
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   lowerCAmelCase_						(					_snake_case      :  Tuple      ,   _snake_case      :  Union[str, Any]      ,   _snake_case      :  Optional[int]   )				->		str:







	'''simple docstring'''


	if isinstance(_snake_case      ,   torch.Tensor   ):
		return image
	elif isinstance(_snake_case      ,   PIL.Image.Image   ):
		__magic_name__		:		List[Any]	   =     [image]

	if isinstance(image[0]      ,   PIL.Image.Image   ):
		__magic_name__		:		Any	   =     [np.array(i.resize((w, h)      ,   resample=PIL_INTERPOLATION["lanczos"]   )   )[None, :] for i in image]
		__magic_name__		:		str	   =     np.concatenate(_snake_case      ,   axis=0   )
		__magic_name__		:		Tuple	   =     np.array(_snake_case   ).astype(np.floataa   ) / 255.0
		__magic_name__		:		Optional[int]	   =     image.transpose(0      ,   3      ,   1      ,   2   )
		__magic_name__		:		List[str]	   =     2.0 * image - 1.0
		__magic_name__		:		Optional[int]	   =     torch.from_numpy(_snake_case   )
	elif isinstance(image[0]      ,   torch.Tensor   ):
		__magic_name__		:		Union[str, Any]	   =     torch.cat(_snake_case      ,   dim=0   )
	return image






def   lowerCAmelCase_						(					_snake_case      :  List[Any]      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  Optional[int]=0.9_995   )				->		Union[str, Any]:







	'''simple docstring'''


	if not isinstance(_snake_case      ,   np.ndarray   ):
		__magic_name__		:		List[str]	   =     True
		__magic_name__		:		Tuple	   =     va.device
		__magic_name__		:		int	   =     va.cpu().numpy()
		__magic_name__		:		Optional[Any]	   =     va.cpu().numpy()

	__magic_name__		:		Optional[Any]	   =     np.sum(va * va / (np.linalg.norm(_snake_case   ) * np.linalg.norm(_snake_case   ))   )
	if np.abs(_snake_case   ) > DOT_THRESHOLD:
		__magic_name__		:		Optional[Any]	   =     (1 - t) * va + t * va
	else:
		__magic_name__		:		Tuple	   =     np.arccos(_snake_case   )
		__magic_name__		:		Tuple	   =     np.sin(_snake_case   )
		__magic_name__		:		List[Any]	   =     theta_a * t
		__magic_name__		:		Tuple	   =     np.sin(_snake_case   )
		__magic_name__		:		Dict	   =     np.sin(theta_a - theta_t   ) / sin_theta_a
		__magic_name__		:		List[str]	   =     sin_theta_t / sin_theta_a
		__magic_name__		:		Tuple	   =     sa * va + sa * va

	if inputs_are_torch:
		__magic_name__		:		int	   =     torch.from_numpy(_snake_case   ).to(_snake_case   )

	return va






def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  List[Any]   )				->		Union[str, Any]:







	'''simple docstring'''


	__magic_name__		:		Optional[Any]	   =     F.normalize(_snake_case      ,   dim=-1   )
	__magic_name__		:		Dict	   =     F.normalize(_snake_case      ,   dim=-1   )
	return (x - y).norm(dim=-1   ).div(2   ).arcsin().pow(2   ).mul(2   )






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  Optional[Any]   )				->		Any:







	'''simple docstring'''


	for param in model.parameters():
		__magic_name__		:		int	   =     value





class       _snake_case					(     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						,   )
				__magic_name__		:		str	   =     (
				    feature_extractor.size
				    if isinstance(feature_extractor.size						,   _a		)
				    else feature_extractor.size["shortest_edge"]
				)
				__magic_name__		:		str	   =     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 			SCREAMING_SNAKE_CASE    (				self						,   _a = "auto"		):
				if slice_size == "auto":
					# half the attention head size is usually a good trade-off between
					# speed and memory
					__magic_name__		:		Dict	   =     self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(_a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				self.enable_attention_slicing(_a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				set_requires_grad(self.vae						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				set_requires_grad(self.vae						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				set_requires_grad(self.unet						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				set_requires_grad(self.unet						,   _a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a		):
				# get the original timestep using init_timestep
				__magic_name__		:		int	   =     min(int(num_inference_steps * strength		)						,   _a		)

