



from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE           =  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__SCREAMING_SNAKE_CASE           =  {
    "vocab_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
    },
    "merges_file": {
        "allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
    },
}

__SCREAMING_SNAKE_CASE           =  {"allegro/herbert-base-cased": 514}
__SCREAMING_SNAKE_CASE           =  {}

class 	lowerCamelCase_    ( __UpperCAmelCase						):






						'''simple docstring'''
						a__   =  VOCAB_FILES_NAMES
						a__   =  PRETRAINED_VOCAB_FILES_MAP
						a__   =  PRETRAINED_INIT_CONFIGURATION
						a__   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a__   =  HerbertTokenizer







						def __init__(	self      :			List[Any]	,	__lowerCamelCase      :			Tuple=None	,	__lowerCamelCase      :			int=None	,	__lowerCamelCase      :			int=None	,	__lowerCamelCase      :			Optional[Any]="<s>"	,	__lowerCamelCase      :			Tuple="<unk>"	,	__lowerCamelCase      :			str="<pad>"	,	__lowerCamelCase      :			List[Any]="<mask>"	,	__lowerCamelCase      :			Optional[int]="</s>"	,	**__lowerCamelCase      :			str	,	)      ->				str:
									super().__init__(
									    lowerCAmelCase_	,	lowerCAmelCase_	,	tokenizer_file=lowerCAmelCase_	,	cls_token=lowerCAmelCase_	,	unk_token=lowerCAmelCase_	,	pad_token=lowerCAmelCase_	,	mask_token=lowerCAmelCase_	,	sep_token=lowerCAmelCase_	,	**lowerCAmelCase_	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	__lowerCamelCase      :			List[int]	,	__lowerCamelCase      :			Optional[List[int]] = None )      ->				Any:
									A							:					Optional[int]              =						[self.cls_token_id]
									A							:					Optional[Any]              =						[self.sep_token_id]
									if token_ids_a is None:
												return cls + token_ids_a + sep

									return cls + token_ids_a + sep + token_ids_a + sep







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any	,	__lowerCamelCase      :			List[int]	,	__lowerCamelCase      :			Optional[List[int]] = None	,	__lowerCamelCase      :			bool = False )      ->				List[Any]:







									if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=lowerCAmelCase_	,	token_ids_a=lowerCAmelCase_	,	already_has_special_tokens=lowerCAmelCase_ )

									if token_ids_a is None:
												return [1] + ([0] * len(lowerCAmelCase_ )) + [1]
									return [1] + ([0] * len(lowerCAmelCase_ )) + [1] + ([0] * len(lowerCAmelCase_ )) + [1]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	__lowerCamelCase      :			List[int]	,	__lowerCamelCase      :			Optional[List[int]] = None )      ->				int:
									A							:					List[Any]              =						[self.sep_token_id]
									A							:					Optional[int]              =						[self.cls_token_id]

									if token_ids_a is None:
												return len(cls + token_ids_a + sep ) * [0]
									return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			Optional[str] = None )      ->				Optional[Any]:
									A							:					int              =						self._tokenizer.model.save(lowerCAmelCase_	,	name=lowerCAmelCase_ )
									return tuple(lowerCAmelCase_ )




import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch

@require_torch
class 	lowerCamelCase_    :






						'''simple docstring'''







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				str:
									return self.get_dummy_input()







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[Any]:
									if self.block_type == "down":
												return (4, 32, 16, 16)
									elif self.block_type == "mid":
												return (4, 32, 32, 32)
									elif self.block_type == "up":
												return (4, 32, 64, 64)

									raise ValueError(F"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'.""" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			Union[str, Any]=True	,	__lowerCamelCase      :			int=False	,	__lowerCamelCase      :			int=False	,	__lowerCamelCase      :			Optional[int]=False	,	)      ->				Dict:
									A							:					Optional[Any]              =						4
									A							:					List[str]              =						32
									A							:					Any              =						(32, 32)

									A							:					str              =						torch.manual_seed(0 )
									A							:					int              =						torch.device(__lowerCamelCase )
									A							:					List[str]              =						(batch_size, num_channels) + sizes
									A							:					Dict              =						randn_tensor(__lowerCamelCase	,	generator=__lowerCamelCase	,	device=__lowerCamelCase )
									A							:					int              =						{"hidden_states": hidden_states}

									if include_temb:
												A							:					Any              =						1_28
												A							:					List[str]              =						randn_tensor((batch_size, temb_channels)	,	generator=__lowerCamelCase	,	device=__lowerCamelCase )

									if include_res_hidden_states_tuple:
												A							:					str              =						torch.manual_seed(1 )
												A							:					Tuple              =						(randn_tensor(__lowerCamelCase	,	generator=__lowerCamelCase	,	device=__lowerCamelCase ),)

									if include_encoder_hidden_states:
												A							:					Dict              =						floats_tensor((batch_size, 32, 32) ).to(__lowerCamelCase )

									if include_skip_sample:
												A							:					Optional[int]              =						randn_tensor(((batch_size, 3) + sizes)	,	generator=__lowerCamelCase	,	device=__lowerCamelCase )

									return dummy_input







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Union[str, Any]:
									A							:					Dict              =						{
									    "in_channels": 32,
									    "out_channels": 32,
									    "temb_channels": 1_28,
									}
									if self.block_type == "up":
												A							:					Dict              =						32

									if self.block_type == "mid":
												init_dict.pop("out_channels" )

									A							:					str              =						self.dummy_input
									return init_dict, inputs_dict







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			Optional[int] )      ->				Union[str, Any]:
									A       , A							:					str              =						self.prepare_init_args_and_inputs_for_common()
									A							:					List[Any]              =						self.block_class(**__lowerCamelCase )
									unet_block.to(__lowerCamelCase )
									unet_block.eval()

									with torch.no_grad():
												A							:					int              =						unet_block(**__lowerCamelCase )

									if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
												A							:					Union[str, Any]              =						output[0]

									self.assertEqual(output.shape	,	self.output_shape )

									A							:					Any              =						output[0, -1, -3:, -3:]
									A							:					Union[str, Any]              =						torch.tensor(__lowerCamelCase ).to(__lowerCamelCase )
									assert torch_all_close(output_slice.flatten()	,	__lowerCamelCase	,	atol=5e-3 )







						@unittest.skipIf(torch_device == "mps"	,	"Training is not supported in mps" )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Dict:
									A       , A							:					Tuple              =						self.prepare_init_args_and_inputs_for_common()
									A							:					str              =						self.block_class(**__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.train()
									A							:					Optional[int]              =						model(**__lowerCamelCase )

									if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
												A							:					Optional[Any]              =						output[0]

									A							:					List[str]              =						torch.device(__lowerCamelCase )
									A							:					List[str]              =						randn_tensor(output.shape	,	device=__lowerCamelCase )
									A							:					Dict              =						torch.nn.functional.mse_loss(__lowerCamelCase	,	__lowerCamelCase )
									loss.backward()