
"""simple docstring"""


import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation






def  _snake_case			(						snake_case__				:       torch.Tensor		, snake_case__				:       int		, snake_case__				:       bool = False		, snake_case__				:       float = 1		, snake_case__				:       float = 1		, snake_case__				:       int = 1_0000		, ):
	assert len(timesteps.shape       ) == 1, "Timesteps should be a 1d-array"

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

	A  				=				torch.exp(snake_case__       )
	A  				=				timesteps[:, None].float() * emb[None, :]

	# scale embeddings
	A  				=				scale * emb

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

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

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






def  _snake_case			(						snake_case__				:       List[str]		, snake_case__				:       Optional[int]		, snake_case__				:       Optional[Any]=False		, snake_case__				:       Optional[Any]=0       ):
	A  				=				np.arange(snake_case__		, dtype=np.floataa       )
	A  				=				np.arange(snake_case__		, dtype=np.floataa       )
	A  				=				np.meshgrid(snake_case__		, snake_case__       )  # here w goes first
	A  				=				np.stack(snake_case__		, axis=0       )

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






def  _snake_case			(						snake_case__				:       Optional[int]		, snake_case__				:       Tuple       ):
	if embed_dim % 2 != 0:
		raise ValueError('embed_dim must be divisible by 2'       )

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

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






def  _snake_case			(						snake_case__				:       Any		, snake_case__				:       List[Any]       ):
	if embed_dim % 2 != 0:
		raise ValueError('embed_dim must be divisible by 2'       )

	A  				=				np.arange(embed_dim // 2		, dtype=np.floataa       )
	omega /= embed_dim / 2.0
	A  				=				1.0 / 1_0000**omega  # (D/2,)

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

	A  				=				np.sin(snake_case__       )  # (M, D/2)
	A  				=				np.cos(snake_case__       )  # (M, D/2)

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







class 			lowerCAmelCase_			( nn.Module      ):



						'''simple docstring'''


						def __init__(	self       : Tuple					,A_       : Any=224					,A_       : List[Any]=224					,A_       : List[str]=16					,A_       : Dict=3					,A_       : int=768					,A_       : List[Any]=False					,A_       : Optional[Any]=True					,A_       : List[Any]=True					,)       ->				List[Any]:
							super().__init__()

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

							A  				=				nn.Convad(
							    A_					,A_					,kernel_size=(patch_size, patch_size)					,stride=A_					,bias=A_ )
							if layer_norm:
								A  				=				nn.LayerNorm(A_					,elementwise_affine=A_					,eps=1e-6 )
							else:
								A  				=				None

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




						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any]					,A_       : Dict )       ->				Optional[Any]:
							A  				=				self.proj(A_ )
							if self.flatten:
								A  				=				latent.flatten(2 ).transpose(1					,2 )  # BCHW -> BNC
							if self.layer_norm:
								A  				=				self.norm(A_ )
							return latent + self.pos_embed







class 			lowerCAmelCase_			( nn.Module      ):



						'''simple docstring'''


						def __init__(	self       : Optional[int]					,A_       : int					,A_       : int					,A_       : str = "silu"					,A_       : int = None					,A_       : Optional[str] = None					,A_       : Optional[int]=None					,)       ->				Optional[int]:
							super().__init__()

							A  				=				nn.Linear(A_					,A_ )

							if cond_proj_dim is not None:
								A  				=				nn.Linear(A_					,A_					,bias=A_ )
							else:
								A  				=				None

							A  				=				get_activation(A_ )

							if out_dim is not None:
								A  				=				out_dim
							else:
								A  				=				time_embed_dim
							A  				=				nn.Linear(A_					,A_ )

							if post_act_fn is None:
								A  				=				None
							else:
								A  				=				get_activation(A_ )




						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int]					,A_       : List[Any]					,A_       : Any=None )       ->				int:
							if condition is not None:
								A  				=				sample + self.cond_proj(A_ )
							A  				=				self.linear_a(A_ )

							if self.act is not None:
								A  				=				self.act(A_ )

							A  				=				self.linear_a(A_ )

							if self.post_act is not None:
								A  				=				self.post_act(A_ )
							return sample







class 			lowerCAmelCase_			( nn.Module      ):



