






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 DetaImageProcessor
class  lowerCAmelCase_       (				unittest.TestCase					):




							def __init__(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Any ,							SCREAMING_SNAKE_CASE_     :   Any=7 ,							SCREAMING_SNAKE_CASE_     :   Any=3 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=30 ,							SCREAMING_SNAKE_CASE_     :   Any=400 ,							SCREAMING_SNAKE_CASE_     :   Dict=True ,							SCREAMING_SNAKE_CASE_     :   List[str]=None ,							SCREAMING_SNAKE_CASE_     :   int=True ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=[0.5, 0.5, 0.5] ,							SCREAMING_SNAKE_CASE_     :   str=[0.5, 0.5, 0.5] ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=True ,							SCREAMING_SNAKE_CASE_     :   Tuple=1 / 255 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=True ,							):
											# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
											lowerCAmelCase__      			=						size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
											lowerCAmelCase__      			=						parent
											lowerCAmelCase__      			=						batch_size
											lowerCAmelCase__      			=						num_channels
											lowerCAmelCase__      			=						min_resolution
											lowerCAmelCase__      			=						max_resolution
											lowerCAmelCase__      			=						do_resize
											lowerCAmelCase__      			=						size
											lowerCAmelCase__      			=						do_normalize
											lowerCAmelCase__      			=						image_mean
											lowerCAmelCase__      			=						image_std
											lowerCAmelCase__      			=						do_rescale
											lowerCAmelCase__      			=						rescale_factor
											lowerCAmelCase__      			=						do_pad




							def 						__snake_case					(   self     :   Optional[Any]	):
											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 						__snake_case					(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   Dict=False	):
											if not batched:
															lowerCAmelCase__      			=						image_inputs[0]
															if isinstance(SCREAMING_SNAKE_CASE_ ,							Image.Image	):
																			lowerCAmelCase__			,		lowerCAmelCase__      			=						image.size
															else:
																			lowerCAmelCase__			,		lowerCAmelCase__      			=						image.shape[1], image.shape[2]
															if w < h:
																			lowerCAmelCase__      			=						int(self.size['''shortest_edge'''] * h / w	)
																			lowerCAmelCase__      			=						self.size['''shortest_edge''']
															elif w > h:
																			lowerCAmelCase__      			=						self.size['''shortest_edge''']
																			lowerCAmelCase__      			=						int(self.size['''shortest_edge'''] * w / h	)
															else:
																			lowerCAmelCase__      			=						self.size['''shortest_edge''']
																			lowerCAmelCase__      			=						self.size['''shortest_edge''']

											else:
															lowerCAmelCase__      			=						[]
															for image in image_inputs:
																			lowerCAmelCase__			,		lowerCAmelCase__      			=						self.get_expected_values([image]	)
																			expected_values.append((expected_height, expected_width)	)
															lowerCAmelCase__      			=						max(SCREAMING_SNAKE_CASE_ ,							key=lambda SCREAMING_SNAKE_CASE_	: item[0]	)[0]
															lowerCAmelCase__      			=						max(SCREAMING_SNAKE_CASE_ ,							key=lambda SCREAMING_SNAKE_CASE_	: item[1]	)[1]

											return expected_height, expected_width



@require_torch
@require_vision
class  lowerCAmelCase_       (				snake_case__     ,    unittest.TestCase					):
							UpperCamelCase_		:List[Any]		      =    DetaImageProcessor if is_vision_available() else None




							def 						__snake_case					(   self     :   Optional[Any]	):
											lowerCAmelCase__      			=						DetaImageProcessingTester(self	)




