





'''simple docstring'''






from typing import TYPE_CHECKING

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


A :			Dict							   =      {
    'configuration_speech_to_text_2': ['SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2Text2Config'],
    'processing_speech_to_text_2': ['Speech2Text2Processor'],
    'tokenization_speech_to_text_2': ['Speech2Text2Tokenizer'],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A :			Optional[int]							   =      [
		    'SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Speech2Text2ForCausalLM',
		    'Speech2Text2PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
		from .processing_speech_to_text_a import SpeechaTextaProcessor
		from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speech_to_text_a import (
				    SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SpeechaTextaForCausalLM,
				    SpeechaTextaPreTrainedModel,
				)

else:
		import sys

		A :			Optional[int]							   =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
class 							snake_case_		(      nn.Module):







		def __init__(		self      ,  __lowercase      ,  __lowercase=1.0 )					-> List[str]:
				super().__init__()

				lowerCamelCase :			Optional[Any]    		=cut_size
				lowerCamelCase :			List[str]    		=cut_power







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase )					-> int:
				lowerCamelCase		, lowerCamelCase :			Any    		=pixel_values.shape[2:4]
				lowerCamelCase :			Any    		=min(__lowercase      ,  __lowercase )
				lowerCamelCase :			int    		=min(__lowercase      ,  __lowercase      ,  self.cut_size )
				lowerCamelCase :			Dict    		=[]
				for _ in range(__lowercase ):
						lowerCamelCase :			Union[str, Any]    		=int(torch.rand([] ) ** self.cut_power * (max_size - min_size) + min_size )
						lowerCamelCase :			Any    		=torch.randint(0      ,  sideX - size + 1      ,  () )
						lowerCamelCase :			Any    		=torch.randint(0      ,  sideY - size + 1      ,  () )
						lowerCamelCase :			Optional[Any]    		=pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
						cutouts.append(F.adaptive_avg_poolad(__lowercase      ,  self.cut_size ) )
				return torch.cat(__lowercase )


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Optional[Any]:
		lowerCamelCase :			str    		=F.normalize(SCREAMING_SNAKE_CASE_				,							dim=-1		)
		lowerCamelCase :			Optional[Any]    		=F.normalize(SCREAMING_SNAKE_CASE_				,							dim=-1		)
		return (x - y).norm(dim=-1		).div(2		).arcsin().pow(2		).mul(2		)


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  List[str]:
		for param in model.parameters():
				lowerCamelCase :			List[Any]    		=value



class 							snake_case_		(      _A):







		def __init__(		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  )					-> List[Any]:
				super().__init__()
				self.register_modules(
				    vae=__lowercase      ,  text_encoder=__lowercase      ,  clip_model=__lowercase      ,  tokenizer=__lowercase      ,  unet=__lowercase      ,  scheduler=__lowercase      ,  feature_extractor=__lowercase      ,  )

				lowerCamelCase :			Optional[Any]    		=transforms.Normalize(mean=feature_extractor.image_mean      ,  std=feature_extractor.image_std )
				lowerCamelCase :			int    		=(
				    feature_extractor.size
				    if isinstance(feature_extractor.size      ,  __lowercase )
				    else feature_extractor.size['''shortest_edge''']
				)
				lowerCamelCase :			int    		=MakeCutouts(self.cut_out_size )

				set_requires_grad(self.text_encoder      ,  __lowercase )
				set_requires_grad(self.clip_model      ,  __lowercase )







		def       __lowercase (		self      ,  __lowercase = "auto" )					-> Dict:
				if slice_size == "auto":
						# half the attention head size is usually a good trade-off between
						# speed and memory
						lowerCamelCase :			int    		=self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(__lowercase )







		def       __lowercase (		self )					-> List[str]:
				self.enable_attention_slicing(__lowercase )







		def       __lowercase (		self )					-> List[str]:
				set_requires_grad(self.vae      ,  __lowercase )







		def       __lowercase (		self )					-> List[str]:
				set_requires_grad(self.vae      ,  __lowercase )







		def       __lowercase (		self )					-> Dict:
				set_requires_grad(self.unet      ,  __lowercase )







		def       __lowercase (		self )					-> Union[str, Any]:
				set_requires_grad(self.unet      ,  __lowercase )







		@torch.enable_grad()
		def       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase=True      ,  )					-> Union[str, Any]:
				lowerCamelCase :			Dict    		=latents.detach().requires_grad_()

				lowerCamelCase :			int    		=self.scheduler.scale_model_input(__lowercase      ,  __lowercase )

