


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							:       Union[str, Any]     			=      logging.get_logger(__name__)


snake_case							:       Union[str, Any]     			=      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

snake_case							:       Any     			=      {
    "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							:       int     			=      {
    "allenai/led-base-16384": 1_63_84,
}







class        snake_case_   (a_      ):
					UpperCAmelCase__					:    Any          =			VOCAB_FILES_NAMES
					UpperCAmelCase__					:    Tuple          =			PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__					:    int          =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__					:    Any          =			LEDTokenizer
					UpperCAmelCase__					:    Any          =			['''input_ids''', '''attention_mask''']
					def __init__(      self   :Optional[int]			,__snake_case   :str=None			,__snake_case   :str=None			,__snake_case   :Dict=None			,__snake_case   :Optional[Any]="replace"			,__snake_case   :Dict="<s>"			,__snake_case   :Dict="</s>"			,__snake_case   :Dict="</s>"			,__snake_case   :Optional[int]="<s>"			,__snake_case   :List[str]="<unk>"			,__snake_case   :Union[str, Any]="<pad>"			,__snake_case   :int="<mask>"			,__snake_case   :List[str]=False			,__snake_case   :List[str]=True			,**__snake_case   :Any			,)					->    Optional[int]:
												super().__init__(
												    _A			,_A			,tokenizer_file=_A			,errors=_A			,bos_token=_A			,eos_token=_A			,sep_token=_A			,cls_token=_A			,unk_token=_A			,pad_token=_A			,mask_token=_A			,add_prefix_space=_A			,trim_offsets=_A			,**_A			,)

												a__             =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
												if pre_tok_state.get('add_prefix_space'			,_A      ) != add_prefix_space:
																			a__             =     getattr(_A			,pre_tok_state.pop('type'      )      )
																			a__             =     add_prefix_space
																			a__             =     pre_tok_class(**_A      )

												a__             =     add_prefix_space

												# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
												a__             =     'post_processor'
												a__             =     getattr(self.backend_tokenizer			,_A			,_A      )
												if tokenizer_component_instance:
																			a__             =     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__             =     tuple(state['sep']      )
																			if "cls" in state:
																										a__             =     tuple(state['cls']      )

																			a__             =     False

																			if state.get('add_prefix_space'			,_A      ) != add_prefix_space:
																										a__             =     add_prefix_space
																										a__             =     True

																			if state.get('trim_offsets'			,_A      ) != trim_offsets:
																										a__             =     trim_offsets
																										a__             =     True

																			if changes_to_apply:
																										a__             =     getattr(_A			,state.pop('type'      )      )
																										a__             =     component_class(**_A      )
																										setattr(self.backend_tokenizer			,_A			,_A      )
					@property
					# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
					def  lowerCamelCase__(      self   :List[str]      )					->    List[Any]:







												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  lowerCamelCase__(      self   :Dict			,__snake_case   :List[Any]      )					->    str:
												a__             =     AddedToken(_A			,lstrip=_A			,rstrip=_A      ) if isinstance(_A			,_A      ) else value
												a__             =     value
					def  lowerCamelCase__(      self   :Tuple			,*__snake_case   :List[str]			,**__snake_case   :List[Any]      )					->    Tuple:
												a__             =     kwargs.get('is_split_into_words'			,_A      )

												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(*_A			,**_A      )
					def  lowerCamelCase__(      self   :str			,*__snake_case   :Dict			,**__snake_case   :Tuple      )					->    str:
												a__             =     kwargs.get('is_split_into_words'			,_A      )

												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(*_A			,**_A      )
					def  lowerCamelCase__(      self   :List[Any]			,__snake_case   :Optional[Any]			,__snake_case   :Optional[Any] = None      )					->    Dict:
												a__             =     self._tokenizer.model.save(_A			,name=_A      )
												return tuple(_A      )
					def  lowerCamelCase__(      self   :Union[str, Any]			,__snake_case   :Tuple			,__snake_case   :int=None      )					->    Any:
												a__             =     [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  lowerCamelCase__(      self   :int			,__snake_case   :Tuple			,__snake_case   :Tuple = None      )					->    Optional[int]:
												a__             =     [self.sep_token_id]
												a__             =     [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  lowerCamelCase__(      self   :Optional[Any]			,__snake_case   :List[str]			,__snake_case   :List[str] = None			,__snake_case   :Any = PaddingStrategy.DO_NOT_PAD			,__snake_case   :Optional[int] = None			,__snake_case   :Optional[int] = None			,)					->    List[Any]:
												a__             =     super()._pad(
												    encoded_inputs=_A			,max_length=_A			,padding_strategy=_A			,pad_to_multiple_of=_A			,return_attention_mask=_A			,)

