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			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__=1.0						)					-> Dict:

							'''simple docstring'''


							super().__init__()

							__lowercase			      =							cut_size
							__lowercase			      =							cut_power





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Union[str, Any]:

							'''simple docstring'''


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





def     UpperCAmelCase						(   lowercase							,      lowercase							):







				"""simple docstring"""



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





def     UpperCAmelCase						(   lowercase							,      lowercase							):







				"""simple docstring"""



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




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       )					-> Union[str, Any]:

							'''simple docstring'''


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

							__lowercase			      =							transforms.Normalize(mean=feature_extractor.image_mean				,       std=feature_extractor.image_std						)
							__lowercase			      =							(
							    feature_extractor.size
							    if isinstance(feature_extractor.size				,       lowerCAmelCase__						)
							    else feature_extractor.size['''shortest_edge''']
							)
							__lowercase			      =							MakeCutouts(self.cut_out_size						)

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





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__ = "auto"						)					-> List[Any]:

							'''simple docstring'''


							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(lowerCAmelCase__						)





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

							'''simple docstring'''


							self.enable_attention_slicing(lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[Any]:

							'''simple docstring'''


							set_requires_grad(self.vae				,       lowerCAmelCase__						)





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

							'''simple docstring'''


							set_requires_grad(self.vae				,       lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


							set_requires_grad(self.unet				,       lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Dict:

							'''simple docstring'''


							set_requires_grad(self.unet				,       lowerCAmelCase__						)





			@torch.enable_grad()
			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__=True				,       )					-> Any:

							'''simple docstring'''


							__lowercase			      =							latents.detach().requires_grad_()

							__lowercase			      =							self.scheduler.scale_model_input(lowerCAmelCase__				,       lowerCAmelCase__						)

							# predict the noise residual
							__lowercase			      =							self.unet(lowerCAmelCase__				,       lowerCAmelCase__				,       encoder_hidden_states=lowerCAmelCase__						).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(lowerCAmelCase__						)
											__lowercase			      =							pred_original_sample * (fac) + latents * (1 - fac)
							elif isinstance(self.scheduler				,       lowerCAmelCase__						):
											__lowercase			      =							self.scheduler.sigmas[index]
											__lowercase			      =							latents - sigma * noise_pred
							else:
											raise ValueError(F"scheduler type {type(self.scheduler						)} not supported"						)

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

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

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

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

							__lowercase			      =							-torch.autograd.grad(lowerCAmelCase__				,       lowerCAmelCase__						)[0]

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





			@torch.no_grad()
			def __call__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = 5_12				,       lowerCAmelCase__ = 5_12				,       lowerCAmelCase__ = 50				,       lowerCAmelCase__ = 7.5				,       lowerCAmelCase__ = 1				,       lowerCAmelCase__ = 0.0				,       lowerCAmelCase__ = 1_00				,       lowerCAmelCase__ = None				,       lowerCAmelCase__ = 4				,       lowerCAmelCase__ = True				,       lowerCAmelCase__ = None				,       lowerCAmelCase__ = None				,       lowerCAmelCase__ = "pil"				,       lowerCAmelCase__ = True				,       )					-> List[Any]:

							'''simple docstring'''


							if isinstance(lowerCAmelCase__				,       lowerCAmelCase__						):
											__lowercase			      =							1
							elif isinstance(lowerCAmelCase__				,       lowerCAmelCase__						):
											__lowercase			      =							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
							__lowercase			      =							self.tokenizer(
							    lowerCAmelCase__				,       padding='''max_length'''				,       max_length=self.tokenizer.model_max_length				,       truncation=lowerCAmelCase__				,       return_tensors='''pt'''				,       )
							__lowercase			      =							self.text_encoder(text_input.input_ids.to(self.device						)						)[0]
							# duplicate text embeddings for each generation per prompt
							__lowercase			      =							text_embeddings.repeat_interleave(lowerCAmelCase__				,       dim=0						)

							if clip_guidance_scale > 0:
											if clip_prompt is not None:
															__lowercase			      =							self.tokenizer(
															    lowerCAmelCase__				,       padding='''max_length'''				,       max_length=self.tokenizer.model_max_length				,       truncation=lowerCAmelCase__				,       return_tensors='''pt'''				,       ).input_ids.to(self.device						)
											else:
															__lowercase			      =							text_input.input_ids.to(self.device						)
											__lowercase			      =							self.clip_model.get_text_features(lowerCAmelCase__						)
											__lowercase			      =							text_embeddings_clip / text_embeddings_clip.norm(p=2				,       dim=-1				,       keepdim=lowerCAmelCase__						)
											# duplicate text embeddings clip for each generation per prompt
											__lowercase			      =							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.
							__lowercase			      =							guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
											__lowercase			      =							text_input.input_ids.shape[-1]
											__lowercase			      =							self.tokenizer(['''''']				,       padding='''max_length'''				,       max_length=lowerCAmelCase__				,       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(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
											__lowercase			      =							torch.cat([uncond_embeddings, text_embeddings]						)

