

import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


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





class     snake_case_			(				_a							):


							"""simple docstring"""





							def __init__( self					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A					,						_A					,						):
												super().__init__()

												if safety_checker is None:
																	logger.warning(
																	    F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
																	    ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
																	    ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
																	    ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
																	    ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
																	    ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'							)

												self.register_modules(
												    speech_model=_A					,						speech_processor=_A					,						vae=_A					,						text_encoder=_A					,						tokenizer=_A					,						unet=_A					,						scheduler=_A					,						feature_extractor=_A					,						)




							def        A__  ( self					,						_A = "auto"							):
												if slice_size == "auto":
																	__lowerCAmelCase					=							self.unet.config.attention_head_dim // 2
												self.unet.set_attention_slice(_A							)




							def        A__  ( self							):
												self.enable_attention_slicing(_A							)




							@torch.no_grad()
							def __call__( self					,						_A					,						_A=1_6_0_0_0					,						_A = 5_1_2					,						_A = 5_1_2					,						_A = 5_0					,						_A = 7.5					,						_A = None					,						_A = 1					,						_A = 0.0					,						_A = None					,						_A = None					,						_A = "pil"					,						_A = True					,						_A = None					,						_A = 1					,						**_A					,						):
												__lowerCAmelCase					=							self.speech_processor.feature_extractor(
												    _A					,						return_tensors='pt'					,						sampling_rate=_A							).input_features.to(self.device							)
												__lowerCAmelCase					=							self.speech_model.generate(_A					,						max_length=4_8_0_0_0_0							)

												__lowerCAmelCase					=							self.speech_processor.tokenizer.batch_decode(_A					,						skip_special_tokens=_A					,						normalize=_A							)[
												    0
												]

												if isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							1
												elif isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							len(_A							)
												else:
																	raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(_A							)}"""							)

												if height % 8 != 0 or width % 8 != 0:
																	raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""							)

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

												# get prompt text embeddings
												__lowerCAmelCase					=							self.tokenizer(
												    _A					,						padding='max_length'					,						max_length=self.tokenizer.model_max_length					,						return_tensors='pt'					,						)
												__lowerCAmelCase					=							text_inputs.input_ids

												if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																	__lowerCAmelCase					=							self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
																	logger.warning(
																	    'The following part of your input was truncated because CLIP can only handle sequences up to'
																	    F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""							)
																	__lowerCAmelCase					=							text_input_ids[:, : self.tokenizer.model_max_length]
												__lowerCAmelCase					=							self.text_encoder(text_input_ids.to(self.device							)							)[0]

												# duplicate text embeddings for each generation per prompt, using mps friendly method
												__lowerCAmelCase,	__lowerCAmelCase,	__lowerCAmelCase					=							text_embeddings.shape
												__lowerCAmelCase					=							text_embeddings.repeat(1					,						_A					,						1							)
												__lowerCAmelCase					=							text_embeddings.view(bs_embed * num_images_per_prompt					,						_A					,						-1							)

												# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
												# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
												# corresponds to doing no classifier free guidance.
												__lowerCAmelCase					=							guidance_scale > 1.0
												# get unconditional embeddings for classifier free guidance
												if do_classifier_free_guidance:
																	__lowerCAmelCase					=							4_2
																	if negative_prompt is None:
																						__lowerCAmelCase					=							[''] * batch_size
																	elif type(_A							) is not type(_A							):
																						raise TypeError(
																						    F"""`negative_prompt` should be the same type to `prompt`, but got {type(_A							)} !="""
																						    F""" {type(_A							)}."""							)
																	elif isinstance(_A					,						_A							):
																						__lowerCAmelCase					=							[negative_prompt]
																	elif batch_size != len(_A							):
																						raise ValueError(
																						    F"""`negative_prompt`: {negative_prompt} has batch size {len(_A							)}, but `prompt`:"""
																						    F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																						    ' the batch size of `prompt`.'							)
																	else:
																						__lowerCAmelCase					=							negative_prompt

																	__lowerCAmelCase					=							text_input_ids.shape[-1]
																	__lowerCAmelCase					=							self.tokenizer(
																	    _A					,						padding='max_length'					,						max_length=_A					,						truncation=_A					,						return_tensors='pt'					,						)
																	__lowerCAmelCase					=							self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

																	# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																	__lowerCAmelCase					=							uncond_embeddings.shape[1]
																	__lowerCAmelCase					=							uncond_embeddings.repeat(1					,						_A					,						1							)
																	__lowerCAmelCase					=							uncond_embeddings.view(batch_size * num_images_per_prompt					,						_A					,						-1							)

																	# For classifier free guidance, we need to do two forward passes.
																	# Here we concatenate the unconditional and text embeddings into a single batch
																	# to avoid doing two forward passes
																	__lowerCAmelCase					=							torch.cat([uncond_embeddings, text_embeddings]							)

												# get the initial random noise unless the user supplied it

												# Unlike in other pipelines, latents need to be generated in the target device
												# for 1-to-1 results reproducibility with the CompVis implementation.
												# However this currently doesn't work in `mps`.
												__lowerCAmelCase					=							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
												__lowerCAmelCase					=							text_embeddings.dtype
												if latents is None:
																	if self.device.type == "mps":
																						# randn does not exist on mps
																						__lowerCAmelCase					=							torch.randn(_A					,						generator=_A					,						device='cpu'					,						dtype=_A							).to(
																						    self.device							)
																	else:
																						__lowerCAmelCase					=							torch.randn(_A					,						generator=_A					,						device=self.device					,						dtype=_A							)
												else:
																	if latents.shape != latents_shape:
																						raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""							)
																	__lowerCAmelCase					=							latents.to(self.device							)

