



'''simple docstring'''



import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase_     :       int								=						get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase_							(					snake_case_    ,				unittest.TestCase			):
						_lowerCAmelCase     :   Optional[int]	     =				XLMRobertaTokenizer
						_lowerCAmelCase     :   Optional[Any]	     =				XLMRobertaTokenizerFast
						_lowerCAmelCase     :   List[str]	     =				True
						_lowerCAmelCase     :   Union[str, Any]	     =				True







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													super().setUp()

													# We have a SentencePiece fixture for testing
													SCREAMING_SNAKE_CASE					:      str     					=			XLMRobertaTokenizer(lowerCAmelCase__				,    keep_accents=lowerCAmelCase__				)
													tokenizer.save_pretrained(self.tmpdirname				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			'''<pad>'''
													SCREAMING_SNAKE_CASE					:      Tuple     					=			1

													self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__				)				,    lowerCAmelCase__				)
													self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__				)				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			list(self.get_tokenizer().get_vocab().keys()				)

													self.assertEqual(vocab_keys[0]				,    '''<s>'''				)
													self.assertEqual(vocab_keys[1]				,    '''<pad>'''				)
													self.assertEqual(vocab_keys[-1]				,    '''<mask>'''				)
													self.assertEqual(len(lowerCAmelCase__				)				,    10_02				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													self.assertEqual(self.get_tokenizer().vocab_size				,    10_02				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			XLMRobertaTokenizer(lowerCAmelCase__				,    keep_accents=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer.tokenize('''This is a test'''				)
													self.assertListEqual(lowerCAmelCase__				,    ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

													self.assertListEqual(
													    tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)				,    [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]				,    )

													SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
													self.assertListEqual(
													    lowerCAmelCase__				,    [
													        SPIECE_UNDERLINE + '''I''',
													        SPIECE_UNDERLINE + '''was''',
													        SPIECE_UNDERLINE + '''b''',
													        '''or''',
													        '''n''',
													        SPIECE_UNDERLINE + '''in''',
													        SPIECE_UNDERLINE + '''''',
													        '''9''',
													        '''2''',
													        '''0''',
													        '''0''',
													        '''0''',
													        ''',''',
													        SPIECE_UNDERLINE + '''and''',
													        SPIECE_UNDERLINE + '''this''',
													        SPIECE_UNDERLINE + '''is''',
													        SPIECE_UNDERLINE + '''f''',
													        '''al''',
													        '''s''',
													        '''é''',
													        '''.''',
													    ]				,    )
													SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)
													self.assertListEqual(
													    lowerCAmelCase__				,    [
													        value + tokenizer.fairseq_offset
													        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
													        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
													    ]				,    )

													SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer.convert_ids_to_tokens(lowerCAmelCase__				)
													self.assertListEqual(
													    lowerCAmelCase__				,    [
													        SPIECE_UNDERLINE + '''I''',
													        SPIECE_UNDERLINE + '''was''',
													        SPIECE_UNDERLINE + '''b''',
													        '''or''',
													        '''n''',
													        SPIECE_UNDERLINE + '''in''',
													        SPIECE_UNDERLINE + '''''',
													        '''<unk>''',
													        '''2''',
													        '''0''',
													        '''0''',
													        '''0''',
													        ''',''',
													        SPIECE_UNDERLINE + '''and''',
													        SPIECE_UNDERLINE + '''this''',
													        SPIECE_UNDERLINE + '''is''',
													        SPIECE_UNDERLINE + '''f''',
													        '''al''',
													        '''s''',
													        '''<unk>''',
													        '''.''',
													    ]				,    )







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													if not self.test_slow_tokenizer:
																				# as we don't have a slow version, we can't compare the outputs between slow and fast versions
																				return

													SCREAMING_SNAKE_CASE					:      Tuple     					=			(self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																				with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
																											SCREAMING_SNAKE_CASE					:      int     					=			self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__				,    **lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Dict     					=			self.tokenizer_class.from_pretrained(lowerCAmelCase__				,    **lowerCAmelCase__				)