							# get the initial random noise unless the user supplied it

							# Unlike in other pipelines, latents need to be generated in the target device
							# for 1-to-1 results reproducibility with the CompVis implementation.
							# However this currently doesn't work in `mps`.
							__lowercase			      =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
							__lowercase			      =							text_embeddings.dtype
							if latents is None:
											if self.device.type == "mps":
															# randn does not work reproducibly on mps
															__lowercase			      =							torch.randn(lowerCAmelCase__				,       generator=lowerCAmelCase__				,       device='''cpu'''				,       dtype=lowerCAmelCase__						).to(
															    self.device						)
											else:
															__lowercase			      =							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}"						)
											__lowercase			      =							latents.to(self.device						)

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

							# scale the initial noise by the standard deviation required by the scheduler
							__lowercase			      =							latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							__lowercase			      =							'''eta''' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
							__lowercase			      =							{}
							if accepts_eta:
											__lowercase			      =							eta

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

							for i, t in enumerate(self.progress_bar(lowerCAmelCase__						)						):
											# 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(lowerCAmelCase__				,       lowerCAmelCase__						)

											# predict the noise residual
											__lowercase			      =							self.unet(lowerCAmelCase__				,       lowerCAmelCase__				,       encoder_hidden_states=lowerCAmelCase__						).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(
															    lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       )

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

							# scale and decode the image latents with vae
							__lowercase			      =							1 / self.vae.config.scaling_factor * latents
							__lowercase			      =							self.vae.decode(lowerCAmelCase__						).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(lowerCAmelCase__						)

							if not return_dict:
											return (image, None)

							return StableDiffusionPipelineOutput(images=lowerCAmelCase__				,       nsfw_content_detected=lowerCAmelCase__						)
import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


__a  :					List[Any]          =  {0: """null""", 1: """short""", 2: """long""", 3: """yes""", 4: """no"""}
__a  :					List[Any]          =  set("""""".join(["""‘""", """’""", """´""", """`""", """.""", """,""", """-""", """\""""]))





def     UpperCAmelCase						(   lowercase							,      lowercase=0							,      lowercase=None							):







				"""simple docstring"""



				return [" ".join(x.split(''' '''							)[begin:end]							) for x in answers if len(x.split(''' '''							)							) > 1]





def     UpperCAmelCase						(   lowercase							,      lowercase=False							):







				"""simple docstring"""



				if make_sub_answers:
								# if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
								# *e.g.* if the correct answer contains a prefix such as "the", or "a"
								__lowercase			      =							(
								    given_answers + get_sub_answers(lowercase							,      begin=1							) + get_sub_answers(lowercase							,      end=-1							)
								)
				__lowercase			      =							[]
				for answer in given_answers:
								__lowercase			      =							answer.replace('''_'''							,      ''' '''							).lower()
								__lowercase			      =							''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias							)
								answers.append(''' '''.join(alias.split()							).strip()							)
				return set(lowercase							)





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase=1							,      lowercase=100							):







				"""simple docstring"""



				__lowercase      ,					__lowercase			      =							jax.lax.top_k(lowercase							,      lowercase							)
				__lowercase      ,					__lowercase			      =							jax.lax.top_k(lowercase							,      lowercase							)

				__lowercase			      =							best_end_idx[:, None] - best_start_idx[None, :]
				__lowercase			      =							jnp.logical_or(widths < 0							,      widths > max_size							)
				__lowercase			      =							(best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
				__lowercase			      =							jnp.argmax(lowercase							).item()

				return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				__lowercase			      =							sample['''question''']['''text''']
				__lowercase			      =							sample['''document''']['''tokens''']['''token''']
				__lowercase			      =							sample['''document''']['''tokens''']['''is_html''']
				__lowercase			      =							sample['''annotations''']['''long_answer''']
				__lowercase			      =							sample['''annotations''']['''short_answers''']

				__lowercase			      =							''' '''.join([context[i] for i in range(len(lowercase							)							) if not is_html[i]]							)

