






"""simple docstring"""







import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


__a       :	Any             = get_tests_dir('fixtures/dummy_feature_extractor_config.json')
__a       :	Optional[Any]             = get_tests_dir('fixtures/vocab.json')
__a       :	List[Any]             = get_tests_dir('fixtures')







class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  0




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''    )
												self.assertIsInstance(__A				,		__A    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												with tempfile.TemporaryDirectory() as tmpdirname:
																	a__          =  WavaVecaConfig()
																	a__          =  AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''    )

																	# save in new folder
																	model_config.save_pretrained(__A    )
																	processor.save_pretrained(__A    )

																	a__          =  AutoProcessor.from_pretrained(__A    )

												self.assertIsInstance(__A				,		__A    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												with tempfile.TemporaryDirectory() as tmpdirname:
																	# copy relevant files
																	copyfile(__A				,		os.path.join(__A				,		__A    )    )
																	copyfile(__A				,		os.path.join(__A				,		'''vocab.json'''    )    )

																	a__          =  AutoProcessor.from_pretrained(__A    )

												self.assertIsInstance(__A				,		__A    )




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												with tempfile.TemporaryDirectory() as tmpdirname:
																	a__          =  WavaVecaFeatureExtractor()
																	a__          =  AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''    )

																	a__          =  WavaVecaProcessor(__A				,		__A    )

																	# save in new folder
																	processor.save_pretrained(__A    )

																	# drop `processor_class` in tokenizer
																	with open(os.path.join(__A				,		__A    )				,		'''r'''    ) as f:
																						a__          =  json.load(__A    )
																						config_dict.pop('''processor_class'''    )

																	with open(os.path.join(__A				,		__A    )				,		'''w'''    ) as f:
																						f.write(json.dumps(__A    )    )

																	a__          =  AutoProcessor.from_pretrained(__A    )

												self.assertIsInstance(__A				,		__A    )




							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												with tempfile.TemporaryDirectory() as tmpdirname:
																	a__          =  WavaVecaFeatureExtractor()
																	a__          =  AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''    )

																	a__          =  WavaVecaProcessor(__A				,		__A    )

																	# save in new folder
																	processor.save_pretrained(__A    )

																	# drop `processor_class` in feature extractor
																	with open(os.path.join(__A				,		__A    )				,		'''r'''    ) as f:
																						a__          =  json.load(__A    )
																						config_dict.pop('''processor_class'''    )

																	with open(os.path.join(__A				,		__A    )				,		'''w'''    ) as f:
																						f.write(json.dumps(__A    )    )

																	a__          =  AutoProcessor.from_pretrained(__A    )

												self.assertIsInstance(__A				,		__A    )




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												with tempfile.TemporaryDirectory() as tmpdirname:
																	a__          =  WavaVecaConfig(processor_class='''Wav2Vec2Processor'''    )
																	model_config.save_pretrained(__A    )
																	# copy relevant files
																	copyfile(__A				,		os.path.join(__A				,		'''vocab.json'''    )    )
																	# create emtpy sample processor
																	with open(os.path.join(__A				,		__A    )				,		'''w'''    ) as f:
																						f.write('''{}'''    )

																	a__          =  AutoProcessor.from_pretrained(__A    )

												self.assertIsInstance(__A				,		__A    )




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												with self.assertRaises(__A    ):
																	a__          =  AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''    )
												# If remote code is disabled, we can't load this config.
												with self.assertRaises(__A    ):
																	a__          =  AutoProcessor.from_pretrained(
																	    '''hf-internal-testing/test_dynamic_processor'''				,		trust_remote_code=__A    )

												a__          =  AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''				,		trust_remote_code=__A    )
												self.assertTrue(processor.special_attribute_present    )
												self.assertEqual(processor.__class__.__name__				,		'''NewProcessor'''    )

												a__          =  processor.feature_extractor
												self.assertTrue(feature_extractor.special_attribute_present    )
												self.assertEqual(feature_extractor.__class__.__name__				,		'''NewFeatureExtractor'''    )

												a__          =  processor.tokenizer
												self.assertTrue(tokenizer.special_attribute_present    )
												if is_tokenizers_available():
																	self.assertEqual(tokenizer.__class__.__name__				,		'''NewTokenizerFast'''    )

																	# Test we can also load the slow version
																	a__          =  AutoProcessor.from_pretrained(
																	    '''hf-internal-testing/test_dynamic_processor'''				,		trust_remote_code=__A				,		use_fast=__A    )
																	a__          =  new_processor.tokenizer
																	self.assertTrue(new_tokenizer.special_attribute_present    )
																	self.assertEqual(new_tokenizer.__class__.__name__				,		'''NewTokenizer'''    )
												else:
																	self.assertEqual(tokenizer.__class__.__name__				,		'''NewTokenizer'''    )




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												try:
																	AutoConfig.register('''custom'''				,		__A    )
																	AutoFeatureExtractor.register(__A				,		__A    )
																	AutoTokenizer.register(__A				,		slow_tokenizer_class=__A    )
																	AutoProcessor.register(__A				,		__A    )
																	# Trying to register something existing in the Transformers library will raise an error
																	with self.assertRaises(__A    ):
																						AutoProcessor.register(__A				,		__A    )

