

"""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_				:      Optional[Any]           =			get_tests_dir("fixtures/dummy_feature_extractor_config.json")
A_				:      List[Any]           =			get_tests_dir("fixtures/vocab.json")
A_				:      Union[str, Any]           =			get_tests_dir("fixtures")





class  lowerCamelCase    (unittest.TestCase      ):
			lowerCamelCase__ :		str       		=				['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']

			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   0

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     Optional[int]:
						SCREAMING_SNAKE_CASE__										=   AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""							)
						self.assertIsInstance(__UpperCAmelCase     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     List[str]:
						with tempfile.TemporaryDirectory() as tmpdirname:
									SCREAMING_SNAKE_CASE__										=   WavaVecaConfig()
									SCREAMING_SNAKE_CASE__										=   AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""							)

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

									SCREAMING_SNAKE_CASE__										=   AutoProcessor.from_pretrained(__UpperCAmelCase							)

						self.assertIsInstance(__UpperCAmelCase     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     Union[str, Any]:
						with tempfile.TemporaryDirectory() as tmpdirname:
									# copy relevant files
									copyfile(__UpperCAmelCase     ,					os.path.join(__UpperCAmelCase     ,					__UpperCAmelCase							)							)
									copyfile(__UpperCAmelCase     ,					os.path.join(__UpperCAmelCase     ,					"""vocab.json"""							)							)

									SCREAMING_SNAKE_CASE__										=   AutoProcessor.from_pretrained(__UpperCAmelCase							)

						self.assertIsInstance(__UpperCAmelCase     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Any:
						with tempfile.TemporaryDirectory() as tmpdirname:
									SCREAMING_SNAKE_CASE__										=   WavaVecaFeatureExtractor()
									SCREAMING_SNAKE_CASE__										=   AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""							)

									SCREAMING_SNAKE_CASE__										=   WavaVecaProcessor(__UpperCAmelCase     ,					__UpperCAmelCase							)

									# save in new folder
									processor.save_pretrained(__UpperCAmelCase							)

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

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

									SCREAMING_SNAKE_CASE__										=   AutoProcessor.from_pretrained(__UpperCAmelCase							)

						self.assertIsInstance(__UpperCAmelCase     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     Dict:
						with tempfile.TemporaryDirectory() as tmpdirname:
									SCREAMING_SNAKE_CASE__										=   WavaVecaFeatureExtractor()
									SCREAMING_SNAKE_CASE__										=   AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""							)

									SCREAMING_SNAKE_CASE__										=   WavaVecaProcessor(__UpperCAmelCase     ,					__UpperCAmelCase							)

									# save in new folder
									processor.save_pretrained(__UpperCAmelCase							)

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

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

									SCREAMING_SNAKE_CASE__										=   AutoProcessor.from_pretrained(__UpperCAmelCase							)

						self.assertIsInstance(__UpperCAmelCase     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     Dict:
						with tempfile.TemporaryDirectory() as tmpdirname:
									SCREAMING_SNAKE_CASE__										=   WavaVecaConfig(processor_class="""Wav2Vec2Processor"""							)
									model_config.save_pretrained(__UpperCAmelCase							)
									# copy relevant files
									copyfile(__UpperCAmelCase     ,					os.path.join(__UpperCAmelCase     ,					"""vocab.json"""							)							)
									# create emtpy sample processor
									with open(os.path.join(__UpperCAmelCase     ,					__UpperCAmelCase							)     ,					"""w"""							) as f:
												f.write("""{}"""							)

									SCREAMING_SNAKE_CASE__										=   AutoProcessor.from_pretrained(__UpperCAmelCase							)

						self.assertIsInstance(__UpperCAmelCase     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     List[Any]:
						# If remote code is not set, we will time out when asking whether to load the model.
						with self.assertRaises(__UpperCAmelCase							):
									SCREAMING_SNAKE_CASE__										=   AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""							)
						# If remote code is disabled, we can't load this config.
						with self.assertRaises(__UpperCAmelCase							):
									SCREAMING_SNAKE_CASE__										=   AutoProcessor.from_pretrained(
									    """hf-internal-testing/test_dynamic_processor"""     ,					trust_remote_code=__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""     ,					trust_remote_code=__UpperCAmelCase							)
						self.assertTrue(processor.special_attribute_present							)
						self.assertEqual(processor.__class__.__name__     ,					"""NewProcessor"""							)