				# 0 - No ; 1 - Yes
				for answer in sample["annotations"]["yes_no_answer"]:
								if answer == 0 or answer == 1:
												return {
												    "question": question,
												    "context": context_string,
												    "short": [],
												    "long": [],
												    "category": "no" if answer == 0 else "yes",
												}

				__lowercase			      =							[]
				for s in short_answers:
								short_targets.extend(s['''text''']							)
				__lowercase			      =							list(set(lowercase							)							)

				__lowercase			      =							[]
				for s in long_answers:
								if s["start_token"] == -1:
												continue
								__lowercase			      =							context[s['''start_token'''] : s['''end_token''']]
								__lowercase			      =							is_html[s['''start_token'''] : s['''end_token''']]
								__lowercase			      =							''' '''.join([answer[i] for i in range(len(lowercase							)							) if not html[i]]							)
								if new_answer not in long_targets:
												long_targets.append(lowercase							)

				__lowercase			      =							'''long_short''' if len(short_targets + long_targets							) > 0 else '''null'''

				return {
				    "question": question,
				    "context": context_string,
				    "short": short_targets,
				    "long": long_targets,
				    "category": category,
				}





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							load_from_disk('''natural-questions-validation'''							)
				__lowercase			      =							dataset.map(lowercase							).remove_columns(['''annotations''', '''document''', '''id''']							)
				print(lowercase							)

				__lowercase			      =							dataset.filter(lambda lowercase							: (len(x['''question''']							) + len(x['''context''']							)) < 4 * 4096							)
				__lowercase			      =							short_validation_dataset.filter(lambda lowercase							: x["category"] != "null"							)
				short_validation_dataset

				__lowercase			      =							'''vasudevgupta/flax-bigbird-natural-questions'''
				__lowercase			      =							FlaxBigBirdForNaturalQuestions.from_pretrained(lowercase							)
				__lowercase			      =							BigBirdTokenizerFast.from_pretrained(lowercase							)

				@jax.jit
				def forward(*lowercase							,      **lowercase							):
								__lowercase      ,					__lowercase      ,					__lowercase			      =							model(*lowercase							,      **lowercase							)
								return start_logits, end_logits, jnp.argmax(lowercase							,      axis=-1							)

				def evaluate(lowercase							):
								# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
								__lowercase			      =							tokenizer(
								    example['''question''']							,      example['''context''']							,      return_tensors='''np'''							,      max_length=4096							,      padding='''max_length'''							,      truncation=lowercase							,      )

								__lowercase      ,					__lowercase      ,					__lowercase			      =							forward(**lowercase							)

								__lowercase			      =							CATEGORY_MAPPING[category.item()]

								__lowercase			      =							example['''long'''] + example['''short''']
								if example["category"] in ["yes", "no", "null"]:
												__lowercase			      =							[example['''category''']]
								__lowercase			      =							example['''category'''] != '''null'''
								# Now target can be: "yes", "no", "null", "list of long & short answers"

								if predicted_category in ["yes", "no", "null"]:
												__lowercase			      =							[predicted_category]
												__lowercase			      =							example['''output'''] == example['''targets''']
												__lowercase			      =							predicted_category != '''null'''
												return example

								__lowercase			      =							38 if predicted_category == '''short''' else 1024
								__lowercase      ,					__lowercase			      =							get_best_valid_start_end_idx(
								    start_scores[0]							,      end_scores[0]							,      top_k=8							,      max_size=lowercase							)

								__lowercase			      =							inputs['''input_ids'''][0].tolist()
								__lowercase			      =							[tokenizer.decode(input_ids[start_score : end_score + 1]							)]

								__lowercase			      =							expand_to_aliases(example['''targets''']							,      make_sub_answers=lowercase							)
								__lowercase			      =							expand_to_aliases(example['''output''']							)

								# some preprocessing to both prediction and answer
								__lowercase			      =							{''''''.join(a.split()							) for a in answers}
								__lowercase			      =							{''''''.join(p.split()							) for p in predictions}
								__lowercase			      =							{s for s in predictions if s not in ['''``''', '''\'\'''', '''`''', '''\'''']}

								# if there is a common element, it's a exact match
								__lowercase			      =							len(list(answers & predictions							)							) > 0
								__lowercase			      =							predicted_category != '''null''' and len(lowercase							) > 0

								return example

				__lowercase			      =							short_validation_dataset.map(lowercase							)

				__lowercase			      =							len(lowercase							)
				__lowercase			      =							len(short_validation_dataset.filter(lambda lowercase							: x["match"] == 1							)							)
				print('''EM score:'''							,      (matched / total) * 100							,      '''%'''							)


if __name__ == "__main__":
		main()