
import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)







class        A_   (			unittest.TestCase   ):




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    ['a', 'b', 'c']

		# Defaults to last layer if both are None
		lowercase  ,					lowercase       =    get_aligned_output_features_output_indices(snake_case		,    snake_case		,    snake_case       )
		self.assertEqual(snake_case		,    ['c']       )
		self.assertEqual(snake_case		,    [2]       )

		# Out indices set to match out features
		lowercase  ,					lowercase       =    get_aligned_output_features_output_indices(['a', 'c']		,    snake_case		,    snake_case       )
		self.assertEqual(snake_case		,    ['a', 'c']       )
		self.assertEqual(snake_case		,    [0, 2]       )

		# Out features set to match out indices
		lowercase  ,					lowercase       =    get_aligned_output_features_output_indices(snake_case		,    [0, 2]		,    snake_case       )
		self.assertEqual(snake_case		,    ['a', 'c']       )
		self.assertEqual(snake_case		,    [0, 2]       )

		# Out features selected from negative indices
		lowercase  ,					lowercase       =    get_aligned_output_features_output_indices(snake_case		,    [-3, -1]		,    snake_case       )
		self.assertEqual(snake_case		,    ['a', 'c']       )
		self.assertEqual(snake_case		,    [-3, -1]       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		# Stage names must be set
		with self.assertRaises(snake_case       ):
			verify_out_features_out_indices(['a', 'b']		,    (0, 1)		,    snake_case       )

		# Out features must be a list
		with self.assertRaises(snake_case       ):
			verify_out_features_out_indices(('a', 'b')		,    (0, 1)		,    ['a', 'b']       )

		# Out features must be a subset of stage names
		with self.assertRaises(snake_case       ):
			verify_out_features_out_indices(['a', 'b']		,    (0, 1)		,    ['a']       )

		# Out indices must be a list or tuple
		with self.assertRaises(snake_case       ):
			verify_out_features_out_indices(snake_case		,    0		,    ['a', 'b']       )

		# Out indices must be a subset of stage names
		with self.assertRaises(snake_case       ):
			verify_out_features_out_indices(snake_case		,    (0, 1)		,    ['a']       )

		# Out features and out indices must be the same length
		with self.assertRaises(snake_case       ):
			verify_out_features_out_indices(['a', 'b']		,    (0,)		,    ['a', 'b', 'c']       )

		# Out features should match out indices
		with self.assertRaises(snake_case       ):
			verify_out_features_out_indices(['a', 'b']		,    (0, 2)		,    ['a', 'b', 'c']       )

		# Out features and out indices should be in order
		with self.assertRaises(snake_case       ):
			verify_out_features_out_indices(['b', 'a']		,    (0, 1)		,    ['a', 'b']       )

		# Check passes with valid inputs
		verify_out_features_out_indices(['a', 'b', 'd']		,    (0, 1, -1)		,    ['a', 'b', 'c', 'd']       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    BackboneMixin()

		lowercase       =    ['a', 'b', 'c']
		lowercase       =    ['a', 'c']
		lowercase       =    [0, 2]

		# Check that the output features and indices are set correctly
		self.assertEqual(backbone.out_features		,    ['a', 'c']       )
		self.assertEqual(backbone.out_indices		,    [0, 2]       )

		# Check out features and indices are updated correctly
		lowercase       =    ['a', 'b']
		self.assertEqual(backbone.out_features		,    ['a', 'b']       )
		self.assertEqual(backbone.out_indices		,    [0, 1]       )

		lowercase       =    [-3, -1]
		self.assertEqual(backbone.out_features		,    ['a', 'c']       )
		self.assertEqual(backbone.out_indices		,    [-3, -1]       )



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


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







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    snake_case		,    ):
		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=snake_case		,    speech_processor=snake_case		,    vae=snake_case		,    text_encoder=snake_case		,    tokenizer=snake_case		,    unet=snake_case		,    scheduler=snake_case		,    feature_extractor=snake_case		,    )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case = "auto"       ):
		if slice_size == "auto":
			lowercase       =    self.unet.config.attention_head_dim // 2
		self.unet.set_attention_slice(snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		self.enable_attention_slicing(snake_case       )





