
import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					if isinstance(lowerCAmelCase__			,      torch.Tensor		):
										return image
					elif isinstance(lowerCAmelCase__			,      PIL.Image.Image		):
										lowercase						     =						[image]

					if isinstance(image[0]			,      PIL.Image.Image		):
										lowercase						     =						[np.array(i.resize((w, h)			,      resample=PIL_INTERPOLATION['''lanczos''']		)		)[None, :] for i in image]
										lowercase						     =						np.concatenate(lowerCAmelCase__			,      axis=0		)
										lowercase						     =						np.array(lowerCAmelCase__		).astype(np.floataa		) / 2_55.0
										lowercase						     =						image.transpose(0			,      3			,      1			,      2		)
										lowercase						     =						2.0 * image - 1.0
										lowercase						     =						torch.from_numpy(lowerCAmelCase__		)
					elif isinstance(image[0]			,      torch.Tensor		):
										lowercase						     =						torch.cat(lowerCAmelCase__			,      dim=0		)
					return image



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__=0.99_95		):
					'''simple docstring'''
					if not isinstance(lowerCAmelCase__			,      np.ndarray		):
										lowercase						     =						True
										lowercase						     =						va.device
										lowercase						     =						va.cpu().numpy()
										lowercase						     =						va.cpu().numpy()

					lowercase						     =						np.sum(va * va / (np.linalg.norm(lowerCAmelCase__		) * np.linalg.norm(lowerCAmelCase__		))		)
					if np.abs(lowerCAmelCase__		) > DOT_THRESHOLD:
										lowercase						     =						(1 - t) * va + t * va
					else:
										lowercase						     =						np.arccos(lowerCAmelCase__		)
										lowercase						     =						np.sin(lowerCAmelCase__		)
										lowercase						     =						theta_a * t
										lowercase						     =						np.sin(lowerCAmelCase__		)
										lowercase						     =						np.sin(theta_a - theta_t		) / sin_theta_a
										lowercase						     =						sin_theta_t / sin_theta_a
										lowercase						     =						sa * va + sa * va

					if inputs_are_torch:
										lowercase						     =						torch.from_numpy(lowerCAmelCase__		).to(lowerCAmelCase__		)

					return va



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						F.normalize(lowerCAmelCase__			,      dim=-1		)
					lowercase						     =						F.normalize(lowerCAmelCase__			,      dim=-1		)
					return (x - y).norm(dim=-1		).div(2		).arcsin().pow(2		).mul(2		)



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					for param in model.parameters():
										lowercase						     =						value







class     lowercase		( SCREAMING_SNAKE_CASE__		):




							def __init__(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__=None       ,A__=None       ,A__=None       ,):
												super().__init__()
												self.register_modules(
												    vae=A__       ,text_encoder=A__       ,clip_model=A__       ,tokenizer=A__       ,unet=A__       ,scheduler=A__       ,feature_extractor=A__       ,coca_model=A__       ,coca_tokenizer=A__       ,coca_transform=A__       ,)
												lowercase						     =						(
												    feature_extractor.size
												    if isinstance(feature_extractor.size       ,A__)
												    else feature_extractor.size['''shortest_edge''']
												)
												lowercase						     =						transforms.Normalize(mean=feature_extractor.image_mean       ,std=feature_extractor.image_std)
												set_requires_grad(self.text_encoder       ,A__)
												set_requires_grad(self.clip_model       ,A__)




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




							def 			A__				(    self):
												self.enable_attention_slicing(A__)




							def 			A__				(    self):
												set_requires_grad(self.vae       ,A__)




							def 			A__				(    self):
												set_requires_grad(self.vae       ,A__)




							def 			A__				(    self):
												set_requires_grad(self.unet       ,A__)




							def 			A__				(    self):
												set_requires_grad(self.unet       ,A__)




							def 			A__				(    self       ,A__       ,A__       ,A__):
												# get the original timestep using init_timestep
												lowercase						     =						min(int(num_inference_steps * strength)       ,A__)

												lowercase						     =						max(num_inference_steps - init_timestep       ,0)
												lowercase						     =						self.scheduler.timesteps[t_start:]

												return timesteps, num_inference_steps - t_start




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__=None):
												if not isinstance(A__       ,torch.Tensor):
																	raise ValueError(f'`image` has to be of type `torch.Tensor` but is {type(A__)}')

												lowercase						     =						image.to(device=A__       ,dtype=A__)

												if isinstance(A__       ,A__):
																	lowercase						     =						[
																	    self.vae.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(A__)
																	]
																	lowercase						     =						torch.cat(A__       ,dim=0)
												else:
																	lowercase						     =						self.vae.encode(A__).latent_dist.sample(A__)

												# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
												lowercase						     =						0.18215 * init_latents
												lowercase						     =						init_latents.repeat_interleave(A__       ,dim=0)

												lowercase						     =						randn_tensor(init_latents.shape       ,generator=A__       ,device=A__       ,dtype=A__)