						'''simple docstring'''


						def __init__(	self       : Union[str, Any]					,A_       : int					,A_       : bool					,A_       : float )       ->				Any:
							super().__init__()
							A  				=				num_channels
							A  				=				flip_sin_to_cos
							A  				=				downscale_freq_shift




						def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any]					,A_       : Any )       ->				Tuple:
							A  				=				get_timestep_embedding(
							    A_					,self.num_channels					,flip_sin_to_cos=self.flip_sin_to_cos					,downscale_freq_shift=self.downscale_freq_shift					,)
							return t_emb







class 			lowerCAmelCase_			( nn.Module      ):



						'''simple docstring'''


						def __init__(	self       : Dict					,A_       : int = 256					,A_       : float = 1.0					,A_       : str=True					,A_       : int=True					,A_       : List[str]=False )       ->				Any:
							super().__init__()
							A  				=				nn.Parameter(torch.randn(A_ ) * scale					,requires_grad=A_ )
							A  				=				log
							A  				=				flip_sin_to_cos

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

								A  				=				self.W




						def   _SCREAMING_SNAKE_CASE (	self       : List[str]					,A_       : Dict )       ->				Any:
							if self.log:
								A  				=				torch.log(A_ )

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

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







class 			lowerCAmelCase_			( nn.Module      ):



						'''simple docstring'''


						def __init__(	self       : Union[str, Any]					,A_       : int					,A_       : int					,A_       : int					,A_       : int					,)       ->				int:
							super().__init__()

							A  				=				height
							A  				=				width
							A  				=				num_embed
							A  				=				embed_dim

							A  				=				nn.Embedding(self.num_embed					,A_ )
							A  				=				nn.Embedding(self.height					,A_ )
							A  				=				nn.Embedding(self.width					,A_ )




						def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : Tuple )       ->				Tuple:
							A  				=				self.emb(A_ )

							A  				=				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
							A  				=				height_emb.unsqueeze(2 )

							A  				=				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
							A  				=				width_emb.unsqueeze(1 )

							A  				=				height_emb + width_emb

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

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

							return emb







class 			lowerCAmelCase_			( nn.Module      ):



						'''simple docstring'''


						def __init__(	self       : str					,A_       : Dict					,A_       : Dict					,A_       : int )       ->				Tuple:
							super().__init__()
							A  				=				dropout_prob > 0
							A  				=				nn.Embedding(num_classes + use_cfg_embedding					,A_ )
							A  				=				num_classes
							A  				=				dropout_prob


						def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : Dict					,A_       : Tuple=None )       ->				List[str]:
							if force_drop_ids is None:
								A  				=				torch.rand(labels.shape[0]					,device=labels.device ) < self.dropout_prob
							else:
								A  				=				torch.tensor(force_drop_ids == 1 )
							A  				=				torch.where(A_					,self.num_classes					,A_ )
							return labels




						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int]					,A_       : torch.LongTensor					,A_       : List[Any]=None )       ->				Optional[int]:
							A  				=				self.dropout_prob > 0
							if (self.training and use_dropout) or (force_drop_ids is not None):
								A  				=				self.token_drop(A_					,A_ )
							A  				=				self.embedding_table(A_ )
							return embeddings







class 			lowerCAmelCase_			( nn.Module      ):



						'''simple docstring'''


						def __init__(	self       : Tuple					,A_       : int = 1024					,A_       : int = 768					,A_       : int = 768					,A_       : int = 10					,)       ->				str:
							super().__init__()

							A  				=				num_image_text_embeds
							A  				=				nn.Linear(A_					,self.num_image_text_embeds * cross_attention_dim )
							A  				=				nn.Linear(A_					,A_ )




						def   _SCREAMING_SNAKE_CASE (	self       : Dict					,A_       : torch.FloatTensor					,A_       : torch.FloatTensor )       ->				Any:
							A  				=				text_embeds.shape[0]

							# image
							A  				=				self.image_embeds(A_ )
							A  				=				image_text_embeds.reshape(A_					,self.num_image_text_embeds					,-1 )

							# text
							A  				=				self.text_proj(A_ )

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







class 			lowerCAmelCase_			( nn.Module      ):



						'''simple docstring'''


						def __init__(	self       : Optional[Any]					,A_       : int = 768					,A_       : int = 768					,A_       : int = 32					,)       ->				List[str]:
							super().__init__()

							A  				=				num_image_text_embeds
							A  				=				nn.Linear(A_					,self.num_image_text_embeds * cross_attention_dim )
							A  				=				nn.LayerNorm(A_ )




