


import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


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

enable_full_determinism()



class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
					UpperCAmelCase__ :  List[Any]						      =	UNetaDModel
					UpperCAmelCase__ :  Tuple						      =	"sample"





					@property
					def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
								__lowerCamelCase:				List[Any]     =	4
								__lowerCamelCase:				Optional[int]     =	3
								__lowerCamelCase:				Dict     =	(32, 32)

								__lowerCamelCase:				Union[str, Any]     =	floats_tensor((batch_size, num_channels) + sizes							).to(SCREAMING_SNAKE_CASE_							)
								__lowerCamelCase:				Optional[Any]     =	torch.tensor([10]							).to(SCREAMING_SNAKE_CASE_							)

								return {"sample": noise, "timestep": time_step}





					@property
					def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
								return (3, 32, 32)





					@property
					def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
								return (3, 32, 32)







					def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
								__lowerCamelCase:				Dict     =	{
								    """block_out_channels""": (32, 64),
								    """down_block_types""": ("""DownBlock2D""", """AttnDownBlock2D"""),
								    """up_block_types""": ("""AttnUpBlock2D""", """UpBlock2D"""),
								    """attention_head_dim""": 3,
								    """out_channels""": 3,
								    """in_channels""": 3,
								    """layers_per_block""": 2,
								    """sample_size""": 32,
								}
								__lowerCamelCase:				str     =	self.dummy_input
								return init_dict, inputs_dict



class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
					UpperCAmelCase__ :  List[Any]						      =	UNetaDModel
					UpperCAmelCase__ :  List[Any]						      =	"sample"





					@property
					def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
								__lowerCamelCase:				str     =	4
								__lowerCamelCase:				str     =	4
								__lowerCamelCase:				str     =	(32, 32)

								__lowerCamelCase:				Dict     =	floats_tensor((batch_size, num_channels) + sizes							).to(SCREAMING_SNAKE_CASE_							)
								__lowerCamelCase:				List[str]     =	torch.tensor([10]							).to(SCREAMING_SNAKE_CASE_							)

								return {"sample": noise, "timestep": time_step}





					@property
					def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
								return (4, 32, 32)





					@property
					def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
								return (4, 32, 32)





					def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
								__lowerCamelCase:				Optional[Any]     =	{
								    """sample_size""": 32,
								    """in_channels""": 4,
								    """out_channels""": 4,
								    """layers_per_block""": 2,
								    """block_out_channels""": (32, 64),
								    """attention_head_dim""": 32,
								    """down_block_types""": ("""DownBlock2D""", """DownBlock2D"""),
								    """up_block_types""": ("""UpBlock2D""", """UpBlock2D"""),
								}
								__lowerCamelCase:				Any     =	self.dummy_input
								return init_dict, inputs_dict





					def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
								__lowerCamelCase:				List[Any]     =	UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""    ,	output_loading_info=SCREAMING_SNAKE_CASE_							)

								self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)
								self.assertEqual(len(loading_info["""missing_keys"""]							)    ,	0							)

								model.to(SCREAMING_SNAKE_CASE_							)
								__lowerCamelCase:				List[Any]     =	model(**self.dummy_input							).sample

								assert image is not None, "Make sure output is not None"





					@unittest.skipIf(torch_device != """cuda"""    ,	"""This test is supposed to run on GPU"""							)
					def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
								__lowerCamelCase:				str     =	UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""    ,	output_loading_info=SCREAMING_SNAKE_CASE_							)
								model.to(SCREAMING_SNAKE_CASE_							)
								__lowerCamelCase:				Any     =	model(**self.dummy_input							).sample

								assert image is not None, "Make sure output is not None"





					@unittest.skipIf(torch_device != """cuda"""    ,	"""This test is supposed to run on GPU"""							)
					def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
								# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
								__lowerCamelCase:				Optional[Any]     =	UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""    ,	output_loading_info=SCREAMING_SNAKE_CASE_							)
								model_accelerate.to(SCREAMING_SNAKE_CASE_							)
								model_accelerate.eval()

								__lowerCamelCase:				Optional[Any]     =	torch.randn(
								    1    ,	model_accelerate.config.in_channels    ,	model_accelerate.config.sample_size    ,	model_accelerate.config.sample_size    ,	generator=torch.manual_seed(0							)    ,	)
								__lowerCamelCase:				List[str]     =	noise.to(SCREAMING_SNAKE_CASE_							)
								__lowerCamelCase:				Union[str, Any]     =	torch.tensor([10] * noise.shape[0]							).to(SCREAMING_SNAKE_CASE_							)