						SCREAMING_SNAKE_CASE__										=   processor.feature_extractor
						self.assertTrue(feature_extractor.special_attribute_present							)
						self.assertEqual(feature_extractor.__class__.__name__     ,					"""NewFeatureExtractor"""							)

						SCREAMING_SNAKE_CASE__										=   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
									SCREAMING_SNAKE_CASE__										=   AutoProcessor.from_pretrained(
									    """hf-internal-testing/test_dynamic_processor"""     ,					trust_remote_code=__UpperCAmelCase     ,					use_fast=__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   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        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     Optional[Any]:
						try:
									AutoConfig.register("""custom"""     ,					__UpperCAmelCase							)
									AutoFeatureExtractor.register(__UpperCAmelCase     ,					__UpperCAmelCase							)
									AutoTokenizer.register(__UpperCAmelCase     ,					slow_tokenizer_class=__UpperCAmelCase							)
									AutoProcessor.register(__UpperCAmelCase     ,					__UpperCAmelCase							)
									# Trying to register something existing in the Transformers library will raise an error
									with self.assertRaises(__UpperCAmelCase							):
												AutoProcessor.register(__UpperCAmelCase     ,					__UpperCAmelCase							)

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

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

									SCREAMING_SNAKE_CASE__										=   CustomProcessor(__UpperCAmelCase     ,					__UpperCAmelCase							)

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

						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        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     str:





						class  lowerCamelCase    (A__      ):
									lowerCamelCase__ :		List[Any]       		=				False





						class  lowerCamelCase    (A__      ):
									lowerCamelCase__ :		List[str]       		=				False





						class  lowerCamelCase    (A__      ):
									lowerCamelCase__ :		int       		=				'AutoFeatureExtractor'
									lowerCamelCase__ :		Optional[Any]       		=				'AutoTokenizer'
									lowerCamelCase__ :		Optional[Any]       		=				False

						try:
									AutoConfig.register("""custom"""     ,					__UpperCAmelCase							)
									AutoFeatureExtractor.register(__UpperCAmelCase     ,					__UpperCAmelCase							)
									AutoTokenizer.register(__UpperCAmelCase     ,					slow_tokenizer_class=__UpperCAmelCase							)
									AutoProcessor.register(__UpperCAmelCase     ,					__UpperCAmelCase							)
									# If remote code is not set, the default is to use local classes.
									SCREAMING_SNAKE_CASE__										=   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.
									SCREAMING_SNAKE_CASE__										=   AutoProcessor.from_pretrained(
									    """hf-internal-testing/test_dynamic_processor"""     ,					trust_remote_code=__UpperCAmelCase							)
									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.
									SCREAMING_SNAKE_CASE__										=   AutoProcessor.from_pretrained(
									    """hf-internal-testing/test_dynamic_processor"""     ,					trust_remote_code=__UpperCAmelCase							)
									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        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert"""							)
						self.assertEqual(processor.__class__.__name__     ,					"""BertTokenizerFast"""							)





			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     Dict:
						SCREAMING_SNAKE_CASE__										=   AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext"""							)
						self.assertEqual(processor.__class__.__name__     ,					"""ConvNextImageProcessor"""							)





@is_staging_test
class  lowerCamelCase    (unittest.TestCase      ):
			lowerCamelCase__ :		Union[str, Any]       		=				['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']

			@classmethod
			def        SCREAMING_SNAKE_CASE					(       cls					:    Optional[int]							) ->     Union[str, Any]:
						SCREAMING_SNAKE_CASE__										=   TOKEN
						HfFolder.save_token(__UpperCAmelCase							)

