"""simple docstring"""


import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

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






class        __UpperCAmelCase						(     nn.Module     ):

				def __init__(				self     ,			_lowerCamelCase     ,			_lowerCamelCase=1.0				):
										super().__init__()

										lowerCamelCase__ =cut_size
										lowerCamelCase__ =cut_power






				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase				):
										lowerCamelCase__ ,				lowerCamelCase__ =pixel_values.shape[2:4]
										lowerCamelCase__ =min(_lowerCamelCase     ,			_lowerCamelCase				)
										lowerCamelCase__ =min(_lowerCamelCase     ,			_lowerCamelCase     ,			self.cut_size				)
										lowerCamelCase__ =[]
										for _ in range(_lowerCamelCase				):
																lowerCamelCase__ =int(torch.rand([]				) ** self.cut_power * (max_size - min_size) + min_size				)
																lowerCamelCase__ =torch.randint(0     ,			sideX - size + 1     ,			()				)
																lowerCamelCase__ =torch.randint(0     ,			sideY - size + 1     ,			()				)
																lowerCamelCase__ =pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
																cutouts.append(F.adaptive_avg_poolad(_lowerCamelCase     ,			self.cut_size				)				)
										return torch.cat(_lowerCamelCase				)





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[Any]:




						'''simple docstring'''



						lowerCamelCase__ =F.normalize(__lowerCAmelCase       ,   dim=-1    )
						lowerCamelCase__ =F.normalize(__lowerCAmelCase       ,   dim=-1    )
						return (x - y).norm(dim=-1    ).div(2    ).arcsin().pow(2    ).mul(2    )





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						for param in model.parameters():
												lowerCamelCase__ =value






class        __UpperCAmelCase						(     __lowerCAmelCase     ):

				def __init__(				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			):
										super().__init__()
										self.register_modules(
										    vae=_lowerCamelCase     ,			text_encoder=_lowerCamelCase     ,			clip_model=_lowerCamelCase     ,			tokenizer=_lowerCamelCase     ,			unet=_lowerCamelCase     ,			scheduler=_lowerCamelCase     ,			feature_extractor=_lowerCamelCase     ,			)

										lowerCamelCase__ =transforms.Normalize(mean=feature_extractor.image_mean     ,			std=feature_extractor.image_std				)
										lowerCamelCase__ =(
										    feature_extractor.size
										    if isinstance(feature_extractor.size     ,			_lowerCamelCase				)
										    else feature_extractor.size["shortest_edge"]
										)
										lowerCamelCase__ =MakeCutouts(self.cut_out_size				)

										set_requires_grad(self.text_encoder     ,			_lowerCamelCase				)
										set_requires_grad(self.clip_model     ,			_lowerCamelCase				)

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

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

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

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

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

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

				@torch.enable_grad()
				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase=True     ,			):
										lowerCamelCase__ =latents.detach().requires_grad_()

										lowerCamelCase__ =self.scheduler.scale_model_input(_lowerCamelCase     ,			_lowerCamelCase				)

										# predict the noise residual
										lowerCamelCase__ =self.unet(_lowerCamelCase     ,			_lowerCamelCase     ,			encoder_hidden_states=_lowerCamelCase				).sample

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

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

										lowerCamelCase__ =1 / self.vae.config.scaling_factor * sample
										lowerCamelCase__ =self.vae.decode(_lowerCamelCase				).sample
										lowerCamelCase__ =(image / 2 + 0.5).clamp(0     ,			1				)

										if use_cutouts:
																lowerCamelCase__ =self.make_cutouts(_lowerCamelCase     ,			_lowerCamelCase				)
										else:
																lowerCamelCase__ =transforms.Resize(self.cut_out_size				)(_lowerCamelCase				)
										lowerCamelCase__ =self.normalize(_lowerCamelCase				).to(latents.dtype				)

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

										if use_cutouts:
																lowerCamelCase__ =spherical_dist_loss(_lowerCamelCase     ,			_lowerCamelCase				)
																lowerCamelCase__ =dists.view([num_cutouts, sample.shape[0], -1]				)
																lowerCamelCase__ =dists.sum(2				).mean(0				).sum() * clip_guidance_scale
										else:
																lowerCamelCase__ =spherical_dist_loss(_lowerCamelCase     ,			_lowerCamelCase				).mean() * clip_guidance_scale

										lowerCamelCase__ =-torch.autograd.grad(_lowerCamelCase     ,			_lowerCamelCase				)[0]

