



"""simple docstring"""






import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


__snake_case							:     Dict   =					logging.get_logger(__name__)


__snake_case							:     str   =					{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__snake_case							:     List[str]   =					{
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

__snake_case							:     Optional[Any]   =					{
    'allenai/led-base-16384': 16_384,
}



class     A__     (						_A							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE					   =			PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE					   =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE					   =			LEDTokenizer
	SCREAMING_SNAKE_CASE					   =			['input_ids', 'attention_mask']

	def __init__(  self:	int ,    _SCREAMING_SNAKE_CASE:	List[str]=None ,    _SCREAMING_SNAKE_CASE:	Optional[int]=None ,    _SCREAMING_SNAKE_CASE:	Optional[int]=None ,    _SCREAMING_SNAKE_CASE:	str="replace" ,    _SCREAMING_SNAKE_CASE:	Optional[int]="<s>" ,    _SCREAMING_SNAKE_CASE:	str="</s>" ,    _SCREAMING_SNAKE_CASE:	Any="</s>" ,    _SCREAMING_SNAKE_CASE:	Dict="<s>" ,    _SCREAMING_SNAKE_CASE:	Optional[int]="<unk>" ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]="<pad>" ,    _SCREAMING_SNAKE_CASE:	Dict="<mask>" ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=False ,    _SCREAMING_SNAKE_CASE:	str=True ,    **_SCREAMING_SNAKE_CASE:	Union[str, Any] ,    )       -> Optional[int]:

				"""simple docstring"""

				super().__init__(
				    __lowerCamelCase ,    __lowerCamelCase ,    tokenizer_file=__lowerCamelCase ,    errors=__lowerCamelCase ,    bos_token=__lowerCamelCase ,    eos_token=__lowerCamelCase ,    sep_token=__lowerCamelCase ,    cls_token=__lowerCamelCase ,    unk_token=__lowerCamelCase ,    pad_token=__lowerCamelCase ,    mask_token=__lowerCamelCase ,    add_prefix_space=__lowerCamelCase ,    trim_offsets=__lowerCamelCase ,    **__lowerCamelCase ,    )

				__lowerCAmelCase   :						Union[str, Any]				= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
				if pre_tok_state.get("add_prefix_space" ,    __lowerCamelCase) != add_prefix_space:
							__lowerCAmelCase   :						Any				= getattr(__lowerCamelCase ,    pre_tok_state.pop("type"))
							__lowerCAmelCase   :						Any				= add_prefix_space
							__lowerCAmelCase   :						Tuple				= pre_tok_class(**__lowerCamelCase)

				__lowerCAmelCase   :						str				= add_prefix_space

				# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
				__lowerCAmelCase   :						List[str]				= "post_processor"
				__lowerCAmelCase   :						Union[str, Any]				= getattr(self.backend_tokenizer ,    __lowerCamelCase ,    __lowerCamelCase)
				if tokenizer_component_instance:
							__lowerCAmelCase   :						Dict				= json.loads(tokenizer_component_instance.__getstate__())

							# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
							if "sep" in state:
										__lowerCAmelCase   :						Union[str, Any]				= tuple(state["sep"])
							if "cls" in state:
										__lowerCAmelCase   :						str				= tuple(state["cls"])

							__lowerCAmelCase   :						int				= False

							if state.get("add_prefix_space" ,    __lowerCamelCase) != add_prefix_space:
										__lowerCAmelCase   :						List[Any]				= add_prefix_space
										__lowerCAmelCase   :						Dict				= True

							if state.get("trim_offsets" ,    __lowerCamelCase) != trim_offsets:
										__lowerCAmelCase   :						Dict				= trim_offsets
										__lowerCAmelCase   :						str				= True

							if changes_to_apply:
										__lowerCAmelCase   :						int				= getattr(__lowerCamelCase ,    state.pop("type"))
										__lowerCAmelCase   :						Dict				= component_class(**__lowerCamelCase)
										setattr(self.backend_tokenizer ,    __lowerCamelCase ,    __lowerCamelCase)

	@property
	# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> str:

				"""simple docstring"""

				if self._mask_token is None:
							if self.verbose:
										logger.error("Using mask_token, but it is not set yet.")
							return None
				return str(self._mask_token)

	@mask_token.setter
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	Any)       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= AddedToken(__lowerCamelCase ,    lstrip=__lowerCamelCase ,    rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase ,    __lowerCamelCase) else value
				__lowerCAmelCase   :						Tuple				= value

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any] ,    *_SCREAMING_SNAKE_CASE:	Optional[Any] ,    **_SCREAMING_SNAKE_CASE:	List[str])       -> BatchEncoding:

				"""simple docstring"""

				__lowerCAmelCase   :						List[str]				= kwargs.get("is_split_into_words" ,    __lowerCamelCase)

				if is_split_into_words and not self.add_prefix_space:
							raise ValueError(
							    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
							    "to use it with pretokenized inputs.")

				return super()._batch_encode_plus(*__lowerCamelCase ,    **__lowerCamelCase)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    *_SCREAMING_SNAKE_CASE:	str ,    **_SCREAMING_SNAKE_CASE:	Optional[Any])       -> BatchEncoding:

				"""simple docstring"""

				__lowerCAmelCase   :						List[str]				= kwargs.get("is_split_into_words" ,    __lowerCamelCase)

				if is_split_into_words and not self.add_prefix_space:
							raise ValueError(
							    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
							    "to use it with pretokenized inputs.")

				return super()._encode_plus(*__lowerCamelCase ,    **__lowerCamelCase)

