
"""simple docstring"""




from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCAmelCase__				:   int   							=			logging.get_logger(__name__)  # pylint: disable=invalid-name


UpperCAmelCase__				:   Union[str, Any]   							=			'\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = "cuda"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample.png")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")\n        ```\n'



class      lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def __init__(self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		None:



											"""simple docstring"""




											super().__init__()

											self.register_modules(
											    unet=SCREAMING_SNAKE_CASE__       ,	scheduler=SCREAMING_SNAKE_CASE__       ,	)

											SCREAMING_SNAKE_CASE__						:	Tuple									=       None



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__=0   ) ->		Any:



											"""simple docstring"""




											if is_accelerate_available() and is_accelerate_version(""">="""       ,	"""0.14.0"""   ):
																		from accelerate import cpu_offload
											else:
																		raise ImportError("""`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher"""   )

											SCREAMING_SNAKE_CASE__						:	int									=       torch.device(F'''cuda:{gpu_id}'''   )

											if self.device.type != "cpu":
																		self.to("""cpu"""       ,	silence_dtype_warnings=SCREAMING_SNAKE_CASE__   )
																		torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

											for cpu_offloaded_model in [self.unet]:
																		cpu_offload(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )

											if self.safety_checker is not None:
																		cpu_offload(self.safety_checker       ,	execution_device=SCREAMING_SNAKE_CASE__       ,	offload_buffers=SCREAMING_SNAKE_CASE__   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__=0   ) ->		Union[str, Any]:



											"""simple docstring"""




											if is_accelerate_available() and is_accelerate_version(""">="""       ,	"""0.17.0.dev0"""   ):
																		from accelerate import cpu_offload_with_hook
											else:
																		raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""   )

											SCREAMING_SNAKE_CASE__						:	Any									=       torch.device(F'''cuda:{gpu_id}'''   )

											if self.device.type != "cpu":
																		self.to("""cpu"""       ,	silence_dtype_warnings=SCREAMING_SNAKE_CASE__   )
																		torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

											SCREAMING_SNAKE_CASE__						:	Tuple									=       None
											for cpu_offloaded_model in [self.unet]:
																		SCREAMING_SNAKE_CASE__						:	Optional[int]									=       cpu_offload_with_hook(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	prev_module_hook=SCREAMING_SNAKE_CASE__   )

											if self.safety_checker is not None:
																		SCREAMING_SNAKE_CASE__						:	int									=       cpu_offload_with_hook(self.safety_checker       ,	SCREAMING_SNAKE_CASE__       ,	prev_module_hook=SCREAMING_SNAKE_CASE__   )

											# We'll offload the last model manually.
											SCREAMING_SNAKE_CASE__						:	List[str]									=       hook



				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def      __magic_name__   (self   ) ->		List[Any]:



											"""simple docstring"""




											if not hasattr(self.unet       ,	"""_hf_hook"""   ):
																		return self.device
											for module in self.unet.modules():
																		if (
																		    hasattr(SCREAMING_SNAKE_CASE__       ,	"""_hf_hook"""   )
																		    and hasattr(module._hf_hook       ,	"""execution_device"""   )
																		    and module._hf_hook.execution_device is not None
																		):
																									return torch.device(module._hf_hook.execution_device   )
											return self.device



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__=None   ) ->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	str									=       (batch_size, num_channels, height, width)
											if isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) and len(SCREAMING_SNAKE_CASE__   ) != batch_size:
																		raise ValueError(
																		    F'''You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE__   )}, but requested an effective batch'''
																		    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''   )

											if latents is None:
																		SCREAMING_SNAKE_CASE__						:	Dict									=       randn_tensor(SCREAMING_SNAKE_CASE__       ,	generator=SCREAMING_SNAKE_CASE__       ,	device=SCREAMING_SNAKE_CASE__       ,	dtype=SCREAMING_SNAKE_CASE__   )
											else:
																		SCREAMING_SNAKE_CASE__						:	Dict									=       latents.to(device=SCREAMING_SNAKE_CASE__       ,	dtype=SCREAMING_SNAKE_CASE__   )

											# scale the initial noise by the standard deviation required by the scheduler
											SCREAMING_SNAKE_CASE__						:	Any									=       latents * self.scheduler.init_noise_sigma
											return latents



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = "pil"   ) ->		Any:



											"""simple docstring"""




											if output_type not in ["pt", "np", "pil"]:
																		raise ValueError(
																		    F'''output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'''   )