												# set timesteps
												self.scheduler.set_timesteps(_A							)

												# Some schedulers like PNDM have timesteps as arrays
												# It's more optimized to move all timesteps to correct device beforehand
												__lowerCAmelCase					=							self.scheduler.timesteps.to(self.device							)

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

												# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
												# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
												# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
												# and should be between [0, 1]
												__lowerCAmelCase					=							'eta' in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
												__lowerCAmelCase					=							{}
												if accepts_eta:
																	__lowerCAmelCase					=							eta

												for i, t in enumerate(self.progress_bar(_A							)							):
																	# expand the latents if we are doing classifier free guidance
																	__lowerCAmelCase					=							torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
																	__lowerCAmelCase					=							self.scheduler.scale_model_input(_A					,						_A							)

																	# predict the noise residual
																	__lowerCAmelCase					=							self.unet(_A					,						_A					,						encoder_hidden_states=_A							).sample

																	# perform guidance
																	if do_classifier_free_guidance:
																						__lowerCAmelCase,	__lowerCAmelCase					=							noise_pred.chunk(2							)
																						__lowerCAmelCase					=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																	# compute the previous noisy sample x_t -> x_t-1
																	__lowerCAmelCase					=							self.scheduler.step(_A					,						_A					,						_A					,						**_A							).prev_sample

																	# call the callback, if provided
																	if callback is not None and i % callback_steps == 0:
																						callback(_A					,						_A					,						_A							)

												__lowerCAmelCase					=							1 / 0.1_8215 * latents
												__lowerCAmelCase					=							self.vae.decode(_A							).sample

												__lowerCAmelCase					=							(image / 2 + 0.5).clamp(0					,						1							)

												# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
												__lowerCAmelCase					=							image.cpu().permute(0					,						2					,						3					,						1							).float().numpy()

												if output_type == "pil":
																	__lowerCAmelCase					=							self.numpy_to_pil(_A							)

												if not return_dict:
																	return image

												return StableDiffusionPipelineOutput(images=_A					,						nsfw_content_detected=_A							)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase      				=     {
    '''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
    '''configuration_data2vec_text''': [
        '''DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecTextConfig''',
        '''Data2VecTextOnnxConfig''',
    ],
    '''configuration_data2vec_vision''': [
        '''DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecVisionConfig''',
        '''Data2VecVisionOnnxConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase      				=     [
		    '''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''Data2VecAudioForAudioFrameClassification''',
		    '''Data2VecAudioForCTC''',
		    '''Data2VecAudioForSequenceClassification''',
		    '''Data2VecAudioForXVector''',
		    '''Data2VecAudioModel''',
		    '''Data2VecAudioPreTrainedModel''',
		]
		lowerCamelCase      				=     [
		    '''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''Data2VecTextForCausalLM''',
		    '''Data2VecTextForMaskedLM''',
		    '''Data2VecTextForMultipleChoice''',
		    '''Data2VecTextForQuestionAnswering''',
		    '''Data2VecTextForSequenceClassification''',
		    '''Data2VecTextForTokenClassification''',
		    '''Data2VecTextModel''',
		    '''Data2VecTextPreTrainedModel''',
		]
		lowerCamelCase      				=     [
		    '''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''Data2VecVisionForImageClassification''',
		    '''Data2VecVisionForMaskedImageModeling''',
		    '''Data2VecVisionForSemanticSegmentation''',
		    '''Data2VecVisionModel''',
		    '''Data2VecVisionPreTrainedModel''',
		]

if is_tf_available():
		lowerCamelCase      				=     [
		    '''TFData2VecVisionForImageClassification''',
		    '''TFData2VecVisionForSemanticSegmentation''',
		    '''TFData2VecVisionModel''',
		    '''TFData2VecVisionPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
		from .configuration_dataavec_text import (
		    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DataaVecTextConfig,
		    DataaVecTextOnnxConfig,
		)
		from .configuration_dataavec_vision import (
		    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DataaVecVisionConfig,
		    DataaVecVisionOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dataavec_audio import (
				    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecAudioForAudioFrameClassification,
				    DataaVecAudioForCTC,
				    DataaVecAudioForSequenceClassification,
				    DataaVecAudioForXVector,
				    DataaVecAudioModel,
				    DataaVecAudioPreTrainedModel,
				)
				from .modeling_dataavec_text import (
				    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecTextForCausalLM,
				    DataaVecTextForMaskedLM,
				    DataaVecTextForMultipleChoice,
				    DataaVecTextForQuestionAnswering,
				    DataaVecTextForSequenceClassification,
				    DataaVecTextForTokenClassification,
				    DataaVecTextModel,
				    DataaVecTextPreTrainedModel,
				)
				from .modeling_dataavec_vision import (
				    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecVisionForImageClassification,
				    DataaVecVisionForMaskedImageModeling,
				    DataaVecVisionForSemanticSegmentation,
				    DataaVecVisionModel,
				    DataaVecVisionPreTrainedModel,
				)
		if is_tf_available():
				from .modeling_tf_dataavec_vision import (
				    TFDataaVecVisionForImageClassification,
				    TFDataaVecVisionForSemanticSegmentation,
				    TFDataaVecVisionModel,
				    TFDataaVecVisionPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase      				=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






