
'''simple docstring'''


import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


__SCREAMING_SNAKE_CASE						=      {0: "null", 1: "short", 2: "long", 3: "yes", 4: "no"}
__SCREAMING_SNAKE_CASE						=      set("".join(["‘", "’", "´", "`", ".", ",", "-", "\""]))
def 					__magic_name__(       UpperCAmelCase__ :       List[str]							,				UpperCAmelCase__ :       List[str]=0							,				UpperCAmelCase__ :       int=None	):
						return [" ".join(x.split(""" """	)[begin:end]	) for x in answers if len(x.split(""" """	)	) > 1]
def 					__magic_name__(       UpperCAmelCase__ :       Optional[Any]							,				UpperCAmelCase__ :       List[Any]=False	):
						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"
												UpperCamelCase_     :	Dict					     =     (
												    given_answers + get_sub_answers(UpperCAmelCase__							,				begin=1	) + get_sub_answers(UpperCAmelCase__							,				end=-1	)
												)
						UpperCamelCase_     :	Union[str, Any]					     =     []
						for answer in given_answers:
												UpperCamelCase_     :	Dict					     =     answer.replace("""_"""							,				""" """	).lower()
												UpperCamelCase_     :	int					     =     """""".join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else """ """ for c in alias	)
												answers.append(""" """.join(alias.split()	).strip()	)
						return set(UpperCAmelCase__	)
def 					__magic_name__(       UpperCAmelCase__ :       Tuple							,				UpperCAmelCase__ :       List[str]							,				UpperCAmelCase__ :       Dict=1							,				UpperCAmelCase__ :       List[str]=100	):
						UpperCamelCase_     ,UpperCamelCase_     :	Optional[Any]					     =     jax.lax.top_k(UpperCAmelCase__							,				UpperCAmelCase__	)
						UpperCamelCase_     ,UpperCamelCase_     :	Union[str, Any]					     =     jax.lax.top_k(UpperCAmelCase__							,				UpperCAmelCase__	)

						UpperCamelCase_     :	Any					     =     best_end_idx[:, None] - best_start_idx[None, :]
						UpperCamelCase_     :	List[str]					     =     jnp.logical_or(widths < 0							,				widths > max_size	)
						UpperCamelCase_     :	Optional[int]					     =     (best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
						UpperCamelCase_     :	str					     =     jnp.argmax(UpperCAmelCase__	).item()

						return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]
def 					__magic_name__(       UpperCAmelCase__ :       Optional[Any]	):
						UpperCamelCase_     :	int					     =     sample["""question"""]["""text"""]
						UpperCamelCase_     :	int					     =     sample["""document"""]["""tokens"""]["""token"""]
						UpperCamelCase_     :	Union[str, Any]					     =     sample["""document"""]["""tokens"""]["""is_html"""]
						UpperCamelCase_     :	Optional[Any]					     =     sample["""annotations"""]["""long_answer"""]
						UpperCamelCase_     :	Dict					     =     sample["""annotations"""]["""short_answers"""]

						UpperCamelCase_     :	int					     =     """ """.join([context[i] for i in range(len(UpperCAmelCase__	)	) 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",
																		}

						UpperCamelCase_     :	Optional[int]					     =     []
						for s in short_answers:
												short_targets.extend(s["""text"""]	)
						UpperCamelCase_     :	Dict					     =     list(set(UpperCAmelCase__	)	)

						UpperCamelCase_     :	List[Any]					     =     []
						for s in long_answers:
												if s["start_token"] == -1:
																		continue
												UpperCamelCase_     :	List[str]					     =     context[s["""start_token"""] : s["""end_token"""]]
												UpperCamelCase_     :	List[Any]					     =     is_html[s["""start_token"""] : s["""end_token"""]]
												UpperCamelCase_     :	int					     =     """ """.join([answer[i] for i in range(len(UpperCAmelCase__	)	) if not html[i]]	)
												if new_answer not in long_targets:
																		long_targets.append(UpperCAmelCase__	)

						UpperCamelCase_     :	List[str]					     =     """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 					__magic_name__(       ):
						UpperCamelCase_     :	Optional[int]					     =     load_from_disk("""natural-questions-validation"""	)
						UpperCamelCase_     :	Optional[int]					     =     dataset.map(UpperCAmelCase__	).remove_columns(["""annotations""", """document""", """id"""]	)
						print(UpperCAmelCase__	)

