






import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


a_							=    logging.get_logger(__name__)


a_							=    {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
a_							=    {
    '''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''',
    },
}

a_							=    {
    '''allenai/led-base-16384''': 1_6384,
}


@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def        _a  (     ) ->   Dict:
						"""simple docstring"""







						lowerCAmelCase__       =						(
						    list(range(ord("!"   )    ,   ord("~"   ) + 1   )   ) + list(range(ord("¡"   )    ,   ord("¬"   ) + 1   )   ) + list(range(ord("®"   )    ,   ord("ÿ"   ) + 1   )   )
						)
						lowerCAmelCase__       =						bs[:]
						lowerCAmelCase__       =						0
						for b in range(2**8   ):
												if b not in bs:
																		bs.append(UpperCamelCase_   )
																		cs.append(2**8 + n   )
																		n += 1
						lowerCAmelCase__       =						[chr(UpperCamelCase_   ) for n in cs]
						return dict(zip(UpperCamelCase_    ,   UpperCamelCase_   )   )


def        _a  (     UpperCamelCase_					:				List[str]   ) ->   Union[str, Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						set()
						lowerCAmelCase__       =						word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)   )
												lowerCAmelCase__       =						char
						return pairs





class      lowercase__     (			_UpperCAmelCase	):
	a_         =VOCAB_FILES_NAMES
	a_         =PRETRAINED_VOCAB_FILES_MAP
	a_         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a_         =["""input_ids""", """attention_mask"""]

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase="replace"		,    __UpperCAmelCase="<s>"		,    __UpperCAmelCase="</s>"		,    __UpperCAmelCase="</s>"		,    __UpperCAmelCase="<s>"		,    __UpperCAmelCase="<unk>"		,    __UpperCAmelCase="<pad>"		,    __UpperCAmelCase="<mask>"		,    __UpperCAmelCase=False		,    **__UpperCAmelCase		,    )->					Dict:







							'''simple docstring'''





							lowerCAmelCase__       =						AddedToken(__UpperCAmelCase		,    lstrip=__UpperCAmelCase		,    rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ) else bos_token
							lowerCAmelCase__       =						AddedToken(__UpperCAmelCase		,    lstrip=__UpperCAmelCase		,    rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ) else eos_token
							lowerCAmelCase__       =						AddedToken(__UpperCAmelCase		,    lstrip=__UpperCAmelCase		,    rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ) else sep_token
							lowerCAmelCase__       =						AddedToken(__UpperCAmelCase		,    lstrip=__UpperCAmelCase		,    rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ) else cls_token
							lowerCAmelCase__       =						AddedToken(__UpperCAmelCase		,    lstrip=__UpperCAmelCase		,    rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ) else unk_token
							lowerCAmelCase__       =						AddedToken(__UpperCAmelCase		,    lstrip=__UpperCAmelCase		,    rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							lowerCAmelCase__       =						AddedToken(__UpperCAmelCase		,    lstrip=__UpperCAmelCase		,    rstrip=__UpperCAmelCase       ) if isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ) else mask_token

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

							with open(__UpperCAmelCase		,    encoding="utf-8"       ) as vocab_handle:
													lowerCAmelCase__       =						json.load(__UpperCAmelCase       )
							lowerCAmelCase__       =						{v: k for k, v in self.encoder.items()}
							lowerCAmelCase__       =						errors  # how to handle errors in decoding
							lowerCAmelCase__       =						bytes_to_unicode()
							lowerCAmelCase__       =						{v: k for k, v in self.byte_encoder.items()}
							with open(__UpperCAmelCase		,    encoding="utf-8"       ) as merges_handle:
													lowerCAmelCase__       =						merges_handle.read().split("\n"       )[1:-1]
							lowerCAmelCase__       =						[tuple(merge.split()       ) for merge in bpe_merges]
							lowerCAmelCase__       =						dict(zip(__UpperCAmelCase		,    range(len(__UpperCAmelCase       )       )       )       )
							lowerCAmelCase__       =						{}
							lowerCAmelCase__       =						add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							lowerCAmelCase__       =						re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"       )

	@property
	# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
	def 		UpperCAmelCase  (							self       )->					List[str]:







							'''simple docstring'''





							return len(self.encoder       )

	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							return dict(self.encoder		,    **self.added_tokens_encoder       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					Dict:







							'''simple docstring'''





							if token in self.cache:
													return self.cache[token]
							lowerCAmelCase__       =						tuple(__UpperCAmelCase       )
							lowerCAmelCase__       =						get_pairs(__UpperCAmelCase       )

							if not pairs:
													return token

							while True:
													lowerCAmelCase__       =						min(__UpperCAmelCase		,    key=lambda __UpperCAmelCase       : self.bpe_ranks.get(__UpperCAmelCase		,    float("inf"       )       )       )
													if bigram not in self.bpe_ranks:
																			break
													lowerCAmelCase__      ,							lowerCAmelCase__       =						bigram
													lowerCAmelCase__       =						[]
													lowerCAmelCase__       =						0
													while i < len(__UpperCAmelCase       ):
																			try:
																									lowerCAmelCase__       =						word.index(__UpperCAmelCase		,    __UpperCAmelCase       )
																			except ValueError:
																									new_word.extend(word[i:]       )
																									break
																			else:
																									new_word.extend(word[i:j]       )
																									lowerCAmelCase__       =						j