								__lowerCamelCase:				Optional[int]     =	model_accelerate(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)["""sample"""]

								# two models don't need to stay in the device at the same time
								del model_accelerate
								torch.cuda.empty_cache()
								gc.collect()

								__lowerCamelCase:				List[str]     =	UNetaDModel.from_pretrained(
								    """fusing/unet-ldm-dummy-update"""    ,	output_loading_info=SCREAMING_SNAKE_CASE_    ,	low_cpu_mem_usage=SCREAMING_SNAKE_CASE_							)
								model_normal_load.to(SCREAMING_SNAKE_CASE_							)
								model_normal_load.eval()
								__lowerCamelCase:				List[Any]     =	model_normal_load(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)["""sample"""]

								assert torch_all_close(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	rtol=1E-3							)







					def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
								__lowerCamelCase:				List[str]     =	UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""							)
								model.eval()
								model.to(SCREAMING_SNAKE_CASE_							)

								__lowerCamelCase:				int     =	torch.randn(
								    1    ,	model.config.in_channels    ,	model.config.sample_size    ,	model.config.sample_size    ,	generator=torch.manual_seed(0							)    ,	)
								__lowerCamelCase:				List[Any]     =	noise.to(SCREAMING_SNAKE_CASE_							)
								__lowerCamelCase:				Union[str, Any]     =	torch.tensor([10] * noise.shape[0]							).to(SCREAMING_SNAKE_CASE_							)

								with torch.no_grad():
											__lowerCamelCase:				Optional[Any]     =	model(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							).sample

								__lowerCamelCase:				Optional[Any]     =	output[0, -1, -3:, -3:].flatten().cpu()
								# fmt: off
								__lowerCamelCase:				Any     =	torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]							)
								# fmt: on

								self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	rtol=1E-3							)							)



class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
					UpperCAmelCase__ :  int						      =	UNetaDModel
					UpperCAmelCase__ :  int						      =	"sample"





					@property
					def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=(32, 32)							):
								__lowerCamelCase:				Optional[int]     =	4
								__lowerCamelCase:				int     =	3

								__lowerCamelCase:				Tuple     =	floats_tensor((batch_size, num_channels) + sizes							).to(SCREAMING_SNAKE_CASE_							)
								__lowerCamelCase:				Tuple     =	torch.tensor(batch_size * [10]							).to(dtype=torch.intaa    ,	device=SCREAMING_SNAKE_CASE_							)

								return {"sample": noise, "timestep": time_step}





					@property
					def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
								return (3, 32, 32)





					@property
					def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
								return (3, 32, 32)





					def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
								__lowerCamelCase:				List[Any]     =	{
								    """block_out_channels""": [32, 64, 64, 64],
								    """in_channels""": 3,
								    """layers_per_block""": 1,
								    """out_channels""": 3,
								    """time_embedding_type""": """fourier""",
								    """norm_eps""": 1E-6,
								    """mid_block_scale_factor""": math.sqrt(2.0							),
								    """norm_num_groups""": None,
								    """down_block_types""": [
								        """SkipDownBlock2D""",
								        """AttnSkipDownBlock2D""",
								        """SkipDownBlock2D""",
								        """SkipDownBlock2D""",
								    ],
								    """up_block_types""": [
								        """SkipUpBlock2D""",
								        """SkipUpBlock2D""",
								        """AttnSkipUpBlock2D""",
								        """SkipUpBlock2D""",
								    ],
								}
								__lowerCamelCase:				Dict     =	self.dummy_input
								return init_dict, inputs_dict





					@slow
					def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
								__lowerCamelCase:				Any     =	UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""    ,	output_loading_info=SCREAMING_SNAKE_CASE_							)
								self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)
								self.assertEqual(len(loading_info["""missing_keys"""]							)    ,	0							)

								model.to(SCREAMING_SNAKE_CASE_							)
								__lowerCamelCase:				List[str]     =	self.dummy_input
								__lowerCamelCase:				int     =	floats_tensor((4, 3) + (256, 256)							).to(SCREAMING_SNAKE_CASE_							)
								__lowerCamelCase:				Union[str, Any]     =	noise
								__lowerCamelCase:				str     =	model(**SCREAMING_SNAKE_CASE_							)

								assert image is not None, "Make sure output is not None"





					@slow
					def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
								__lowerCamelCase:				Optional[int]     =	UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""							)
								model.to(SCREAMING_SNAKE_CASE_							)

								__lowerCamelCase:				str     =	4
								__lowerCamelCase:				List[str]     =	3
								__lowerCamelCase:				str     =	(256, 256)