																											SCREAMING_SNAKE_CASE					:      int     					=			tempfile.mkdtemp()

																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			tokenizer_r.save_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer_p.save_pretrained(lowerCAmelCase__				)

																											# Checks it save with the same files + the tokenizer.json file for the fast one
																											self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files				)				)
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f				)
																											self.assertSequenceEqual(lowerCAmelCase__				,    lowerCAmelCase__				)

																											# Checks everything loads correctly in the same way
																											SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer_r.from_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      int     					=			tokenizer_p.from_pretrained(lowerCAmelCase__				)

																											# Check special tokens are set accordingly on Rust and Python
																											for key in tokenizer_pp.special_tokens_map:
																																		self.assertTrue(hasattr(lowerCAmelCase__				,    lowerCAmelCase__				)				)
																																		# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																																		# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																											shutil.rmtree(lowerCAmelCase__				)

																											# Save tokenizer rust, legacy_format=True
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			tempfile.mkdtemp()

																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			tokenizer_r.save_pretrained(lowerCAmelCase__				,    legacy_format=lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tokenizer_p.save_pretrained(lowerCAmelCase__				)

																											# Checks it save with the same files
																											self.assertSequenceEqual(lowerCAmelCase__				,    lowerCAmelCase__				)

																											# Checks everything loads correctly in the same way
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			tokenizer_r.from_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      str     					=			tokenizer_p.from_pretrained(lowerCAmelCase__				)

																											# Check special tokens are set accordingly on Rust and Python
																											for key in tokenizer_pp.special_tokens_map:
																																		self.assertTrue(hasattr(lowerCAmelCase__				,    lowerCAmelCase__				)				)

																											shutil.rmtree(lowerCAmelCase__				)

																											# Save tokenizer rust, legacy_format=False
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			tempfile.mkdtemp()

																											SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer_r.save_pretrained(lowerCAmelCase__				,    legacy_format=lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tokenizer_p.save_pretrained(lowerCAmelCase__				)

																											# Checks it saved the tokenizer.json file
																											self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files				)				)

																											# Checks everything loads correctly in the same way
																											SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer_r.from_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer_p.from_pretrained(lowerCAmelCase__				)

																											# Check special tokens are set accordingly on Rust and Python
																											for key in tokenizer_pp.special_tokens_map:
																																		self.assertTrue(hasattr(lowerCAmelCase__				,    lowerCAmelCase__				)				)

																											shutil.rmtree(lowerCAmelCase__				)







						@cached_property
						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''				)







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													with tempfile.NamedTemporaryFile() as f:
																				shutil.copyfile(lowerCAmelCase__				,    f.name				)
																				SCREAMING_SNAKE_CASE					:      str     					=			XLMRobertaTokenizer(f.name				,    keep_accents=lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      str     					=			pickle.dumps(lowerCAmelCase__				)
													pickle.loads(lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													if not self.test_rust_tokenizer:
																				return

													SCREAMING_SNAKE_CASE					:      int     					=			self.get_tokenizer()
													SCREAMING_SNAKE_CASE					:      int     					=			self.get_rust_tokenizer()

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			'''I was born in 92000, and this is falsé.'''

													SCREAMING_SNAKE_CASE					:      int     					=			tokenizer.tokenize(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			rust_tokenizer.tokenize(lowerCAmelCase__				)
													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer.encode(lowerCAmelCase__				,    add_special_tokens=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			rust_tokenizer.encode(lowerCAmelCase__				,    add_special_tokens=lowerCAmelCase__				)
													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      str     					=			self.get_rust_tokenizer()
													SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer.encode(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			rust_tokenizer.encode(lowerCAmelCase__				)
													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)







						@slow
						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			'''Hello World!'''
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[0, 3_53_78, 66_61, 38, 2]
													# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
													# xlmr.eval()
													# xlmr.encode(symbols)

													self.assertListEqual(lowerCAmelCase__				,    self.big_tokenizer.encode(lowerCAmelCase__				)				)







