
'''simple docstring'''



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


UpperCamelCase__   :		Optional[Any]											=	logging.get_logger(__name__)

UpperCamelCase__   :		List[Any]											=	{
    "google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}

class  _a					(lowercase_):





       """simple docstring"""




       SCREAMING_SNAKE_CASE   =	'canine'






       def __init__(				self							,				A__=7_68							,				A__=12							,				A__=12							,				A__=30_72							,				A__="gelu"							,				A__=0.1							,				A__=0.1							,				A__=1_63_84							,				A__=16							,				A__=0.02							,				A__=1E-12							,				A__=0							,				A__=0Xe_0_0_0							,				A__=0Xe_0_0_1							,				A__=4							,				A__=4							,				A__=8							,				A__=1_63_84							,				A__=1_28							,				**A__							,				)     ->      List[Any]:
          super().__init__(pad_token_id=_lowercase							,				bos_token_id=_lowercase							,				eos_token_id=_lowercase							,				**_lowercase   )

          _SCREAMING_SNAKE_CASE  			=					max_position_embeddings
          _SCREAMING_SNAKE_CASE  			=					hidden_size
          _SCREAMING_SNAKE_CASE  			=					num_hidden_layers
          _SCREAMING_SNAKE_CASE  			=					num_attention_heads
          _SCREAMING_SNAKE_CASE  			=					intermediate_size
          _SCREAMING_SNAKE_CASE  			=					hidden_act
          _SCREAMING_SNAKE_CASE  			=					hidden_dropout_prob
          _SCREAMING_SNAKE_CASE  			=					attention_probs_dropout_prob
          _SCREAMING_SNAKE_CASE  			=					initializer_range
          _SCREAMING_SNAKE_CASE  			=					type_vocab_size
          _SCREAMING_SNAKE_CASE  			=					layer_norm_eps

          # Character config:
          _SCREAMING_SNAKE_CASE  			=					downsampling_rate
          _SCREAMING_SNAKE_CASE  			=					upsampling_kernel_size
          _SCREAMING_SNAKE_CASE  			=					num_hash_functions
          _SCREAMING_SNAKE_CASE  			=					num_hash_buckets
          _SCREAMING_SNAKE_CASE  			=					local_transformer_stride









'''simple docstring'''

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

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


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


SCREAMING_SNAKE_CASE							:				int     	=			get_tests_dir("fixtures")
SCREAMING_SNAKE_CASE							:				str     	=			get_tests_dir("fixtures/dummy_feature_extractor_config.json")
SCREAMING_SNAKE_CASE							:				str     	=			get_tests_dir("fixtures/dummy-config.json")






class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



				def   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				0



				def   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h'       )
											self.assertIsInstance(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase       )
											self.assertIsInstance(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							str:
											with tempfile.TemporaryDirectory() as tmpdirname:
																		SCREAMING_SNAKE_CASE_				=				WavaVecaConfig()

																		# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase       ).to_dict()

																		config_dict.pop('feature_extractor_type'       )
																		SCREAMING_SNAKE_CASE_				=				WavaVecaFeatureExtractor(**_lowercase       )

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

																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase       )

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

											self.assertIsInstance(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase       )
											self.assertIsInstance(_lowercase,     _lowercase       )



				def   a__					(    self       )     ->							Any:
											with self.assertRaisesRegex(
											    _lowercase,     'bert-base is not a local folder and is not a valid model identifier'       ):
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained('bert-base'       )



				def   a__					(    self       )     ->							List[Any]:
											with self.assertRaisesRegex(
											    _lowercase,     R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'       ):
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase,     revision='aaaaaa'       )



				def   a__					(    self       )     ->							List[Any]:
											with self.assertRaisesRegex(
											    _lowercase,     'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.',     ):
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained('hf-internal-testing/config-no-model'       )



				def   a__					(    self       )     ->							List[str]:
											# If remote code is not set, we will time out when asking whether to load the model.
											with self.assertRaises(_lowercase       ):
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor'       )
											# If remote code is disabled, we can't load this config.
											with self.assertRaises(_lowercase       ):
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor',     trust_remote_code=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
											    'hf-internal-testing/test_dynamic_feature_extractor',     trust_remote_code=_lowercase       )
											self.assertEqual(feature_extractor.__class__.__name__,     'NewFeatureExtractor'       )

											# Test feature extractor can be reloaded.
											with tempfile.TemporaryDirectory() as tmp_dir:
																		feature_extractor.save_pretrained(_lowercase       )
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase,     trust_remote_code=_lowercase       )
											self.assertEqual(reloaded_feature_extractor.__class__.__name__,     'NewFeatureExtractor'       )



				def   a__					(    self       )     ->							int:
											try:
																		AutoConfig.register('custom',     _lowercase       )
																		AutoFeatureExtractor.register(_lowercase,     _lowercase       )
																		# Trying to register something existing in the Transformers library will raise an error
																		with self.assertRaises(_lowercase       ):
																									AutoFeatureExtractor.register(_lowercase,     _lowercase       )

																		# Now that the config is registered, it can be used as any other config with the auto-API
																		SCREAMING_SNAKE_CASE_				=				CustomFeatureExtractor.from_pretrained(_lowercase       )
																		with tempfile.TemporaryDirectory() as tmp_dir:
																									feature_extractor.save_pretrained(_lowercase       )
																									SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(_lowercase       )
																									self.assertIsInstance(_lowercase,     _lowercase       )

											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]





				def   a__					(    self       )     ->							str:






											class    snake_case     (      lowercase_		):






															"""simple docstring"""
															_a    	=     True

											try:
																		AutoConfig.register('custom',     _lowercase       )
																		AutoFeatureExtractor.register(_lowercase,     _lowercase       )
																		# If remote code is not set, the default is to use local
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor'       )
																		self.assertEqual(feature_extractor.__class__.__name__,     'NewFeatureExtractor'       )
																		self.assertTrue(feature_extractor.is_local       )

																		# If remote code is disabled, we load the local one.
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor',     trust_remote_code=_lowercase       )
																		self.assertEqual(feature_extractor.__class__.__name__,     'NewFeatureExtractor'       )
																		self.assertTrue(feature_extractor.is_local       )

																		# If remote is enabled, we load from the Hub
																		SCREAMING_SNAKE_CASE_				=				AutoFeatureExtractor.from_pretrained(
																		    'hf-internal-testing/test_dynamic_feature_extractor',     trust_remote_code=_lowercase       )
																		self.assertEqual(feature_extractor.__class__.__name__,     'NewFeatureExtractor'       )
																		self.assertTrue(not hasattr(_lowercase,     'is_local'       )       )

											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]