						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : torch.FloatTensor )       ->				Tuple:
							A  				=				image_embeds.shape[0]

							# image
							A  				=				self.image_embeds(A_ )
							A  				=				image_embeds.reshape(A_					,self.num_image_text_embeds					,-1 )
							A  				=				self.norm(A_ )
							return image_embeds







class 			lowerCAmelCase_			( nn.Module      ):



						'''simple docstring'''


						def __init__(	self       : List[Any]					,A_       : Optional[Any]					,A_       : str					,A_       : List[str]=0.1 )       ->				List[str]:
							super().__init__()

							A  				=				Timesteps(num_channels=256					,flip_sin_to_cos=A_					,downscale_freq_shift=1 )
							A  				=				TimestepEmbedding(in_channels=256					,time_embed_dim=A_ )
							A  				=				LabelEmbedding(A_					,A_					,A_ )




						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : Tuple					,A_       : List[Any]					,A_       : List[Any]=None )       ->				str:
							A  				=				self.time_proj(A_ )
							A  				=				self.timestep_embedder(timesteps_proj.to(dtype=A_ ) )  # (N, D)

							A  				=				self.class_embedder(A_ )  # (N, D)

							A  				=				timesteps_emb + class_labels  # (N, D)

							return conditioning







class 			lowerCAmelCase_			( nn.Module      ):



						'''simple docstring'''


						def __init__(	self       : Optional[Any]					,A_       : int					,A_       : int					,A_       : int = 64 )       ->				List[str]:
							super().__init__()
							A  				=				nn.LayerNorm(A_ )
							A  				=				AttentionPooling(A_					,A_ )
							A  				=				nn.Linear(A_					,A_ )
							A  				=				nn.LayerNorm(A_ )




						def   _SCREAMING_SNAKE_CASE (	self       : Any					,A_       : str )       ->				Dict:
							A  				=				self.norma(A_ )
							A  				=				self.pool(A_ )
							A  				=				self.proj(A_ )
							A  				=				self.norma(A_ )
							return hidden_states







class 			lowerCAmelCase_			( nn.Module      ):



						'''simple docstring'''


						def __init__(	self       : Optional[Any]					,A_       : int = 768					,A_       : int = 768					,A_       : int = 1536 )       ->				Tuple:
							super().__init__()
							A  				=				nn.Linear(A_					,A_ )
							A  				=				nn.LayerNorm(A_ )
							A  				=				nn.Linear(A_					,A_ )




						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : torch.FloatTensor					,A_       : torch.FloatTensor )       ->				Optional[int]:
							# text
							A  				=				self.text_proj(A_ )
							A  				=				self.text_norm(A_ )

							# image
							A  				=				self.image_proj(A_ )

							return time_image_embeds + time_text_embeds







class 			lowerCAmelCase_			( nn.Module      ):



						'''simple docstring'''


						def __init__(	self       : Any					,A_       : int = 768					,A_       : int = 1536 )       ->				str:
							super().__init__()
							A  				=				nn.Linear(A_					,A_ )
							A  				=				nn.LayerNorm(A_ )




						def   _SCREAMING_SNAKE_CASE (	self       : List[Any]					,A_       : torch.FloatTensor )       ->				Union[str, Any]:
							# image
							A  				=				self.image_proj(A_ )
							A  				=				self.image_norm(A_ )
							return time_image_embeds







class 			lowerCAmelCase_			( nn.Module      ):



						'''simple docstring'''


						def __init__(	self       : Any					,A_       : int = 768					,A_       : int = 1536 )       ->				Union[str, Any]:
							super().__init__()
							A  				=				nn.Linear(A_					,A_ )
							A  				=				nn.LayerNorm(A_ )
							A  				=				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					,256					,3					,padding=1					,stride=2 )					,nn.SiLU()					,nn.Convad(256					,4					,3					,padding=1 )					,)




						def   _SCREAMING_SNAKE_CASE (	self       : Any					,A_       : torch.FloatTensor					,A_       : torch.FloatTensor )       ->				Optional[int]:
							# image
							A  				=				self.image_proj(A_ )
							A  				=				self.image_norm(A_ )
							A  				=				self.input_hint_block(A_ )
							return time_image_embeds, hint







class 			lowerCAmelCase_			( nn.Module      ):



