from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__ = 0.93				,       lowerCAmelCase__ = True				,       )					-> Any:

							'''simple docstring'''


							__lowercase			      =							mixing_coeff
							__lowercase			      =							leapfrog_steps

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

							__lowercase			      =							2 ** (len(self.vae.config.block_out_channels						) - 1)
							__lowercase			      =							VaeImageProcessor(vae_scale_factor=self.vae_scale_factor						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = None				,       lowerCAmelCase__ = False						)					-> List[Any]:

							'''simple docstring'''


							__lowercase			      =							self.tokenizer(
							    lowerCAmelCase__				,       padding='''max_length'''				,       max_length=self.tokenizer.model_max_length				,       truncation=lowerCAmelCase__				,       return_tensors='''pt'''				,       )

							__lowercase			      =							self.text_encoder(text_inputs.input_ids.to(self.device						)						).last_hidden_state

							__lowercase			      =							prompt_embeds.to(dtype=self.text_encoder.dtype				,       device=self.device						)

							if do_classifier_free_guidance:
											__lowercase			      =							'''''' if negative_prompt is None else negative_prompt

											__lowercase			      =							self.tokenizer(
											    lowerCAmelCase__				,       padding='''max_length'''				,       max_length=self.tokenizer.model_max_length				,       truncation=lowerCAmelCase__				,       return_tensors='''pt'''				,       )

											__lowercase			      =							self.text_encoder(uncond_input.input_ids.to(self.device						)						).last_hidden_state

											__lowercase			      =							torch.cat([negative_prompt_embeds, prompt_embeds]						)

							return prompt_embeds





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Dict:

							'''simple docstring'''


							__lowercase			      =							self.mixing_coeff * x + (1 - self.mixing_coeff) * y
							__lowercase			      =							self.mixing_coeff * y + (1 - self.mixing_coeff) * x

							return [x, y]





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Dict:

							'''simple docstring'''


							__lowercase			      =							(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
							__lowercase			      =							(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

							return [x, y]





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

							'''simple docstring'''


							__lowercase			      =							int(lowerCAmelCase__						)

							__lowercase			      =							self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

							return alpha_prod, 1 - alpha_prod





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       )					-> int:

							'''simple docstring'''


							__lowercase			      =							timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

							__lowercase      ,					__lowercase			      =							self._get_alpha_and_beta(lowerCAmelCase__						)
							__lowercase      ,					__lowercase			      =							self._get_alpha_and_beta(lowerCAmelCase__						)

							__lowercase			      =							(alpha_prod_t_prev / alpha_prod_t) ** 0.5
							__lowercase			      =							-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

							__lowercase			      =							(base - b_t * model_output) / a_t

							return model_input, next_model_input.to(base.dtype						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       )					-> Dict:

							'''simple docstring'''


							__lowercase			      =							timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

							__lowercase      ,					__lowercase			      =							self._get_alpha_and_beta(lowerCAmelCase__						)
							__lowercase      ,					__lowercase			      =							self._get_alpha_and_beta(lowerCAmelCase__						)

							__lowercase			      =							(alpha_prod_t_prev / alpha_prod_t) ** 0.5
							__lowercase			      =							-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
							__lowercase			      =							a_t * base + b_t * model_output

							return model_input, next_model_input.to(base.dtype						)





			@torch.no_grad()
			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> str:

							'''simple docstring'''


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





			@torch.no_grad()
			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__ = None				,       )					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							guidance_scale > 1.0

							__lowercase			      =							image.to(device=self.device				,       dtype=text_embeds.dtype						)
							__lowercase			      =							self.vae.encode(lowerCAmelCase__						).latent_dist.sample(lowerCAmelCase__						)

							__lowercase			      =							self.vae.config.scaling_factor * latent

							__lowercase			      =							[latent.clone(), latent.clone()]

							for i, t in tqdm(enumerate(lowerCAmelCase__						)				,       total=len(lowerCAmelCase__						)						):
											__lowercase			      =							self.noise_mixing_layer(x=coupled_latents[0]				,       y=coupled_latents[1]						)