						@slow
						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			(
													    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
													    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
													)
													SCREAMING_SNAKE_CASE					:      Any     					=			[
													    0,
													    32_93,
													    83,
													    10,
													    45_52,
													    49_89,
													    79_86,
													    6_78,
													    10,
													    59_15,
													    1_11,
													    17_94_59,
													    12_48_50,
													    4,
													    60_44,
													    2_37,
													    12,
													    6,
													    5,
													    6,
													    4,
													    67_80,
													    7_05,
													    15,
													    13_88,
													    44,
													    3_78,
													    1_01_14,
													    7_11,
													    1_52,
													    20,
													    6,
													    5,
													    2_23_76,
													    6_42,
													    12_21,
													    1_51_90,
													    3_41_53,
													    4_50,
													    56_08,
													    9_59,
													    11_19,
													    5_77_02,
													    1_36,
													    1_86,
													    47,
													    10_98,
													    2_93_67,
													    47,
													    # 4426, # What fairseq tokenizes from "<unk>": "_<"
													    # 3678, # What fairseq tokenizes from "<unk>": "unk"
													    # 2740, # What fairseq tokenizes from "<unk>": ">"
													    3,  # What we tokenize from "<unk>": "<unk>"
													    6,  # Residue from the tokenization: an extra sentencepiece underline
													    4,
													    60_44,
													    2_37,
													    62_84,
													    5_09_01,
													    5_28,
													    31,
													    90,
													    34,
													    9_27,
													    2,
													]
													# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
													# xlmr.eval()
													# xlmr.encode(symbols)

													self.assertListEqual(lowerCAmelCase__				,    self.big_tokenizer.encode(lowerCAmelCase__				)				)







						@slow
						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													# fmt: off
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			{'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
													# fmt: on

													self.tokenizer_integration_test_util(
													    expected_encoding=lowerCAmelCase__				,    model_name='''xlm-roberta-base'''				,    revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''				,    )









'''simple docstring'''



import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


lowerCAmelCase_     :       str								=						logging.get_logger(__name__)

lowerCAmelCase_     :       List[Any]								=						OrderedDict(
    [
        ('align', 'EfficientNetImageProcessor'),
        ('beit', 'BeitImageProcessor'),
        ('bit', 'BitImageProcessor'),
        ('blip', 'BlipImageProcessor'),
        ('blip-2', 'BlipImageProcessor'),
        ('bridgetower', 'BridgeTowerImageProcessor'),
        ('chinese_clip', 'ChineseCLIPImageProcessor'),
        ('clip', 'CLIPImageProcessor'),
        ('clipseg', 'ViTImageProcessor'),
        ('conditional_detr', 'ConditionalDetrImageProcessor'),
        ('convnext', 'ConvNextImageProcessor'),
        ('convnextv2', 'ConvNextImageProcessor'),
        ('cvt', 'ConvNextImageProcessor'),
        ('data2vec-vision', 'BeitImageProcessor'),
        ('deformable_detr', 'DeformableDetrImageProcessor'),
        ('deit', 'DeiTImageProcessor'),
        ('deta', 'DetaImageProcessor'),
        ('detr', 'DetrImageProcessor'),
        ('dinat', 'ViTImageProcessor'),
        ('donut-swin', 'DonutImageProcessor'),
        ('dpt', 'DPTImageProcessor'),
        ('efficientformer', 'EfficientFormerImageProcessor'),
        ('efficientnet', 'EfficientNetImageProcessor'),
        ('flava', 'FlavaImageProcessor'),
        ('focalnet', 'BitImageProcessor'),
        ('git', 'CLIPImageProcessor'),
        ('glpn', 'GLPNImageProcessor'),
        ('groupvit', 'CLIPImageProcessor'),
        ('imagegpt', 'ImageGPTImageProcessor'),
        ('instructblip', 'BlipImageProcessor'),
        ('layoutlmv2', 'LayoutLMv2ImageProcessor'),
        ('layoutlmv3', 'LayoutLMv3ImageProcessor'),
        ('levit', 'LevitImageProcessor'),
        ('mask2former', 'Mask2FormerImageProcessor'),
        ('maskformer', 'MaskFormerImageProcessor'),
        ('mgp-str', 'ViTImageProcessor'),
        ('mobilenet_v1', 'MobileNetV1ImageProcessor'),
        ('mobilenet_v2', 'MobileNetV2ImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevit', 'MobileViTImageProcessor'),
        ('mobilevitv2', 'MobileViTImageProcessor'),
        ('nat', 'ViTImageProcessor'),
        ('oneformer', 'OneFormerImageProcessor'),
        ('owlvit', 'OwlViTImageProcessor'),
        ('perceiver', 'PerceiverImageProcessor'),
        ('pix2struct', 'Pix2StructImageProcessor'),
        ('poolformer', 'PoolFormerImageProcessor'),
        ('regnet', 'ConvNextImageProcessor'),
        ('resnet', 'ConvNextImageProcessor'),
        ('sam', 'SamImageProcessor'),
        ('segformer', 'SegformerImageProcessor'),
        ('swiftformer', 'ViTImageProcessor'),
        ('swin', 'ViTImageProcessor'),
        ('swin2sr', 'Swin2SRImageProcessor'),
        ('swinv2', 'ViTImageProcessor'),
        ('table-transformer', 'DetrImageProcessor'),
        ('timesformer', 'VideoMAEImageProcessor'),
        ('tvlt', 'TvltImageProcessor'),
        ('upernet', 'SegformerImageProcessor'),
        ('van', 'ConvNextImageProcessor'),
        ('videomae', 'VideoMAEImageProcessor'),
        ('vilt', 'ViltImageProcessor'),
        ('vit', 'ViTImageProcessor'),
        ('vit_hybrid', 'ViTHybridImageProcessor'),
        ('vit_mae', 'ViTImageProcessor'),
        ('vit_msn', 'ViTImageProcessor'),
        ('xclip', 'CLIPImageProcessor'),
        ('yolos', 'YolosImageProcessor'),
    ]
)

