

"""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


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





class      __A     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      Tuple		,       UpperCAmelCase_							:      WhisperForConditionalGeneration		,       UpperCAmelCase_							:      WhisperProcessor		,       UpperCAmelCase_							:      AutoencoderKL		,       UpperCAmelCase_							:      CLIPTextModel		,       UpperCAmelCase_							:      CLIPTokenizer		,       UpperCAmelCase_							:      UNetaDConditionModel		,       UpperCAmelCase_							:      Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]		,       UpperCAmelCase_							:      StableDiffusionSafetyChecker		,       UpperCAmelCase_							:      CLIPImageProcessor		,       )					->Any:



														"""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=UpperCAmelCase_		,       speech_processor=UpperCAmelCase_		,       vae=UpperCAmelCase_		,       text_encoder=UpperCAmelCase_		,       tokenizer=UpperCAmelCase_		,       unet=UpperCAmelCase_		,       scheduler=UpperCAmelCase_		,       feature_extractor=UpperCAmelCase_		,       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]		,       UpperCAmelCase_							:      Optional[Union[str, int]] = "auto"       )					->Dict:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      str       )					->List[Any]:



														"""simple docstring"""



														self.enable_attention_slicing(UpperCAmelCase_       )



							@torch.no_grad()
							def __call__(				self							:      str		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Any=16_000		,       UpperCAmelCase_							:      int = 512		,       UpperCAmelCase_							:      int = 512		,       UpperCAmelCase_							:      int = 50		,       UpperCAmelCase_							:      float = 7.5		,       UpperCAmelCase_							:      Optional[Union[str, List[str]]] = None		,       UpperCAmelCase_							:      Optional[int] = 1		,       UpperCAmelCase_							:      float = 0.0		,       UpperCAmelCase_							:      Optional[torch.Generator] = None		,       UpperCAmelCase_							:      Optional[torch.FloatTensor] = None		,       UpperCAmelCase_							:      Optional[str] = "pil"		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      Optional[Callable[[int, int, torch.FloatTensor], None]] = None		,       UpperCAmelCase_							:      int = 1		,       **UpperCAmelCase_							:      int		,       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		self.speech_processor.feature_extractor(
														    UpperCAmelCase_		,       return_tensors="""pt"""		,       sampling_rate=UpperCAmelCase_       ).input_features.to(self.device       )
														snake_case_           =		self.speech_model.generate(UpperCAmelCase_		,       max_length=480_000       )

														snake_case_           =		self.speech_processor.tokenizer.batch_decode(UpperCAmelCase_		,       skip_special_tokens=UpperCAmelCase_		,       normalize=UpperCAmelCase_       )[
														    0
														]

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

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

														# get prompt text embeddings
														snake_case_           =		self.tokenizer(
														    UpperCAmelCase_		,       padding="""max_length"""		,       max_length=self.tokenizer.model_max_length		,       return_tensors="""pt"""		,       )
														snake_case_           =		text_inputs.input_ids

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

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

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

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

														# set timesteps
														self.scheduler.set_timesteps(UpperCAmelCase_       )

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

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

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

																					# predict the noise residual
																					snake_case_           =		self.unet(UpperCAmelCase_		,       UpperCAmelCase_		,       encoder_hidden_states=UpperCAmelCase_       ).sample

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

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

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

														snake_case_           =		1 / 0.18_215 * latents
														snake_case_           =		self.vae.decode(UpperCAmelCase_       ).sample

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

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

														if output_type == "pil":
																					snake_case_           =		self.numpy_to_pil(UpperCAmelCase_       )

														if not return_dict:
																					return image

														return StableDiffusionPipelineOutput(images=UpperCAmelCase_		,       nsfw_content_detected=UpperCAmelCase_       )







"""simple docstring"""
import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import ImageGPTImageProcessor





class      __A     (unittest.TestCase):





