import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase = False							,      lowercase = 1							,      lowercase = 1							,      lowercase = 10000							,      ):







				"""simple docstring"""



				assert len(timesteps.shape							) == 1, "Timesteps should be a 1d-array"

				__lowercase			      =							embedding_dim // 2
				__lowercase			      =							-math.log(_lowerCAmelCase							) * torch.arange(
				    start=0							,      end=_lowerCAmelCase							,      dtype=torch.floataa							,      device=timesteps.device							)
				__lowercase			      =							exponent / (half_dim - downscale_freq_shift)

				__lowercase			      =							torch.exp(_lowerCAmelCase							)
				__lowercase			      =							timesteps[:, None].float() * emb[None, :]

				# scale embeddings
				__lowercase			      =							scale * emb

				# concat sine and cosine embeddings
				__lowercase			      =							torch.cat([torch.sin(_lowerCAmelCase							), torch.cos(_lowerCAmelCase							)]							,      dim=-1							)

				# flip sine and cosine embeddings
				if flip_sin_to_cos:
								__lowercase			      =							torch.cat([emb[:, half_dim:], emb[:, :half_dim]]							,      dim=-1							)

				# zero pad
				if embedding_dim % 2 == 1:
								__lowercase			      =							torch.nn.functional.pad(_lowerCAmelCase							,      (0, 1, 0, 0)							)
				return emb





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase=False							,      lowercase=0							):







				"""simple docstring"""



				__lowercase			      =							np.arange(_lowerCAmelCase							,      dtype=np.floataa							)
				__lowercase			      =							np.arange(_lowerCAmelCase							,      dtype=np.floataa							)
				__lowercase			      =							np.meshgrid(_lowerCAmelCase							,      _lowerCAmelCase							)  # here w goes first
				__lowercase			      =							np.stack(_lowerCAmelCase							,      axis=0							)

				__lowercase			      =							grid.reshape([2, 1, grid_size, grid_size]							)
				__lowercase			      =							get_ad_sincos_pos_embed_from_grid(_lowerCAmelCase							,      _lowerCAmelCase							)
				if cls_token and extra_tokens > 0:
								__lowercase			      =							np.concatenate([np.zeros([extra_tokens, embed_dim]							), pos_embed]							,      axis=0							)
				return pos_embed





def     UpperCAmelCase						(   lowercase							,      lowercase							):







				"""simple docstring"""



				if embed_dim % 2 != 0:
								raise ValueError('''embed_dim must be divisible by 2'''							)

				# use half of dimensions to encode grid_h
				__lowercase			      =							get_ad_sincos_pos_embed_from_grid(embed_dim // 2							,      grid[0]							)  # (H*W, D/2)
				__lowercase			      =							get_ad_sincos_pos_embed_from_grid(embed_dim // 2							,      grid[1]							)  # (H*W, D/2)

				__lowercase			      =							np.concatenate([emb_h, emb_w]							,      axis=1							)  # (H*W, D)
				return emb





def     UpperCAmelCase						(   lowercase							,      lowercase							):







				"""simple docstring"""



				if embed_dim % 2 != 0:
								raise ValueError('''embed_dim must be divisible by 2'''							)

				__lowercase			      =							np.arange(embed_dim // 2							,      dtype=np.floataa							)
				omega /= embed_dim / 2.0
				__lowercase			      =							1.0 / 10000**omega  # (D/2,)

				__lowercase			      =							pos.reshape(-1							)  # (M,)
				__lowercase			      =							np.einsum('''m,d->md'''							,      _lowerCAmelCase							,      _lowerCAmelCase							)  # (M, D/2), outer product

				__lowercase			      =							np.sin(_lowerCAmelCase							)  # (M, D/2)
				__lowercase			      =							np.cos(_lowerCAmelCase							)  # (M, D/2)

				__lowercase			      =							np.concatenate([emb_sin, emb_cos]							,      axis=1							)  # (M, D)
				return emb




class        _UpperCamelCase				(						nn.Module			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__=2_24				,       lowerCAmelCase__=2_24				,       lowerCAmelCase__=16				,       lowerCAmelCase__=3				,       lowerCAmelCase__=7_68				,       lowerCAmelCase__=False				,       lowerCAmelCase__=True				,       lowerCAmelCase__=True				,       )					-> Tuple:

