


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				:       Tuple      =  get_tests_dir('fixtures/dummy_feature_extractor_config.json')
A				:       Dict      =  get_tests_dir('fixtures/vocab.json')
A				:       Any      =  get_tests_dir('fixtures')






class      UpperCamelCase(	unittest.TestCase ):
					snake_case_		:					Optional[int]    					=						["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Any:




											'''simple docstring'''

											__snake_case				=		0


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h"    )
											self.assertIsInstance(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Optional[int]:




											'''simple docstring'''

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

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

																	__snake_case				=		AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE    )

											self.assertIsInstance(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						Union[str, Any]:




											'''simple docstring'''

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

																	__snake_case				=		AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE    )

											self.assertIsInstance(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						str:




											'''simple docstring'''

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

																	__snake_case				=		WavaVecaProcessor(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )

																	# save in new folder
																	processor.save_pretrained(SCREAMING_SNAKE_CASE    )

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

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

																	__snake_case				=		AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE    )

											self.assertIsInstance(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]    )      ->						Tuple:




											'''simple docstring'''

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

																	__snake_case				=		WavaVecaProcessor(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )

																	# save in new folder
																	processor.save_pretrained(SCREAMING_SNAKE_CASE    )

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

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

																	__snake_case				=		AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE    )

											self.assertIsInstance(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Union[str, Any]:




											'''simple docstring'''

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

																	__snake_case				=		AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE    )

											self.assertIsInstance(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						List[str]:




											'''simple docstring'''

											with self.assertRaises(SCREAMING_SNAKE_CASE    ):
																	__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(SCREAMING_SNAKE_CASE    ):
																	__snake_case				=		AutoProcessor.from_pretrained(
																	    "hf-internal-testing/test_dynamic_processor"  ,     trust_remote_code=SCREAMING_SNAKE_CASE    )

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

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

											__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
																	__snake_case				=		AutoProcessor.from_pretrained(
																	    "hf-internal-testing/test_dynamic_processor"  ,     trust_remote_code=SCREAMING_SNAKE_CASE  ,     use_fast=SCREAMING_SNAKE_CASE    )
																	__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    )      ->						Union[str, Any]:




											'''simple docstring'''

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

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

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

																	__snake_case				=		CustomProcessor(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )

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

											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						:		Any    )      ->						int:




											'''simple docstring'''

											class      UpperCamelCase(	_a ):
																snake_case_		:					int    					=						False






											class      UpperCamelCase(	_a ):
																snake_case_		:					int    					=						False






											class      UpperCamelCase(	_a ):
																snake_case_		:					str    					=						"""AutoFeatureExtractor"""
																snake_case_		:					Optional[int]    					=						"""AutoTokenizer"""
																snake_case_		:					str    					=						False

											try:
																	AutoConfig.register("custom"  ,     SCREAMING_SNAKE_CASE    )
																	AutoFeatureExtractor.register(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )
																	AutoTokenizer.register(SCREAMING_SNAKE_CASE  ,     slow_tokenizer_class=SCREAMING_SNAKE_CASE    )
																	AutoProcessor.register(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )
																	# If remote code is not set, the default is to use local classes.
																	__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.
																	__snake_case				=		AutoProcessor.from_pretrained(
																	    "hf-internal-testing/test_dynamic_processor"  ,     trust_remote_code=SCREAMING_SNAKE_CASE    )
																	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.
																	__snake_case				=		AutoProcessor.from_pretrained(
																	    "hf-internal-testing/test_dynamic_processor"  ,     trust_remote_code=SCREAMING_SNAKE_CASE    )
																	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						:		Tuple    )      ->						int:




											'''simple docstring'''

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






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						str:




											'''simple docstring'''

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






@is_staging_test
class      UpperCamelCase(	unittest.TestCase ):
					snake_case_		:					Optional[int]    					=						["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]


					@classmethod
					def 				SCREAMING_SNAKE_CASE_	(    cls						:		str    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		TOKEN
											HfFolder.save_token(SCREAMING_SNAKE_CASE    )


					@classmethod
					def 				SCREAMING_SNAKE_CASE_	(    cls						:		List[Any]    )      ->						int:




											'''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 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						str:




											'''simple docstring'''

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

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						Any:




											'''simple docstring'''

											__snake_case				=		WavaVecaProcessor.from_pretrained(SCREAMING_SNAKE_CASE    )

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

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






					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]    )      ->						Any:




											'''simple docstring'''

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

											__snake_case				=		CustomFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE    )

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

											__snake_case				=		CustomProcessor(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )

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

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

																	repo.push_to_hub()

											__snake_case				=		AutoProcessor.from_pretrained(f'''{USER}/test-dynamic-processor'''  ,     trust_remote_code=SCREAMING_SNAKE_CASE    )
											# 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"    )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A				:       List[str]      =  {
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A				:       str      =  [
	    'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TimeSeriesTransformerForPrediction',
	    'TimeSeriesTransformerModel',
	    'TimeSeriesTransformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	A				:       str      =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