	@torch.no_grad()
	def __call__(       self		,    snake_case		,    snake_case=1_6000		,    snake_case = 512		,    snake_case = 512		,    snake_case = 50		,    snake_case = 7.5		,    snake_case = None		,    snake_case = 1		,    snake_case = 0.0		,    snake_case = None		,    snake_case = None		,    snake_case = "pil"		,    snake_case = True		,    snake_case = None		,    snake_case = 1		,    **snake_case		,    ):
		lowercase       =    self.speech_processor.feature_extractor(
		    snake_case		,    return_tensors='pt'		,    sampling_rate=snake_case       ).input_features.to(self.device       )
		lowercase       =    self.speech_model.generate(snake_case		,    max_length=48_0000       )

		lowercase       =    self.speech_processor.tokenizer.batch_decode(snake_case		,    skip_special_tokens=snake_case		,    normalize=snake_case       )[
		    0
		]

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

		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(snake_case		,    snake_case       ) or callback_steps <= 0)
		):
			raise ValueError(
			    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
			    F''' {type(snake_case       )}.'''       )

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

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

		# duplicate text embeddings for each generation per prompt, using mps friendly method
		lowercase  ,					lowercase  ,					lowercase       =    text_embeddings.shape
		lowercase       =    text_embeddings.repeat(1		,    snake_case		,    1       )
		lowercase       =    text_embeddings.view(bs_embed * num_images_per_prompt		,    snake_case		,    -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.
		lowercase       =    guidance_scale > 1.0
		# get unconditional embeddings for classifier free guidance
		if do_classifier_free_guidance:
			lowercase       =    42
			if negative_prompt is None:
				lowercase       =    [''] * batch_size
			elif type(snake_case       ) is not type(snake_case       ):
				raise TypeError(
				    F'''`negative_prompt` should be the same type to `prompt`, but got {type(snake_case       )} !='''
				    F''' {type(snake_case       )}.'''       )
			elif isinstance(snake_case		,    snake_case       ):
				lowercase       =    [negative_prompt]
			elif batch_size != len(snake_case       ):
				raise ValueError(
				    F'''`negative_prompt`: {negative_prompt} has batch size {len(snake_case       )}, but `prompt`:'''
				    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
				    ' the batch size of `prompt`.'       )
			else:
				lowercase       =    negative_prompt

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

			# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
			lowercase       =    uncond_embeddings.shape[1]
			lowercase       =    uncond_embeddings.repeat(1		,    snake_case		,    1       )
			lowercase       =    uncond_embeddings.view(batch_size * num_images_per_prompt		,    snake_case		,    -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
			lowercase       =    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`.
		lowercase       =    (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
		lowercase       =    text_embeddings.dtype
		if latents is None:
			if self.device.type == "mps":
				# randn does not exist on mps
				lowercase       =    torch.randn(snake_case		,    generator=snake_case		,    device='cpu'		,    dtype=snake_case       ).to(
				    self.device       )
			else:
				lowercase       =    torch.randn(snake_case		,    generator=snake_case		,    device=self.device		,    dtype=snake_case       )
		else:
			if latents.shape != latents_shape:
				raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''       )
			lowercase       =    latents.to(self.device       )

		# set timesteps
		self.scheduler.set_timesteps(snake_case       )

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

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

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

			# predict the noise residual
			lowercase       =    self.unet(snake_case		,    snake_case		,    encoder_hidden_states=snake_case       ).sample

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

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

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

		lowercase       =    1 / 0.18_215 * latents
		lowercase       =    self.vae.decode(snake_case       ).sample

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

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

		if output_type == "pil":
			lowercase       =    self.numpy_to_pil(snake_case       )

		if not return_dict:
			return image

		return StableDiffusionPipelineOutput(images=snake_case		,    nsfw_content_detected=snake_case       )

