import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402
class     lowerCAmelCase_     (       unittest.TestCase ):







							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     0







							def  UpperCamelCase_						(   self      :     Tuple		):
												_UpperCamelCase			     =     AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''		)
												self.assertIsInstance(_A ,			_A		)







							def  UpperCamelCase_						(   self      :     str		):
												with tempfile.TemporaryDirectory() as tmpdirname:
																	_UpperCamelCase			     =     Path(_A		) / '''preprocessor_config.json'''
																	_UpperCamelCase			     =     Path(_A		) / '''config.json'''
																	json.dump(
																	    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} ,			open(_A ,			'''w'''		) ,			)
																	json.dump({'''model_type''': '''clip'''} ,			open(_A ,			'''w'''		)		)

																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained(_A		)
																	self.assertIsInstance(_A ,			_A		)







							def  UpperCamelCase_						(   self      :     Tuple		):
												# Ensure we can load the image processor from the feature extractor config
												with tempfile.TemporaryDirectory() as tmpdirname:
																	_UpperCamelCase			     =     Path(_A		) / '''preprocessor_config.json'''
																	_UpperCamelCase			     =     Path(_A		) / '''config.json'''
																	json.dump(
																	    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} ,			open(_A ,			'''w'''		) ,			)
																	json.dump({'''model_type''': '''clip'''} ,			open(_A ,			'''w'''		)		)

																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained(_A		)
																	self.assertIsInstance(_A ,			_A		)







							def  UpperCamelCase_						(   self      :     List[str]		):
												with tempfile.TemporaryDirectory() as tmpdirname:
																	_UpperCamelCase			     =     CLIPConfig()

																	# Create a dummy config file with image_proceesor_type
																	_UpperCamelCase			     =     Path(_A		) / '''preprocessor_config.json'''
																	_UpperCamelCase			     =     Path(_A		) / '''config.json'''
																	json.dump(
																	    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} ,			open(_A ,			'''w'''		) ,			)
																	json.dump({'''model_type''': '''clip'''} ,			open(_A ,			'''w'''		)		)

																	# remove image_processor_type to make sure config.json alone is enough to load image processor locally
																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained(_A		).to_dict()

																	config_dict.pop('''image_processor_type'''		)
																	_UpperCamelCase			     =     CLIPImageProcessor(**_A		)

																	# save in new folder
																	model_config.save_pretrained(_A		)
																	config.save_pretrained(_A		)

																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained(_A		)

																	# make sure private variable is not incorrectly saved
																	_UpperCamelCase			     =     json.loads(config.to_json_string()		)
																	self.assertTrue('''_processor_class''' not in dict_as_saved		)

												self.assertIsInstance(_A ,			_A		)







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												with tempfile.TemporaryDirectory() as tmpdirname:
																	_UpperCamelCase			     =     Path(_A		) / '''preprocessor_config.json'''
																	json.dump(
																	    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} ,			open(_A ,			'''w'''		) ,			)

																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained(_A		)
																	self.assertIsInstance(_A ,			_A		)







							def  UpperCamelCase_						(   self      :     List[Any]		):
												with self.assertRaisesRegex(
												    _A ,			'''clip-base is not a local folder and is not a valid model identifier'''		):
																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained('''clip-base'''		)







							def  UpperCamelCase_						(   self      :     Dict		):
												with self.assertRaisesRegex(
												    _A ,			R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''		):
																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained(_A ,			revision='''aaaaaa'''		)







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												with self.assertRaisesRegex(
												    _A ,			'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' ,			):
																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model'''		)







							def  UpperCamelCase_						(   self      :     List[Any]		):
												# If remote code is not set, we will time out when asking whether to load the model.
												with self.assertRaises(_A		):
																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''		)
												# If remote code is disabled, we can't load this config.
												with self.assertRaises(_A		):
																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained(
																	    '''hf-internal-testing/test_dynamic_image_processor''' ,			trust_remote_code=_A		)