																	# Now that the config is registered, it can be used as any other config with the auto-API
																	a__          =  CustomFeatureExtractor.from_pretrained(__A    )

																	with tempfile.TemporaryDirectory() as tmp_dir:
																						a__          =  os.path.join(__A				,		'''vocab.txt'''    )
																						with open(__A				,		'''w'''				,		encoding='''utf-8'''    ) as vocab_writer:
																											vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]    )    )
																						a__          =  CustomTokenizer(__A    )

																	a__          =  CustomProcessor(__A				,		__A    )

																	with tempfile.TemporaryDirectory() as tmp_dir:
																						processor.save_pretrained(__A    )
																						a__          =  AutoProcessor.from_pretrained(__A    )
																						self.assertIsInstance(__A				,		__A    )

												finally:
																	if "custom" in CONFIG_MAPPING._extra_content:
																						del CONFIG_MAPPING._extra_content["custom"]
																	if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																						del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
																	if CustomConfig in TOKENIZER_MAPPING._extra_content:
																						del TOKENIZER_MAPPING._extra_content[CustomConfig]
																	if CustomConfig in PROCESSOR_MAPPING._extra_content:
																						del PROCESSOR_MAPPING._extra_content[CustomConfig]




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





																			"""simple docstring"""





																			_SCREAMING_SNAKE_CASE    =False







												class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





																			"""simple docstring"""





																			_SCREAMING_SNAKE_CASE    =False







												class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





																			"""simple docstring"""





																			_SCREAMING_SNAKE_CASE    ='AutoFeatureExtractor'
																			_SCREAMING_SNAKE_CASE    ='AutoTokenizer'
																			_SCREAMING_SNAKE_CASE    =False

												try:
																	AutoConfig.register('''custom'''				,		__A    )
																	AutoFeatureExtractor.register(__A				,		__A    )
																	AutoTokenizer.register(__A				,		slow_tokenizer_class=__A    )
																	AutoProcessor.register(__A				,		__A    )
																	# If remote code is not set, the default is to use local classes.
																	a__          =  AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''    )
																	self.assertEqual(processor.__class__.__name__				,		'''NewProcessor'''    )
																	self.assertFalse(processor.special_attribute_present    )
																	self.assertFalse(processor.feature_extractor.special_attribute_present    )
																	self.assertFalse(processor.tokenizer.special_attribute_present    )

																	# If remote code is disabled, we load the local ones.
																	a__          =  AutoProcessor.from_pretrained(
																	    '''hf-internal-testing/test_dynamic_processor'''				,		trust_remote_code=__A    )
																	self.assertEqual(processor.__class__.__name__				,		'''NewProcessor'''    )
																	self.assertFalse(processor.special_attribute_present    )
																	self.assertFalse(processor.feature_extractor.special_attribute_present    )
																	self.assertFalse(processor.tokenizer.special_attribute_present    )

																	# If remote is enabled, we load from the Hub.
																	a__          =  AutoProcessor.from_pretrained(
																	    '''hf-internal-testing/test_dynamic_processor'''				,		trust_remote_code=__A    )
																	self.assertEqual(processor.__class__.__name__				,		'''NewProcessor'''    )
																	self.assertTrue(processor.special_attribute_present    )
																	self.assertTrue(processor.feature_extractor.special_attribute_present    )
																	self.assertTrue(processor.tokenizer.special_attribute_present    )

												finally:
																	if "custom" in CONFIG_MAPPING._extra_content:
																						del CONFIG_MAPPING._extra_content["custom"]
																	if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																						del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
																	if CustomConfig in TOKENIZER_MAPPING._extra_content:
																						del TOKENIZER_MAPPING._extra_content[CustomConfig]
																	if CustomConfig in PROCESSOR_MAPPING._extra_content:
																						del PROCESSOR_MAPPING._extra_content[CustomConfig]




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-bert'''    )
												self.assertEqual(processor.__class__.__name__				,		'''BertTokenizerFast'''    )





							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-convnext'''    )
												self.assertEqual(processor.__class__.__name__				,		'''ConvNextImageProcessor'''    )







@is_staging_test
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']




							@classmethod
							def  lowercase		(   cls:     List[Any]    ):

												'''simple docstring'''

												a__          =  TOKEN
												HfFolder.save_token(__A    )




							@classmethod
							def  lowercase		(   cls:     Tuple    ):

												'''simple docstring'''

												try:
																	delete_repo(token=cls._token				,		repo_id='''test-processor'''    )
												except HTTPError:
																	pass

												try:
																	delete_repo(token=cls._token				,		repo_id='''valid_org/test-processor-org'''    )
												except HTTPError:
																	pass

												try:
																	delete_repo(token=cls._token				,		repo_id='''test-dynamic-processor'''    )
												except HTTPError:
																	pass




