


"""simple docstring"""


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


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







		def __init__(					self      :       int    ,   a__      :       WhisperForConditionalGeneration    ,   a__      :       WhisperProcessor    ,   a__      :       AutoencoderKL    ,   a__      :       CLIPTextModel    ,   a__      :       CLIPTokenizer    ,   a__      :       UNetaDConditionModel    ,   a__      :       Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]    ,   a__      :       StableDiffusionSafetyChecker    ,   a__      :       CLIPImageProcessor    ,   )       ->						List[str]:





						'''simple docstring'''







						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      :       List[str]    ,   a__      :       Optional[Union[str, int]] = "auto"		)       ->						List[str]:





						'''simple docstring'''







						if slice_size == "auto":
										_A					  =			self.unet.config.attention_head_dim // 2
						self.unet.set_attention_slice(a__		)







		def  a_ (					self      :       Union[str, Any]		)       ->						Optional[Any]:





						'''simple docstring'''







						self.enable_attention_slicing(a__		)







		@torch.no_grad()
		def __call__(					self      :       Dict    ,   a__      :       Optional[int]    ,   a__      :       List[str]=1_60_00    ,   a__      :       int = 5_12    ,   a__      :       int = 5_12    ,   a__      :       int = 50    ,   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__      :       Any    ,   )       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			self.speech_processor.feature_extractor(
						    a__    ,   return_tensors="pt"    ,   sampling_rate=a__		).input_features.to(self.device		)
						_A					  =			self.speech_model.generate(a__    ,   max_length=48_00_00		)

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

						if isinstance(a__    ,   a__		):
										_A					  =			1
						elif isinstance(a__    ,   a__		):
										_A					  =			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
						_A					  =			self.tokenizer(
						    a__    ,   padding="max_length"    ,   max_length=self.tokenizer.model_max_length    ,   return_tensors="pt"    ,   )
						_A					  =			text_inputs.input_ids

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

						# duplicate text embeddings for each generation per prompt, using mps friendly method
						_A  ,		_A  ,		_A					  =			text_embeddings.shape
						_A					  =			text_embeddings.repeat(1    ,   a__    ,   1		)
						_A					  =			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.
						_A					  =			guidance_scale > 1.0
						# get unconditional embeddings for classifier free guidance
						if do_classifier_free_guidance:
										_A					  =			42
										if negative_prompt is None:
														_A					  =			[""] * 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__		):
														_A					  =			[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:
														_A					  =			negative_prompt

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

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

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

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

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

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

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

						_A					  =			1 / 0.1_8_2_1_5 * latents
						_A					  =			self.vae.decode(a__		).sample

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

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

						if output_type == "pil":
										_A					  =			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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_       							=    {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    ["RoFormerTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "RoFormerForCausalLM",
		    "RoFormerForMaskedLM",
		    "RoFormerForMultipleChoice",
		    "RoFormerForQuestionAnswering",
		    "RoFormerForSequenceClassification",
		    "RoFormerForTokenClassification",
		    "RoFormerLayer",
		    "RoFormerModel",
		    "RoFormerPreTrainedModel",
		    "load_tf_weights_in_roformer",
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFRoFormerForCausalLM",
		    "TFRoFormerForMaskedLM",
		    "TFRoFormerForMultipleChoice",
		    "TFRoFormerForQuestionAnswering",
		    "TFRoFormerForSequenceClassification",
		    "TFRoFormerForTokenClassification",
		    "TFRoFormerLayer",
		    "TFRoFormerModel",
		    "TFRoFormerPreTrainedModel",
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "FlaxRoFormerForMaskedLM",
		    "FlaxRoFormerForMultipleChoice",
		    "FlaxRoFormerForQuestionAnswering",
		    "FlaxRoFormerForSequenceClassification",
		    "FlaxRoFormerForTokenClassification",
		    "FlaxRoFormerModel",
		    "FlaxRoFormerPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
		from .tokenization_roformer import RoFormerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roformer_fast import RoFormerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roformer import (
				    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoFormerForCausalLM,
				    RoFormerForMaskedLM,
				    RoFormerForMultipleChoice,
				    RoFormerForQuestionAnswering,
				    RoFormerForSequenceClassification,
				    RoFormerForTokenClassification,
				    RoFormerLayer,
				    RoFormerModel,
				    RoFormerPreTrainedModel,
				    load_tf_weights_in_roformer,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roformer import (
				    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerLayer,
				    TFRoFormerModel,
				    TFRoFormerPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roformer import (
				    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				    FlaxRoFormerPreTrainedModel,
				)


else:
		import sys

		a_       							=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)