												# get latents
												lowercase						     =						self.scheduler.add_noise(A__       ,A__       ,A__)
												lowercase						     =						init_latents

												return latents




							def 			A__				(    self       ,A__):
												lowercase						     =						self.coca_transform(A__).unsqueeze(0)
												with torch.no_grad(), torch.cuda.amp.autocast():
																	lowercase						     =						self.coca_model.generate(transformed_image.to(device=self.device       ,dtype=self.coca_model.dtype))
												lowercase						     =						self.coca_tokenizer.decode(generated[0].cpu().numpy())
												return generated.split('''<end_of_text>''')[0].replace('''<start_of_text>'''       ,'''''').rstrip(''' .,''')




							def 			A__				(    self       ,A__       ,A__):
												lowercase						     =						self.feature_extractor.preprocess(A__)
												lowercase						     =						torch.from_numpy(clip_image_input['''pixel_values'''][0]).unsqueeze(0).to(self.device).half()
												lowercase						     =						self.clip_model.get_image_features(A__)
												lowercase						     =						image_embeddings_clip / image_embeddings_clip.norm(p=2       ,dim=-1       ,keepdim=A__)
												lowercase						     =						image_embeddings_clip.repeat_interleave(A__       ,dim=0)
												return image_embeddings_clip




							@torch.enable_grad()
							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,):
												lowercase						     =						latents.detach().requires_grad_()

												lowercase						     =						self.scheduler.scale_model_input(A__       ,A__)

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

												if isinstance(self.scheduler       ,(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)):
																	lowercase						     =						self.scheduler.alphas_cumprod[timestep]
																	lowercase						     =						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
																	lowercase						     =						(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

																	lowercase						     =						torch.sqrt(A__)
																	lowercase						     =						pred_original_sample * (fac) + latents * (1 - fac)
												elif isinstance(self.scheduler       ,A__):
																	lowercase						     =						self.scheduler.sigmas[index]
																	lowercase						     =						latents - sigma * noise_pred
												else:
																	raise ValueError(f'scheduler type {type(self.scheduler)} not supported')

												# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
												lowercase						     =						1 / 0.18215 * sample
												lowercase						     =						self.vae.decode(A__).sample
												lowercase						     =						(image / 2 + 0.5).clamp(0       ,1)

												lowercase						     =						transforms.Resize(self.feature_extractor_size)(A__)
												lowercase						     =						self.normalize(A__).to(latents.dtype)

												lowercase						     =						self.clip_model.get_image_features(A__)
												lowercase						     =						image_embeddings_clip / image_embeddings_clip.norm(p=2       ,dim=-1       ,keepdim=A__)

												lowercase						     =						spherical_dist_loss(A__       ,A__).mean() * clip_guidance_scale

												lowercase						     =						-torch.autograd.grad(A__       ,A__)[0]

												if isinstance(self.scheduler       ,A__):
																	lowercase						     =						latents.detach() + grads * (sigma**2)
																	lowercase						     =						noise_pred_original
												else:
																	lowercase						     =						noise_pred_original - torch.sqrt(A__) * grads
												return noise_pred, latents





							@torch.no_grad()
							def __call__(    self       ,A__       ,A__       ,A__ = None       ,A__ = None       ,A__ = 5_1_2       ,A__ = 5_1_2       ,A__ = 0.6       ,A__ = 5_0       ,A__ = 7.5       ,A__ = 1       ,A__ = 0.0       ,A__ = 1_0_0       ,A__ = None       ,A__ = "pil"       ,A__ = True       ,A__ = 0.8       ,A__ = 0.1       ,A__ = 0.1       ,):
												if isinstance(A__       ,A__) and len(A__) != batch_size:
																	raise ValueError(f'You have passed {batch_size} batch_size, but only {len(A__)} generators.')

												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 isinstance(A__       ,torch.Generator) and batch_size > 1:
																	lowercase						     =						[generator] + [None] * (batch_size - 1)

												lowercase						     =						[
												    ('''model''', self.coca_model is None),
												    ('''tokenizer''', self.coca_tokenizer is None),
												    ('''transform''', self.coca_transform is None),
												]
												lowercase						     =						[x[0] for x in coca_is_none if x[1]]
												lowercase						     =						''', '''.join(A__)
												# generate prompts with coca model if prompt is None
												if content_prompt is None:
																	if len(A__):
																						raise ValueError(
																						    f'Content prompt is None and CoCa [{coca_is_none_str}] is None.'
																						    f'Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.')
																	lowercase						     =						self.get_image_description(A__)
												if style_prompt is None:
																	if len(A__):
																						raise ValueError(
																						    f'Style prompt is None and CoCa [{coca_is_none_str}] is None.'
																						    f' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.')
																	lowercase						     =						self.get_image_description(A__)