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  WavaVecaProcessor.from_pretrained(__A    )
												with tempfile.TemporaryDirectory() as tmp_dir:
																	processor.save_pretrained(
																	    os.path.join(__A				,		'''test-processor'''    )				,		push_to_hub=__A				,		use_auth_token=self._token    )

																	a__          =  WavaVecaProcessor.from_pretrained(F'{USER}/test-processor'    )
																	for k, v in processor.feature_extractor.__dict__.items():
																						self.assertEqual(__A				,		getattr(new_processor.feature_extractor				,		__A    )    )
																	self.assertDictEqual(new_processor.tokenizer.get_vocab()				,		processor.tokenizer.get_vocab()    )




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  WavaVecaProcessor.from_pretrained(__A    )

												with tempfile.TemporaryDirectory() as tmp_dir:
																	processor.save_pretrained(
																	    os.path.join(__A				,		'''test-processor-org'''    )				,		push_to_hub=__A				,		use_auth_token=self._token				,		organization='''valid_org'''				,		)

																	a__          =  WavaVecaProcessor.from_pretrained('''valid_org/test-processor-org'''    )
																	for k, v in processor.feature_extractor.__dict__.items():
																						self.assertEqual(__A				,		getattr(new_processor.feature_extractor				,		__A    )    )
																	self.assertDictEqual(new_processor.tokenizer.get_vocab()				,		processor.tokenizer.get_vocab()    )





							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												CustomFeatureExtractor.register_for_auto_class()
												CustomTokenizer.register_for_auto_class()
												CustomProcessor.register_for_auto_class()

												a__          =  CustomFeatureExtractor.from_pretrained(__A    )

												with tempfile.TemporaryDirectory() as tmp_dir:
																	a__          =  os.path.join(__A				,		'''vocab.txt'''    )
																	with open(__A				,		'''w'''				,		encoding='''utf-8'''    ) as vocab_writer:
																						vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]    )    )
																	a__          =  CustomTokenizer(__A    )

												a__          =  CustomProcessor(__A				,		__A    )

												with tempfile.TemporaryDirectory() as tmp_dir:
																	create_repo(F'{USER}/test-dynamic-processor'				,		token=self._token    )
																	a__          =  Repository(__A				,		clone_from=F'{USER}/test-dynamic-processor'				,		token=self._token    )
																	processor.save_pretrained(__A    )

																	# This has added the proper auto_map field to the feature extractor config
																	self.assertDictEqual(
																	    processor.feature_extractor.auto_map				,		{
																	        '''AutoFeatureExtractor''': '''custom_feature_extraction.CustomFeatureExtractor''',
																	        '''AutoProcessor''': '''custom_processing.CustomProcessor''',
																	    }				,		)

																	# This has added the proper auto_map field to the tokenizer config
																	with open(os.path.join(__A				,		'''tokenizer_config.json'''    )    ) as f:
																						a__          =  json.load(__A    )
																	self.assertDictEqual(
																	    tokenizer_config['''auto_map''']				,		{
																	        '''AutoTokenizer''': ['''custom_tokenization.CustomTokenizer''', None],
																	        '''AutoProcessor''': '''custom_processing.CustomProcessor''',
																	    }				,		)

																	# The code has been copied from fixtures
																	self.assertTrue(os.path.isfile(os.path.join(__A				,		'''custom_feature_extraction.py'''    )    )    )
																	self.assertTrue(os.path.isfile(os.path.join(__A				,		'''custom_tokenization.py'''    )    )    )
																	self.assertTrue(os.path.isfile(os.path.join(__A				,		'''custom_processing.py'''    )    )    )

																	repo.push_to_hub()

												a__          =  AutoProcessor.from_pretrained(F'{USER}/test-dynamic-processor'				,		trust_remote_code=__A    )
												# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
												self.assertEqual(new_processor.__class__.__name__				,		'''CustomProcessor'''    )










"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

__a       :	int             = {
    'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='vit_mae'




							def __init__(   self:     str				,		__A:     str=768				,		__A:     Union[str, Any]=12				,		__A:     str=12				,		__A:     List[Any]=3072				,		__A:     List[Any]="gelu"				,		__A:     Union[str, Any]=0.0				,		__A:     Tuple=0.0				,		__A:     str=0.0_2				,		__A:     Tuple=1e-12				,		__A:     Dict=224				,		__A:     List[Any]=16				,		__A:     Tuple=3				,		__A:     str=True				,		__A:     Optional[Any]=16				,		__A:     List[Any]=512				,		__A:     Optional[Any]=8				,		__A:     Dict=2048				,		__A:     str=0.7_5				,		__A:     str=False				,		**__A:     Tuple				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  num_attention_heads
												a__          =  intermediate_size
												a__          =  hidden_act
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  initializer_range
												a__          =  layer_norm_eps
												a__          =  image_size
												a__          =  patch_size
												a__          =  num_channels
												a__          =  qkv_bias
												a__          =  decoder_num_attention_heads
												a__          =  decoder_hidden_size
												a__          =  decoder_num_hidden_layers
												a__          =  decoder_intermediate_size
												a__          =  mask_ratio
												a__          =  norm_pix_loss


