






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ConditionalDetrImageProcessor



class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					def __init__(      self  :						List[Any]							,	__A  :						Dict							,	__A  :						int=7							,	__A  :						Optional[Any]=3							,	__A  :						List[str]=3_0							,	__A  :						List[Any]=4_0_0							,	__A  :						Union[str, Any]=True							,	__A  :						List[Any]=None							,	__A  :						Optional[Any]=True							,	__A  :						Tuple=[0.5, 0.5, 0.5]							,	__A  :						Union[str, Any]=[0.5, 0.5, 0.5]							,	__A  :						List[str]=True							,	__A  :						Any=1 / 2_5_5							,	__A  :						Optional[int]=True							,	):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						snake_case__				:   List[str]     =						size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
						snake_case__				:   Dict     =						parent
						snake_case__				:   Optional[int]     =						batch_size
						snake_case__				:   Union[str, Any]     =						num_channels
						snake_case__				:   str     =						min_resolution
						snake_case__				:   Tuple     =						max_resolution
						snake_case__				:   List[Any]     =						do_resize
						snake_case__				:   Dict     =						size
						snake_case__				:   List[str]     =						do_normalize
						snake_case__				:   Optional[int]     =						image_mean
						snake_case__				:   Optional[int]     =						image_std
						snake_case__				:   Any     =						do_rescale
						snake_case__				:   Optional[int]     =						rescale_factor
						snake_case__				:   int     =						do_pad



					def  _lowercase  (      self  :						Dict ):
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						}






					def  _lowercase  (      self  :						Optional[int]							,	__A  :						Dict							,	__A  :						List[Any]=False ):
						if not batched:
							snake_case__				:   List[str]     =						image_inputs[0]
							if isinstance(__A							,	Image.Image ):
								snake_case__,		snake_case__				:   Tuple     =						image.size
							else:
								snake_case__,		snake_case__				:   List[str]     =						image.shape[1], image.shape[2]
							if w < h:
								snake_case__				:   Dict     =						int(self.size["shortest_edge"] * h / w )
								snake_case__				:   Optional[int]     =						self.size["shortest_edge"]
							elif w > h:
								snake_case__				:   List[Any]     =						self.size["shortest_edge"]
								snake_case__				:   Union[str, Any]     =						int(self.size["shortest_edge"] * w / h )
							else:
								snake_case__				:   Dict     =						self.size["shortest_edge"]
								snake_case__				:   Dict     =						self.size["shortest_edge"]

						else:
							snake_case__				:   str     =						[]
							for image in image_inputs:
								snake_case__,		snake_case__				:   str     =						self.get_expected_values([image] )
								expected_values.append((expected_height, expected_width) )
							snake_case__				:   Dict     =						max(__A							,	key=lambda __A : item[0] )[0]
							snake_case__				:   Tuple     =						max(__A							,	key=lambda __A : item[1] )[1]

						return expected_height, expected_width



@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	ConditionalDetrImageProcessor if is_vision_available() else None



					def  _lowercase  (      self  :						int ):
						snake_case__				:   Tuple     =						ConditionalDetrImageProcessingTester(self )



					@property
					def  _lowercase  (      self  :						Any ):
						return self.image_processor_tester.prepare_image_processor_dict()



					def  _lowercase  (      self  :						Any ):
						snake_case__				:   Dict     =						self.image_processing_class(**self.image_processor_dict )
						self.assertTrue(hasattr(__A							,	"image_mean" ) )
						self.assertTrue(hasattr(__A							,	"image_std" ) )
						self.assertTrue(hasattr(__A							,	"do_normalize" ) )
						self.assertTrue(hasattr(__A							,	"do_resize" ) )
						self.assertTrue(hasattr(__A							,	"size" ) )