lowerCAmelCase_     :       List[str]								=						_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def 					UpperCAmelCase   (						A  :				str				):
							for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
														if class_name in extractors:
																					SCREAMING_SNAKE_CASE					:      Tuple     					=			model_type_to_module_name(A				)

																					SCREAMING_SNAKE_CASE					:      List[Any]     					=			importlib.import_module(F""".{module_name}"""					,		'''transformers.models'''				)
																					try:
																												return getattr(A					,		A				)
																					except AttributeError:
																												continue

							for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
														if getattr(A					,		'''__name__'''					,		A				) == class_name:
																					return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
							SCREAMING_SNAKE_CASE					:      List[str]     					=			importlib.import_module('''transformers'''				)
							if hasattr(A					,		A				):
														return getattr(A					,		A				)

							return None
def 					UpperCAmelCase   (						A  :				Union[str, os.PathLike]					,		A  :				Optional[Union[str, os.PathLike]] = None					,		A  :				bool = False					,		A  :				bool = False					,		A  :				Optional[Dict[str, str]] = None					,		A  :				Optional[Union[bool, str]] = None					,		A  :				Optional[str] = None					,		A  :				bool = False					,		**A  :				List[str]					,		):
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			get_file_from_repo(
							    A					,		A					,		cache_dir=A					,		force_download=A					,		resume_download=A					,		proxies=A					,		use_auth_token=A					,		revision=A					,		local_files_only=A					,		)
							if resolved_config_file is None:
														logger.info(
														    '''Could not locate the image processor configuration file, will try to use the model config instead.'''				)
														return {}

							with open(A					,		encoding='''utf-8'''				) as reader:
														return json.load(A				)






class 	lowerCamelCase_							:







						def __init__(  self   :      Tuple				):

													"""simple docstring"""
													raise EnvironmentError(
													    '''AutoImageProcessor is designed to be instantiated '''
													    '''using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''				)







						@classmethod
						@replace_list_option_in_docstrings(lowerCAmelCase__				)
						def 					__lowercase    (  cls   :      int				,    lowerCAmelCase__   :      Optional[int]				,    **lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			kwargs.pop('''config'''				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			kwargs.pop('''trust_remote_code'''				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			True