								__lowerCamelCase:				int     =	torch.ones((batch_size, num_channels) + sizes							).to(SCREAMING_SNAKE_CASE_							)
								__lowerCamelCase:				int     =	torch.tensor(batch_size * [1E-4]							).to(SCREAMING_SNAKE_CASE_							)

								with torch.no_grad():
											__lowerCamelCase:				List[Any]     =	model(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							).sample

								__lowerCamelCase:				Optional[Any]     =	output[0, -3:, -3:, -1].flatten().cpu()
								# fmt: off
								__lowerCamelCase:				int     =	torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10980.7129, -20028.8535, 8148.2822, 2342.2905, 567.7608]							)
								# fmt: on

								self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	rtol=1E-2							)							)





					def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
								__lowerCamelCase:				int     =	UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""							)
								model.to(SCREAMING_SNAKE_CASE_							)

								__lowerCamelCase:				Dict     =	4
								__lowerCamelCase:				Optional[int]     =	3
								__lowerCamelCase:				Optional[int]     =	(32, 32)

								__lowerCamelCase:				Dict     =	torch.ones((batch_size, num_channels) + sizes							).to(SCREAMING_SNAKE_CASE_							)
								__lowerCamelCase:				str     =	torch.tensor(batch_size * [1E-4]							).to(SCREAMING_SNAKE_CASE_							)

								with torch.no_grad():
											__lowerCamelCase:				List[str]     =	model(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							).sample

								__lowerCamelCase:				List[str]     =	output[0, -3:, -3:, -1].flatten().cpu()
								# fmt: off
								__lowerCamelCase:				List[Any]     =	torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]							)
								# fmt: on

								self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	rtol=1E-2							)							)







					def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
								# not required for this model
								pass






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		:				List[str]									=       logging.get_logger(__name__)


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

# See all LED models at https://huggingface.co/models?filter=LED
_A		:				int									=       {
    '''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		:				Dict									=       {
    '''allenai/led-base-16384''': 16_384,
}



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



def     __lowerCAmelCase						(							snake_case	:    List[str] )       ->							str:
   __lowerCamelCase:				int     =	set()
   __lowerCamelCase:				str     =	word[0]
   for char in word[1:]:
      pairs.add((prev_char, char) )
      __lowerCamelCase:				Any     =	char
   return pairs







class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  Optional[Any]						      =	VOCAB_FILES_NAMES
     UpperCAmelCase__ :  Optional[Any]						      =	PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__ :  List[str]						      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__ :  List[str]						      =	["input_ids", "attention_mask"]





     def __init__(    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Dict="replace"    ,	SCREAMING_SNAKE_CASE_					:				Dict="<s>"    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]="</s>"    ,	SCREAMING_SNAKE_CASE_					:				int="</s>"    ,	SCREAMING_SNAKE_CASE_					:				Any="<s>"    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]="<unk>"    ,	SCREAMING_SNAKE_CASE_					:				Tuple="<pad>"    ,	SCREAMING_SNAKE_CASE_					:				Dict="<mask>"    ,	SCREAMING_SNAKE_CASE_					:				int=False    ,	**SCREAMING_SNAKE_CASE_					:				List[str]    ,	):
        __lowerCamelCase:				Tuple     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else bos_token
        __lowerCamelCase:				Union[str, Any]     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else eos_token
        __lowerCamelCase:				List[Any]     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else sep_token
        __lowerCamelCase:				Dict     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else cls_token
        __lowerCamelCase:				Any     =	AddedToken(SCREAMING_SNAKE_CASE_    ,	lstrip=SCREAMING_SNAKE_CASE_    ,	rstrip=SCREAMING_SNAKE_CASE_							) if isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) else unk_token
        __lowerCamelCase:				Tuple     =	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
        __lowerCamelCase:				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:
           __lowerCamelCase:				str     =	json.load(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Optional[Any]     =	{v: k for k, v in self.encoder.items()}
        __lowerCamelCase:				Any     =	errors  # how to handle errors in decoding
        __lowerCamelCase:				List[Any]     =	bytes_to_unicode()
        __lowerCamelCase:				Optional[int]     =	{v: k for k, v in self.byte_encoder.items()}
        with open(SCREAMING_SNAKE_CASE_    ,	encoding="""utf-8"""							) as merges_handle:
           __lowerCamelCase:				List[Any]     =	merges_handle.read().split("""\n"""							)[1:-1]
        __lowerCamelCase:				Tuple     =	[tuple(merge.split()							) for merge in bpe_merges]
        __lowerCamelCase:				str     =	dict(zip(SCREAMING_SNAKE_CASE_    ,	range(len(SCREAMING_SNAKE_CASE_							)							)							)							)
        __lowerCamelCase:				List[str]     =	{}
        __lowerCamelCase:				Optional[int]     =	add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        __lowerCamelCase:				str     =	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  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        return len(self.encoder							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        return dict(self.encoder    ,	**self.added_tokens_encoder							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				int							):
        if token in self.cache:
           return self.cache[token]
        __lowerCamelCase:				List[str]     =	tuple(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	get_pairs(SCREAMING_SNAKE_CASE_							)