				# predict the noise residual
				lowerCamelCase :			Union[str, Any]    		=self.unet(__lowercase      ,  __lowercase      ,  encoder_hidden_states=__lowercase ).sample

				if isinstance(self.scheduler      ,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
						lowerCamelCase :			Optional[int]    		=self.scheduler.alphas_cumprod[timestep]
						lowerCamelCase :			List[str]    		=1 - alpha_prod_t
						# compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						lowerCamelCase :			Tuple    		=(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

						lowerCamelCase :			List[str]    		=torch.sqrt(__lowercase )
						lowerCamelCase :			str    		=pred_original_sample * (fac) + latents * (1 - fac)
				elif isinstance(self.scheduler      ,  __lowercase ):
						lowerCamelCase :			Optional[int]    		=self.scheduler.sigmas[index]
						lowerCamelCase :			str    		=latents - sigma * noise_pred
				else:
						raise ValueError(F"scheduler type {type(self.scheduler )} not supported" )

				lowerCamelCase :			List[Any]    		=1 / self.vae.config.scaling_factor * sample
				lowerCamelCase :			Optional[Any]    		=self.vae.decode(__lowercase ).sample
				lowerCamelCase :			Dict    		=(image / 2 + 0.5).clamp(0      ,  1 )

				if use_cutouts:
						lowerCamelCase :			Tuple    		=self.make_cutouts(__lowercase      ,  __lowercase )
				else:
						lowerCamelCase :			str    		=transforms.Resize(self.cut_out_size )(__lowercase )
				lowerCamelCase :			List[Any]    		=self.normalize(__lowercase ).to(latents.dtype )

				lowerCamelCase :			Optional[Any]    		=self.clip_model.get_image_features(__lowercase )
				lowerCamelCase :			Optional[int]    		=image_embeddings_clip / image_embeddings_clip.norm(p=2      ,  dim=-1      ,  keepdim=__lowercase )

				if use_cutouts:
						lowerCamelCase :			str    		=spherical_dist_loss(__lowercase      ,  __lowercase )
						lowerCamelCase :			Union[str, Any]    		=dists.view([num_cutouts, sample.shape[0], -1] )
						lowerCamelCase :			Tuple    		=dists.sum(2 ).mean(0 ).sum() * clip_guidance_scale
				else:
						lowerCamelCase :			Union[str, Any]    		=spherical_dist_loss(__lowercase      ,  __lowercase ).mean() * clip_guidance_scale

				lowerCamelCase :			List[Any]    		=-torch.autograd.grad(__lowercase      ,  __lowercase )[0]

				if isinstance(self.scheduler      ,  __lowercase ):
						lowerCamelCase :			Any    		=latents.detach() + grads * (sigma**2)
						lowerCamelCase :			Dict    		=noise_pred_original
				else:
						lowerCamelCase :			Union[str, Any]    		=noise_pred_original - torch.sqrt(__lowercase ) * grads
				return noise_pred, latents







		@torch.no_grad()
		def __call__(		self      ,  __lowercase      ,  __lowercase = 5_1_2      ,  __lowercase = 5_1_2      ,  __lowercase = 5_0      ,  __lowercase = 7.5      ,  __lowercase = 1      ,  __lowercase = 0.0      ,  __lowercase = 1_0_0      ,  __lowercase = None      ,  __lowercase = 4      ,  __lowercase = True      ,  __lowercase = None      ,  __lowercase = None      ,  __lowercase = "pil"      ,  __lowercase = True      ,  )					-> Dict:
				if isinstance(__lowercase      ,  __lowercase ):
						lowerCamelCase :			List[str]    		=1
				elif isinstance(__lowercase      ,  __lowercase ):
						lowerCamelCase :			Dict    		=len(__lowercase )
				else:
						raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(__lowercase )}" )

				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}." )

				# get prompt text embeddings
				lowerCamelCase :			Optional[int]    		=self.tokenizer(
				    __lowercase      ,  padding='''max_length'''      ,  max_length=self.tokenizer.model_max_length      ,  truncation=__lowercase      ,  return_tensors='''pt'''      ,  )
				lowerCamelCase :			Any    		=self.text_encoder(text_input.input_ids.to(self.device ) )[0]
				# duplicate text embeddings for each generation per prompt
				lowerCamelCase :			Union[str, Any]    		=text_embeddings.repeat_interleave(__lowercase      ,  dim=0 )

				if clip_guidance_scale > 0:
						if clip_prompt is not None:
								lowerCamelCase :			Dict    		=self.tokenizer(
								    __lowercase      ,  padding='''max_length'''      ,  max_length=self.tokenizer.model_max_length      ,  truncation=__lowercase      ,  return_tensors='''pt'''      ,  ).input_ids.to(self.device )
						else:
								lowerCamelCase :			List[Any]    		=text_input.input_ids.to(self.device )
						lowerCamelCase :			Dict    		=self.clip_model.get_text_features(__lowercase )
						lowerCamelCase :			int    		=text_embeddings_clip / text_embeddings_clip.norm(p=2      ,  dim=-1      ,  keepdim=__lowercase )
						# duplicate text embeddings clip for each generation per prompt
						lowerCamelCase :			List[Any]    		=text_embeddings_clip.repeat_interleave(__lowercase      ,  dim=0 )