												_UpperCamelCase			     =     AutoImageProcessor.from_pretrained(
												    '''hf-internal-testing/test_dynamic_image_processor''' ,			trust_remote_code=_A		)
												self.assertEqual(image_processor.__class__.__name__ ,			'''NewImageProcessor'''		)

												# Test image processor can be reloaded.
												with tempfile.TemporaryDirectory() as tmp_dir:
																	image_processor.save_pretrained(_A		)
																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained(_A ,			trust_remote_code=_A		)
												self.assertEqual(reloaded_image_processor.__class__.__name__ ,			'''NewImageProcessor'''		)







							def  UpperCamelCase_						(   self      :     List[Any]		):
												try:
																	AutoConfig.register('''custom''' ,			_A		)
																	AutoImageProcessor.register(_A ,			_A		)
																	# Trying to register something existing in the Transformers library will raise an error
																	with self.assertRaises(_A		):
																						AutoImageProcessor.register(_A ,			_A		)

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						_UpperCamelCase			     =     Path(_A		) / '''preprocessor_config.json'''
																						_UpperCamelCase			     =     Path(_A		) / '''config.json'''
																						json.dump(
																						    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} ,			open(_A ,			'''w'''		) ,			)
																						json.dump({'''model_type''': '''clip'''} ,			open(_A ,			'''w'''		)		)

																						_UpperCamelCase			     =     CustomImageProcessor.from_pretrained(_A		)

																	# Now that the config is registered, it can be used as any other config with the auto-API
																	with tempfile.TemporaryDirectory() as tmp_dir:
																						image_processor.save_pretrained(_A		)
																						_UpperCamelCase			     =     AutoImageProcessor.from_pretrained(_A		)
																						self.assertIsInstance(_A ,			_A		)

												finally:
																	if "custom" in CONFIG_MAPPING._extra_content:
																						del CONFIG_MAPPING._extra_content["custom"]
																	if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
																						del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]







							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												class     lowerCAmelCase_     (       __lowercase ):
																			UpperCAmelCase				   =     True

												try:
																	AutoConfig.register('''custom''' ,			_A		)
																	AutoImageProcessor.register(_A ,			_A		)
																	# If remote code is not set, the default is to use local
																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''		)
																	self.assertEqual(image_processor.__class__.__name__ ,			'''NewImageProcessor'''		)
																	self.assertTrue(image_processor.is_local		)

																	# If remote code is disabled, we load the local one.
																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained(
																	    '''hf-internal-testing/test_dynamic_image_processor''' ,			trust_remote_code=_A		)
																	self.assertEqual(image_processor.__class__.__name__ ,			'''NewImageProcessor'''		)
																	self.assertTrue(image_processor.is_local		)

																	# If remote is enabled, we load from the Hub
																	_UpperCamelCase			     =     AutoImageProcessor.from_pretrained(
																	    '''hf-internal-testing/test_dynamic_image_processor''' ,			trust_remote_code=_A		)
																	self.assertEqual(image_processor.__class__.__name__ ,			'''NewImageProcessor'''		)
																	self.assertTrue(not hasattr(_A ,			'''is_local'''		)		)

												finally:
																	if "custom" in CONFIG_MAPPING._extra_content:
																						del CONFIG_MAPPING._extra_content["custom"]
																	if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
																						del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]

import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase       =		"▁"

_lowerCAmelCase       =		get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
class     lowerCAmelCase_     (       __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     BertGenerationTokenizer
							UpperCAmelCase				   =     False
							UpperCAmelCase				   =     True







							def  UpperCamelCase_						(   self      :     List[str]		):
												super().setUp()

												_UpperCamelCase			     =     BertGenerationTokenizer(_A ,			keep_accents=_A		)
												tokenizer.save_pretrained(self.tmpdirname		)







							def  UpperCamelCase_						(   self      :     Dict		):
												_UpperCamelCase			     =     '''<s>'''
												_UpperCamelCase			     =     1