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

				return timesteps, num_inference_steps - t_start







			def 			SCREAMING_SNAKE_CASE    (				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		)}'''		)

				__magic_name__		:		Union[str, Any]	   =     image.to(device=_a						,   dtype=_a		)

				if isinstance(_a						,   _a		):
					__magic_name__		:		List[Any]	   =     [
					    self.vae.encode(image[i : i + 1]		).latent_dist.sample(generator[i]		) for i in range(_a		)
					]
					__magic_name__		:		Tuple	   =     torch.cat(_a						,   dim=0		)
				else:
					__magic_name__		:		Dict	   =     self.vae.encode(_a		).latent_dist.sample(_a		)

				# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
				__magic_name__		:		List[str]	   =     0.1_82_15 * init_latents
				__magic_name__		:		List[Any]	   =     init_latents.repeat_interleave(_a						,   dim=0		)

				__magic_name__		:		Tuple	   =     randn_tensor(init_latents.shape						,   generator=_a						,   device=_a						,   dtype=_a		)

				# get latents
				__magic_name__		:		List[str]	   =     self.scheduler.add_noise(_a						,   _a						,   _a		)
				__magic_name__		:		Optional[Any]	   =     init_latents

				return latents







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		str	   =     self.coca_transform(_a		).unsqueeze(0		)
				with torch.no_grad(), torch.cuda.amp.autocast():
					__magic_name__		:		Any	   =     self.coca_model.generate(transformed_image.to(device=self.device						,   dtype=self.coca_model.dtype		)		)
				__magic_name__		:		Tuple	   =     self.coca_tokenizer.decode(generated[0].cpu().numpy()		)
				return generated.split("<end_of_text>"		)[0].replace("<start_of_text>"						,   ""		).rstrip(" .,"		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a		):
				__magic_name__		:		Tuple	   =     self.feature_extractor.preprocess(_a		)
				__magic_name__		:		List[str]	   =     torch.from_numpy(clip_image_input["pixel_values"][0]		).unsqueeze(0		).to(self.device		).half()
				__magic_name__		:		List[str]	   =     self.clip_model.get_image_features(_a		)
				__magic_name__		:		List[Any]	   =     image_embeddings_clip / image_embeddings_clip.norm(p=2						,   dim=-1						,   keepdim=_a		)
				__magic_name__		:		Tuple	   =     image_embeddings_clip.repeat_interleave(_a						,   dim=0		)
				return image_embeddings_clip







			@torch.enable_grad()
			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   ):
				__magic_name__		:		Optional[Any]	   =     latents.detach().requires_grad_()

				__magic_name__		:		List[str]	   =     self.scheduler.scale_model_input(_a						,   _a		)

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

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

					__magic_name__		:		int	   =     torch.sqrt(_a		)
					__magic_name__		:		Union[str, Any]	   =     pred_original_sample * (fac) + latents * (1 - fac)
				elif isinstance(self.scheduler						,   _a		):
					__magic_name__		:		Union[str, Any]	   =     self.scheduler.sigmas[index]
					__magic_name__		:		Tuple	   =     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
				__magic_name__		:		Dict	   =     1 / 0.1_82_15 * sample
				__magic_name__		:		Any	   =     self.vae.decode(_a		).sample
				__magic_name__		:		List[str]	   =     (image / 2 + 0.5).clamp(0						,   1		)

				__magic_name__		:		List[Any]	   =     transforms.Resize(self.feature_extractor_size		)(_a		)
				__magic_name__		:		Any	   =     self.normalize(_a		).to(latents.dtype		)

				__magic_name__		:		Any	   =     self.clip_model.get_image_features(_a		)
				__magic_name__		:		List[Any]	   =     image_embeddings_clip / image_embeddings_clip.norm(p=2						,   dim=-1						,   keepdim=_a		)

