

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:    int 							=			logging.get_logger(__name__)


_a:    int 							=			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
_a:    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""",
    },
}

_a:    Optional[int] 							=			{
    """allenai/led-base-16384""": 1_6384,
}



@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def       __lowerCAmelCase      (						):
	UpperCAmelCase_       				=			(
	    list(range(ord("!"    )       ,     ord("~"    ) + 1    )    ) + list(range(ord("¡"    )       ,     ord("¬"    ) + 1    )    ) + list(range(ord("®"    )       ,     ord("ÿ"    ) + 1    )    )
	)
	UpperCAmelCase_       				=			bs[:]
	UpperCAmelCase_       				=			0
	for b in range(2**8    ):
		if b not in bs:
			bs.append(A    )
			cs.append(2**8 + n    )
			n += 1
	UpperCAmelCase_       				=			[chr(A    ) for n in cs]
	return dict(zip(A       ,     A    )    )



def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			set()
	UpperCAmelCase_       				=			word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)    )
		UpperCAmelCase_       				=			char
	return pairs




class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE__    				=     PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE__    				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE__    				=     ['input_ids', 'attention_mask']




	def __init__(		self	:						Optional[int]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						Optional[int]="replace"		,	lowerCAmelCase	:						Optional[int]="<s>"		,	lowerCAmelCase	:						Union[str, Any]="</s>"		,	lowerCAmelCase	:						Optional[Any]="</s>"		,	lowerCAmelCase	:						Optional[int]="<s>"		,	lowerCAmelCase	:						Tuple="<unk>"		,	lowerCAmelCase	:						List[str]="<pad>"		,	lowerCAmelCase	:						Optional[int]="<mask>"		,	lowerCAmelCase	:						Tuple=False		,	**lowerCAmelCase	:						Optional[Any]		,	):


		'''simple docstring'''







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

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

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

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

		# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
		UpperCAmelCase_       				=			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       __A					(		self	:						Dict							):


		'''simple docstring'''







		return len(self.encoder							)




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







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




	def       __A					(		self	:						Dict		,	lowerCAmelCase	:						Any							):


		'''simple docstring'''







		if token in self.cache:
			return self.cache[token]
		UpperCAmelCase_       				=			tuple(lowerCAmelCase							)
		UpperCAmelCase_       				=			get_pairs(lowerCAmelCase							)

		if not pairs:
			return token

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

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




	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			[]
		for token in re.findall(self.pat		,	lowerCAmelCase							):
			UpperCAmelCase_       				=			"".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(lowerCAmelCase							).split(" "							)							)
		return bpe_tokens




	def       __A					(		self	:						int		,	lowerCAmelCase	:						Dict							):


		'''simple docstring'''







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




	def       __A					(		self	:						Any		,	lowerCAmelCase	:						Optional[int]							):


		'''simple docstring'''







		return self.decoder.get(lowerCAmelCase							)




	def       __A					(		self	:						Any		,	lowerCAmelCase	:						Dict							):


		'''simple docstring'''







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




	def       __A					(		self	:						int		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Optional[str] = None							):


		'''simple docstring'''







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

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

		UpperCAmelCase_       				=			0
		with open(lowerCAmelCase		,	"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 lowerCAmelCase							: 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!"							)
					UpperCAmelCase_       				=			token_index
				writer.write(" ".join(lowerCAmelCase							) + "\n"							)
				index += 1

		return vocab_file, merge_file




	def       __A					(		self	:						int		,	lowerCAmelCase	:						List[int]		,	lowerCAmelCase	:						Optional[List[int]] = None							):


		'''simple docstring'''







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




	def       __A					(		self	:						Any		,	lowerCAmelCase	:						List[int]		,	lowerCAmelCase	:						Optional[List[int]] = None		,	lowerCAmelCase	:						bool = False							):


		'''simple docstring'''







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

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




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						List[int]		,	lowerCAmelCase	:						Optional[List[int]] = None							):


		'''simple docstring'''







		UpperCAmelCase_       				=			[self.sep_token_id]
		UpperCAmelCase_       				=			[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	:						Any		,	lowerCAmelCase	:						Union[str, Any]		,	lowerCAmelCase	:						int=False		,	**lowerCAmelCase	:						Union[str, Any]							):


		'''simple docstring'''







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




	def       __A					(		self	:						Dict		,	lowerCAmelCase	:						Union[Dict[str, EncodedInput], BatchEncoding]		,	lowerCAmelCase	:						Optional[int] = None		,	lowerCAmelCase	:						PaddingStrategy = PaddingStrategy.DO_NOT_PAD		,	lowerCAmelCase	:						Optional[int] = None		,	lowerCAmelCase	:						Optional[bool] = None		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			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:
			UpperCAmelCase_       				=			"attention_mask" in self.model_input_names

