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






class 						A   (   UpperCAmelCase   ):





							def __init__(      self				:							Optional[int]     ,		__a				:							WhisperForConditionalGeneration     ,		__a				:							WhisperProcessor     ,		__a				:							AutoencoderKL     ,		__a				:							CLIPTextModel     ,		__a				:							CLIPTokenizer     ,		__a				:							UNetaDConditionModel     ,		__a				:							Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]     ,		__a				:							StableDiffusionSafetyChecker     ,		__a				:							CLIPImageProcessor     ,		)							->	Any:
								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       snake_case__						(      self				:							Any     ,		__a				:							Optional[Union[str, int]] = "auto"	)							->	Dict:
								if slice_size == "auto":
									__UpperCAmelCase							=			self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(__a	)





							def       snake_case__						(      self				:							List[str]	)							->	Union[str, Any]:
								self.enable_attention_slicing(__a	)






							@torch.no_grad()
							def __call__(      self				:							Optional[int]     ,		__a				:							int     ,		__a				:							Union[str, Any]=1_6_0_0_0     ,		__a				:							int = 5_1_2     ,		__a				:							int = 5_1_2     ,		__a				:							int = 5_0     ,		__a				:							float = 7.5     ,		__a				:							Optional[Union[str, List[str]]] = None     ,		__a				:							Optional[int] = 1     ,		__a				:							float = 0.0     ,		__a				:							Optional[torch.Generator] = None     ,		__a				:							Optional[torch.FloatTensor] = None     ,		__a				:							Optional[str] = "pil"     ,		__a				:							bool = True     ,		__a				:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,		__a				:							int = 1     ,		**__a				:							Optional[int]     ,		)							->	Tuple:
								__UpperCAmelCase							=			self.speech_processor.feature_extractor(
								    __a     ,		return_tensors='''pt'''     ,		sampling_rate=__a	).input_features.to(self.device	)
								__UpperCAmelCase							=			self.speech_model.generate(__a     ,		max_length=4_8_0_0_0_0	)

								__UpperCAmelCase							=			self.speech_processor.tokenizer.batch_decode(__a     ,		skip_special_tokens=__a     ,		normalize=__a	)[
								    0
								]

								if isinstance(__a     ,		__a	):
									__UpperCAmelCase							=			1
								elif isinstance(__a     ,		__a	):
									__UpperCAmelCase							=			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
								__UpperCAmelCase							=			self.tokenizer(
								    __a     ,		padding='''max_length'''     ,		max_length=self.tokenizer.model_max_length     ,		return_tensors='''pt'''     ,		)
								__UpperCAmelCase							=			text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
									__UpperCAmelCase							=			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}"""	)
									__UpperCAmelCase							=			text_input_ids[:, : self.tokenizer.model_max_length]
								__UpperCAmelCase							=			self.text_encoder(text_input_ids.to(self.device	)	)[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								__UpperCAmelCase				,			__UpperCAmelCase				,			__UpperCAmelCase							=			text_embeddings.shape
								__UpperCAmelCase							=			text_embeddings.repeat(1     ,		__a     ,		1	)
								__UpperCAmelCase							=			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.
								__UpperCAmelCase							=			guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
									__UpperCAmelCase							=			4_2
									if negative_prompt is None:
										__UpperCAmelCase							=			[''''''] * 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	):
										__UpperCAmelCase							=			[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:
										__UpperCAmelCase							=			negative_prompt

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

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
									__UpperCAmelCase							=			uncond_embeddings.shape[1]
									__UpperCAmelCase							=			uncond_embeddings.repeat(1     ,		__a     ,		1	)
									__UpperCAmelCase							=			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
									__UpperCAmelCase							=			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`.
								__UpperCAmelCase							=			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								__UpperCAmelCase							=			text_embeddings.dtype
								if latents is None:
									if self.device.type == "mps":
										# randn does not exist on mps
										__UpperCAmelCase							=			torch.randn(__a     ,		generator=__a     ,		device='''cpu'''     ,		dtype=__a	).to(
										    self.device	)
									else:
										__UpperCAmelCase							=			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}"""	)
									__UpperCAmelCase							=			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
								__UpperCAmelCase							=			self.scheduler.timesteps.to(self.device	)

								# scale the initial noise by the standard deviation required by the scheduler
								__UpperCAmelCase							=			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]
								__UpperCAmelCase							=			'''eta''' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
								__UpperCAmelCase							=			{}
								if accepts_eta:
									__UpperCAmelCase							=			eta

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

									# predict the noise residual
									__UpperCAmelCase							=			self.unet(__a     ,		__a     ,		encoder_hidden_states=__a	).sample

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

									# compute the previous noisy sample x_t -> x_t-1
									__UpperCAmelCase							=			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	)

								__UpperCAmelCase							=			1 / 0.1_8_2_1_5 * latents
								__UpperCAmelCase							=			self.vae.decode(__a	).sample

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

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

								if output_type == "pil":
									__UpperCAmelCase							=			self.numpy_to_pil(__a	)

								if not return_dict:
									return image

								return StableDiffusionPipelineOutput(images=__a     ,		nsfw_content_detected=__a	)

'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCAmelCase	:       Optional[Any]						       =	{
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       str						       =	["LlamaTokenizer"]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       str						       =	["LlamaTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       Union[str, Any]						       =	[
					    "LlamaForCausalLM",
					    "LlamaModel",
					    "LlamaPreTrainedModel",
					    "LlamaForSequenceClassification",
					]


if TYPE_CHECKING:
					from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama_fast import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
					import sys

					__lowerCAmelCase	:       Optional[Any]						       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