							'''simple docstring'''


							super().__init__()

							__lowercase			      =							(height // patch_size) * (width // patch_size)
							__lowercase			      =							flatten
							__lowercase			      =							layer_norm

							__lowercase			      =							nn.Convad(
							    snake_case__				,       snake_case__				,       kernel_size=(patch_size, patch_size)				,       stride=snake_case__				,       bias=snake_case__						)
							if layer_norm:
											__lowercase			      =							nn.LayerNorm(snake_case__				,       elementwise_affine=snake_case__				,       eps=1E-6						)
							else:
											__lowercase			      =							None

							__lowercase			      =							get_ad_sincos_pos_embed(snake_case__				,       int(num_patches**0.5						)						)
							self.register_buffer('''pos_embed'''				,       torch.from_numpy(snake_case__						).float().unsqueeze(0						)				,       persistent=snake_case__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> str:

							'''simple docstring'''


							__lowercase			      =							self.proj(snake_case__						)
							if self.flatten:
											__lowercase			      =							latent.flatten(2						).transpose(1				,       2						)  # BCHW -> BNC
							if self.layer_norm:
											__lowercase			      =							self.norm(snake_case__						)
							return latent + self.pos_embed




class        _UpperCamelCase				(						nn.Module			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__ = "silu"				,       lowerCAmelCase__ = None				,       lowerCAmelCase__ = None				,       lowerCAmelCase__=None				,       )					-> List[str]:

							'''simple docstring'''


							super().__init__()

							__lowercase			      =							nn.Linear(snake_case__				,       snake_case__						)

							if cond_proj_dim is not None:
											__lowercase			      =							nn.Linear(snake_case__				,       snake_case__				,       bias=snake_case__						)
							else:
											__lowercase			      =							None

							__lowercase			      =							get_activation(snake_case__						)

							if out_dim is not None:
											__lowercase			      =							out_dim
							else:
											__lowercase			      =							time_embed_dim
							__lowercase			      =							nn.Linear(snake_case__				,       snake_case__						)

							if post_act_fn is None:
											__lowercase			      =							None
							else:
											__lowercase			      =							get_activation(snake_case__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__=None						)					-> Tuple:

							'''simple docstring'''


							if condition is not None:
											__lowercase			      =							sample + self.cond_proj(snake_case__						)
							__lowercase			      =							self.linear_a(snake_case__						)

							if self.act is not None:
											__lowercase			      =							self.act(snake_case__						)

							__lowercase			      =							self.linear_a(snake_case__						)

							if self.post_act is not None:
											__lowercase			      =							self.post_act(snake_case__						)
							return sample




class        _UpperCamelCase				(						nn.Module			):


			"""simple docstring"""






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

							'''simple docstring'''


							super().__init__()
							__lowercase			      =							num_channels
							__lowercase			      =							flip_sin_to_cos
							__lowercase			      =							downscale_freq_shift





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

							'''simple docstring'''


							__lowercase			      =							get_timestep_embedding(
							    snake_case__				,       self.num_channels				,       flip_sin_to_cos=self.flip_sin_to_cos				,       downscale_freq_shift=self.downscale_freq_shift				,       )
							return t_emb




class        _UpperCamelCase				(						nn.Module			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__ = 2_56				,       lowerCAmelCase__ = 1.0				,       lowerCAmelCase__=True				,       lowerCAmelCase__=True				,       lowerCAmelCase__=False						)					-> Union[str, Any]:

							'''simple docstring'''


							super().__init__()
							__lowercase			      =							nn.Parameter(torch.randn(snake_case__						) * scale				,       requires_grad=snake_case__						)
							__lowercase			      =							log
							__lowercase			      =							flip_sin_to_cos

							if set_W_to_weight:
											# to delete later
											__lowercase			      =							nn.Parameter(torch.randn(snake_case__						) * scale				,       requires_grad=snake_case__						)

