






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


SCREAMING_SNAKE_CASE__      :			Dict							=		logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__      :			List[Any]							=		{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__      :			int							=		{
    """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""",
    },
}

SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		{
    """allenai/led-base-16384""": 1_63_84,
}
class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  VOCAB_FILES_NAMES
				a__					       =  PRETRAINED_VOCAB_FILES_MAP
				a__					       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				a__					       =  LEDTokenizer
				a__					       =  ['''input_ids''', '''attention_mask''']







				def __init__(						self       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase="replace"       ,					__lowerCAmelCase="<s>"       ,					__lowerCAmelCase="</s>"       ,					__lowerCAmelCase="</s>"       ,					__lowerCAmelCase="<s>"       ,					__lowerCAmelCase="<unk>"       ,					__lowerCAmelCase="<pad>"       ,					__lowerCAmelCase="<mask>"       ,					__lowerCAmelCase=False       ,					__lowerCAmelCase=True       ,					**__lowerCAmelCase       ,					):
								"""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       ,					)

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

								__magic_name__		:Tuple        =      add_prefix_space

								# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
								__magic_name__		:Union[str, Any]        =      '''post_processor'''
								__magic_name__		:Union[str, Any]        =      getattr(self.backend_tokenizer       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )
								if tokenizer_component_instance:
												__magic_name__		:int        =      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:
																__magic_name__		:List[str]        =      tuple(state['''sep''']     )
												if "cls" in state:
																__magic_name__		:List[Any]        =      tuple(state['''cls''']     )

												__magic_name__		:Optional[Any]        =      False

												if state.get('''add_prefix_space'''       ,					__lowerCAmelCase     ) != add_prefix_space:
																__magic_name__		:Optional[int]        =      add_prefix_space
																__magic_name__		:Optional[Any]        =      True

												if state.get('''trim_offsets'''       ,					__lowerCAmelCase     ) != trim_offsets:
																__magic_name__		:str        =      trim_offsets
																__magic_name__		:Tuple        =      True

												if changes_to_apply:
																__magic_name__		:Optional[int]        =      getattr(__lowerCAmelCase       ,					state.pop('''type'''     )     )
																__magic_name__		:Optional[int]        =      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 			A	(						self     ):
								"""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 			A	(						self       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      AddedToken(__lowerCAmelCase       ,					lstrip=__lowerCAmelCase       ,					rstrip=__lowerCAmelCase     ) if isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     ) else value
								__magic_name__		:List[Any]        =      value







				def 			A	(						self       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      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 			A	(						self       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		: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 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None     ):
								"""simple docstring"""
								__magic_name__		:str        =      self._tokenizer.model.save(__lowerCAmelCase       ,					name=__lowerCAmelCase     )
								return tuple(__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=None     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      [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 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      [self.sep_token_id]
								__magic_name__		:Union[str, Any]        =      [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 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = PaddingStrategy.DO_NOT_PAD       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					):
								"""simple docstring"""
								__magic_name__		:int        =      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:
												__magic_name__		:Any        =      '''attention_mask''' in self.model_input_names

								if return_attention_mask and "global_attention_mask" in encoded_inputs:
												__magic_name__		:Union[str, Any]        =      encoded_inputs[self.model_input_names[0]]
												# `global_attention_mask` need to have the same length as other (sequential) inputs.
												__magic_name__		:Dict        =      len(encoded_inputs['''global_attention_mask''']     ) != len(__lowerCAmelCase     )

												if needs_to_be_padded:
																__magic_name__		:Union[str, 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`
																				__magic_name__		:Any        =      (
																				    encoded_inputs['''global_attention_mask'''] + [-1] * difference
																				)
																elif self.padding_side == "left":
																				__magic_name__		:Any        =      [-1] * difference + encoded_inputs[
																				    '''global_attention_mask'''
																				]
																else:
																				raise ValueError('''Invalid padding strategy:''' + str(self.padding_side     )     )

								return encoded_inputs














import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class 						lowerCamelCase_				(      unittest.TestCase ):







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								torch.manual_seed(0     )
								__magic_name__		:Dict        =      UNetaDModel(
								    block_out_channels=(3_2, 6_4)       ,					layers_per_block=2       ,					sample_size=3_2       ,					in_channels=3       ,					out_channels=3       ,					down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')       ,					up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')       ,					)
								return model







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								torch.manual_seed(0     )
								__magic_name__		:List[str]        =      VQModel(
								    block_out_channels=[3_2, 6_4]       ,					in_channels=3       ,					out_channels=3       ,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,					latent_channels=3       ,					)
								return model







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								torch.manual_seed(0     )
								__magic_name__		:int        =      CLIPTextConfig(
								    bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=3_2       ,					intermediate_size=3_7       ,					layer_norm_eps=1E-05       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					pad_token_id=1       ,					vocab_size=1_0_0_0       ,					)
								return CLIPTextModel(__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      self.dummy_uncond_unet
								__magic_name__		:Optional[int]        =      DDIMScheduler()
								__magic_name__		:List[str]        =      self.dummy_vq_model

								__magic_name__		:Tuple        =      LDMPipeline(unet=__lowerCAmelCase       ,					vqvae=__lowerCAmelCase       ,					scheduler=__lowerCAmelCase     )
								ldm.to(__lowerCAmelCase     )
								ldm.set_progress_bar_config(disable=__lowerCAmelCase     )

								__magic_name__		:List[Any]        =      torch.manual_seed(0     )
								__magic_name__		:List[str]        =      ldm(generator=__lowerCAmelCase       ,					num_inference_steps=2       ,					output_type='''numpy'''     ).images

								__magic_name__		:List[Any]        =      torch.manual_seed(0     )
								__magic_name__		:Any        =      ldm(generator=__lowerCAmelCase       ,					num_inference_steps=2       ,					output_type='''numpy'''       ,					return_dict=__lowerCAmelCase     )[0]

								__magic_name__		:Any        =      image[0, -3:, -3:, -1]
								__magic_name__		:Dict        =      image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 6_4, 6_4, 3)
								__magic_name__		:Union[str, Any]        =      np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]     )
								__magic_name__		:Any        =      1E-2 if torch_device != '''mps''' else 3E-2

								assert np.abs(image_slice.flatten() - expected_slice     ).max() < tolerance
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < tolerance







@slow
@require_torch
class 						lowerCamelCase_				(      unittest.TestCase ):







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256'''     )
								ldm.to(__lowerCAmelCase     )
								ldm.set_progress_bar_config(disable=__lowerCAmelCase     )

								__magic_name__		:Optional[Any]        =      torch.manual_seed(0     )
								__magic_name__		:Optional[int]        =      ldm(generator=__lowerCAmelCase       ,					num_inference_steps=5       ,					output_type='''numpy'''     ).images

								__magic_name__		:Optional[Any]        =      image[0, -3:, -3:, -1]

								assert image.shape == (1, 2_5_6, 2_5_6, 3)
								__magic_name__		:List[str]        =      np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]     )
								__magic_name__		:Tuple        =      1E-2 if torch_device != '''mps''' else 3E-2

								assert np.abs(image_slice.flatten() - expected_slice     ).max() < tolerance