											# Equivalent to diffusers.VaeImageProcessor.denormalize
											SCREAMING_SNAKE_CASE__						:	Dict									=       (sample / 2 + 0.5).clamp(0       ,	1   )
											if output_type == "pt":
																		return sample

											# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       sample.cpu().permute(0       ,	2       ,	3       ,	1   ).numpy()
											if output_type == "np":
																		return sample

											# Output_type must be 'pil'
											SCREAMING_SNAKE_CASE__						:	List[str]									=       self.numpy_to_pil(SCREAMING_SNAKE_CASE__   )
											return sample



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__=None   ) ->		Any:



											"""simple docstring"""




											if self.unet.config.num_class_embeds is not None:
																		if isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ):
																									SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(SCREAMING_SNAKE_CASE__       ,	dtype=torch.int   )
																		elif isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ):
																									assert batch_size == 1, "Batch size must be 1 if classes is an int"
																									SCREAMING_SNAKE_CASE__						:	List[str]									=       torch.tensor([class_labels]       ,	dtype=torch.int   )
																		elif class_labels is None:
																									# Randomly generate batch_size class labels
																									# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
																									SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       torch.randint(0       ,	self.unet.config.num_class_embeds       ,	size=(batch_size,)   )
																		SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       class_labels.to(SCREAMING_SNAKE_CASE__   )
											else:
																		SCREAMING_SNAKE_CASE__						:	Tuple									=       None
											return class_labels



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) ->		Tuple:



											"""simple docstring"""




											if num_inference_steps is None and timesteps is None:
																		raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""   )

											if num_inference_steps is not None and timesteps is not None:
																		logger.warning(
																		    F'''Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'''
																		    """ `timesteps` will be used over `num_inference_steps`."""   )

											if latents is not None:
																		SCREAMING_SNAKE_CASE__						:	Optional[int]									=       (batch_size, 3, img_size, img_size)
																		if latents.shape != expected_shape:
																									raise ValueError(F'''The shape of latents is {latents.shape} but is expected to be {expected_shape}.'''   )

											if (callback_steps is None) or (
											    callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) or callback_steps <= 0)
											):
																		raise ValueError(
																		    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
																		    F''' {type(SCREAMING_SNAKE_CASE__   )}.'''   )



				@torch.no_grad()
				@replace_example_docstring(SCREAMING_SNAKE_CASE__   )
				def __call__(self       ,	SCREAMING_SNAKE_CASE__ = 1       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = 1       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = "pil"       ,	SCREAMING_SNAKE_CASE__ = True       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = 1       ,	) ->		Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	str									=       self.unet.config.sample_size
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       self._execution_device

											# 1. Check inputs
											self.check_inputs(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )

											# 2. Prepare image latents
											# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       self.prepare_latents(
											    batch_size=SCREAMING_SNAKE_CASE__       ,	num_channels=self.unet.config.in_channels       ,	height=SCREAMING_SNAKE_CASE__       ,	width=SCREAMING_SNAKE_CASE__       ,	dtype=self.unet.dtype       ,	device=SCREAMING_SNAKE_CASE__       ,	generator=SCREAMING_SNAKE_CASE__       ,	latents=SCREAMING_SNAKE_CASE__       ,	)

											# 3. Handle class_labels for class-conditional models
											SCREAMING_SNAKE_CASE__						:	int									=       self.prepare_class_labels(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	class_labels=SCREAMING_SNAKE_CASE__   )

											# 4. Prepare timesteps
											if timesteps is not None:
																		self.scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE__       ,	device=SCREAMING_SNAKE_CASE__   )
																		SCREAMING_SNAKE_CASE__						:	List[str]									=       self.scheduler.timesteps
																		SCREAMING_SNAKE_CASE__						:	List[Any]									=       len(SCREAMING_SNAKE_CASE__   )
											else:
																		self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )
																		SCREAMING_SNAKE_CASE__						:	Any									=       self.scheduler.timesteps

											# 5. Denoising loop
											# Multistep sampling: implements Algorithm 1 in the paper
											with self.progress_bar(total=SCREAMING_SNAKE_CASE__   ) as progress_bar:
																		for i, t in enumerate(SCREAMING_SNAKE_CASE__   ):
																									SCREAMING_SNAKE_CASE__						:	Tuple									=       self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )
																									SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       self.unet(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	class_labels=SCREAMING_SNAKE_CASE__       ,	return_dict=SCREAMING_SNAKE_CASE__   )[0]

																									SCREAMING_SNAKE_CASE__						:	Any									=       self.scheduler.step(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	generator=SCREAMING_SNAKE_CASE__   )[0]