													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      str     					=			ImageProcessingMixin.get_image_processor_dict(lowerCAmelCase__				,    **lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			config_dict.get('''image_processor_type'''				,    lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      str     					=			None
													if "AutoImageProcessor" in config_dict.get('''auto_map'''				,    {}				):
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			config_dict['''auto_map''']['''AutoImageProcessor''']

													# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
													# and if so, infer the image processor class from there.
													if image_processor_class is None and image_processor_auto_map is None:
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			config_dict.pop('''feature_extractor_type'''				,    lowerCAmelCase__				)
																				if feature_extractor_class is not None:
																											logger.warning(
																											    '''Could not find image processor class in the image processor config or the model config. Loading'''
																											    ''' based on pattern matching with the model\'s feature extractor configuration.'''				)
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			feature_extractor_class.replace('''FeatureExtractor'''				,    '''ImageProcessor'''				)
																				if "AutoFeatureExtractor" in config_dict.get('''auto_map'''				,    {}				):
																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			config_dict['''auto_map''']['''AutoFeatureExtractor''']
																											SCREAMING_SNAKE_CASE					:      str     					=			feature_extractor_auto_map.replace('''FeatureExtractor'''				,    '''ImageProcessor'''				)
																											logger.warning(
																											    '''Could not find image processor auto map in the image processor config or the model config.'''
																											    ''' Loading based on pattern matching with the model\'s feature extractor configuration.'''				)

        # If we don't find the image processor class in the image processor config, let's try the model config.
													if image_processor_class is None and image_processor_auto_map is None:
																				if not isinstance(lowerCAmelCase__				,    lowerCAmelCase__				):
																											SCREAMING_SNAKE_CASE					:      List[str]     					=			AutoConfig.from_pretrained(lowerCAmelCase__				,    **lowerCAmelCase__				)
																				# It could be in `config.image_processor_type``
																				SCREAMING_SNAKE_CASE					:      Any     					=			getattr(lowerCAmelCase__				,    '''image_processor_type'''				,    lowerCAmelCase__				)
																				if hasattr(lowerCAmelCase__				,    '''auto_map'''				) and "AutoImageProcessor" in config.auto_map:
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			config.auto_map['''AutoImageProcessor''']

													if image_processor_class is not None:
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			image_processor_class_from_name(lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			image_processor_auto_map is not None
													SCREAMING_SNAKE_CASE					:      str     					=			image_processor_class is not None or type(lowerCAmelCase__				) in IMAGE_PROCESSOR_MAPPING
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			resolve_trust_remote_code(
													    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)

													if has_remote_code and trust_remote_code:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			get_class_from_dynamic_module(
																				    lowerCAmelCase__				,    lowerCAmelCase__				,    **lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			kwargs.pop('''code_revision'''				,    lowerCAmelCase__				)
																				if os.path.isdir(lowerCAmelCase__				):
																											image_processor_class.register_for_auto_class()
																				return image_processor_class.from_dict(lowerCAmelCase__				,    **lowerCAmelCase__				)
													elif image_processor_class is not None:
																				return image_processor_class.from_dict(lowerCAmelCase__				,    **lowerCAmelCase__				)
													# Last try: we use the IMAGE_PROCESSOR_MAPPING.
													elif type(lowerCAmelCase__				) in IMAGE_PROCESSOR_MAPPING:
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			IMAGE_PROCESSOR_MAPPING[type(lowerCAmelCase__				)]
																				return image_processor_class.from_dict(lowerCAmelCase__				,    **lowerCAmelCase__				)

													raise ValueError(
													    F"""Unrecognized image processor in {pretrained_model_name_or_path}. Should have a """
													    F"""`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following """
													    F"""`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()				)}"""				)







						@staticmethod
						def 					__lowercase    (  lowerCAmelCase__   :      Optional[int]				,    lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													IMAGE_PROCESSOR_MAPPING.register(lowerCAmelCase__				,    lowerCAmelCase__				)