						'''simple docstring'''


						def __init__(	self       : Union[str, Any]					,A_       : Tuple					,A_       : int					,A_       : Tuple=None )       ->				Dict:
							super().__init__()
							A  				=				dtype
							A  				=				nn.Parameter(torch.randn(1					,A_ ) / embed_dim**0.5 )
							A  				=				nn.Linear(A_					,A_					,dtype=self.dtype )
							A  				=				nn.Linear(A_					,A_					,dtype=self.dtype )
							A  				=				nn.Linear(A_					,A_					,dtype=self.dtype )
							A  				=				num_heads
							A  				=				embed_dim // self.num_heads




						def   _SCREAMING_SNAKE_CASE (	self       : Any					,A_       : Dict )       ->				Optional[Any]:
							A				, A				, A  				=				x.size()

							def shape(A_       : Any ):
								# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
								A  				=				x.view(A_					,-1					,self.num_heads					,self.dim_per_head )
								# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
								A  				=				x.transpose(1					,2 )
								# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
								A  				=				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)
								A  				=				x.transpose(1					,2 )
								return x

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

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

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

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

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

							return a[:, 0, :]  # cls_token

"""simple docstring"""


import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 			lowerCAmelCase_			( _lowercase						,   unittest.TestCase      ):



						'''simple docstring'''


						_lowerCamelCase:      List[str] =      HerbertTokenizer
						_lowerCamelCase:      Any =      HerbertTokenizerFast
						_lowerCamelCase:      Optional[int] =      True


						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				Tuple:
							super().setUp()

							# Use a simpler test file without japanese/chinese characters
							with open(F'{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'					,encoding='utf-8' ) as f_data:
								A  				=				f_data.read().replace('\n\n'					,'\n' ).strip()

							A  				=				[
							    '<s>',
							    '</s>',
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    'w</w>',
							    'r</w>',
							    't</w>',
							    'lo',
							    'low',
							    'er</w>',
							    'low</w>',
							    'lowest</w>',
							    'newer</w>',
							    'wider</w>',
							    ',</w>',
							    '<unk>',
							]
							A  				=				dict(zip(A_					,range(len(A_ ) ) ) )
							A  				=				['l o 123', 'lo w 1456', 'e r</w> 1789', '']

							A  				=				os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['vocab_file'] )
							A  				=				os.path.join(self.tmpdirname					,VOCAB_FILES_NAMES['merges_file'] )
							with open(self.vocab_file					,'w' ) as fp:
								fp.write(json.dumps(A_ ) )
							with open(self.merges_file					,'w' ) as fp:
								fp.write('\n'.join(A_ ) )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple					,A_       : List[str] )       ->				int:
							A  				=				'lower newer'
							A  				=				'lower newer'
							return input_text, output_text


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				int:
							A  				=				self.tokenizer_class(vocab_file=self.vocab_file					,merges_file=self.merges_file )

							A  				=				'lower'
							A  				=				['low', 'er</w>']
							A  				=				tokenizer.tokenize(A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				tokens + ['<unk>']
							A  				=				[16, 17, 23]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_ )					,A_ )


						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				Optional[int]:
							if not self.test_rust_tokenizer:
								return

							A  				=				self.get_tokenizer()
							A  				=				self.get_rust_tokenizer()

							A  				=				'lower,newer'

							A  				=				tokenizer.tokenize(A_ )
							A  				=				rust_tokenizer.tokenize(A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				tokenizer.encode(A_					,add_special_tokens=A_ )
							A  				=				rust_tokenizer.encode(A_					,add_special_tokens=A_ )
							self.assertListEqual(A_					,A_ )

							A  				=				self.get_rust_tokenizer()
							A  				=				tokenizer.encode(A_ )
							A  				=				rust_tokenizer.encode(A_ )
							self.assertListEqual(A_					,A_ )




						@slow
						def   _SCREAMING_SNAKE_CASE (	self       : str )       ->				List[Any]:
							A  				=				self.tokenizer_class.from_pretrained('allegro/herbert-base-cased' )

							A  				=				tokenizer.encode('konstruowanie sekwencji'					,add_special_tokens=A_ )
							A  				=				tokenizer.encode('konstruowanie wielu sekwencji'					,add_special_tokens=A_ )

							A  				=				tokenizer.build_inputs_with_special_tokens(A_ )
							A  				=				tokenizer.build_inputs_with_special_tokens(A_					,A_ )

							assert encoded_sentence == [0] + text + [2]
							assert encoded_pair == [0] + text + [2] + text_a + [2]