					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   Optional[Any]     =						self.image_processing_class.from_dict(self.image_processor_dict )
						self.assertEqual(image_processor.size							,	{"shortest_edge": 1_8, "longest_edge": 1_3_3_3} )
						self.assertEqual(image_processor.do_pad							,	__A )

						snake_case__				:   Any     =						self.image_processing_class.from_dict(
						    self.image_processor_dict							,	size=4_2							,	max_size=8_4							,	pad_and_return_pixel_mask=__A )
						self.assertEqual(image_processor.size							,	{"shortest_edge": 4_2, "longest_edge": 8_4} )
						self.assertEqual(image_processor.do_pad							,	__A )



					def  _lowercase  (      self  :						Union[str, Any] ):
						pass



					def  _lowercase  (      self  :						List[str] ):
						# Initialize image_processing
						snake_case__				:   Dict     =						self.image_processing_class(**self.image_processor_dict )
						# create random PIL images
						snake_case__				:   Dict     =						prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A )
						for image in image_inputs:
							self.assertIsInstance(__A							,	Image.Image )

						# Test not batched input
						snake_case__				:   Union[str, Any]     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   Union[str, Any]     =						self.image_processor_tester.get_expected_values(__A )

						self.assertEqual(
						    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

						# Test batched
						snake_case__,		snake_case__				:   Tuple     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

						snake_case__				:   int     =						image_processing(__A							,	return_tensors="pt" ).pixel_values
						self.assertEqual(
						    encoded_images.shape							,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )							,	)



					def  _lowercase  (      self  :						Tuple ):
						# Initialize image_processing
						snake_case__				:   List[str]     =						self.image_processing_class(**self.image_processor_dict )
						# create random numpy tensors
						snake_case__				:   List[str]     =						prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A							,	numpify=__A )
						for image in image_inputs:
							self.assertIsInstance(__A							,	np.ndarray )

						# Test not batched input
						snake_case__				:   int     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   Dict     =						self.image_processor_tester.get_expected_values(__A )

						self.assertEqual(
						    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

						# Test batched
						snake_case__				:   Optional[Any]     =						image_processing(__A							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   str     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

						self.assertEqual(
						    encoded_images.shape							,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )							,	)



					def  _lowercase  (      self  :						Tuple ):
						# Initialize image_processing
						snake_case__				:   Optional[Any]     =						self.image_processing_class(**self.image_processor_dict )
						# create random PyTorch tensors
						snake_case__				:   str     =						prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A							,	torchify=__A )
						for image in image_inputs:
							self.assertIsInstance(__A							,	torch.Tensor )

						# Test not batched input
						snake_case__				:   Tuple     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   Optional[int]     =						self.image_processor_tester.get_expected_values(__A )

						self.assertEqual(
						    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

						# Test batched
						snake_case__				:   Dict     =						image_processing(__A							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   int     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

						self.assertEqual(
						    encoded_images.shape							,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )							,	)



					@slow
					def  _lowercase  (      self  :						List[Any] ):
						# prepare image and target
						snake_case__				:   Union[str, Any]     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
						with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"							,	"r" ) as f:
							snake_case__				:   Union[str, Any]     =						json.loads(f.read() )

						snake_case__				:   Optional[Any]     =						{"image_id": 3_9_7_6_9, "annotations": target}

						# encode them
						snake_case__				:   Tuple     =						ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50" )
						snake_case__				:   int     =						image_processing(images=__A							,	annotations=__A							,	return_tensors="pt" )

						# verify pixel values
						snake_case__				:   str     =						torch.Size([1, 3, 8_0_0, 1_0_6_6] )
						self.assertEqual(encoding["pixel_values"].shape							,	__A )

						snake_case__				:   Tuple     =						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,	__A							,	atol=1e-4 ) )

						# verify area
						snake_case__				:   Optional[int]     =						torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,	__A ) )
						# verify boxes
						snake_case__				:   Tuple     =						torch.Size([6, 4] )
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,	__A )
						snake_case__				:   List[Any]     =						torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,	__A							,	atol=1e-3 ) )
						# verify image_id
						snake_case__				:   str     =						torch.tensor([3_9_7_6_9] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,	__A ) )
						# verify is_crowd
						snake_case__				:   List[Any]     =						torch.tensor([0, 0, 0, 0, 0, 0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,	__A ) )
						# verify class_labels
						snake_case__				:   Optional[int]     =						torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,	__A ) )
						# verify orig_size
						snake_case__				:   Dict     =						torch.tensor([4_8_0, 6_4_0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,	__A ) )
						# verify size
						snake_case__				:   List[str]     =						torch.tensor([8_0_0, 1_0_6_6] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,	__A ) )






