


"""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							:      Optional[Any]						=      get_tests_dir('fixtures/dummy_feature_extractor_config.json')
__SCREAMING_SNAKE_CASE							:      int						=      get_tests_dir('fixtures/dummy-config.json')





class        __A       (unittest.TestCase):





							'''simple docstring'''

							def 						lowerCAmelCase    (			self							: str    )   ->str:



														"""simple docstring"""



														snake_case_          =						0



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



														"""simple docstring"""



														snake_case_          =						AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h"""    )
														self.assertIsInstance(UpperCAmelCase_						,    UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: Dict    )   ->str:



														"""simple docstring"""



														snake_case_          =						AutoFeatureExtractor.from_pretrained(UpperCAmelCase_    )
														self.assertIsInstance(UpperCAmelCase_						,    UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: List[Any]    )   ->Optional[Any]:



														"""simple docstring"""



														with tempfile.TemporaryDirectory() as tmpdirname:
																					snake_case_          =						WavaVecaConfig()

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

																					config_dict.pop("""feature_extractor_type"""    )
																					snake_case_          =						WavaVecaFeatureExtractor(**UpperCAmelCase_    )

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

																					snake_case_          =						AutoFeatureExtractor.from_pretrained(UpperCAmelCase_    )

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

														self.assertIsInstance(UpperCAmelCase_						,    UpperCAmelCase_    )



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



														"""simple docstring"""



														snake_case_          =						AutoFeatureExtractor.from_pretrained(UpperCAmelCase_    )
														self.assertIsInstance(UpperCAmelCase_						,    UpperCAmelCase_    )



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



														"""simple docstring"""



														with self.assertRaisesRegex(
														    UpperCAmelCase_						,    """bert-base is not a local folder and is not a valid model identifier"""    ):
																					snake_case_          =						AutoFeatureExtractor.from_pretrained("""bert-base"""    )



							def 						lowerCAmelCase    (			self							: Tuple    )   ->Optional[int]:



														"""simple docstring"""



														with self.assertRaisesRegex(
														    UpperCAmelCase_						,    R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""    ):
																					snake_case_          =						AutoFeatureExtractor.from_pretrained(UpperCAmelCase_						,    revision="""aaaaaa"""    )



							def 						lowerCAmelCase    (			self							: str    )   ->Dict:



														"""simple docstring"""



														with self.assertRaisesRegex(
														    UpperCAmelCase_						,    """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""						,    ):
																					snake_case_          =						AutoFeatureExtractor.from_pretrained("""hf-internal-testing/config-no-model"""    )



							def 						lowerCAmelCase    (			self							: str    )   ->List[str]:



														"""simple docstring"""



														with self.assertRaises(UpperCAmelCase_    ):
																					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(UpperCAmelCase_    ):
																					snake_case_          =						AutoFeatureExtractor.from_pretrained(
																					    """hf-internal-testing/test_dynamic_feature_extractor"""						,    trust_remote_code=UpperCAmelCase_    )

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

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



							def 						lowerCAmelCase    (			self							: int    )   ->int:



														"""simple docstring"""



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

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



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



														"""simple docstring"""



														class        __A       (snake_case__):





																					'''simple docstring'''

																					__lowercase:						List[str]							 =      True

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

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







"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

__SCREAMING_SNAKE_CASE							:      Tuple						=      [
    'cross_validation.py',
    'gradient_accumulation.py',
    'local_sgd.py',
    'multi_process_metrics.py',
    'memory.py',
    'automatic_gradient_accumulation.py',
    'fsdp_with_peak_mem_tracking.py',
    'deepspeed_with_config_support.py',
    'megatron_lm_gpt_pretraining.py',
]





class        __A       (unittest.TestCase):





							'''simple docstring'''

							def 						lowerCAmelCase    (			self							: str						,    UpperCAmelCase_							: str						,    UpperCAmelCase_							: bool						,    UpperCAmelCase_							: str = None						,    UpperCAmelCase_							: list = None    )   ->List[Any]:



														"""simple docstring"""



														snake_case_          =						None
														snake_case_          =						os.path.abspath(os.path.join("""examples"""						,    """by_feature"""    )    )
														snake_case_          =						os.path.abspath("""examples"""    )
														for item in os.listdir(UpperCAmelCase_    ):
																					if item not in EXCLUDE_EXAMPLES:
																												snake_case_          =						os.path.join(UpperCAmelCase_						,    UpperCAmelCase_    )
																												if os.path.isfile(UpperCAmelCase_    ) and ".py" in item_path:
																																			with self.subTest(
																																			    tested_script=UpperCAmelCase_						,    feature_script=UpperCAmelCase_						,    tested_section="""main()""" if parser_only else """training_function()"""						,    ):
																																										snake_case_          =						compare_against_test(
																																										    os.path.join(UpperCAmelCase_						,    UpperCAmelCase_    )						,    UpperCAmelCase_						,    UpperCAmelCase_						,    UpperCAmelCase_    )
																																										snake_case_          =						"""\n""".join(UpperCAmelCase_    )
																																										if special_strings is not None:
																																																	for string in special_strings:
																																																								snake_case_          =						diff.replace(UpperCAmelCase_						,    """"""    )
																																										self.assertEqual(UpperCAmelCase_						,    """"""    )



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



														"""simple docstring"""



														self.one_complete_example("""complete_nlp_example.py"""						,    UpperCAmelCase_    )
														self.one_complete_example("""complete_nlp_example.py"""						,    UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: Tuple    )   ->Dict:



														"""simple docstring"""



														snake_case_          =						os.path.abspath(os.path.join("""examples"""						,    """cv_example.py"""    )    )
														snake_case_          =						[
														    """ """ * 16 + """{\n\n""",
														    """ """ * 20 + """\"accuracy\": eval_metric[\"accuracy\"],\n\n""",
														    """ """ * 20 + """\"f1\": eval_metric[\"f1\"],\n\n""",
														    """ """ * 20 + """\"train_loss\": total_loss.item() / len(train_dataloader),\n\n""",
														    """ """ * 20 + """\"epoch\": epoch,\n\n""",
														    """ """ * 16 + """},\n\n""",
														    """ """ * 16 + """step=epoch,\n""",
														    """ """ * 12,
														    """ """ * 8 + """for step, batch in enumerate(active_dataloader):\n""",
														]
														self.one_complete_example("""complete_cv_example.py"""						,    UpperCAmelCase_						,    UpperCAmelCase_						,    UpperCAmelCase_    )
														self.one_complete_example("""complete_cv_example.py"""						,    UpperCAmelCase_						,    UpperCAmelCase_						,    UpperCAmelCase_    )





@mock.patch.dict(os.environ  ,						{"""TESTING_MOCKED_DATALOADERS""": """1"""})
class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						str							 =      False



							@classmethod
							def 						lowerCAmelCase    (			cls							: Any    )   ->List[str]:



														"""simple docstring"""



														super().setUpClass()
														snake_case_          =						tempfile.mkdtemp()
														snake_case_          =						os.path.join(cls._tmpdir						,    """default_config.yml"""    )

														write_basic_config(save_location=cls.configPath    )
														snake_case_          =						["""accelerate""", """launch""", """--config_file""", cls.configPath]



							@classmethod
							def 						lowerCAmelCase    (			cls							: List[str]    )   ->int:



														"""simple docstring"""



														super().tearDownClass()
														shutil.rmtree(cls._tmpdir    )



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



														"""simple docstring"""



														snake_case_          =						F"""
        examples/by_feature/checkpointing.py
        --checkpointing_steps epoch
        --output_dir {self.tmpdir}
        """.split()
														run_command(self._launch_args + testargs    )
														self.assertTrue(os.path.exists(os.path.join(self.tmpdir						,    """epoch_0"""    )    )    )



							def 						lowerCAmelCase    (			self							: Any    )   ->Optional[Any]:



														"""simple docstring"""



														snake_case_          =						F"""
        examples/by_feature/checkpointing.py
        --checkpointing_steps 1
        --output_dir {self.tmpdir}
        """.split()
														snake_case_          =						run_command(self._launch_args + testargs    )
														self.assertTrue(os.path.exists(os.path.join(self.tmpdir						,    """step_2"""    )    )    )



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



														"""simple docstring"""



														snake_case_          =						F"""
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir						,    "epoch_0"    )}
        """.split()
														snake_case_          =						run_command(self._launch_args + testargs						,    return_stdout=UpperCAmelCase_    )
														self.assertNotIn("""epoch 0:"""						,    UpperCAmelCase_    )
														self.assertIn("""epoch 1:"""						,    UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->Tuple:



														"""simple docstring"""



														snake_case_          =						F"""
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir						,    "step_2"    )}
        """.split()
														snake_case_          =						run_command(self._launch_args + testargs						,    return_stdout=UpperCAmelCase_    )
														if torch.cuda.is_available():
																					snake_case_          =						torch.cuda.device_count()
														else:
																					snake_case_          =						1
														if num_processes > 1:
																					self.assertNotIn("""epoch 0:"""						,    UpperCAmelCase_    )
																					self.assertIn("""epoch 1:"""						,    UpperCAmelCase_    )
														else:
																					self.assertIn("""epoch 0:"""						,    UpperCAmelCase_    )
																					self.assertIn("""epoch 1:"""						,    UpperCAmelCase_    )



							@slow
							def 						lowerCAmelCase    (			self							: Dict    )   ->Dict:



														"""simple docstring"""



														snake_case_          =						"""
        examples/by_feature/cross_validation.py
        --num_folds 2
        """.split()
														with mock.patch.dict(os.environ						,    {"""TESTING_MOCKED_DATALOADERS""": """0"""}    ):
																					snake_case_          =						run_command(self._launch_args + testargs						,    return_stdout=UpperCAmelCase_    )
																					snake_case_          =						re.findall("""({.+})"""						,    UpperCAmelCase_    )
																					snake_case_          =						[r for r in results if """accuracy""" in r][-1]
																					snake_case_          =						ast.literal_eval(UpperCAmelCase_    )
																					self.assertGreaterEqual(results["""accuracy"""]						,    0.75    )



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->int:



														"""simple docstring"""



														snake_case_          =						["""examples/by_feature/multi_process_metrics.py"""]
														run_command(self._launch_args + testargs    )



							@require_trackers
							@mock.patch.dict(os.environ						,    {"""WANDB_MODE""": """offline"""}    )
							def 						lowerCAmelCase    (			self							: Dict    )   ->Optional[Any]:



														"""simple docstring"""



														with tempfile.TemporaryDirectory() as tmpdir:
																					snake_case_          =						F"""
            examples/by_feature/tracking.py
            --with_tracking
            --project_dir {tmpdir}
            """.split()
																					run_command(self._launch_args + testargs    )
																					self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase_						,    """tracking"""    )    )    )



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



														"""simple docstring"""



														snake_case_          =						["""examples/by_feature/gradient_accumulation.py"""]
														run_command(self._launch_args + testargs    )



							def 						lowerCAmelCase    (			self							: str    )   ->Dict:



														"""simple docstring"""



														snake_case_          =						["""examples/by_feature/local_sgd.py"""]
														run_command(self._launch_args + testargs    )