							'''simple docstring'''

							def __init__(				self							:      int		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Tuple=7		,       UpperCAmelCase_							:      Dict=3		,       UpperCAmelCase_							:      Tuple=18		,       UpperCAmelCase_							:      Optional[Any]=30		,       UpperCAmelCase_							:      str=400		,       UpperCAmelCase_							:      int=True		,       UpperCAmelCase_							:      Optional[int]=None		,       UpperCAmelCase_							:      Optional[Any]=True		,       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		size if size is not None else {"""height""": 18, """width""": 18}
														snake_case_           =		parent
														snake_case_           =		batch_size
														snake_case_           =		num_channels
														snake_case_           =		image_size
														snake_case_           =		min_resolution
														snake_case_           =		max_resolution
														snake_case_           =		do_resize
														snake_case_           =		size
														snake_case_           =		do_normalize



							def 		lowerCAmelCase       (				self							:      List[str]       )					->Optional[Any]:



														"""simple docstring"""



														return {
														    # here we create 2 clusters for the sake of simplicity
														    "clusters": np.asarray(
														        [
														            [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
														            [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
														        ]       ),
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_normalize": self.do_normalize,
														}





@require_torch
@require_vision
class      __A     (snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		List[Any]							      =    ImageGPTImageProcessor if is_vision_available() else None



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		ImageGPTImageProcessingTester(self       )



							@property
							def 		lowerCAmelCase       (				self							:      Tuple       )					->List[Any]:



														"""simple docstring"""



														return self.image_processor_tester.prepare_image_processor_dict()



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """clusters"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """do_resize"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """size"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """do_normalize"""       )       )



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class.from_dict(self.image_processor_dict       )
														self.assertEqual(image_processor.size		,       {"""height""": 18, """width""": 18}       )

														snake_case_           =		self.image_processing_class.from_dict(self.image_processor_dict		,       size=42       )
														self.assertEqual(image_processor.size		,       {"""height""": 42, """width""": 42}       )



							def 		lowerCAmelCase       (				self							:      Any       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )
														snake_case_           =		json.loads(image_processor.to_json_string()       )
														for key, value in self.image_processor_dict.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(UpperCAmelCase_		,       obj[key]       )       )
																					else:
																												self.assertEqual(obj[key]		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					snake_case_           =		os.path.join(UpperCAmelCase_		,       """image_processor.json"""       )
																					image_processor_first.to_json_file(UpperCAmelCase_       )
																					snake_case_           =		self.image_processing_class.from_json_file(UpperCAmelCase_       ).to_dict()

														snake_case_           =		image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(UpperCAmelCase_		,       image_processor_second[key]       )       )
																					else:
																												self.assertEqual(image_processor_first[key]		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					image_processor_first.save_pretrained(UpperCAmelCase_       )
																					snake_case_           =		self.image_processing_class.from_pretrained(UpperCAmelCase_       ).to_dict()

														snake_case_           =		image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(UpperCAmelCase_		,       image_processor_second[key]       )       )
																					else:
																												self.assertEqual(image_processor_first[key]		,       UpperCAmelCase_       )



							@unittest.skip("""ImageGPT requires clusters at initialization"""       )
							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Tuple:



														"""simple docstring"""



														pass







def 						_a				(      )    ->				str:
							snake_case_           =		load_dataset("""hf-internal-testing/fixtures_image_utils"""				, split="""test"""				)

							snake_case_           =		Image.open(dataset[4]["""file"""]				)
							snake_case_           =		Image.open(dataset[5]["""file"""]				)

							snake_case_           =		[imagea, imagea]

							return images





@require_vision
@require_torch
class      __A     (unittest.TestCase):





							'''simple docstring'''

							@slow
							def 		lowerCAmelCase       (				self							:      Tuple       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""       )

														snake_case_           =		prepare_images()

														# test non-batched
														snake_case_           =		image_processing(images[0]		,       return_tensors="""pt"""       )

														self.assertIsInstance(encoding.input_ids		,       torch.LongTensor       )
														self.assertEqual(encoding.input_ids.shape		,       (1, 1_024)       )

														snake_case_           =		[306, 191, 191]
														self.assertEqual(encoding.input_ids[0, :3].tolist()		,       UpperCAmelCase_       )

														# test batched
														snake_case_           =		image_processing(UpperCAmelCase_		,       return_tensors="""pt"""       )

														self.assertIsInstance(encoding.input_ids		,       torch.LongTensor       )
														self.assertEqual(encoding.input_ids.shape		,       (2, 1_024)       )

														snake_case_           =		[303, 13, 13]
														self.assertEqual(encoding.input_ids[1, -3:].tolist()		,       UpperCAmelCase_       )



