



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           =  logging.get_logger(__name__)


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

__SCREAMING_SNAKE_CASE           =  {
    """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           =  {
    """allenai/led-base-16384""": 16384,
}

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  VOCAB_FILES_NAMES
						a__   =  PRETRAINED_VOCAB_FILES_MAP
						a__   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a__   =  LEDTokenizer
						a__   =  ["input_ids", "attention_mask"]







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

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

									A							:					str              =						add_prefix_space

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

												A							:					int              =						False

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

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

												if changes_to_apply:
															A							:					int              =						getattr(__lowerCamelCase	,	state.pop("type" ) )
															A							:					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:
									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]	,	__lowerCamelCase      :			Any )      ->				Dict:
									A							:					Tuple              =						AddedToken(__lowerCamelCase	,	lstrip=__lowerCamelCase	,	rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase	,	__lowerCamelCase ) else value
									A							:					Tuple              =						value







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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			List[Any]=None )      ->				List[str]:
									A							:					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]	,	__lowerCamelCase      :			List[int]	,	__lowerCamelCase      :			Optional[List[int]] = None )      ->				List[int]:
									A							:					str              =						[self.sep_token_id]
									A							:					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	,	__lowerCamelCase      :			Union[Dict[str, EncodedInput], BatchEncoding]	,	__lowerCamelCase      :			Optional[int] = None	,	__lowerCamelCase      :			PaddingStrategy = PaddingStrategy.DO_NOT_PAD	,	__lowerCamelCase      :			Optional[int] = None	,	__lowerCamelCase      :			Optional[bool] = None	,	)      ->				dict:
									A							:					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:
												A							:					List[Any]              =						"attention_mask" in self.model_input_names

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

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

									return encoded_inputs




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  (PNDMScheduler,)
						a__   =  (("num_inference_steps", 50),)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	**__lowerCamelCase      :			str )      ->				Optional[Any]:
									A							:					Union[str, Any]              =						{
									    "num_train_timesteps": 10_00,
									    "beta_start": 0.0001,
									    "beta_end": 0.02,
									    "beta_schedule": "linear",
									}

									config.update(**__lowerCamelCase )
									return config







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			List[str]=0	,	**__lowerCamelCase      :			Any )      ->				Tuple:
									A							:					Dict              =						dict(self.forward_default_kwargs )
									A							:					Dict              =						kwargs.pop("num_inference_steps"	,	__lowerCamelCase )
									A							:					Union[str, Any]              =						self.dummy_sample
									A							:					List[Any]              =						0.1 * sample
									A							:					List[Any]              =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
												A							:					Any              =						self.get_scheduler_config(**__lowerCamelCase )
												A							:					int              =						scheduler_class(**__lowerCamelCase )
												scheduler.set_timesteps(__lowerCamelCase )
												# copy over dummy past residuals
												A							:					Tuple              =						dummy_past_residuals[:]

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(__lowerCamelCase )
															A							:					Dict              =						scheduler_class.from_pretrained(__lowerCamelCase )
															new_scheduler.set_timesteps(__lowerCamelCase )
															# copy over dummy past residuals
															A							:					Tuple              =						dummy_past_residuals[:]

												A							:					Dict              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					str              =						new_scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

												assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

												A							:					int              =						scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					List[str]              =						new_scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

												assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Union[str, Any]:
									pass







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			Optional[Any]=0	,	**__lowerCamelCase      :			Tuple )      ->				str:
									A							:					List[str]              =						dict(self.forward_default_kwargs )
									A							:					Optional[int]              =						kwargs.pop("num_inference_steps"	,	__lowerCamelCase )
									A							:					List[str]              =						self.dummy_sample
									A							:					Any              =						0.1 * sample
									A							:					Any              =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
												A							:					Tuple              =						self.get_scheduler_config()
												A							:					Optional[int]              =						scheduler_class(**__lowerCamelCase )
												scheduler.set_timesteps(__lowerCamelCase )

												# copy over dummy past residuals (must be after setting timesteps)
												A							:					Optional[int]              =						dummy_past_residuals[:]

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(__lowerCamelCase )
															A							:					str              =						scheduler_class.from_pretrained(__lowerCamelCase )
															# copy over dummy past residuals
															new_scheduler.set_timesteps(__lowerCamelCase )

															# copy over dummy past residual (must be after setting timesteps)
															A							:					Optional[Any]              =						dummy_past_residuals[:]

												A							:					Union[str, Any]              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					Dict              =						new_scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

												assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

												A							:					Union[str, Any]              =						scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					List[Any]              =						new_scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

												assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	**__lowerCamelCase      :			Any )      ->				Union[str, Any]:
									A							:					Optional[Any]              =						self.scheduler_classes[0]
									A							:					List[Any]              =						self.get_scheduler_config(**__lowerCamelCase )
									A							:					str              =						scheduler_class(**__lowerCamelCase )

