


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


__lowerCAmelCase	: Dict  				=logging.get_logger(__name__)

__lowerCAmelCase	: int  				={
    'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class        _lowercase      (			lowerCamelCase_       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			int						=		'''gpt_bigcode'''
				SCREAMING_SNAKE_CASE__				:			str						=		['''past_key_values''']
				SCREAMING_SNAKE_CASE__				:			str						=		{
				    '''hidden_size''': '''n_embd''',
				    '''max_position_embeddings''': '''n_positions''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}


				def __init__(       self			:Optional[Any]			,			lowerCAmelCase__			:Tuple=50_257			,			lowerCAmelCase__			:str=1_024			,			lowerCAmelCase__			:Dict=768			,			lowerCAmelCase__			:Tuple=12			,			lowerCAmelCase__			:str=12			,			lowerCAmelCase__			:Optional[int]=None			,			lowerCAmelCase__			:Dict="gelu_pytorch_tanh"			,			lowerCAmelCase__			:Tuple=0.1			,			lowerCAmelCase__			:Tuple=0.1			,			lowerCAmelCase__			:Union[str, Any]=0.1			,			lowerCAmelCase__			:Tuple=1E-5			,			lowerCAmelCase__			:str=0.02			,			lowerCAmelCase__			:Dict=True			,			lowerCAmelCase__			:Union[str, Any]=True			,			lowerCAmelCase__			:Optional[int]=50_256			,			lowerCAmelCase__			:Optional[int]=50_256			,			lowerCAmelCase__			:Union[str, Any]=True			,			lowerCAmelCase__			:Dict=True			,			lowerCAmelCase__			:Union[str, Any]=True			,			**lowerCAmelCase__			:List[Any]			,			)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		vocab_size
										__SCREAMING_SNAKE_CASE						:     Dict					=		n_positions
										__SCREAMING_SNAKE_CASE						:     Tuple					=		n_embd
										__SCREAMING_SNAKE_CASE						:     int					=		n_layer
										__SCREAMING_SNAKE_CASE						:     Tuple					=		n_head
										__SCREAMING_SNAKE_CASE						:     str					=		n_inner
										__SCREAMING_SNAKE_CASE						:     List[str]					=		activation_function
										__SCREAMING_SNAKE_CASE						:     str					=		resid_pdrop
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		embd_pdrop
										__SCREAMING_SNAKE_CASE						:     Tuple					=		attn_pdrop
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		layer_norm_epsilon
										__SCREAMING_SNAKE_CASE						:     int					=		initializer_range
										__SCREAMING_SNAKE_CASE						:     List[str]					=		scale_attn_weights
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		use_cache
										__SCREAMING_SNAKE_CASE						:     str					=		attention_softmax_in_fpaa
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		scale_attention_softmax_in_fpaa
										__SCREAMING_SNAKE_CASE						:     str					=		multi_query

										__SCREAMING_SNAKE_CASE						:     str					=		bos_token_id
										__SCREAMING_SNAKE_CASE						:     Any					=		eos_token_id

										super().__init__(bos_token_id=__a			,			eos_token_id=__a			,			**__a	)







'''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


lowerCAmelCase_					:    Any      	=			get_tests_dir('fixtures')
lowerCAmelCase_					:    Union[str, Any]      	=			get_tests_dir('fixtures/dummy_feature_extractor_config.json')
lowerCAmelCase_					:    Dict      	=			get_tests_dir('fixtures/dummy-config.json')
class 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= 0




				def   UpperCamelCase__   (  self     :					str					):
								_a								= AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h"					)
								self.assertIsInstance(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= AutoFeatureExtractor.from_pretrained(__a					)
								self.assertIsInstance(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								with tempfile.TemporaryDirectory() as tmpdirname:
												_a								= WavaVecaConfig()

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

												config_dict.pop("feature_extractor_type"					)
												_a								= WavaVecaFeatureExtractor(**__a					)

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

												_a								= AutoFeatureExtractor.from_pretrained(__a					)

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

								self.assertIsInstance(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= AutoFeatureExtractor.from_pretrained(__a					)
								self.assertIsInstance(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								with self.assertRaisesRegex(
								    __a					,				"bert-base is not a local folder and is not a valid model identifier"					):
												_a								= AutoFeatureExtractor.from_pretrained("bert-base"					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					):
								with self.assertRaisesRegex(
								    __a					,				r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"					):
												_a								= AutoFeatureExtractor.from_pretrained(__a					,				revision="aaaaaa"					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								with self.assertRaisesRegex(
								    __a					,				"hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."					,				):
												_a								= AutoFeatureExtractor.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					):
												_a								= AutoFeatureExtractor.from_pretrained(
												    "hf-internal-testing/test_dynamic_feature_extractor"					)
								# If remote code is disabled, we can't load this config.
								with self.assertRaises(__a					):
												_a								= AutoFeatureExtractor.from_pretrained(
												    "hf-internal-testing/test_dynamic_feature_extractor"					,				trust_remote_code=__a					)

								_a								= AutoFeatureExtractor.from_pretrained(
								    "hf-internal-testing/test_dynamic_feature_extractor"					,				trust_remote_code=__a					)
								self.assertEqual(feature_extractor.__class__.__name__					,				"NewFeatureExtractor"					)

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




				def   UpperCamelCase__   (  self     :					Any					):
								try:
												AutoConfig.register("custom"					,				__a					)
												AutoFeatureExtractor.register(__a					,				__a					)
												# Trying to register something existing in the Transformers library will raise an error
												with self.assertRaises(__a					):
																AutoFeatureExtractor.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:
																feature_extractor.save_pretrained(__a					)
																_a								= AutoFeatureExtractor.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]




				def   UpperCamelCase__   (  self     :					Tuple					):
								class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






												"""simple docstring"""







												__a            =True

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

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