				# 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 :			int    		=guidance_scale > 1.0
				# get unconditional embeddings for classifier free guidance
				if do_classifier_free_guidance:
						lowerCamelCase :			Union[str, Any]    		=text_input.input_ids.shape[-1]
						lowerCamelCase :			str    		=self.tokenizer(['''''']      ,  padding='''max_length'''      ,  max_length=__lowercase      ,  return_tensors='''pt''' )
						lowerCamelCase :			Any    		=self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
						# duplicate unconditional embeddings for each generation per prompt
						lowerCamelCase :			str    		=uncond_embeddings.repeat_interleave(__lowercase      ,  dim=0 )

						# 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 :			Optional[Any]    		=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 :			Optional[int]    		=(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
				lowerCamelCase :			List[Any]    		=text_embeddings.dtype
				if latents is None:
						if self.device.type == "mps":
								# randn does not work reproducibly on mps
								lowerCamelCase :			Optional[int]    		=torch.randn(__lowercase      ,  generator=__lowercase      ,  device='''cpu'''      ,  dtype=__lowercase ).to(
								    self.device )
						else:
								lowerCamelCase :			int    		=torch.randn(__lowercase      ,  generator=__lowercase      ,  device=self.device      ,  dtype=__lowercase )
				else:
						if latents.shape != latents_shape:
								raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}" )
						lowerCamelCase :			Optional[int]    		=latents.to(self.device )

				# set timesteps
				lowerCamelCase :			Optional[Any]    		='''offset''' in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
				lowerCamelCase :			List[Any]    		={}
				if accepts_offset:
						lowerCamelCase :			Tuple    		=1

				self.scheduler.set_timesteps(__lowercase      ,  **__lowercase )

				# Some schedulers like PNDM have timesteps as arrays
				# It's more optimized to move all timesteps to correct device beforehand
				lowerCamelCase :			Optional[int]    		=self.scheduler.timesteps.to(self.device )

				# scale the initial noise by the standard deviation required by the scheduler
				lowerCamelCase :			List[Any]    		=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 :			Tuple    		='''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
				lowerCamelCase :			Union[str, Any]    		={}
				if accepts_eta:
						lowerCamelCase :			List[str]    		=eta

				# check if the scheduler accepts generator
				lowerCamelCase :			Tuple    		='''generator''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
				if accepts_generator:
						lowerCamelCase :			int    		=generator

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

						# predict the noise residual
						lowerCamelCase :			Optional[int]    		=self.unet(__lowercase      ,  __lowercase      ,  encoder_hidden_states=__lowercase ).sample

						# perform classifier free guidance
						if do_classifier_free_guidance:
								lowerCamelCase		, lowerCamelCase :			Optional[int]    		=noise_pred.chunk(2 )
								lowerCamelCase :			Union[str, Any]    		=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

						# perform clip guidance
						if clip_guidance_scale > 0:
								lowerCamelCase :			List[Any]    		=(
								    text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
								)
								lowerCamelCase		, lowerCamelCase :			Union[str, Any]    		=self.cond_fn(
								    __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  )

						# compute the previous noisy sample x_t -> x_t-1
						lowerCamelCase :			Dict    		=self.scheduler.step(__lowercase      ,  __lowercase      ,  __lowercase      ,  **__lowercase ).prev_sample

				# scale and decode the image latents with vae
				lowerCamelCase :			Tuple    		=1 / self.vae.config.scaling_factor * latents
				lowerCamelCase :			str    		=self.vae.decode(__lowercase ).sample

				lowerCamelCase :			Optional[int]    		=(image / 2 + 0.5).clamp(0      ,  1 )
				lowerCamelCase :			Union[str, Any]    		=image.cpu().permute(0      ,  2      ,  3      ,  1 ).numpy()

				if output_type == "pil":
						lowerCamelCase :			Optional[Any]    		=self.numpy_to_pil(__lowercase )

				if not return_dict:
						return (image, None)

				return StableDiffusionPipelineOutput(images=__lowercase      ,  nsfw_content_detected=__lowercase )