												# get prompt text embeddings for content and style
												lowercase						     =						self.tokenizer(
												    A__       ,padding='''max_length'''       ,max_length=self.tokenizer.model_max_length       ,truncation=A__       ,return_tensors='''pt'''       ,)
												lowercase						     =						self.text_encoder(content_text_input.input_ids.to(self.device))[0]

												lowercase						     =						self.tokenizer(
												    A__       ,padding='''max_length'''       ,max_length=self.tokenizer.model_max_length       ,truncation=A__       ,return_tensors='''pt'''       ,)
												lowercase						     =						self.text_encoder(style_text_input.input_ids.to(self.device))[0]

												lowercase						     =						slerp(A__       ,A__       ,A__)

												# duplicate text embeddings for each generation per prompt
												lowercase						     =						text_embeddings.repeat_interleave(A__       ,dim=0)

												# set timesteps
												lowercase						     =						'''offset''' in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
												lowercase						     =						{}
												if accepts_offset:
																	lowercase						     =						1

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

												lowercase     ,						lowercase						     =						self.get_timesteps(A__       ,A__       ,self.device)
												lowercase						     =						timesteps[:1].repeat(A__)

												# Preprocess image
												lowercase						     =						preprocess(A__       ,A__       ,A__)
												lowercase						     =						self.prepare_latents(
												    A__       ,A__       ,A__       ,text_embeddings.dtype       ,self.device       ,A__)

												lowercase						     =						preprocess(A__       ,A__       ,A__)
												lowercase						     =						self.prepare_latents(
												    A__       ,A__       ,A__       ,text_embeddings.dtype       ,self.device       ,A__)

												lowercase						     =						slerp(A__       ,A__       ,A__)

												if clip_guidance_scale > 0:
																	lowercase						     =						self.get_clip_image_embeddings(A__       ,A__)
																	lowercase						     =						self.get_clip_image_embeddings(A__       ,A__)
																	lowercase						     =						slerp(
																	    A__       ,A__       ,A__)

												# 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						     =						content_text_input.input_ids.shape[-1]
																	lowercase						     =						self.tokenizer(['''''']       ,padding='''max_length'''       ,max_length=A__       ,return_tensors='''pt''')
																	lowercase						     =						self.text_encoder(uncond_input.input_ids.to(self.device))[0]
																	# duplicate unconditional embeddings for each generation per prompt
																	lowercase						     =						uncond_embeddings.repeat_interleave(A__       ,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
																	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, 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 work reproducibly on mps
																						lowercase						     =						torch.randn(A__       ,generator=A__       ,device='''cpu'''       ,dtype=A__).to(
																						    self.device)
																	else:
																						lowercase						     =						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}')
																	lowercase						     =						latents.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

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

												with self.progress_bar(total=A__):
																	for i, t in enumerate(A__):
																						# 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(A__       ,A__)

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

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

																						# perform clip guidance
																						if clip_guidance_scale > 0:
																											lowercase						     =						(
																											    text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
																											)
																											lowercase     ,						lowercase						     =						self.cond_fn(
																											    A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,)

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
												lowercase						     =						1 / 0.18215 * latents
												lowercase						     =						self.vae.decode(A__).sample

												lowercase						     =						(image / 2 + 0.5).clamp(0       ,1)
												lowercase						     =						image.cpu().permute(0       ,2       ,3       ,1).numpy()

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

												if not return_dict:
																	return (image, None)

												return StableDiffusionPipelineOutput(images=A__       ,nsfw_content_detected=A__)






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
							import numpy as np
							import tensorflow as tf

							from transformers import TFXLMRobertaModel







@require_tf
@require_sentencepiece
@require_tokenizers
class     lowercase		( unittest.TestCase		):




							@slow
							def 			A__				(    self):
												lowercase						     =						TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base''')

												lowercase						     =						{
												    '''input_ids''': tf.convert_to_tensor([[0, 2_6_4_6, 1_0_2_6_9, 8_3, 9_9_9_4_2, 2]]       ,dtype=tf.intaa),  # "My dog is cute"
												    '''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]       ,dtype=tf.intaa),
												}

												lowercase						     =						model(A__)['''last_hidden_state''']
												lowercase						     =						tf.TensorShape((1, 6, 7_6_8))
												self.assertEqual(output.shape       ,A__)
												# compare the actual values for a slice.
												lowercase						     =						tf.convert_to_tensor(
												    [
												        [
												            [0.0681762, 0.10894451, 0.06772504],
												            [-0.06423668, 0.02366615, 0.04329344],
												            [-0.06057295, 0.09974135, -0.00070584],
												        ]
												    ]       ,dtype=tf.floataa       ,)

												self.assertTrue(np.allclose(output[:, :3, :3].numpy()       ,expected_slice.numpy()       ,atol=1E-4))