							@property
							def 						__snake_case					(   self     :   Dict	):
											return self.image_processor_tester.prepare_image_processor_dict()




							def 						__snake_case					(   self     :   Tuple	):
											lowerCAmelCase__      			=						self.image_processing_class(**self.image_processor_dict	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''image_mean'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''image_std'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''do_normalize'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''do_resize'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''do_rescale'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''do_pad'''	)	)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,							'''size'''	)	)




							def 						__snake_case					(   self     :   Union[str, Any]	):
											lowerCAmelCase__      			=						self.image_processing_class.from_dict(self.image_processor_dict	)
											self.assertEqual(image_processor.size ,							{'''shortest_edge''': 18, '''longest_edge''': 1_333}	)
											self.assertEqual(image_processor.do_pad ,							SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Tuple	):
											pass




							def 						__snake_case					(   self     :   List[Any]	):
											# Initialize image_processing
											lowerCAmelCase__      			=						self.image_processing_class(**self.image_processor_dict	)
											# create random PIL images
											lowerCAmelCase__      			=						prepare_image_inputs(self.image_processor_tester ,							equal_resolution=SCREAMING_SNAKE_CASE_	)
											for image in image_inputs:
															self.assertIsInstance(SCREAMING_SNAKE_CASE_ ,							Image.Image	)

											# Test not batched input
											lowerCAmelCase__      			=						image_processing(image_inputs[0] ,							return_tensors='''pt'''	).pixel_values

											lowerCAmelCase__			,		lowerCAmelCase__      			=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_	)

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

											# Test batched
											lowerCAmelCase__			,		lowerCAmelCase__      			=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ ,							batched=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						image_processing(SCREAMING_SNAKE_CASE_ ,							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 						__snake_case					(   self     :   List[Any]	):
											# Initialize image_processing
											lowerCAmelCase__      			=						self.image_processing_class(**self.image_processor_dict	)
											# create random numpy tensors
											lowerCAmelCase__      			=						prepare_image_inputs(self.image_processor_tester ,							equal_resolution=SCREAMING_SNAKE_CASE_ ,							numpify=SCREAMING_SNAKE_CASE_	)
											for image in image_inputs:
															self.assertIsInstance(SCREAMING_SNAKE_CASE_ ,							np.ndarray	)

											# Test not batched input
											lowerCAmelCase__      			=						image_processing(image_inputs[0] ,							return_tensors='''pt'''	).pixel_values

											lowerCAmelCase__			,		lowerCAmelCase__      			=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_	)

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

											# Test batched
											lowerCAmelCase__      			=						image_processing(SCREAMING_SNAKE_CASE_ ,							return_tensors='''pt'''	).pixel_values

											lowerCAmelCase__			,		lowerCAmelCase__      			=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ ,							batched=SCREAMING_SNAKE_CASE_	)

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




							def 						__snake_case					(   self     :   Tuple	):
											# Initialize image_processing
											lowerCAmelCase__      			=						self.image_processing_class(**self.image_processor_dict	)
											# create random PyTorch tensors
											lowerCAmelCase__      			=						prepare_image_inputs(self.image_processor_tester ,							equal_resolution=SCREAMING_SNAKE_CASE_ ,							torchify=SCREAMING_SNAKE_CASE_	)
											for image in image_inputs:
															self.assertIsInstance(SCREAMING_SNAKE_CASE_ ,							torch.Tensor	)

											# Test not batched input
											lowerCAmelCase__      			=						image_processing(image_inputs[0] ,							return_tensors='''pt'''	).pixel_values

											lowerCAmelCase__			,		lowerCAmelCase__      			=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_	)

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

											# Test batched
											lowerCAmelCase__      			=						image_processing(SCREAMING_SNAKE_CASE_ ,							return_tensors='''pt'''	).pixel_values

											lowerCAmelCase__			,		lowerCAmelCase__      			=						self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ ,							batched=SCREAMING_SNAKE_CASE_	)

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




							@slow
							def 						__snake_case					(   self     :   Dict	):
											# prepare image and target
											lowerCAmelCase__      			=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
											with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' ,							'''r'''	) as f:
															lowerCAmelCase__      			=						json.loads(f.read()	)

											lowerCAmelCase__      			=						{'''image_id''': 39_769, '''annotations''': target}