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






				@torch.no_grad()
				def __call__(				self     ,			_lowerCamelCase     ,			_lowerCamelCase = 512     ,			_lowerCamelCase = 512     ,			_lowerCamelCase = 50     ,			_lowerCamelCase = 7.5     ,			_lowerCamelCase = 1     ,			_lowerCamelCase = 0.0     ,			_lowerCamelCase = 100     ,			_lowerCamelCase = None     ,			_lowerCamelCase = 4     ,			_lowerCamelCase = True     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = "pil"     ,			_lowerCamelCase = True     ,			):
										if isinstance(_lowerCamelCase     ,			_lowerCamelCase				):
																lowerCamelCase__ =1
										elif isinstance(_lowerCamelCase     ,			_lowerCamelCase				):
																lowerCamelCase__ =len(_lowerCamelCase				)
										else:
																raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(_lowerCamelCase				)}'''				)

										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}.'''				)

										# get prompt text embeddings
										lowerCamelCase__ =self.tokenizer(
										    _lowerCamelCase     ,			padding="max_length"     ,			max_length=self.tokenizer.model_max_length     ,			truncation=_lowerCamelCase     ,			return_tensors="pt"     ,			)
										lowerCamelCase__ =self.text_encoder(text_input.input_ids.to(self.device				)				)[0]
										# duplicate text embeddings for each generation per prompt
										lowerCamelCase__ =text_embeddings.repeat_interleave(_lowerCamelCase     ,			dim=0				)

										if clip_guidance_scale > 0:
																if clip_prompt is not None:
																						lowerCamelCase__ =self.tokenizer(
																						    _lowerCamelCase     ,			padding="max_length"     ,			max_length=self.tokenizer.model_max_length     ,			truncation=_lowerCamelCase     ,			return_tensors="pt"     ,			).input_ids.to(self.device				)
																else:
																						lowerCamelCase__ =text_input.input_ids.to(self.device				)
																lowerCamelCase__ =self.clip_model.get_text_features(_lowerCamelCase				)
																lowerCamelCase__ =text_embeddings_clip / text_embeddings_clip.norm(p=2     ,			dim=-1     ,			keepdim=_lowerCamelCase				)
																# duplicate text embeddings clip for each generation per prompt
																lowerCamelCase__ =text_embeddings_clip.repeat_interleave(_lowerCamelCase     ,			dim=0				)

										# 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.
										lowerCamelCase__ =guidance_scale > 1.0
										# get unconditional embeddings for classifier free guidance
										if do_classifier_free_guidance:
																lowerCamelCase__ =text_input.input_ids.shape[-1]
																lowerCamelCase__ =self.tokenizer([""]     ,			padding="max_length"     ,			max_length=_lowerCamelCase     ,			return_tensors="pt"				)
																lowerCamelCase__ =self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]
																# duplicate unconditional embeddings for each generation per prompt
																lowerCamelCase__ =uncond_embeddings.repeat_interleave(_lowerCamelCase     ,			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
																lowerCamelCase__ =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`.
										lowerCamelCase__ =(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
										lowerCamelCase__ =text_embeddings.dtype
										if latents is None:
																if self.device.type == "mps":
																						# randn does not work reproducibly on mps
																						lowerCamelCase__ =torch.randn(_lowerCamelCase     ,			generator=_lowerCamelCase     ,			device="cpu"     ,			dtype=_lowerCamelCase				).to(
																						    self.device				)
																else:
																						lowerCamelCase__ =torch.randn(_lowerCamelCase     ,			generator=_lowerCamelCase     ,			device=self.device     ,			dtype=_lowerCamelCase				)
										else:
																if latents.shape != latents_shape:
																						raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''				)
																lowerCamelCase__ =latents.to(self.device				)

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

										self.scheduler.set_timesteps(_lowerCamelCase     ,			**_lowerCamelCase				)

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

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

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

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

																# predict the noise residual
																lowerCamelCase__ =self.unet(_lowerCamelCase     ,			_lowerCamelCase     ,			encoder_hidden_states=_lowerCamelCase				).sample

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

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

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

										# scale and decode the image latents with vae
										lowerCamelCase__ =1 / self.vae.config.scaling_factor * latents
										lowerCamelCase__ =self.vae.decode(_lowerCamelCase				).sample

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

										if output_type == "pil":
																lowerCamelCase__ =self.numpy_to_pil(_lowerCamelCase				)

										if not return_dict:
																return (image, None)

										return StableDiffusionPipelineOutput(images=_lowerCamelCase     ,			nsfw_content_detected=_lowerCamelCase				)



"""simple docstring"""


from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

a							={
    name: (getattr(transformers, name), getattr(transformers, name + 'Fast')) for name in SLOW_TO_FAST_CONVERTERS
}

a							=datasets.load_dataset('xnli', split='test+validation')

a							=0
a							=0
a							=0
a							=0





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						if spm_diff == list(reversed(__lowerCAmelCase    )    ):
												# AAA -> AA+A vs A+AA case.
												return True
						elif len(__lowerCAmelCase    ) == len(__lowerCAmelCase    ) and fast.decode(__lowerCAmelCase    ) == fast.decode(__lowerCAmelCase    ):
												# Second order OK
												# Barrich -> Barr + ich vs Bar + rich
												return True
						lowerCamelCase__ =slow.encode(slow.decode(__lowerCAmelCase    )    )
						lowerCamelCase__ =fast.encode(fast.decode(__lowerCAmelCase    )    )
						if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
												# Type 3 error.
												# Snehagatha ->
												#       Sne, h, aga, th, a
												#       Sne, ha, gat, ha
												# Encoding the wrong with sp does not even recover what spm gave us
												# It fits tokenizer however...
												return True
						return False