			@classmethod
			def        SCREAMING_SNAKE_CASE					(       cls					:    Optional[Any]							) ->     Dict:
						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        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     List[str]:
						SCREAMING_SNAKE_CASE__										=   WavaVecaProcessor.from_pretrained(__UpperCAmelCase							)
						with tempfile.TemporaryDirectory() as tmp_dir:
									processor.save_pretrained(
									    os.path.join(__UpperCAmelCase     ,					"""test-processor"""							)     ,					push_to_hub=__UpperCAmelCase     ,					use_auth_token=self._token							)

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

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     int:
						SCREAMING_SNAKE_CASE__										=   WavaVecaProcessor.from_pretrained(__UpperCAmelCase							)

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

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





			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[Any]							) ->     List[Any]:
						CustomFeatureExtractor.register_for_auto_class()
						CustomTokenizer.register_for_auto_class()
						CustomProcessor.register_for_auto_class()

						SCREAMING_SNAKE_CASE__										=   CustomFeatureExtractor.from_pretrained(__UpperCAmelCase							)

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

						SCREAMING_SNAKE_CASE__										=   CustomProcessor(__UpperCAmelCase     ,					__UpperCAmelCase							)

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

									# 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(__UpperCAmelCase     ,					"""tokenizer_config.json"""							)							) as f:
												SCREAMING_SNAKE_CASE__										=   json.load(__UpperCAmelCase							)
									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(__UpperCAmelCase     ,					"""custom_feature_extraction.py"""							)							)							)
									self.assertTrue(os.path.isfile(os.path.join(__UpperCAmelCase     ,					"""custom_tokenization.py"""							)							)							)
									self.assertTrue(os.path.isfile(os.path.join(__UpperCAmelCase     ,					"""custom_processing.py"""							)							)							)

									repo.push_to_hub()

						SCREAMING_SNAKE_CASE__										=   AutoProcessor.from_pretrained(F"""{USER}/test-dynamic-processor"""     ,					trust_remote_code=__UpperCAmelCase							)
						# 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 __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


A_				:      Union[str, Any]           =			{
    "susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
    "susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}





class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		int       		=				'ernie_m'
			lowerCamelCase__ :		Dict[str, str]       		=				{"dropout": "classifier_dropout", "num_classes": "num_labels"}

			def __init__(       self					:    List[str]     ,					__UpperCAmelCase					:    int = 2_5_0_0_0_2     ,					__UpperCAmelCase					:    int = 7_6_8     ,					__UpperCAmelCase					:    int = 1_2     ,					__UpperCAmelCase					:    int = 1_2     ,					__UpperCAmelCase					:    int = 3_0_7_2     ,					__UpperCAmelCase					:    str = "gelu"     ,					__UpperCAmelCase					:    float = 0.1     ,					__UpperCAmelCase					:    float = 0.1     ,					__UpperCAmelCase					:    int = 5_1_4     ,					__UpperCAmelCase					:    float = 0.02     ,					__UpperCAmelCase					:    int = 1     ,					__UpperCAmelCase					:    float = 1e-05     ,					__UpperCAmelCase					:    Union[str, Any]=None     ,					__UpperCAmelCase					:    List[Any]=False     ,					__UpperCAmelCase					:    Union[str, Any]=0.0     ,					**__UpperCAmelCase					:    Any     ,					) ->     Optional[Any]:
						super().__init__(pad_token_id=__UpperCAmelCase     ,					**__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   vocab_size
						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__										=   max_position_embeddings
						SCREAMING_SNAKE_CASE__										=   initializer_range
						SCREAMING_SNAKE_CASE__										=   layer_norm_eps
						SCREAMING_SNAKE_CASE__										=   classifier_dropout
						SCREAMING_SNAKE_CASE__										=   is_decoder
						SCREAMING_SNAKE_CASE__										=   act_dropout