																									# call the callback, if provided
																									progress_bar.update()
																									if callback is not None and i % callback_steps == 0:
																																callback(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )

        # 6. Post-process image sample
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       self.postprocess_image(SCREAMING_SNAKE_CASE__       ,	output_type=SCREAMING_SNAKE_CASE__   )

											# Offload last model to CPU
											if hasattr(self       ,	"""final_offload_hook"""   ) and self.final_offload_hook is not None:
																		self.final_offload_hook.offload()

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__   )








"""simple docstring"""




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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase__				:   Optional[Any]   							=			logging.get_logger(__name__)


UpperCAmelCase__				:   Optional[int]   							=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all MVP models at https://huggingface.co/models?filter=mvp
UpperCAmelCase__				:   Optional[Any]   							=			{
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
}

UpperCAmelCase__				:   Dict   							=			{
    'RUCAIBox/mvp': 1_0_2_4,
}

@lru_cache()
def lowercase_  (       ):
							SCREAMING_SNAKE_CASE__						:	Dict									=       (
							    list(range(ord("""!"""						)       ,ord("""~"""						) + 1						)						) + list(range(ord("""¡"""						)       ,ord("""¬"""						) + 1						)						) + list(range(ord("""®"""						)       ,ord("""ÿ"""						) + 1						)						)
							)
							SCREAMING_SNAKE_CASE__						:	str									=       bs[:]
							SCREAMING_SNAKE_CASE__						:	Any									=       0
							for b in range(2**8						):
														if b not in bs:
																					bs.append(_snake_case						)
																					cs.append(2**8 + n						)
																					n += 1
							SCREAMING_SNAKE_CASE__						:	List[str]									=       [chr(_snake_case						) for n in cs]
							return dict(zip(_snake_case       ,_snake_case						)						)

def lowercase_  (       _snake_case						):
							SCREAMING_SNAKE_CASE__						:	Tuple									=       set()
							SCREAMING_SNAKE_CASE__						:	Any									=       word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)						)
														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       char
							return pairs



class      lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase				:						int 	=       VOCAB_FILES_NAMES
				__UpperCamelCase				:						str 	=       PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase				:						str 	=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase				:						List[Any] 	=       ['''input_ids''', '''attention_mask''']



				def __init__(self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__="replace"       ,	SCREAMING_SNAKE_CASE__="<s>"       ,	SCREAMING_SNAKE_CASE__="</s>"       ,	SCREAMING_SNAKE_CASE__="</s>"       ,	SCREAMING_SNAKE_CASE__="<s>"       ,	SCREAMING_SNAKE_CASE__="<unk>"       ,	SCREAMING_SNAKE_CASE__="<pad>"       ,	SCREAMING_SNAKE_CASE__="<mask>"       ,	SCREAMING_SNAKE_CASE__=False       ,	**SCREAMING_SNAKE_CASE__       ,	) ->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       AddedToken(SCREAMING_SNAKE_CASE__       ,	lstrip=SCREAMING_SNAKE_CASE__       ,	rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) else bos_token
											SCREAMING_SNAKE_CASE__						:	str									=       AddedToken(SCREAMING_SNAKE_CASE__       ,	lstrip=SCREAMING_SNAKE_CASE__       ,	rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) else eos_token
											SCREAMING_SNAKE_CASE__						:	List[str]									=       AddedToken(SCREAMING_SNAKE_CASE__       ,	lstrip=SCREAMING_SNAKE_CASE__       ,	rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) else sep_token
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       AddedToken(SCREAMING_SNAKE_CASE__       ,	lstrip=SCREAMING_SNAKE_CASE__       ,	rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) else cls_token
											SCREAMING_SNAKE_CASE__						:	Tuple									=       AddedToken(SCREAMING_SNAKE_CASE__       ,	lstrip=SCREAMING_SNAKE_CASE__       ,	rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) else unk_token
											SCREAMING_SNAKE_CASE__						:	Any									=       AddedToken(SCREAMING_SNAKE_CASE__       ,	lstrip=SCREAMING_SNAKE_CASE__       ,	rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) else pad_token