					@slow
					def  _lowercase  (      self  :						str ):
						# prepare image, target and masks_path
						snake_case__				:   Optional[Any]     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
						with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"							,	"r" ) as f:
							snake_case__				:   int     =						json.loads(f.read() )

						snake_case__				:   Optional[int]     =						{"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

						snake_case__				:   Optional[Any]     =						pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )

						# encode them
						snake_case__				:   Optional[int]     =						ConditionalDetrImageProcessor(format="coco_panoptic" )
						snake_case__				:   Tuple     =						image_processing(images=__A							,	annotations=__A							,	masks_path=__A							,	return_tensors="pt" )

						# verify pixel values
						snake_case__				:   Optional[Any]     =						torch.Size([1, 3, 8_0_0, 1_0_6_6] )
						self.assertEqual(encoding["pixel_values"].shape							,	__A )

						snake_case__				:   List[str]     =						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,	__A							,	atol=1e-4 ) )

						# verify area
						snake_case__				:   Tuple     =						torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,	__A ) )
						# verify boxes
						snake_case__				:   Dict     =						torch.Size([6, 4] )
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,	__A )
						snake_case__				:   int     =						torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,	__A							,	atol=1e-3 ) )
						# verify image_id
						snake_case__				:   str     =						torch.tensor([3_9_7_6_9] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,	__A ) )
						# verify is_crowd
						snake_case__				:   Tuple     =						torch.tensor([0, 0, 0, 0, 0, 0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,	__A ) )
						# verify class_labels
						snake_case__				:   Optional[Any]     =						torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,	__A ) )
						# verify masks
						snake_case__				:   str     =						8_2_2_8_7_3
						self.assertEqual(encoding["labels"][0]["masks"].sum().item()							,	__A )
						# verify orig_size
						snake_case__				:   int     =						torch.tensor([4_8_0, 6_4_0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,	__A ) )
						# verify size
						snake_case__				:   List[Any]     =						torch.tensor([8_0_0, 1_0_6_6] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,	__A ) )











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


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


__lowerCamelCase      :    Optional[Any]   		=   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

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

__lowerCamelCase      :    Dict   		=   {
    """allenai/led-base-16384""": 1_6384,
}







@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Optional[int]     =						(
	    list(range(ord("!"     )  ,						ord("~"     ) + 1     )     ) + list(range(ord("¡"     )  ,						ord("¬"     ) + 1     )     ) + list(range(ord("®"     )  ,						ord("ÿ"     ) + 1     )     )
	)
	snake_case__				:   Optional[int]     =						bs[:]
	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
	snake_case__				:   Dict     =						[chr(snake_case_     ) for n in cs]
	return dict(zip(snake_case_  ,						snake_case_     )     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]     ):
	snake_case__				:   Dict     =						set()
	snake_case__				:   Tuple     =						word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)     )
		snake_case__				:   List[Any]     =						char
	return pairs





class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	VOCAB_FILES_NAMES
					a_			=	PRETRAINED_VOCAB_FILES_MAP
					a_			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a_			=	["input_ids", "attention_mask"]



					def __init__(      self  :						List[str]							,	__A  :						Any							,	__A  :						List[str]							,	__A  :						Optional[Any]="replace"							,	__A  :						Optional[int]="<s>"							,	__A  :						Union[str, Any]="</s>"							,	__A  :						Tuple="</s>"							,	__A  :						List[Any]="<s>"							,	__A  :						Dict="<unk>"							,	__A  :						Any="<pad>"							,	__A  :						Optional[int]="<mask>"							,	__A  :						List[str]=False							,	**__A  :						Union[str, Any]							,	):
						snake_case__				:   List[Any]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else bos_token
						snake_case__				:   List[str]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else eos_token
						snake_case__				:   Any     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else sep_token
						snake_case__				:   List[Any]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else cls_token
						snake_case__				:   Tuple     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else unk_token
						snake_case__				:   List[Any]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else pad_token