        if not pairs:
           return token

        while True:
           __lowerCamelCase:				List[str]     =	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
           __lowerCamelCase   ,     __lowerCamelCase:				List[str]     =	bigram
           __lowerCamelCase:				Optional[int]     =	[]
           __lowerCamelCase:				Optional[Any]     =	0
           while i < len(SCREAMING_SNAKE_CASE_							):
              try:
                 __lowerCamelCase:				int     =	word.index(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
              except ValueError:
                 new_word.extend(word[i:]							)
                 break
              else:
                 new_word.extend(word[i:j]							)
                 __lowerCamelCase:				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
           __lowerCamelCase:				Optional[int]     =	tuple(SCREAMING_SNAKE_CASE_							)
           __lowerCamelCase:				Any     =	new_word
           if len(SCREAMING_SNAKE_CASE_							) == 1:
              break
           else:
              __lowerCamelCase:				Tuple     =	get_pairs(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	""" """.join(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	word
        return word





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Any							):
        __lowerCamelCase:				Any     =	[]
        for token in re.findall(self.pat    ,	SCREAMING_SNAKE_CASE_							):
           __lowerCamelCase:				int     =	"""""".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  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Tuple							):
        return self.encoder.get(SCREAMING_SNAKE_CASE_    ,	self.encoder.get(self.unk_token							)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str							):
        return self.decoder.get(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	"""""".join(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				str     =	bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""    ,	errors=self.errors							)
        return text





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				Optional[str] = None							):
        if not os.path.isdir(SCREAMING_SNAKE_CASE_							):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
           return
        __lowerCamelCase:				Dict     =	os.path.join(
            SCREAMING_SNAKE_CASE_    ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
        __lowerCamelCase:				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"""							)

        __lowerCamelCase:				List[str]     =	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!"""							)
                 __lowerCamelCase:				Tuple     =	token_index
              writer.write(""" """.join(SCREAMING_SNAKE_CASE_							) + """\n"""							)
              index += 1

        return vocab_file, merge_file





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				List[int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[List[int]] = None							):
        if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __lowerCamelCase:				List[str]     =	[self.cls_token_id]
        __lowerCamelCase:				int     =	[self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				List[int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[List[int]] = None    ,	SCREAMING_SNAKE_CASE_					:				bool = False							):
        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  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				List[int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[List[int]] = None							):
        __lowerCamelCase:				int     =	[self.sep_token_id]
        __lowerCamelCase:				Tuple     =	[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					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=False    ,	**SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        __lowerCamelCase:				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()):
           __lowerCamelCase:				Dict     =	""" """ + text
        return (text, kwargs)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Union[Dict[str, EncodedInput], BatchEncoding]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int] = None    ,	SCREAMING_SNAKE_CASE_					:				PaddingStrategy = PaddingStrategy.DO_NOT_PAD    ,	SCREAMING_SNAKE_CASE_					:				Optional[int] = None    ,	SCREAMING_SNAKE_CASE_					:				Optional[bool] = None    ,	):
        __lowerCamelCase:				Optional[Any]     =	super()._pad(
            encoded_inputs=SCREAMING_SNAKE_CASE_    ,	max_length=SCREAMING_SNAKE_CASE_    ,	padding_strategy=SCREAMING_SNAKE_CASE_    ,	pad_to_multiple_of=SCREAMING_SNAKE_CASE_    ,	return_attention_mask=SCREAMING_SNAKE_CASE_    ,	)

        # Load from model defaults
        if return_attention_mask is None:
           __lowerCamelCase:				Optional[Any]     =	"""attention_mask""" in self.model_input_names

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

           if needs_to_be_padded:
              __lowerCamelCase:				Union[str, Any]     =	len(SCREAMING_SNAKE_CASE_							) - 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:				str     =	(
                     encoded_inputs["""global_attention_mask"""] + [-1] * difference
                 )
              elif self.padding_side == "left":
                 __lowerCamelCase:				Optional[int]     =	[-1] * difference + encoded_inputs[
                     """global_attention_mask"""
                 ]
              else:
                 raise ValueError("""Invalid padding strategy:""" + str(self.padding_side							)							)

        return encoded_inputs