						UpperCamelCase_     :	int					     =     dataset.filter(lambda UpperCAmelCase__	: (len(x["""question"""]	) + len(x["""context"""]	)) < 4 * 4096	)
						UpperCamelCase_     :	Dict					     =     short_validation_dataset.filter(lambda UpperCAmelCase__	: x["category"] != "null"	)
						short_validation_dataset

						UpperCamelCase_     :	str					     =     """vasudevgupta/flax-bigbird-natural-questions"""
						UpperCamelCase_     :	Tuple					     =     FlaxBigBirdForNaturalQuestions.from_pretrained(UpperCAmelCase__	)
						UpperCamelCase_     :	int					     =     BigBirdTokenizerFast.from_pretrained(UpperCAmelCase__	)

						@jax.jit
						def forward(*UpperCAmelCase__ :       List[str]							,				**UpperCAmelCase__ :       Union[str, Any]	):
												UpperCamelCase_     ,UpperCamelCase_     ,UpperCamelCase_     :	Dict					     =     model(*UpperCAmelCase__							,				**UpperCAmelCase__	)
												return start_logits, end_logits, jnp.argmax(UpperCAmelCase__							,				axis=-1	)

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

												UpperCamelCase_     ,UpperCamelCase_     ,UpperCamelCase_     :	Optional[Any]					     =     forward(**UpperCAmelCase__	)

												UpperCamelCase_     :	int					     =     CATEGORY_MAPPING[category.item()]

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

												if predicted_category in ["yes", "no", "null"]:
																		UpperCamelCase_     :	Dict					     =     [predicted_category]
																		UpperCamelCase_     :	int					     =     example["""output"""] == example["""targets"""]
																		UpperCamelCase_     :	Optional[int]					     =     predicted_category != """null"""
																		return example

												UpperCamelCase_     :	Optional[int]					     =     38 if predicted_category == """short""" else 1024
												UpperCamelCase_     ,UpperCamelCase_     :	str					     =     get_best_valid_start_end_idx(
												    start_scores[0]							,				end_scores[0]							,				top_k=8							,				max_size=UpperCAmelCase__	)

												UpperCamelCase_     :	int					     =     inputs["""input_ids"""][0].tolist()
												UpperCamelCase_     :	Optional[int]					     =     [tokenizer.decode(input_ids[start_score : end_score + 1]	)]

												UpperCamelCase_     :	Dict					     =     expand_to_aliases(example["""targets"""]							,				make_sub_answers=UpperCAmelCase__	)
												UpperCamelCase_     :	Union[str, Any]					     =     expand_to_aliases(example["""output"""]	)

												# some preprocessing to both prediction and answer
												UpperCamelCase_     :	List[Any]					     =     {"""""".join(a.split()	) for a in answers}
												UpperCamelCase_     :	Dict					     =     {"""""".join(p.split()	) for p in predictions}
												UpperCamelCase_     :	List[Any]					     =     {s for s in predictions if s not in ["""``""", """''""", """`""", """'"""]}

												# if there is a common element, it's a exact match
												UpperCamelCase_     :	int					     =     len(list(answers & predictions	)	) > 0
												UpperCamelCase_     :	List[Any]					     =     predicted_category != """null""" and len(UpperCAmelCase__	) > 0

												return example

						UpperCamelCase_     :	List[Any]					     =     short_validation_dataset.map(UpperCAmelCase__	)

						UpperCamelCase_     :	int					     =     len(UpperCAmelCase__	)
						UpperCamelCase_     :	Optional[int]					     =     len(short_validation_dataset.filter(lambda UpperCAmelCase__	: x["match"] == 1	)	)
						print("""EM score:"""							,				(matched / total) * 100							,				"""%"""	)


if __name__ == "__main__":
	main()






'''simple docstring'''


from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


__SCREAMING_SNAKE_CASE						=      logging.get_logger(__name__)  # pylint: disable=invalid-name




class     __snake_case			(    _a	):
		a_          =					["safety_checker", "feature_extractor"]




		def __init__(  self					:str			,    _SCREAMING_SNAKE_CASE					:AutoencoderKL			,    _SCREAMING_SNAKE_CASE					:CLIPTextModel			,    _SCREAMING_SNAKE_CASE					:CLIPTokenizer			,    _SCREAMING_SNAKE_CASE					:UNetaDConditionModel			,    _SCREAMING_SNAKE_CASE					:Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]			,    _SCREAMING_SNAKE_CASE					:StableDiffusionSafetyChecker			,    _SCREAMING_SNAKE_CASE					:CLIPImageProcessor			,    _SCREAMING_SNAKE_CASE					:bool = True			,    )					->			int:






								'''simple docstring'''






								super().__init__()
								if hasattr(scheduler.config			,    """steps_offset""") and scheduler.config.steps_offset != 1:
														UpperCamelCase_     :	Optional[Any]					     =     (
														    F"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
														    F""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
														    """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
														    """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
														    """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
														    """ file"""
														)
														deprecate("""steps_offset!=1"""			,    """1.0.0"""			,    _SCREAMING_SNAKE_CASE			,    standard_warn=_SCREAMING_SNAKE_CASE)
														UpperCamelCase_     :	Union[str, Any]					     =     dict(scheduler.config)
														UpperCamelCase_     :	Optional[Any]					     =     1
														UpperCamelCase_     :	int					     =     FrozenDict(_SCREAMING_SNAKE_CASE)

								self.register_modules(
								    vae=_SCREAMING_SNAKE_CASE			,    text_encoder=_SCREAMING_SNAKE_CASE			,    tokenizer=_SCREAMING_SNAKE_CASE			,    unet=_SCREAMING_SNAKE_CASE			,    scheduler=_SCREAMING_SNAKE_CASE			,    safety_checker=_SCREAMING_SNAKE_CASE			,    feature_extractor=_SCREAMING_SNAKE_CASE			,    )
								self.register_to_config(requires_safety_checker=_SCREAMING_SNAKE_CASE)




		@property
		def 	_snake_case	(  self					:Tuple)					->			Dict[str, Any]:






								'''simple docstring'''






								return {k: getattr(self			,    _SCREAMING_SNAKE_CASE) for k in self.config.keys() if not k.startswith("""_""")}




		def 	_snake_case	(  self					:Optional[int]			,    _SCREAMING_SNAKE_CASE					:Optional[Union[str, int]] = "auto")					->			Union[str, Any]:






								'''simple docstring'''






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




		def 	_snake_case	(  self					:Union[str, Any])					->			Dict:






								'''simple docstring'''






								self.enable_attention_slicing(_SCREAMING_SNAKE_CASE)




		@torch.no_grad()
		def 	_snake_case	(  self					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:Union[str, List[str]]			,    _SCREAMING_SNAKE_CASE					:Union[torch.FloatTensor, PIL.Image.Image]			,    _SCREAMING_SNAKE_CASE					:Union[torch.FloatTensor, PIL.Image.Image]			,    _SCREAMING_SNAKE_CASE					:float = 0.8			,    _SCREAMING_SNAKE_CASE					:Optional[int] = 5_0			,    _SCREAMING_SNAKE_CASE					:Optional[float] = 7.5			,    _SCREAMING_SNAKE_CASE					:Optional[Union[str, List[str]]] = None			,    _SCREAMING_SNAKE_CASE					:Optional[int] = 1			,    _SCREAMING_SNAKE_CASE					:Optional[float] = 0.0			,    _SCREAMING_SNAKE_CASE					:Optional[torch.Generator] = None			,    _SCREAMING_SNAKE_CASE					:Optional[str] = "pil"			,    _SCREAMING_SNAKE_CASE					:bool = True			,    _SCREAMING_SNAKE_CASE					:Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,    _SCREAMING_SNAKE_CASE					:int = 1			,    )					->			List[str]:






								'''simple docstring'''






								return StableDiffusionInpaintPipelineLegacy(**self.components)(
								    prompt=_SCREAMING_SNAKE_CASE			,    image=_SCREAMING_SNAKE_CASE			,    mask_image=_SCREAMING_SNAKE_CASE			,    strength=_SCREAMING_SNAKE_CASE			,    num_inference_steps=_SCREAMING_SNAKE_CASE			,    guidance_scale=_SCREAMING_SNAKE_CASE			,    negative_prompt=_SCREAMING_SNAKE_CASE			,    num_images_per_prompt=_SCREAMING_SNAKE_CASE			,    eta=_SCREAMING_SNAKE_CASE			,    generator=_SCREAMING_SNAKE_CASE			,    output_type=_SCREAMING_SNAKE_CASE			,    return_dict=_SCREAMING_SNAKE_CASE			,    callback=_SCREAMING_SNAKE_CASE			,    )