				__magic_name__		:		Dict	   =     spherical_dist_loss(_a						,   _a		).mean() * clip_guidance_scale

				__magic_name__		:		Tuple	   =     -torch.autograd.grad(_a						,   _a		)[0]

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







			@torch.no_grad()
			def __call__(				self						,   _a						,   _a						,   _a = None						,   _a = None						,   _a = 512						,   _a = 512						,   _a = 0.6						,   _a = 50						,   _a = 7.5						,   _a = 1						,   _a = 0.0						,   _a = 100						,   _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:
					__magic_name__		:		Any	   =     [generator] + [None] * (batch_size - 1)

				__magic_name__		:		Optional[int]	   =     [
				    ("model", self.coca_model is None),
				    ("tokenizer", self.coca_tokenizer is None),
				    ("transform", self.coca_transform is None),
				]
				__magic_name__		:		Optional[int]	   =     [x[0] for x in coca_is_none if x[1]]
				__magic_name__		:		Union[str, Any]	   =     ", ".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.'''		)
					__magic_name__		:		Dict	   =     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.'''		)
					__magic_name__		:		Dict	   =     self.get_image_description(_a		)

				# get prompt text embeddings for content and style
				__magic_name__		:		Tuple	   =     self.tokenizer(
				    _a						,   padding="max_length"						,   max_length=self.tokenizer.model_max_length						,   truncation=_a						,   return_tensors="pt"						,   )
				__magic_name__		:		Optional[Any]	   =     self.text_encoder(content_text_input.input_ids.to(self.device		)		)[0]

				__magic_name__		:		str	   =     self.tokenizer(
				    _a						,   padding="max_length"						,   max_length=self.tokenizer.model_max_length						,   truncation=_a						,   return_tensors="pt"						,   )
				__magic_name__		:		List[str]	   =     self.text_encoder(style_text_input.input_ids.to(self.device		)		)[0]

				__magic_name__		:		Optional[Any]	   =     slerp(_a						,   _a						,   _a		)

				# duplicate text embeddings for each generation per prompt
				__magic_name__		:		Union[str, Any]	   =     text_embeddings.repeat_interleave(_a						,   dim=0		)

				# set timesteps
				__magic_name__		:		int	   =     "offset" in set(inspect.signature(self.scheduler.set_timesteps		).parameters.keys()		)
				__magic_name__		:		Optional[int]	   =     {}
				if accepts_offset:
					__magic_name__		:		Optional[int]	   =     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		)

				__magic_name__   ,    __magic_name__		:		Dict	   =     self.get_timesteps(_a						,   _a						,   self.device		)
				__magic_name__		:		str	   =     timesteps[:1].repeat(_a		)

				# Preprocess image
				__magic_name__		:		Tuple	   =     preprocess(_a						,   _a						,   _a		)
				__magic_name__		:		int	   =     self.prepare_latents(
				    _a						,   _a						,   _a						,   text_embeddings.dtype						,   self.device						,   _a		)

				__magic_name__		:		Dict	   =     preprocess(_a						,   _a						,   _a		)
				__magic_name__		:		Dict	   =     self.prepare_latents(
				    _a						,   _a						,   _a						,   text_embeddings.dtype						,   self.device						,   _a		)

				__magic_name__		:		Tuple	   =     slerp(_a						,   _a						,   _a		)

				if clip_guidance_scale > 0:
					__magic_name__		:		Union[str, Any]	   =     self.get_clip_image_embeddings(_a						,   _a		)
					__magic_name__		:		str	   =     self.get_clip_image_embeddings(_a						,   _a		)
					__magic_name__		:		Dict	   =     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.
				__magic_name__		:		Tuple	   =     guidance_scale > 1.0
				# get unconditional embeddings for classifier free guidance
				if do_classifier_free_guidance:
					__magic_name__		:		Optional[int]	   =     content_text_input.input_ids.shape[-1]
					__magic_name__		:		str	   =     self.tokenizer([""]						,   padding="max_length"						,   max_length=_a						,   return_tensors="pt"		)
					__magic_name__		:		Optional[int]	   =     self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]
					# duplicate unconditional embeddings for each generation per prompt
					__magic_name__		:		Optional[int]	   =     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
					__magic_name__		:		int	   =     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`.
				__magic_name__		:		List[Any]	   =     (batch_size, self.unet.config.in_channels, height // 8, width // 8)
				__magic_name__		:		Optional[int]	   =     text_embeddings.dtype
				if latents is None:
					if self.device.type == "mps":
						# randn does not work reproducibly on mps
						__magic_name__		:		List[str]	   =     torch.randn(_a						,   generator=_a						,   device="cpu"						,   dtype=_a		).to(
						    self.device		)
					else:
						__magic_name__		:		List[Any]	   =     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}'''		)
					__magic_name__		:		Dict	   =     latents.to(self.device		)

