    F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
                    F' {self.sampling_rate} and not {sampling_rate}.'						)
        else:
            logger.warning(
                'It is strongly recommended to pass the `sampling_rate` argument to this function. '
                'Failing to do so can result in silent errors that might be hard to debug.'						)

        A__        =  isinstance(lowercase_,np.ndarray						) and len(raw_speech.shape						) > 1
        if is_batched_numpy and len(raw_speech.shape						) > 2:
            raise ValueError(F'Only mono-channel audio is supported for input to {self}'						)
        A__        =  is_batched_numpy or (
            isinstance(lowercase_,(list, tuple)						) and (isinstance(raw_speech[0],(np.ndarray, tuple, list)						))
        )

        if is_batched:
            A__        =  [np.asarray(lowercase_,dtype=np.floataa						) for speech in raw_speech]
        elif not is_batched and not isinstance(lowercase_,np.ndarray						):
            A__        =  np.asarray(lowercase_,dtype=np.floataa						)
        elif isinstance(lowercase_,np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
            A__        =  raw_speech.astype(np.floataa						)

        # always return batch
        if not is_batched:
            A__        =  [raw_speech]

        # extract fbank features and pad/truncate to max_length
        A__        =  [self._extract_fbank_features(lowercase_,max_length=self.max_length						) for waveform in raw_speech]

        # convert into BatchFeature
        A__        =  BatchFeature({'input_values': features}						)

        # make sure list is in array format
        A__        =  padded_inputs.get('input_values'						)
        if isinstance(input_values[0],lowercase_						):
            A__        =  [np.asarray(lowercase_,dtype=np.floataa						) for feature in input_values]

        # normalization
        if self.do_normalize:
            A__        =  [self.normalize(lowercase_						) for feature in input_values]

        if return_tensors is not None:
            A__        =  padded_inputs.convert_to_tensors(lowercase_						)

        return padded_inputs