											# Mask token behave like a normal word, i.e. include the space before it
											SCREAMING_SNAKE_CASE__						:	List[str]									=       AddedToken(SCREAMING_SNAKE_CASE__       ,	lstrip=SCREAMING_SNAKE_CASE__       ,	rstrip=SCREAMING_SNAKE_CASE__   ) if isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ) else mask_token

											super().__init__(
											    errors=SCREAMING_SNAKE_CASE__       ,	bos_token=SCREAMING_SNAKE_CASE__       ,	eos_token=SCREAMING_SNAKE_CASE__       ,	unk_token=SCREAMING_SNAKE_CASE__       ,	sep_token=SCREAMING_SNAKE_CASE__       ,	cls_token=SCREAMING_SNAKE_CASE__       ,	pad_token=SCREAMING_SNAKE_CASE__       ,	mask_token=SCREAMING_SNAKE_CASE__       ,	add_prefix_space=SCREAMING_SNAKE_CASE__       ,	**SCREAMING_SNAKE_CASE__       ,	)
											with open(SCREAMING_SNAKE_CASE__       ,	encoding="""utf-8"""   ) as vocab_handle:
																		SCREAMING_SNAKE_CASE__						:	Dict									=       json.load(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       {v: k for k, v in self.encoder.items()}
											SCREAMING_SNAKE_CASE__						:	Any									=       errors  # how to handle errors in decoding
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       bytes_to_unicode()
											SCREAMING_SNAKE_CASE__						:	Tuple									=       {v: k for k, v in self.byte_encoder.items()}
											with open(SCREAMING_SNAKE_CASE__       ,	encoding="""utf-8"""   ) as merges_handle:
																		SCREAMING_SNAKE_CASE__						:	List[str]									=       merges_handle.read().split("""\n"""   )[1:-1]
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       [tuple(merge.split()   ) for merge in bpe_merges]
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       dict(zip(SCREAMING_SNAKE_CASE__       ,	range(len(SCREAMING_SNAKE_CASE__   )   )   )   )
											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       {}
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       add_prefix_space

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



				@property
				def      __magic_name__   (self   ) ->		Tuple:



											"""simple docstring"""




											return len(self.encoder   )



				def      __magic_name__   (self   ) ->		Optional[Any]:



											"""simple docstring"""




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



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		Dict:



											"""simple docstring"""




											if token in self.cache:
																		return self.cache[token]
											SCREAMING_SNAKE_CASE__						:	Dict									=       tuple(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	str									=       get_pairs(SCREAMING_SNAKE_CASE__   )

											if not pairs:
																		return token

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

																									if word[i] == first and i < len(SCREAMING_SNAKE_CASE__   ) - 1 and word[i + 1] == second:
																																new_word.append(first + second   )
																																i += 2
																									else:
																																new_word.append(word[i]   )
																																i += 1
																		SCREAMING_SNAKE_CASE__						:	int									=       tuple(SCREAMING_SNAKE_CASE__   )
																		SCREAMING_SNAKE_CASE__						:	Optional[int]									=       new_word
																		if len(SCREAMING_SNAKE_CASE__   ) == 1:
																									break
																		else:
																									SCREAMING_SNAKE_CASE__						:	List[Any]									=       get_pairs(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       """ """.join(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       word
											return word



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       []
											for token in re.findall(self.pat       ,	SCREAMING_SNAKE_CASE__   ):
																		SCREAMING_SNAKE_CASE__						:	Dict									=       """""".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(SCREAMING_SNAKE_CASE__   ).split(""" """   )   )
											return bpe_tokens



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		str:



											"""simple docstring"""




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



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



											"""simple docstring"""




											return self.decoder.get(SCREAMING_SNAKE_CASE__   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	int									=       """""".join(SCREAMING_SNAKE_CASE__   )
											SCREAMING_SNAKE_CASE__						:	List[Any]									=       bytearray([self.byte_decoder[c] for c in text]   ).decode("""utf-8"""       ,	errors=self.errors   )
											return text



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = None   ) ->		Tuple[str]:



											"""simple docstring"""




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

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

											SCREAMING_SNAKE_CASE__						:	Dict									=       0
											with open(SCREAMING_SNAKE_CASE__       ,	"""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 SCREAMING_SNAKE_CASE__   : 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!"""   )
																																SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       token_index
																									writer.write(""" """.join(SCREAMING_SNAKE_CASE__   ) + """\n"""   )
																									index += 1

											return vocab_file, merge_file



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = None   ) ->		List[int]:



											"""simple docstring"""




											if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       [self.cls_token_id]
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       [self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = None       ,	SCREAMING_SNAKE_CASE__ = False   ) ->		List[int]:



											"""simple docstring"""




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

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



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = None   ) ->		List[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Dict									=       [self.sep_token_id]
											SCREAMING_SNAKE_CASE__						:	Optional[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      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__=False       ,	**SCREAMING_SNAKE_CASE__   ) ->		List[Any]:



											"""simple docstring"""




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