												# Load from model defaults
												if return_attention_mask is None:
																			a__             =     'attention_mask' in self.model_input_names

												if return_attention_mask and "global_attention_mask" in encoded_inputs:
																			a__             =     encoded_inputs[self.model_input_names[0]]
																			# `global_attention_mask` need to have the same length as other (sequential) inputs.
																			a__             =     len(encoded_inputs['global_attention_mask']      ) != len(_A      )

																			if needs_to_be_padded:
																										a__             =     len(_A      ) - 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__             =     (
																																	    encoded_inputs['global_attention_mask'] + [-1] * difference
																																	)
																										elif self.padding_side == "left":
																																	a__             =     [-1] * difference + encoded_inputs[
																																	    'global_attention_mask'
																																	]
																										else:
																																	raise ValueError('Invalid padding strategy:' + str(self.padding_side      )      )

												return encoded_inputs











from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 	A_       (a_			):
	UpperCAmelCase__					=			42
	UpperCAmelCase__					=			42
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				super().__init__()
				self.register_modules(unet=_A							,					scheduler=_A		)







	@torch.no_grad()
	def __call__(    self							,					_A = 1							,					_A = 5_0							,					_A = None							,					_A = "pil"							,					_A = True							,					**_A							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				self.unet.config.sample_size
				UpperCAmelCase   		=				(batch_size, 3, img_size, img_size)

				UpperCAmelCase   		=				self.unet

				# sample x_0 ~ N(0, sigma_0^2 * I)
				UpperCAmelCase   		=				randn_tensor(_A							,					generator=_A							,					device=self.device		) * self.scheduler.init_noise_sigma

				self.scheduler.set_timesteps(_A		)

				for t in self.progress_bar(self.scheduler.timesteps		):
							# here sigma_t == t_i from the paper
							UpperCAmelCase   		=				self.scheduler.schedule[t]
							UpperCAmelCase   		=				self.scheduler.schedule[t - 1] if t > 0 else 0

							# 1. Select temporarily increased noise level sigma_hat
							# 2. Add new noise to move from sample_i to sample_hat
							UpperCAmelCase		,			UpperCAmelCase   		=				self.scheduler.add_noise_to_input(_A							,					_A							,					generator=_A		)

							# 3. Predict the noise residual given the noise magnitude `sigma_hat`
							# The model inputs and output are adjusted by following eq. (213) in [1].
							UpperCAmelCase   		=				(sigma_hat / 2) * model((sample_hat + 1) / 2							,					sigma_hat / 2		).sample

							# 4. Evaluate dx/dt at sigma_hat
							# 5. Take Euler step from sigma to sigma_prev
							UpperCAmelCase   		=				self.scheduler.step(_A							,					_A							,					_A							,					_A		)

							if sigma_prev != 0:
										# 6. Apply 2nd order correction
										# The model inputs and output are adjusted by following eq. (213) in [1].
										UpperCAmelCase   		=				(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2							,					sigma_prev / 2		).sample
										UpperCAmelCase   		=				self.scheduler.step_correct(
										    _A							,					_A							,					_A							,					_A							,					step_output.prev_sample							,					step_output['''derivative''']							,					)
							UpperCAmelCase   		=				step_output.prev_sample

				UpperCAmelCase   		=				(sample / 2 + 0.5).clamp(0							,					1		)
				UpperCAmelCase   		=				sample.cpu().permute(0							,					2							,					3							,					1		).numpy()
				if output_type == "pil":
							UpperCAmelCase   		=				self.numpy_to_pil(_A		)

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=_A		)