									A							:					List[str]              =						10
									A							:					Union[str, Any]              =						self.dummy_model()
									A							:					int              =						self.dummy_sample_deter
									scheduler.set_timesteps(__lowerCamelCase )

									for i, t in enumerate(scheduler.prk_timesteps ):
												A							:					Optional[int]              =						model(__lowerCamelCase	,	__lowerCamelCase )
												A							:					Optional[int]              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase ).prev_sample

									for i, t in enumerate(scheduler.plms_timesteps ):
												A							:					Tuple              =						model(__lowerCamelCase	,	__lowerCamelCase )
												A							:					Tuple              =						scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase ).prev_sample

									return sample







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Any:
									A							:					Union[str, Any]              =						dict(self.forward_default_kwargs )

									A							:					Union[str, Any]              =						kwargs.pop("num_inference_steps"	,	__lowerCamelCase )

									for scheduler_class in self.scheduler_classes:
												A							:					Dict              =						self.get_scheduler_config()
												A							:					List[str]              =						scheduler_class(**__lowerCamelCase )

												A							:					List[Any]              =						self.dummy_sample
												A							:					List[Any]              =						0.1 * sample

												if num_inference_steps is not None and hasattr(__lowerCamelCase	,	"set_timesteps" ):
															scheduler.set_timesteps(__lowerCamelCase )
												elif num_inference_steps is not None and not hasattr(__lowerCamelCase	,	"set_timesteps" ):
															A							:					List[str]              =						num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												A							:					List[Any]              =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
												A							:					Tuple              =						dummy_past_residuals[:]

												A							:					Dict              =						scheduler.step_prk(__lowerCamelCase	,	0	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					List[Any]              =						scheduler.step_prk(__lowerCamelCase	,	1	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

												self.assertEqual(output_a.shape	,	sample.shape )
												self.assertEqual(output_a.shape	,	output_a.shape )

												A							:					Any              =						scheduler.step_plms(__lowerCamelCase	,	0	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					str              =						scheduler.step_plms(__lowerCamelCase	,	1	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

												self.assertEqual(output_a.shape	,	sample.shape )
												self.assertEqual(output_a.shape	,	output_a.shape )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Tuple:
									for timesteps in [1_00, 10_00]:
												self.check_over_configs(num_train_timesteps=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				List[str]:
									for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=__lowerCamelCase )

									A							:					Dict              =						self.scheduler_classes[0]
									A							:					Union[str, Any]              =						self.get_scheduler_config(steps_offset=1 )
									A							:					Optional[int]              =						scheduler_class(**__lowerCamelCase )
									scheduler.set_timesteps(10 )
									assert torch.equal(
									    scheduler.timesteps	,	torch.LongTensor(
									        [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] )	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Dict:
									for beta_start, beta_end in zip([0.0001, 0.001]	,	[0.002, 0.02] ):
												self.check_over_configs(beta_start=__lowerCamelCase	,	beta_end=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Union[str, Any]:
									for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				str:
									for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				List[Any]:
									for t in [1, 5, 10]:
												self.check_over_forward(time_step=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				int:
									for t, num_inference_steps in zip([1, 5, 10]	,	[10, 50, 1_00] ):
												self.check_over_forward(num_inference_steps=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Any:
									# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
									A							:					str              =						27

									for scheduler_class in self.scheduler_classes:
												A							:					Tuple              =						self.dummy_sample
												A							:					List[Any]              =						0.1 * sample

												A							:					List[Any]              =						self.get_scheduler_config()
												A							:					List[str]              =						scheduler_class(**__lowerCamelCase )

												scheduler.set_timesteps(__lowerCamelCase )

												# before power of 3 fix, would error on first step, so we only need to do two
												for i, t in enumerate(scheduler.prk_timesteps[:2] ):
															A							:					Dict              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase ).prev_sample







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				int:
									with self.assertRaises(__lowerCamelCase ):
												A							:					Union[str, Any]              =						self.scheduler_classes[0]
												A							:					Union[str, Any]              =						self.get_scheduler_config()
												A							:					List[str]              =						scheduler_class(**__lowerCamelCase )

												scheduler.step_plms(self.dummy_sample	,	1	,	self.dummy_sample ).prev_sample







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Dict:
									A							:					Optional[Any]              =						self.full_loop()
									A							:					Tuple              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Optional[int]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 198.1318 ) < 1e-2
									assert abs(result_mean.item() - 0.2580 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Any:
									A							:					Any              =						self.full_loop(prediction_type="v_prediction" )
									A							:					Union[str, Any]              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					List[str]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 67.3986 ) < 1e-2
									assert abs(result_mean.item() - 0.0878 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				List[str]:
									# We specify different beta, so that the first alpha is 0.99
									A							:					Any              =						self.full_loop(set_alpha_to_one=__lowerCamelCase	,	beta_start=0.01 )
									A							:					Dict              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Any              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 230.0399 ) < 1e-2
									assert abs(result_mean.item() - 0.2995 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Any:
									# We specify different beta, so that the first alpha is 0.99
									A							:					Any              =						self.full_loop(set_alpha_to_one=__lowerCamelCase	,	beta_start=0.01 )
									A							:					List[Any]              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Optional[Any]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 186.9482 ) < 1e-2
									assert abs(result_mean.item() - 0.2434 ) < 1e-3