		@torch.no_grad()
		def 	_snake_case	(  self					:List[str]			,    _SCREAMING_SNAKE_CASE					:Union[str, List[str]]			,    _SCREAMING_SNAKE_CASE					:Union[torch.FloatTensor, PIL.Image.Image]			,    _SCREAMING_SNAKE_CASE					:float = 0.8			,    _SCREAMING_SNAKE_CASE					:Optional[int] = 5_0			,    _SCREAMING_SNAKE_CASE					:Optional[float] = 7.5			,    _SCREAMING_SNAKE_CASE					:Optional[Union[str, List[str]]] = None			,    _SCREAMING_SNAKE_CASE					:Optional[int] = 1			,    _SCREAMING_SNAKE_CASE					:Optional[float] = 0.0			,    _SCREAMING_SNAKE_CASE					:Optional[torch.Generator] = None			,    _SCREAMING_SNAKE_CASE					:Optional[str] = "pil"			,    _SCREAMING_SNAKE_CASE					:bool = True			,    _SCREAMING_SNAKE_CASE					:Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,    _SCREAMING_SNAKE_CASE					:int = 1			,    **_SCREAMING_SNAKE_CASE					:int			,    )					->			Any:






								'''simple docstring'''






								return StableDiffusionImgaImgPipeline(**self.components)(
								    prompt=_SCREAMING_SNAKE_CASE			,    image=_SCREAMING_SNAKE_CASE			,    strength=_SCREAMING_SNAKE_CASE			,    num_inference_steps=_SCREAMING_SNAKE_CASE			,    guidance_scale=_SCREAMING_SNAKE_CASE			,    negative_prompt=_SCREAMING_SNAKE_CASE			,    num_images_per_prompt=_SCREAMING_SNAKE_CASE			,    eta=_SCREAMING_SNAKE_CASE			,    generator=_SCREAMING_SNAKE_CASE			,    output_type=_SCREAMING_SNAKE_CASE			,    return_dict=_SCREAMING_SNAKE_CASE			,    callback=_SCREAMING_SNAKE_CASE			,    callback_steps=_SCREAMING_SNAKE_CASE			,    )





		@torch.no_grad()
		def 	_snake_case	(  self					:Any			,    _SCREAMING_SNAKE_CASE					:Union[str, List[str]]			,    _SCREAMING_SNAKE_CASE					:int = 5_1_2			,    _SCREAMING_SNAKE_CASE					:int = 5_1_2			,    _SCREAMING_SNAKE_CASE					:int = 5_0			,    _SCREAMING_SNAKE_CASE					:float = 7.5			,    _SCREAMING_SNAKE_CASE					:Optional[Union[str, List[str]]] = None			,    _SCREAMING_SNAKE_CASE					:Optional[int] = 1			,    _SCREAMING_SNAKE_CASE					:float = 0.0			,    _SCREAMING_SNAKE_CASE					:Optional[torch.Generator] = None			,    _SCREAMING_SNAKE_CASE					:Optional[torch.FloatTensor] = None			,    _SCREAMING_SNAKE_CASE					:Optional[str] = "pil"			,    _SCREAMING_SNAKE_CASE					:bool = True			,    _SCREAMING_SNAKE_CASE					:Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,    _SCREAMING_SNAKE_CASE					:int = 1			,    )					->			List[Any]:






								'''simple docstring'''






								return StableDiffusionPipeline(**self.components)(
								    prompt=_SCREAMING_SNAKE_CASE			,    height=_SCREAMING_SNAKE_CASE			,    width=_SCREAMING_SNAKE_CASE			,    num_inference_steps=_SCREAMING_SNAKE_CASE			,    guidance_scale=_SCREAMING_SNAKE_CASE			,    negative_prompt=_SCREAMING_SNAKE_CASE			,    num_images_per_prompt=_SCREAMING_SNAKE_CASE			,    eta=_SCREAMING_SNAKE_CASE			,    generator=_SCREAMING_SNAKE_CASE			,    latents=_SCREAMING_SNAKE_CASE			,    output_type=_SCREAMING_SNAKE_CASE			,    return_dict=_SCREAMING_SNAKE_CASE			,    callback=_SCREAMING_SNAKE_CASE			,    callback_steps=_SCREAMING_SNAKE_CASE			,    )