											# encode them
											lowerCAmelCase__      			=						DetaImageProcessor()
											lowerCAmelCase__      			=						image_processing(images=SCREAMING_SNAKE_CASE_ ,							annotations=SCREAMING_SNAKE_CASE_ ,							return_tensors='''pt'''	)

											# verify pixel values
											lowerCAmelCase__      			=						torch.Size([1, 3, 800, 1_066]	)
											self.assertEqual(encoding['''pixel_values'''].shape ,							SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						torch.tensor([0.2_796, 0.3_138, 0.3_481]	)
											self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-4	)	)

											# verify area
											lowerCAmelCase__      			=						torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]	)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] ,							SCREAMING_SNAKE_CASE_	)	)
											# verify boxes
											lowerCAmelCase__      			=						torch.Size([6, 4]	)
											self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,							SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]	)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-3	)	)
											# verify image_id
											lowerCAmelCase__      			=						torch.tensor([39_769]	)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,							SCREAMING_SNAKE_CASE_	)	)
											# verify is_crowd
											lowerCAmelCase__      			=						torch.tensor([0, 0, 0, 0, 0, 0]	)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,							SCREAMING_SNAKE_CASE_	)	)
											# verify class_labels
											lowerCAmelCase__      			=						torch.tensor([75, 75, 63, 65, 17, 17]	)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,							SCREAMING_SNAKE_CASE_	)	)
											# verify orig_size
											lowerCAmelCase__      			=						torch.tensor([480, 640]	)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,							SCREAMING_SNAKE_CASE_	)	)
											# verify size
											lowerCAmelCase__      			=						torch.tensor([800, 1_066]	)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,							SCREAMING_SNAKE_CASE_	)	)




							@slow
							def 						__snake_case					(   self     :   List[str]	):
											# prepare image, target and masks_path
											lowerCAmelCase__      			=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
											with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt''' ,							'''r'''	) as f:
															lowerCAmelCase__      			=						json.loads(f.read()	)

											lowerCAmelCase__      			=						{'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

											lowerCAmelCase__      			=						pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''	)

											# encode them
											lowerCAmelCase__      			=						DetaImageProcessor(format='''coco_panoptic'''	)
											lowerCAmelCase__      			=						image_processing(images=SCREAMING_SNAKE_CASE_ ,							annotations=SCREAMING_SNAKE_CASE_ ,							masks_path=SCREAMING_SNAKE_CASE_ ,							return_tensors='''pt'''	)

											# verify pixel values
											lowerCAmelCase__      			=						torch.Size([1, 3, 800, 1_066]	)
											self.assertEqual(encoding['''pixel_values'''].shape ,							SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						torch.tensor([0.2_796, 0.3_138, 0.3_481]	)
											self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-4	)	)

											# verify area
											lowerCAmelCase__      			=						torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]	)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area'''] ,							SCREAMING_SNAKE_CASE_	)	)
											# verify boxes
											lowerCAmelCase__      			=						torch.Size([6, 4]	)
											self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,							SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]	)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-3	)	)
											# verify image_id
											lowerCAmelCase__      			=						torch.tensor([39_769]	)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,							SCREAMING_SNAKE_CASE_	)	)
											# verify is_crowd
											lowerCAmelCase__      			=						torch.tensor([0, 0, 0, 0, 0, 0]	)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,							SCREAMING_SNAKE_CASE_	)	)
											# verify class_labels
											lowerCAmelCase__      			=						torch.tensor([17, 17, 63, 75, 75, 93]	)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,							SCREAMING_SNAKE_CASE_	)	)
											# verify masks
											lowerCAmelCase__      			=						822_873
											self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() ,							SCREAMING_SNAKE_CASE_	)
											# verify orig_size
											lowerCAmelCase__      			=						torch.tensor([480, 640]	)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,							SCREAMING_SNAKE_CASE_	)	)
											# verify size
											lowerCAmelCase__      			=						torch.tensor([800, 1_066]	)
											self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,							SCREAMING_SNAKE_CASE_	)	)