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

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

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

						# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
						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
					# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
					def  _lowercase  (      self  :						List[Any] ):
						return len(self.encoder )



					def  _lowercase  (      self  :						Any ):
						return dict(self.encoder							,	**self.added_tokens_encoder )



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						Optional[int] ):
						if token in self.cache:
							return self.cache[token]
						snake_case__				:   Union[str, Any]     =						tuple(__A )
						snake_case__				:   List[Any]     =						get_pairs(__A )

						if not pairs:
							return token

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

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



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						Optional[Any] ):
						snake_case__				:   List[str]     =						[]
						for token in re.findall(self.pat							,	__A ):
							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(__A ).split(" " ) )
						return bpe_tokens



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Optional[int] ):
						return self.encoder.get(__A							,	self.encoder.get(self.unk_token ) )



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						Optional[Any] ):
						return self.decoder.get(__A )



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Dict ):
						snake_case__				:   Optional[Any]     =						"".join(__A )
						snake_case__				:   int     =						bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8"							,	errors=self.errors )
						return text



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						str							,	__A  :						Optional[str] = None ):
						if not os.path.isdir(__A ):
							logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
							return
						snake_case__				:   List[Any]     =						os.path.join(
						    __A							,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
						snake_case__				:   str     =						os.path.join(
						    __A							,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )

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

						snake_case__				:   str     =						0
						with open(__A							,	"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 __A : 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!" )
									snake_case__				:   int     =						token_index
								writer.write(" ".join(__A ) + "\n" )
								index += 1

						return vocab_file, merge_file



					def  _lowercase  (      self  :						int							,	__A  :						List[int]							,	__A  :						Optional[List[int]] = None ):
						if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						snake_case__				:   Tuple     =						[self.cls_token_id]
						snake_case__				:   List[Any]     =						[self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						List[int]							,	__A  :						Optional[List[int]] = None							,	__A  :						bool = False ):
						if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=__A							,	token_ids_a=__A							,	already_has_special_tokens=__A )

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



					def  _lowercase  (      self  :						List[Any]							,	__A  :						List[int]							,	__A  :						Optional[List[int]] = None ):
						snake_case__				:   Any     =						[self.sep_token_id]
						snake_case__				:   List[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  _lowercase  (      self  :						Optional[Any]							,	__A  :						int							,	__A  :						int=False							,	**__A  :						Dict ):
						snake_case__				:   Optional[int]     =						kwargs.pop("add_prefix_space"							,	self.add_prefix_space )
						if (is_split_into_words or add_prefix_space) and (len(__A ) > 0 and not text[0].isspace()):
							snake_case__				:   Optional[int]     =						" " + text
						return (text, kwargs)






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

						# Load from model defaults
						if return_attention_mask is None:
							snake_case__				:   Union[str, Any]     =						"attention_mask" in self.model_input_names

						if return_attention_mask and "global_attention_mask" in encoded_inputs:
							snake_case__				:   Union[str, Any]     =						encoded_inputs[self.model_input_names[0]]
							# `global_attention_mask` need to have the same length as other (sequential) inputs.
							snake_case__				:   Tuple     =						len(encoded_inputs["global_attention_mask"] ) != len(__A )

							if needs_to_be_padded:
								snake_case__				:   int     =						len(__A ) - len(encoded_inputs["global_attention_mask"] )

								if self.padding_side == "right":
									# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
									snake_case__				:   int     =						(
									    encoded_inputs["global_attention_mask"] + [-1] * difference
									)
								elif self.padding_side == "left":
									snake_case__				:   Tuple     =						[-1] * difference + encoded_inputs[
									    "global_attention_mask"
									]
								else:
									raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )

						return encoded_inputs