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







							def  UpperCamelCase_						(   self      :     Any		):
												_UpperCamelCase			     =     list(self.get_tokenizer().get_vocab().keys()		)

												self.assertEqual(vocab_keys[0] ,			'''<unk>'''		)
												self.assertEqual(vocab_keys[1] ,			'''<s>'''		)
												self.assertEqual(vocab_keys[-1] ,			'''<pad>'''		)
												self.assertEqual(len(_A		) ,			1002		)







							def  UpperCamelCase_						(   self      :     Dict		):
												self.assertEqual(self.get_tokenizer().vocab_size ,			1000		)







							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     BertGenerationTokenizer(_A ,			keep_accents=_A		)

												_UpperCamelCase			     =     tokenizer.tokenize('''This is a test'''		)
												self.assertListEqual(_A ,			['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']		)

												self.assertListEqual(
												    tokenizer.convert_tokens_to_ids(_A		) ,			[285, 46, 10, 170, 382] ,			)

												_UpperCamelCase			     =     tokenizer.tokenize('''I was born in 92000, and this is falsé.'''		)
												self.assertListEqual(
												    _A ,			[
												        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''',
												        '''.''',
												    ] ,			)
												_UpperCamelCase			     =     tokenizer.convert_tokens_to_ids(_A		)
												self.assertListEqual(
												    _A ,			[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4] ,			)

												_UpperCamelCase			     =     tokenizer.convert_ids_to_tokens(_A		)
												self.assertListEqual(
												    _A ,			[
												        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>''',
												        '''.''',
												    ] ,			)







							@cached_property
							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''		)







							@slow
							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												_UpperCamelCase			     =     '''Hello World!'''
												_UpperCamelCase			     =     [1_8536, 2260, 101]

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







							@slow
							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     (
												    '''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'''
												)
												_UpperCamelCase			     =     [
												    871,
												    419,
												    358,
												    946,
												    991,
												    2521,
												    452,
												    358,
												    1357,
												    387,
												    7751,
												    3536,
												    112,
												    985,
												    456,
												    126,
												    865,
												    938,
												    5400,
												    5734,
												    458,
												    1368,
												    467,
												    786,
												    2462,
												    5246,
												    1159,
												    633,
												    865,
												    4519,
												    457,
												    582,
												    852,
												    2557,
												    427,
												    916,
												    508,
												    405,
												    3_4324,
												    497,
												    391,
												    408,
												    1_1342,
												    1244,
												    385,
												    100,
												    938,
												    985,
												    456,
												    574,
												    362,
												    1_2597,
												    3200,
												    3129,
												    1172,
												]

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







							@require_torch
							@slow
							def  UpperCamelCase_						(   self      :     Dict		):
												import torch

												from transformers import BertGenerationConfig, BertGenerationEncoder

												# Build sequence
												_UpperCamelCase			     =     list(self.big_tokenizer.get_vocab().keys()		)[:10]
												_UpperCamelCase			     =     ''' '''.join(_A		)
												_UpperCamelCase			     =     self.big_tokenizer.encode_plus(_A ,			return_tensors='''pt''' ,			return_token_type_ids=_A		)
												_UpperCamelCase			     =     self.big_tokenizer.batch_encode_plus(
												    [sequence + ''' ''' + sequence] ,			return_tensors='''pt''' ,			return_token_type_ids=_A		)

												_UpperCamelCase			     =     BertGenerationConfig()
												_UpperCamelCase			     =     BertGenerationEncoder(_A		)

												assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

												with torch.no_grad():
																	model(**_A		)
																	model(**_A		)







							@slow
							def  UpperCamelCase_						(   self      :     Dict		):
												# fmt: off
												_UpperCamelCase			     =     {'''input_ids''': [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 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], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 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]], '''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, 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, 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=_A ,			model_name='''google/bert_for_seq_generation_L-24_bbc_encoder''' ,			revision='''c817d1fd1be2ffa69431227a1fe320544943d4db''' ,			)