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

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

		return encoded_inputs


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     (KDPMaDiscreteScheduler,)
	SCREAMING_SNAKE_CASE__    				=     10




	def       __A					(		self	:						Union[str, Any]		,	**lowerCAmelCase	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{
		    "num_train_timesteps": 1_100,
		    "beta_start": 0.0_001,
		    "beta_end": 0.02,
		    "beta_schedule": "linear",
		}

		config.update(**lowerCAmelCase							)
		return config




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		for timesteps in [10, 50, 100, 1_000]:
			self.check_over_configs(num_train_timesteps=lowerCAmelCase							)




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]		,	[0.0_002, 0.002, 0.02]							):
			self.check_over_configs(beta_start=lowerCAmelCase		,	beta_end=lowerCAmelCase							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		for schedule in ["linear", "scaled_linear"]:
			self.check_over_configs(beta_schedule=lowerCAmelCase							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		for prediction_type in ["epsilon", "v_prediction"]:
			self.check_over_configs(prediction_type=lowerCAmelCase							)




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config(prediction_type="v_prediction"							)
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

		scheduler.set_timesteps(self.num_inference_steps							)

		UpperCAmelCase_       				=			self.dummy_model()
		UpperCAmelCase_       				=			self.dummy_sample_deter * scheduler.init_noise_sigma
		UpperCAmelCase_       				=			sample.to(lowerCAmelCase							)

		for i, t in enumerate(scheduler.timesteps							):
			UpperCAmelCase_       				=			scheduler.scale_model_input(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			scheduler.step(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			output.prev_sample

		UpperCAmelCase_       				=			torch.sum(torch.abs(lowerCAmelCase							)							)
		UpperCAmelCase_       				=			torch.mean(torch.abs(lowerCAmelCase							)							)

		if torch_device in ["cpu", "mps"]:
			assert abs(result_sum.item() - 4.6_934e-07							) < 1e-2
			assert abs(result_mean.item() - 6.1_112e-10							) < 1e-3
		else:
			# CUDA
			assert abs(result_sum.item() - 4.693_428_650_170_972e-07							) < 1e-2
			assert abs(result_mean.item() - 0.0_002							) < 1e-3




	def       __A					(		self	:						int							):


		'''simple docstring'''







		if torch_device == "mps":
			return
		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config()
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

		scheduler.set_timesteps(self.num_inference_steps							)

		UpperCAmelCase_       				=			self.dummy_model()
		UpperCAmelCase_       				=			self.dummy_sample_deter * scheduler.init_noise_sigma
		UpperCAmelCase_       				=			sample.to(lowerCAmelCase							)

		for i, t in enumerate(scheduler.timesteps							):
			UpperCAmelCase_       				=			scheduler.scale_model_input(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			scheduler.step(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			output.prev_sample

		UpperCAmelCase_       				=			torch.sum(torch.abs(lowerCAmelCase							)							)
		UpperCAmelCase_       				=			torch.mean(torch.abs(lowerCAmelCase							)							)

		if torch_device in ["cpu", "mps"]:
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3
		else:
			# CUDA
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		if torch_device == "mps":
			return
		UpperCAmelCase_       				=			self.scheduler_classes[0]
		UpperCAmelCase_       				=			self.get_scheduler_config()
		UpperCAmelCase_       				=			scheduler_class(**lowerCAmelCase							)

		scheduler.set_timesteps(self.num_inference_steps		,	device=lowerCAmelCase							)

		UpperCAmelCase_       				=			self.dummy_model()
		UpperCAmelCase_       				=			self.dummy_sample_deter.to(lowerCAmelCase							) * scheduler.init_noise_sigma

		for t in scheduler.timesteps:
			UpperCAmelCase_       				=			scheduler.scale_model_input(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase							)

			UpperCAmelCase_       				=			scheduler.step(lowerCAmelCase		,	lowerCAmelCase		,	lowerCAmelCase							)
			UpperCAmelCase_       				=			output.prev_sample

		UpperCAmelCase_       				=			torch.sum(torch.abs(lowerCAmelCase							)							)
		UpperCAmelCase_       				=			torch.mean(torch.abs(lowerCAmelCase							)							)

		if str(lowerCAmelCase							).startswith("cpu"							):
			# The following sum varies between 148 and 156 on mps. Why?
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3
		else:
			# CUDA
			assert abs(result_sum.item() - 20.4_125							) < 1e-2
			assert abs(result_mean.item() - 0.0_266							) < 1e-3