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	Optional[str] = None)       -> Tuple[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= self._tokenizer.model.save(__lowerCamelCase ,    name=__lowerCamelCase)
				return tuple(__lowerCamelCase)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	List[Any]=None)       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= [self.bos_token_id] + token_ids_a + [self.eos_token_id]
				if token_ids_a is None:
							return output

				return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	List[int] ,    _SCREAMING_SNAKE_CASE:	Optional[List[int]] = None)       -> List[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= [self.sep_token_id]
				__lowerCAmelCase   :						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 + sep + token_ids_a + sep) * [0]







	def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    _SCREAMING_SNAKE_CASE:	Union[Dict[str, EncodedInput], BatchEncoding] ,    _SCREAMING_SNAKE_CASE:	Optional[int] = None ,    _SCREAMING_SNAKE_CASE:	PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,    _SCREAMING_SNAKE_CASE:	Optional[int] = None ,    _SCREAMING_SNAKE_CASE:	Optional[bool] = None ,    )       -> dict:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= super()._pad(
				    encoded_inputs=__lowerCamelCase ,    max_length=__lowerCamelCase ,    padding_strategy=__lowerCamelCase ,    pad_to_multiple_of=__lowerCamelCase ,    return_attention_mask=__lowerCamelCase ,    )

				# Load from model defaults
				if return_attention_mask is None:
							__lowerCAmelCase   :						List[Any]				= "attention_mask" in self.model_input_names

				if return_attention_mask and "global_attention_mask" in encoded_inputs:
							__lowerCAmelCase   :						Optional[int]				= encoded_inputs[self.model_input_names[0]]
							# `global_attention_mask` need to have the same length as other (sequential) inputs.
							__lowerCAmelCase   :						Tuple				= len(encoded_inputs["global_attention_mask"]) != len(__lowerCamelCase)

							if needs_to_be_padded:
										__lowerCAmelCase   :						Any				= len(__lowerCamelCase) - len(encoded_inputs["global_attention_mask"])

										if self.padding_side == "right":
													# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
													__lowerCAmelCase   :						Tuple				= (
													    encoded_inputs["global_attention_mask"] + [-1] * difference
													)
										elif self.padding_side == "left":
													__lowerCAmelCase   :						Tuple				= [-1] * difference + encoded_inputs[
													    "global_attention_mask"
													]
										else:
													raise ValueError("Invalid padding strategy:" + str(self.padding_side))

				return encoded_inputs




"""simple docstring"""






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     A__     :




	'''simple docstring'''





	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> Optional[Any]:

				"""simple docstring"""

				return self.get_dummy_input()

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> str:

				"""simple docstring"""

				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 ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=True ,    _SCREAMING_SNAKE_CASE:	Tuple=False ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=False ,    _SCREAMING_SNAKE_CASE:	Dict=False ,    )       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= 4
				__lowerCAmelCase   :						List[str]				= 32
				__lowerCAmelCase   :						str				= (32, 32)

				__lowerCAmelCase   :						int				= torch.manual_seed(0)
				__lowerCAmelCase   :						Dict				= torch.device(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						str				= (batch_size, num_channels) + sizes
				__lowerCAmelCase   :						Dict				= randn_tensor(_SCREAMING_SNAKE_CASE ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						str				= {"hidden_states": hidden_states}

				if include_temb:
							__lowerCAmelCase   :						List[str]				= 128
							__lowerCAmelCase   :						Dict				= randn_tensor((batch_size, temb_channels) ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE)

				if include_res_hidden_states_tuple:
							__lowerCAmelCase   :						Dict				= torch.manual_seed(1)
							__lowerCAmelCase   :						int				= (randn_tensor(_SCREAMING_SNAKE_CASE ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE),)

				if include_encoder_hidden_states:
							__lowerCAmelCase   :						int				= floats_tensor((batch_size, 32, 32)).to(_SCREAMING_SNAKE_CASE)

				if include_skip_sample:
							__lowerCAmelCase   :						Optional[Any]				= randn_tensor(((batch_size, 3) + sizes) ,    generator=_SCREAMING_SNAKE_CASE ,    device=_SCREAMING_SNAKE_CASE)

				return dummy_input

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= {
				    "in_channels": 32,
				    "out_channels": 32,
				    "temb_channels": 128,
				}
				if self.block_type == "up":
							__lowerCAmelCase   :						Tuple				= 32

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

				__lowerCAmelCase   :						str				= self.dummy_input
				return init_dict, inputs_dict

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

				"""simple docstring"""

				__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= self.prepare_init_args_and_inputs_for_common()
				__lowerCAmelCase   :						Dict				= self.block_class(**_SCREAMING_SNAKE_CASE)
				unet_block.to(_SCREAMING_SNAKE_CASE)
				unet_block.eval()

				with torch.no_grad():
							__lowerCAmelCase   :						int				= unet_block(**_SCREAMING_SNAKE_CASE)

				if isinstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						List[Any]				= output[0]

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

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







	@unittest.skipIf(torch_device == "mps" ,    "Training is not supported in mps")
	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase		,			__lowerCAmelCase   :						Optional[Any]				= self.prepare_init_args_and_inputs_for_common()
				__lowerCAmelCase   :						str				= self.block_class(**_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.train()
				__lowerCAmelCase   :						Union[str, Any]				= model(**_SCREAMING_SNAKE_CASE)

				if isinstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						Union[str, Any]				= output[0]

				__lowerCAmelCase   :						List[Any]				= torch.device(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						int				= randn_tensor(output.shape ,    device=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						int				= torch.nn.functional.mse_loss(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
				loss.backward()