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

				# check if the scheduler accepts generator
				__magic_name__		:		str	   =     "generator" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
				if accepts_generator:
					__magic_name__		:		List[Any]	   =     generator

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

						# predict the noise residual
						__magic_name__		:		Dict	   =     self.unet(_a						,   _a						,   encoder_hidden_states=_a		).sample

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

						# perform clip guidance
						if clip_guidance_scale > 0:
							__magic_name__		:		List[str]	   =     (
							    text_embeddings.chunk(2		)[1] if do_classifier_free_guidance else text_embeddings
							)
							__magic_name__   ,    __magic_name__		:		Tuple	   =     self.cond_fn(
							    _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   )

						# compute the previous noisy sample x_t -> x_t-1
						__magic_name__		:		str	   =     self.scheduler.step(_a						,   _a						,   _a						,   **_a		).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
				__magic_name__		:		List[Any]	   =     1 / 0.1_82_15 * latents
				__magic_name__		:		int	   =     self.vae.decode(_a		).sample

				__magic_name__		:		List[Any]	   =     (image / 2 + 0.5).clamp(0						,   1		)
				__magic_name__		:		Dict	   =     image.cpu().permute(0						,   2						,   3						,   1		).numpy()

				if output_type == "pil":
					__magic_name__		:		List[Any]	   =     self.numpy_to_pil(_a		)

				if not return_dict:
					return (image, None)

				return StableDiffusionPipelineOutput(images=_a						,   nsfw_content_detected=_a		)


def   lowerCAmelCase_						(					_snake_case      :  str      ,   _snake_case      :  str   )				->		bool:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     len(_snake_case   ) + 1
	__magic_name__		:		List[str]	   =     len(_snake_case   ) + 1

	# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
	# length i of input_string matches with prefix string of length j of
	# given pattern.
	# "dp" stands for dynamic programming.
	__magic_name__		:		str	   =     [[0 for i in range(_snake_case   )] for j in range(_snake_case   )]

	# since string of zero length match pattern of zero length
	__magic_name__		:		Optional[int]	   =     1

	# since pattern of zero length will never match with string of non-zero length
	for i in range(1      ,   _snake_case   ):
		__magic_name__		:		Optional[int]	   =     0

	# since string of zero length will match with pattern where there
	# is at least one * alternatively
	for j in range(1      ,   _snake_case   ):
		__magic_name__		:		Union[str, Any]	   =     dp[0][j - 2] if pattern[j - 1] == "*" else 0

	# now using bottom-up approach to find for all remaining lengths
	for i in range(1      ,   _snake_case   ):
		for j in range(1      ,   _snake_case   ):
			if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
				__magic_name__		:		Optional[int]	   =     dp[i - 1][j - 1]

			elif pattern[j - 1] == "*":
				if dp[i][j - 2] == 1:
					__magic_name__		:		Optional[Any]	   =     1
				elif pattern[j - 2] in (input_string[i - 1], "."):
					__magic_name__		:		List[Any]	   =     dp[i - 1][j]
				else:
					__magic_name__		:		Union[str, Any]	   =     0
			else:
				__magic_name__		:		Dict	   =     0

	return bool(dp[-1][-1]   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# inputing the strings
				# input_string = input("input a string :")
				# pattern = input("input a pattern :")

				snake_case :		Optional[Any]							      =  "aab"
				snake_case :		List[str]							      =  "c*a*b"

				# using function to check whether given string matches the given pattern
				if match_pattern(input_string, pattern):
								print(F"{input_string} matches the given pattern {pattern}")
				else:
								print(F"{input_string} does not match with the given pattern {pattern}")