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[str]:




						'''simple docstring'''



						lowerCamelCase__ =fast.encode_plus(__lowerCAmelCase    )[0]
						lowerCamelCase__ =enc.offsets
						lowerCamelCase__ ,				lowerCamelCase__ =offsets[idx], offsets[idx - 1]
						if curr is not None and line[curr[0] : curr[1]] == "\u200f":
												return True
						if prev is not None and line[prev[0] : prev[1]] == "\u200f":
												return True





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Tuple:




						'''simple docstring'''



						for i, (spm_id, tok_id) in enumerate(zip(__lowerCAmelCase       ,   __lowerCAmelCase    )    ):
												if spm_id != tok_id:
																		break
						lowerCamelCase__ =i
						for i, (spm_id, tok_id) in enumerate(zip(reversed(__lowerCAmelCase    )       ,   reversed(__lowerCAmelCase    )    )    ):
												if spm_id != tok_id:
																		break
						lowerCamelCase__ =len(__lowerCAmelCase    ) - i

						lowerCamelCase__ =spm_ids[first:last]
						lowerCamelCase__ =tok_ids[first:last]

						if check_diff(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
												return True

						if check_LTR_mark(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
												return True

						if last - first > 5:
												# We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
												lowerCamelCase__ =Counter(spm_ids[first:last]    )
												lowerCamelCase__ =Counter(tok_ids[first:last]    )

												lowerCamelCase__ ={spm_ for (spm_, si) in spms.items() if toks.get(spm_       ,   0    ) == si}
												lowerCamelCase__ =3
												for i in range(last - first - min_width    ):
																		if all(spm_ids[first + i + j] in removable_tokens for j in range(__lowerCAmelCase    )    ):
																								lowerCamelCase__ =[
																								    k
																								    for k in range(last - first - min_width    )
																								    if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
																								]
																								for j in possible_matches:
																														if check_diff(spm_ids[first : first + i]       ,   tok_ids[first : first + j]       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ) and check_details(
																														    __lowerCAmelCase       ,   spm_ids[first + i : last]       ,   tok_ids[first + j : last]       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   ):
																																				return True

						print(F'''Spm: {[fast.decode([spm_ids[i]]    ) for i in range(__lowerCAmelCase       ,   __lowerCAmelCase    )]}'''    )
						try:
												print(F'''Tok: {[fast.decode([tok_ids[i]]    ) for i in range(__lowerCAmelCase       ,   __lowerCAmelCase    )]}'''    )
						except Exception:
												pass

						lowerCamelCase__ =fast.decode(spm_ids[:first]    )
						lowerCamelCase__ =fast.decode(spm_ids[last:]    )
						lowerCamelCase__ =fast.decode(spm_ids[first:last]    )
						print()
						print(__lowerCAmelCase    )
						return False





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						global perfect
						global imperfect
						global wrong
						global total

						lowerCamelCase__ =slow.encode(__lowerCAmelCase    )
						lowerCamelCase__ =fast.encode(__lowerCAmelCase    )

						lowerCamelCase__ =False
						total += 1

						if slow_ids != fast_ids:
												if check_details(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
																		lowerCamelCase__ =True
																		imperfect += 1
												else:
																		wrong += 1
						else:
												perfect += 1

						if total % 1_0000 == 0:
												print(F'''({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'''    )

						if skip_assert:
												return

						assert (
						    slow_ids == fast_ids
						), F'''line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(__lowerCAmelCase    )}\n{fast.tokenize(__lowerCAmelCase    )}'''





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[str]:




						'''simple docstring'''



						global batch_total
						for i in range(len(__lowerCAmelCase    )    ):
												# premise, all languages
												for text in dataset[i]["premise"].values():
																		test_string(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )

												# hypothesis, all languages
												for text in dataset[i]["hypothesis"]["translation"]:
																		test_string(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )


if __name__ == "__main__":
	for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
		a							=list(slow_class.max_model_input_sizes.keys())
		for checkpoint in checkpoint_names:
			a							=0
			a							=0
			a							=0
			a							=0

			print(F'''========================== Checking {name}: {checkpoint} ==========================''')
			a							=slow_class.from_pretrained(checkpoint, force_download=True)
			a							=fast_class.from_pretrained(checkpoint, force_download=True)
			test_tokenizer(slow, fast)
			print(F'''Accuracy {perfect * 100 / total:.2f}''')