																			if word[i] == first and i < len(__UpperCAmelCase       ) - 1 and word[i + 1] == second:
																									new_word.append(first + second       )
																									i += 2
																			else:
																									new_word.append(word[i]       )
																									i += 1
													lowerCAmelCase__       =						tuple(__UpperCAmelCase       )
													lowerCAmelCase__       =						new_word
													if len(__UpperCAmelCase       ) == 1:
																			break
													else:
																			lowerCAmelCase__       =						get_pairs(__UpperCAmelCase       )
							lowerCAmelCase__       =						" ".join(__UpperCAmelCase       )
							lowerCAmelCase__       =						word
							return word

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						[]
							for token in re.findall(self.pat		,    __UpperCAmelCase       ):
													lowerCAmelCase__       =						"".join(
													    self.byte_encoder[b] for b in token.encode("utf-8"       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
													bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__UpperCAmelCase       ).split(" "       )       )
							return bpe_tokens

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					List[str]:







							'''simple docstring'''





							return self.encoder.get(__UpperCAmelCase		,    self.encoder.get(self.unk_token       )       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					Any:







							'''simple docstring'''





							return self.decoder.get(__UpperCAmelCase       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						"".join(__UpperCAmelCase       )
							lowerCAmelCase__       =						bytearray([self.byte_decoder[c] for c in text]       ).decode("utf-8"		,    errors=self.errors       )
							return text

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = None       )->					Tuple[str]:







							'''simple docstring'''





							if not os.path.isdir(__UpperCAmelCase       ):
													logger.error(F"Vocabulary path ({save_directory}) should be a directory"       )
													return
							lowerCAmelCase__       =						os.path.join(
							    __UpperCAmelCase		,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )
							lowerCAmelCase__       =						os.path.join(
							    __UpperCAmelCase		,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]       )

							with open(__UpperCAmelCase		,    "w"		,    encoding="utf-8"       ) as f:
													f.write(json.dumps(self.encoder		,    indent=2		,    sort_keys=__UpperCAmelCase		,    ensure_ascii=__UpperCAmelCase       ) + "\n"       )

							lowerCAmelCase__       =						0
							with open(__UpperCAmelCase		,    "w"		,    encoding="utf-8"       ) as writer:
													writer.write("#version: 0.2\n"       )
													for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,    key=lambda __UpperCAmelCase       : kv[1]       ):
																			if index != token_index:
																									logger.warning(
																									    F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
																									    " Please check that the tokenizer is not corrupted!"       )
																									lowerCAmelCase__       =						token_index
																			writer.write(" ".join(__UpperCAmelCase       ) + "\n"       )
																			index += 1

							return vocab_file, merge_file

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = None       )->					List[int]:







							'''simple docstring'''





							if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							lowerCAmelCase__       =						[self.cls_token_id]
							lowerCAmelCase__       =						[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    __UpperCAmelCase = False       )->					List[int]:







							'''simple docstring'''





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

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

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = None       )->					List[int]:







							'''simple docstring'''





							lowerCAmelCase__       =						[self.sep_token_id]
							lowerCAmelCase__       =						[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 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase=False		,    **__UpperCAmelCase       )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						kwargs.pop("add_prefix_space"		,    self.add_prefix_space       )
							if (is_split_into_words or add_prefix_space) and (len(__UpperCAmelCase       ) > 0 and not text[0].isspace()):
													lowerCAmelCase__       =						" " + text
							return (text, kwargs)


	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    __UpperCAmelCase = PaddingStrategy.DO_NOT_PAD		,    __UpperCAmelCase = None		,    __UpperCAmelCase = None		,    )->					dict:







							'''simple docstring'''





							lowerCAmelCase__       =						super()._pad(
							    encoded_inputs=__UpperCAmelCase		,    max_length=__UpperCAmelCase		,    padding_strategy=__UpperCAmelCase		,    pad_to_multiple_of=__UpperCAmelCase		,    return_attention_mask=__UpperCAmelCase		,    )

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

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

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

							return encoded_inputs














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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class      lowercase__     (			_UpperCAmelCase	):

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase = None		,    )->					List[str]:







							'''simple docstring'''





							super().__init__()
							self.register_modules(transformer=__UpperCAmelCase		,    vae=__UpperCAmelCase		,    scheduler=__UpperCAmelCase       )

							# create a imagenet -> id dictionary for easier use
							lowerCAmelCase__       =						{}
							if idalabel is not None:
													for key, value in idalabel.items():
																			for label in value.split(","       ):
																									lowerCAmelCase__       =						int(__UpperCAmelCase       )
													lowerCAmelCase__       =						dict(sorted(self.labels.items()       )       )

	def 		UpperCAmelCase  (							self		,    __UpperCAmelCase       )->					List[int]:







							'''simple docstring'''





							if not isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ):
													lowerCAmelCase__       =						list(__UpperCAmelCase       )

							for l in label:
													if l not in self.labels:
																			raise ValueError(
																			    F"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."       )

							return [self.labels[l] for l in label]


	@torch.no_grad()
	def __call__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase = 4.0		,    __UpperCAmelCase = None		,    __UpperCAmelCase = 50		,    __UpperCAmelCase = "pil"		,    __UpperCAmelCase = True		,    )->					Union[ImagePipelineOutput, Tuple]:







							'''simple docstring'''





							lowerCAmelCase__       =						len(__UpperCAmelCase       )
							lowerCAmelCase__       =						self.transformer.config.sample_size
							lowerCAmelCase__       =						self.transformer.config.in_channels

							lowerCAmelCase__       =						randn_tensor(
							    shape=(batch_size, latent_channels, latent_size, latent_size)		,    generator=__UpperCAmelCase		,    device=self.device		,    dtype=self.transformer.dtype		,    )
							lowerCAmelCase__       =						torch.cat([latents] * 2       ) if guidance_scale > 1 else latents

							lowerCAmelCase__       =						torch.tensor(__UpperCAmelCase		,    device=self.device       ).reshape(-1       )
							lowerCAmelCase__       =						torch.tensor([1000] * batch_size		,    device=self.device       )
							lowerCAmelCase__       =						torch.cat([class_labels, class_null]		,    0       ) if guidance_scale > 1 else class_labels

							# set step values
							self.scheduler.set_timesteps(__UpperCAmelCase       )

							for t in self.progress_bar(self.scheduler.timesteps       ):
													if guidance_scale > 1:
																			lowerCAmelCase__       =						latent_model_input[: len(__UpperCAmelCase       ) // 2]
																			lowerCAmelCase__       =						torch.cat([half, half]		,    dim=0       )
													lowerCAmelCase__       =						self.scheduler.scale_model_input(__UpperCAmelCase		,    __UpperCAmelCase       )

													lowerCAmelCase__       =						t
													if not torch.is_tensor(__UpperCAmelCase       ):
																			# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																			# This would be a good case for the `match` statement (Python 3.10+)
																			lowerCAmelCase__       =						latent_model_input.device.type == "mps"
																			if isinstance(__UpperCAmelCase		,    __UpperCAmelCase       ):
																									lowerCAmelCase__       =						torch.floataa if is_mps else torch.floataa
																			else:
																									lowerCAmelCase__       =						torch.intaa if is_mps else torch.intaa
																			lowerCAmelCase__       =						torch.tensor([timesteps]		,    dtype=__UpperCAmelCase		,    device=latent_model_input.device       )
													elif len(timesteps.shape       ) == 0:
																			lowerCAmelCase__       =						timesteps[None].to(latent_model_input.device       )
													# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
													lowerCAmelCase__       =						timesteps.expand(latent_model_input.shape[0]       )
													# predict noise model_output
													lowerCAmelCase__       =						self.transformer(
													    __UpperCAmelCase		,    timestep=__UpperCAmelCase		,    class_labels=__UpperCAmelCase       ).sample

													# perform guidance
													if guidance_scale > 1:
																			lowerCAmelCase__      ,							lowerCAmelCase__       =						noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																			lowerCAmelCase__      ,							lowerCAmelCase__       =						torch.split(__UpperCAmelCase		,    len(__UpperCAmelCase       ) // 2		,    dim=0       )

																			lowerCAmelCase__       =						uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																			lowerCAmelCase__       =						torch.cat([half_eps, half_eps]		,    dim=0       )

																			lowerCAmelCase__       =						torch.cat([eps, rest]		,    dim=1       )

													# learned sigma
													if self.transformer.config.out_channels // 2 == latent_channels:
																			lowerCAmelCase__      ,							lowerCAmelCase__       =						torch.split(__UpperCAmelCase		,    __UpperCAmelCase		,    dim=1       )
													else:
																			lowerCAmelCase__       =						noise_pred

													# compute previous image: x_t -> x_t-1
													lowerCAmelCase__       =						self.scheduler.step(__UpperCAmelCase		,    __UpperCAmelCase		,    __UpperCAmelCase       ).prev_sample

							if guidance_scale > 1:
													lowerCAmelCase__      ,							lowerCAmelCase__       =						latent_model_input.chunk(2		,    dim=0       )
							else:
													lowerCAmelCase__       =						latent_model_input

							lowerCAmelCase__       =						1 / self.vae.config.scaling_factor * latents
							lowerCAmelCase__       =						self.vae.decode(__UpperCAmelCase       ).sample

							lowerCAmelCase__       =						(samples / 2 + 0.5).clamp(0		,    1       )

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							lowerCAmelCase__       =						samples.cpu().permute(0		,    2		,    3		,    1       ).float().numpy()

							if output_type == "pil":
													lowerCAmelCase__       =						self.numpy_to_pil(__UpperCAmelCase       )

							if not return_dict:
													return (samples,)

							return ImagePipelineOutput(images=__UpperCAmelCase       )