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			:		int   				=    logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
_UpperCAmelCase			:		Dict   				=    {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
}

_UpperCAmelCase			:		Tuple   				=    {
    "facebook/bart-base": 1_024,
    "facebook/bart-large": 1_024,
    "facebook/bart-large-mnli": 1_024,
    "facebook/bart-large-cnn": 1_024,
    "facebook/bart-large-xsum": 1_024,
    "yjernite/bart_eli5": 1_024,
}





@lru_cache()
def 	lowerCAmelCase_		()		->    Union[str, Any]:


				'''simple docstring'''




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





def 	lowerCAmelCase_		(lowercase__						:       Optional[int]       )		->    Optional[int]:


				'''simple docstring'''




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




class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:List[str]		      =    VOCAB_FILES_NAMES
							UpperCamelCase_		:Union[str, Any]		      =    PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase_		:Union[str, Any]		      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase_		:str		      =    ['input_ids', 'attention_mask']




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

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




							@property
							def 						__snake_case					(   self     :   List[str]	):
											return len(self.encoder	)




							def 						__snake_case					(   self     :   Union[str, Any]	):
											return dict(self.encoder ,							**self.added_tokens_encoder	)




							def 						__snake_case					(   self     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   Tuple	):
											if token in self.cache:
															return self.cache[token]
											lowerCAmelCase__      			=						tuple(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						get_pairs(SCREAMING_SNAKE_CASE_	)

											if not pairs:
															return token

											while True:
															lowerCAmelCase__      			=						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__      			=						bigram
															lowerCAmelCase__      			=						[]
															lowerCAmelCase__      			=						0
															while i < len(SCREAMING_SNAKE_CASE_	):
																			try:
																							lowerCAmelCase__      			=						word.index(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)
																			except ValueError:
																							new_word.extend(word[i:]	)
																							break
																			else:
																							new_word.extend(word[i:j]	)
																							lowerCAmelCase__      			=						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__      			=						tuple(SCREAMING_SNAKE_CASE_	)
															lowerCAmelCase__      			=						new_word
															if len(SCREAMING_SNAKE_CASE_	) == 1:
																			break
															else:
																			lowerCAmelCase__      			=						get_pairs(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						''' '''.join(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						word
											return word




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




							def 						__snake_case					(   self     :   Any ,							SCREAMING_SNAKE_CASE_     :   List[Any]	):
											return self.encoder.get(SCREAMING_SNAKE_CASE_ ,							self.encoder.get(self.unk_token	)	)




							def 						__snake_case					(   self     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   str	):
											return self.decoder.get(SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Any ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]	):
											lowerCAmelCase__      			=						''''''.join(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						bytearray([self.byte_decoder[c] for c in text]	).decode('''utf-8''' ,							errors=self.errors	)
											return text




							def 						__snake_case					(   self     :   str ,							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__      			=						os.path.join(
											    SCREAMING_SNAKE_CASE_ ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)
											lowerCAmelCase__      			=						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__      			=						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__      			=						token_index
																			writer.write(''' '''.join(SCREAMING_SNAKE_CASE_	) + '''\n'''	)
																			index += 1

											return vocab_file, merge_file




							def 						__snake_case					(   self     :   int ,							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__      			=						[self.cls_token_id]
											lowerCAmelCase__      			=						[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep




							def 						__snake_case					(   self     :   Optional[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 						__snake_case					(   self     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   List[int] ,							SCREAMING_SNAKE_CASE_     :   Optional[List[int]] = None	):
											lowerCAmelCase__      			=						[self.sep_token_id]
											lowerCAmelCase__      			=						[self.cls_token_id]

											if token_ids_a is None:
															return len(cls + token_ids_a + sep	) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]




							def 						__snake_case					(   self     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=False ,							**SCREAMING_SNAKE_CASE_     :   Optional[int]	):
											lowerCAmelCase__      			=						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__      			=						''' ''' + text
											return (text, kwargs)