											__lowercase			      =							self.W





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

							'''simple docstring'''


							if self.log:
											__lowercase			      =							torch.log(snake_case__						)

							__lowercase			      =							x[:, None] * self.weight[None, :] * 2 * np.pi

							if self.flip_sin_to_cos:
											__lowercase			      =							torch.cat([torch.cos(snake_case__						), torch.sin(snake_case__						)]				,       dim=-1						)
							else:
											__lowercase			      =							torch.cat([torch.sin(snake_case__						), torch.cos(snake_case__						)]				,       dim=-1						)
							return out




class        _UpperCamelCase				(						nn.Module			):


			"""simple docstring"""






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

							'''simple docstring'''


							super().__init__()

							__lowercase			      =							height
							__lowercase			      =							width
							__lowercase			      =							num_embed
							__lowercase			      =							embed_dim

							__lowercase			      =							nn.Embedding(self.num_embed				,       snake_case__						)
							__lowercase			      =							nn.Embedding(self.height				,       snake_case__						)
							__lowercase			      =							nn.Embedding(self.width				,       snake_case__						)





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

							'''simple docstring'''


							__lowercase			      =							self.emb(snake_case__						)

							__lowercase			      =							self.height_emb(torch.arange(self.height				,       device=index.device						).view(1				,       self.height						)						)

							# 1 x H x D -> 1 x H x 1 x D
							__lowercase			      =							height_emb.unsqueeze(2						)

							__lowercase			      =							self.width_emb(torch.arange(self.width				,       device=index.device						).view(1				,       self.width						)						)

							# 1 x W x D -> 1 x 1 x W x D
							__lowercase			      =							width_emb.unsqueeze(1						)

							__lowercase			      =							height_emb + width_emb

							# 1 x H x W x D -> 1 x L xD
							__lowercase			      =							pos_emb.view(1				,       self.height * self.width				,       -1						)

							__lowercase			      =							emb + pos_emb[:, : emb.shape[1], :]

							return emb




class        _UpperCamelCase				(						nn.Module			):


			"""simple docstring"""






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

							'''simple docstring'''


							super().__init__()
							__lowercase			      =							dropout_prob > 0
							__lowercase			      =							nn.Embedding(num_classes + use_cfg_embedding				,       snake_case__						)
							__lowercase			      =							num_classes
							__lowercase			      =							dropout_prob





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__=None						)					-> int:

							'''simple docstring'''


							if force_drop_ids is None:
											__lowercase			      =							torch.rand(labels.shape[0]				,       device=labels.device						) < self.dropout_prob
							else:
											__lowercase			      =							torch.tensor(force_drop_ids == 1						)
							__lowercase			      =							torch.where(snake_case__				,       self.num_classes				,       snake_case__						)
							return labels





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__=None						)					-> Union[str, Any]:

							'''simple docstring'''


							__lowercase			      =							self.dropout_prob > 0
							if (self.training and use_dropout) or (force_drop_ids is not None):
											__lowercase			      =							self.token_drop(snake_case__				,       snake_case__						)
							__lowercase			      =							self.embedding_table(snake_case__						)
							return embeddings




class        _UpperCamelCase				(						nn.Module			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__ = 10_24				,       lowerCAmelCase__ = 7_68				,       lowerCAmelCase__ = 7_68				,       lowerCAmelCase__ = 10				,       )					-> Optional[int]:

							'''simple docstring'''


							super().__init__()

							__lowercase			      =							num_image_text_embeds
							__lowercase			      =							nn.Linear(snake_case__				,       self.num_image_text_embeds * cross_attention_dim						)
							__lowercase			      =							nn.Linear(snake_case__				,       snake_case__						)





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

							'''simple docstring'''


							__lowercase			      =							text_embeds.shape[0]

							# image
							__lowercase			      =							self.image_embeds(snake_case__						)
							__lowercase			      =							image_text_embeds.reshape(snake_case__				,       self.num_image_text_embeds				,       -1						)