											# j - model_input index, k - base index
											for j in range(2						):
															__lowercase			      =							j ^ 1

															if self.leapfrog_steps:
																			if i % 2 == 0:
																							__lowercase      ,					__lowercase			      =							j, k

															__lowercase			      =							coupled_latents[j]
															__lowercase			      =							coupled_latents[k]

															__lowercase			      =							torch.cat([model_input] * 2						) if do_classifier_free_guidance else model_input

															__lowercase			      =							self.unet(lowerCAmelCase__				,       lowerCAmelCase__				,       encoder_hidden_states=lowerCAmelCase__						).sample

															if do_classifier_free_guidance:
																			__lowercase      ,					__lowercase			      =							noise_pred.chunk(2						)
																			__lowercase			      =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															__lowercase      ,					__lowercase			      =							self.noise_step(
															    base=lowerCAmelCase__				,       model_input=lowerCAmelCase__				,       model_output=lowerCAmelCase__				,       timestep=lowerCAmelCase__				,       )

															__lowercase			      =							model_input

							return coupled_latents





			@torch.no_grad()
			def __call__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__ = 3.0				,       lowerCAmelCase__ = 50				,       lowerCAmelCase__ = 0.8				,       lowerCAmelCase__ = None				,       lowerCAmelCase__ = None				,       lowerCAmelCase__ = "pil"				,       )					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							guidance_scale > 1.0

							__lowercase			      =							self.image_processor.preprocess(lowerCAmelCase__						)

							__lowercase			      =							self._encode_prompt(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)
							__lowercase			      =							self._encode_prompt(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)

							self.scheduler.set_timesteps(lowerCAmelCase__				,       self.device						)

							__lowercase			      =							num_inference_steps - int(num_inference_steps * strength						)
							__lowercase			      =							self.scheduler.timesteps[t_limit:]
							__lowercase			      =							fwd_timesteps.flip(0						)

							__lowercase			      =							self.prepare_latents(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)

							for i, t in tqdm(enumerate(lowerCAmelCase__						)				,       total=len(lowerCAmelCase__						)						):
											# j - model_input index, k - base index
											for k in range(2						):
															__lowercase			      =							k ^ 1

															if self.leapfrog_steps:
																			if i % 2 == 1:
																							__lowercase      ,					__lowercase			      =							j, k

															__lowercase			      =							coupled_latents[j]
															__lowercase			      =							coupled_latents[k]

															__lowercase			      =							torch.cat([model_input] * 2						) if do_classifier_free_guidance else model_input

															__lowercase			      =							self.unet(lowerCAmelCase__				,       lowerCAmelCase__				,       encoder_hidden_states=lowerCAmelCase__						).sample

															if do_classifier_free_guidance:
																			__lowercase      ,					__lowercase			      =							noise_pred.chunk(2						)
																			__lowercase			      =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															__lowercase      ,					__lowercase			      =							self.denoise_step(
															    base=lowerCAmelCase__				,       model_input=lowerCAmelCase__				,       model_output=lowerCAmelCase__				,       timestep=lowerCAmelCase__				,       )

															__lowercase			      =							model_input

											__lowercase			      =							self.denoise_mixing_layer(x=coupled_latents[0]				,       y=coupled_latents[1]						)

							# either one is fine
							__lowercase			      =							coupled_latents[0]

							if output_type not in ["latent", "pt", "np", "pil"]:
											__lowercase			      =							(
											    F"the output_type {output_type} is outdated. Please make sure to set it to one of these instead: "
											    '''`pil`, `np`, `pt`, `latent`'''
											)
											deprecate('''Unsupported output_type'''				,       '''1.0.0'''				,       lowerCAmelCase__				,       standard_warn=lowerCAmelCase__						)
											__lowercase			      =							'''np'''

							if output_type == "latent":
											__lowercase			      =							final_latent
							else:
											__lowercase			      =							self.decode_latents(lowerCAmelCase__						)
											__lowercase			      =							self.image_processor.postprocess(lowerCAmelCase__				,       output_type=lowerCAmelCase__						)

							return image
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()