							# text
							__lowercase			      =							self.text_proj(snake_case__						)

							return torch.cat([image_text_embeds, text_embeds]				,       dim=1						)




class        _UpperCamelCase				(						nn.Module			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__ = 7_68				,       lowerCAmelCase__ = 7_68				,       lowerCAmelCase__ = 32				,       )					-> Tuple:

							'''simple docstring'''


							super().__init__()

							__lowercase			      =							num_image_text_embeds
							__lowercase			      =							nn.Linear(snake_case__				,       self.num_image_text_embeds * cross_attention_dim						)
							__lowercase			      =							nn.LayerNorm(snake_case__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Any:

							'''simple docstring'''


							__lowercase			      =							image_embeds.shape[0]

							# image
							__lowercase			      =							self.image_embeds(snake_case__						)
							__lowercase			      =							image_embeds.reshape(snake_case__				,       self.num_image_text_embeds				,       -1						)
							__lowercase			      =							self.norm(snake_case__						)
							return image_embeds




class        _UpperCamelCase				(						nn.Module			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__=0.1						)					-> Optional[int]:

							'''simple docstring'''


							super().__init__()

							__lowercase			      =							Timesteps(num_channels=2_56				,       flip_sin_to_cos=snake_case__				,       downscale_freq_shift=1						)
							__lowercase			      =							TimestepEmbedding(in_channels=2_56				,       time_embed_dim=snake_case__						)
							__lowercase			      =							LabelEmbedding(snake_case__				,       snake_case__				,       snake_case__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__=None						)					-> Any:

							'''simple docstring'''


							__lowercase			      =							self.time_proj(snake_case__						)
							__lowercase			      =							self.timestep_embedder(timesteps_proj.to(dtype=snake_case__						)						)  # (N, D)

							__lowercase			      =							self.class_embedder(snake_case__						)  # (N, D)

							__lowercase			      =							timesteps_emb + class_labels  # (N, D)

							return conditioning




class        _UpperCamelCase				(						nn.Module			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__ = 64						)					-> str:

							'''simple docstring'''


							super().__init__()
							__lowercase			      =							nn.LayerNorm(snake_case__						)
							__lowercase			      =							AttentionPooling(snake_case__				,       snake_case__						)
							__lowercase			      =							nn.Linear(snake_case__				,       snake_case__						)
							__lowercase			      =							nn.LayerNorm(snake_case__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Dict:

							'''simple docstring'''


							__lowercase			      =							self.norma(snake_case__						)
							__lowercase			      =							self.pool(snake_case__						)
							__lowercase			      =							self.proj(snake_case__						)
							__lowercase			      =							self.norma(snake_case__						)
							return hidden_states




class        _UpperCamelCase				(						nn.Module			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__ = 7_68				,       lowerCAmelCase__ = 7_68				,       lowerCAmelCase__ = 15_36						)					-> List[str]:

							'''simple docstring'''


							super().__init__()
							__lowercase			      =							nn.Linear(snake_case__				,       snake_case__						)
							__lowercase			      =							nn.LayerNorm(snake_case__						)
							__lowercase			      =							nn.Linear(snake_case__				,       snake_case__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Optional[int]:

							'''simple docstring'''


							__lowercase			      =							self.text_proj(snake_case__						)
							__lowercase			      =							self.text_norm(snake_case__						)

							# image
							__lowercase			      =							self.image_proj(snake_case__						)

							return time_image_embeds + time_text_embeds




class        _UpperCamelCase				(						nn.Module			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__ = 7_68				,       lowerCAmelCase__ = 15_36						)					-> Optional[Any]:

							'''simple docstring'''


							super().__init__()
							__lowercase			      =							nn.Linear(snake_case__				,       snake_case__						)
							__lowercase			      =							nn.LayerNorm(snake_case__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> int:

							'''simple docstring'''


							__lowercase			      =							self.image_proj(snake_case__						)
							__lowercase			      =							self.image_norm(snake_case__						)
							return time_image_embeds




class        _UpperCamelCase				(						nn.Module			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__ = 7_68				,       lowerCAmelCase__ = 15_36						)					-> Union[str, Any]:

							'''simple docstring'''


							super().__init__()
							__lowercase			      =							nn.Linear(snake_case__				,       snake_case__						)
							__lowercase			      =							nn.LayerNorm(snake_case__						)
							__lowercase			      =							nn.Sequential(
							    nn.Convad(3				,       16				,       3				,       padding=1						)				,       nn.SiLU()				,       nn.Convad(16				,       16				,       3				,       padding=1						)				,       nn.SiLU()				,       nn.Convad(16				,       32				,       3				,       padding=1				,       stride=2						)				,       nn.SiLU()				,       nn.Convad(32				,       32				,       3				,       padding=1						)				,       nn.SiLU()				,       nn.Convad(32				,       96				,       3				,       padding=1				,       stride=2						)				,       nn.SiLU()				,       nn.Convad(96				,       96				,       3				,       padding=1						)				,       nn.SiLU()				,       nn.Convad(96				,       2_56				,       3				,       padding=1				,       stride=2						)				,       nn.SiLU()				,       nn.Convad(2_56				,       4				,       3				,       padding=1						)				,       )





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

							'''simple docstring'''


							__lowercase			      =							self.image_proj(snake_case__						)
							__lowercase			      =							self.image_norm(snake_case__						)
							__lowercase			      =							self.input_hint_block(snake_case__						)
							return time_image_embeds, hint




class        _UpperCamelCase				(						nn.Module			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__=None						)					-> List[str]:

							'''simple docstring'''


							super().__init__()
							__lowercase			      =							dtype
							__lowercase			      =							nn.Parameter(torch.randn(1				,       snake_case__						) / embed_dim**0.5						)
							__lowercase			      =							nn.Linear(snake_case__				,       snake_case__				,       dtype=self.dtype						)
							__lowercase			      =							nn.Linear(snake_case__				,       snake_case__				,       dtype=self.dtype						)
							__lowercase			      =							nn.Linear(snake_case__				,       snake_case__				,       dtype=self.dtype						)
							__lowercase			      =							num_heads
							__lowercase			      =							embed_dim // self.num_heads





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Optional[int]:

							'''simple docstring'''


							__lowercase      ,					__lowercase      ,					__lowercase			      =							x.size()

							def shape(lowerCAmelCase__						):
											# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
											__lowercase			      =							x.view(snake_case__				,       -1				,       self.num_heads				,       self.dim_per_head						)
											# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
											__lowercase			      =							x.transpose(1				,       2						)
											# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
											__lowercase			      =							x.reshape(bs * self.num_heads				,       -1				,       self.dim_per_head						)
											# (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
											__lowercase			      =							x.transpose(1				,       2						)
											return x

							__lowercase			      =							x.mean(dim=1				,       keepdim=snake_case__						) + self.positional_embedding.to(x.dtype						)
							__lowercase			      =							torch.cat([class_token, x]				,       dim=1						)  # (bs, length+1, width)

							# (bs*n_heads, class_token_length, dim_per_head)
							__lowercase			      =							shape(self.q_proj(snake_case__						)						)
							# (bs*n_heads, length+class_token_length, dim_per_head)
							__lowercase			      =							shape(self.k_proj(snake_case__						)						)
							__lowercase			      =							shape(self.v_proj(snake_case__						)						)

							# (bs*n_heads, class_token_length, length+class_token_length):
							__lowercase			      =							1 / math.sqrt(math.sqrt(self.dim_per_head						)						)
							__lowercase			      =							torch.einsum('''bct,bcs->bts'''				,       q * scale				,       k * scale						)  # More stable with f16 than dividing afterwards
							__lowercase			      =							torch.softmax(weight.float()				,       dim=-1						).type(weight.dtype						)

							# (bs*n_heads, dim_per_head, class_token_length)
							__lowercase			      =							torch.einsum('''bts,bcs->bct'''				,       snake_case__				,       snake_case__						)

							# (bs, length+1, width)
							__lowercase			      =							a.reshape(snake_case__				,       -1				,       1						).transpose(1				,       2						)

							return a[:, 0, :]  # cls_token






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()