




"""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


lowercase__					:						Tuple												=			get_tests_dir('''fixtures''')
lowercase__					:						List[str]												=			get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
lowercase__					:						Dict												=			get_tests_dir('''fixtures/dummy-config.json''')




class  _UpperCAmelCase (    unittest.TestCase):

 def 		_snake_case      (  self	: Tuple       ):
     snake_case_				:  Optional[Any]       =			0

 def 		_snake_case      (  self	: int       ):
     snake_case_				:  str       =			AutoFeatureExtractor.from_pretrained('''facebook/wav2vec2-base-960h'''       )
     self.assertIsInstance(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: Optional[Any]       ):
     snake_case_				:  Dict       =			AutoFeatureExtractor.from_pretrained(lowercase_       )
     self.assertIsInstance(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: int       ):
     with tempfile.TemporaryDirectory() as tmpdirname:
         snake_case_				:  List[Any]       =			WavaVecaConfig()

         # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
         snake_case_				:  Optional[Any]       =			AutoFeatureExtractor.from_pretrained(lowercase_       ).to_dict()

         config_dict.pop('''feature_extractor_type'''       )
         snake_case_				:  Optional[Any]       =			WavaVecaFeatureExtractor(**lowercase_       )

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

         snake_case_				:  List[Any]       =			AutoFeatureExtractor.from_pretrained(lowercase_       )

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

     self.assertIsInstance(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: Optional[Any]       ):
     snake_case_				:  str       =			AutoFeatureExtractor.from_pretrained(lowercase_       )
     self.assertIsInstance(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: List[Any]       ):
     with self.assertRaisesRegex(
         lowercase_						,  '''bert-base is not a local folder and is not a valid model identifier'''       ):
         snake_case_				:  List[str]       =			AutoFeatureExtractor.from_pretrained('''bert-base'''       )

 def 		_snake_case      (  self	: Any       ):
     with self.assertRaisesRegex(
         lowercase_						,  r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''       ):
         snake_case_				:  Dict       =			AutoFeatureExtractor.from_pretrained(lowercase_						,  revision='''aaaaaa'''       )

 def 		_snake_case      (  self	: Optional[Any]       ):
     with self.assertRaisesRegex(
         lowercase_						,  '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''						,  ):
         snake_case_				:  Dict       =			AutoFeatureExtractor.from_pretrained('''hf-internal-testing/config-no-model'''       )

 def 		_snake_case      (  self	: Optional[int]       ):
     # If remote code is not set, we will time out when asking whether to load the model.
     with self.assertRaises(lowercase_       ):
         snake_case_				:  str       =			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_       ):
         snake_case_				:  str       =			AutoFeatureExtractor.from_pretrained(
             '''hf-internal-testing/test_dynamic_feature_extractor'''						,  trust_remote_code=lowercase_       )

     snake_case_				:  List[str]       =			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_       )
         snake_case_				:  Optional[int]       =			AutoFeatureExtractor.from_pretrained(lowercase_						,  trust_remote_code=lowercase_       )
     self.assertEqual(reloaded_feature_extractor.__class__.__name__						,  '''NewFeatureExtractor'''       )

 def 		_snake_case      (  self	: Optional[Any]       ):
     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
         snake_case_				:  List[Any]       =			CustomFeatureExtractor.from_pretrained(lowercase_       )
         with tempfile.TemporaryDirectory() as tmp_dir:
             feature_extractor.save_pretrained(lowercase_       )
             snake_case_				:  Any       =			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 		_snake_case      (  self	: Union[str, Any]       ):




     class  _UpperCAmelCase (    lowerCAmelCase__):
      _lowerCAmelCase     :    str	      =	True

     try:
         AutoConfig.register('''custom'''						,  lowercase_       )
         AutoFeatureExtractor.register(lowercase_						,  lowercase_       )
         # If remote code is not set, the default is to use local
         snake_case_				:  List[Any]       =			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_				:  Union[str, Any]       =			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
         snake_case_				:  Tuple       =			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]










"""simple docstring"""

import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


lowercase__					:						List[Any]												=			logging.get_logger(__name__)




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Tuple	      =	"""linear"""
 _lowerCAmelCase     :    Optional[int]	      =	"""cosine"""
 _lowerCAmelCase     :    Optional[int]	      =	"""cosine_with_restarts"""
 _lowerCAmelCase     :    Union[str, Any]	      =	"""polynomial"""
 _lowerCAmelCase     :    Optional[Any]	      =	"""constant"""
 _lowerCAmelCase     :    Optional[Any]	      =	"""constant_with_warmup"""
 _lowerCAmelCase     :    Union[str, Any]	      =	"""piecewise_constant"""





def __lowercase    (							_a	,    _a = -1					):
    return LambdaLR(_a	,    lambda _a					: 1	,    last_epoch=_a					)




def __lowercase    (							_a	,    _a	,    _a = -1					):

    def lr_lambda(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1.0	,    _a					)					)
        return 1.0

    return LambdaLR(_a	,    _a	,    last_epoch=_a					)




def __lowercase    (							_a	,    _a	,    _a = -1					):
    snake_case_				:  Dict       =			{}
    snake_case_				:  Optional[Any]       =			step_rules.split(''','''					)
    for rule_str in rule_list[:-1]:
        snake_case_,			snake_case_				:  Union[str, Any]       =			rule_str.split(''':'''					)
        snake_case_				:  Dict       =			int(_a					)
        snake_case_				:  int       =			float(_a					)
        snake_case_				:  Any       =			value
    snake_case_				:  Any       =			float(rule_list[-1]					)

    def create_rules_function(_a	,    _a					):
        def rule_func(_a					) -> float:
            snake_case_				:  Union[str, Any]       =			sorted(rules_dict.keys()					)
            for i, sorted_step in enumerate(_a					):
                if steps < sorted_step:
                    return rules_dict[sorted_steps[i]]
            return last_lr_multiple

        return rule_func

    snake_case_				:  List[str]       =			create_rules_function(_a	,    _a					)

    return LambdaLR(_a	,    _a	,    last_epoch=_a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a=-1					):

    def lr_lambda(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1	,    _a					)					)
        return max(
            0.0	,    float(num_training_steps - current_step					) / float(max(1	,    num_training_steps - num_warmup_steps					)					)					)

    return LambdaLR(_a	,    _a	,    _a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a = 0.5	,    _a = -1					):

    def lr_lambda(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1	,    _a					)					)
        snake_case_				:  str       =			float(current_step - num_warmup_steps					) / float(max(1	,    num_training_steps - num_warmup_steps					)					)
        return max(0.0	,    0.5 * (1.0 + math.cos(math.pi * float(_a					) * 2.0 * progress					))					)

    return LambdaLR(_a	,    _a	,    _a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a = 1	,    _a = -1					):

    def lr_lambda(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1	,    _a					)					)
        snake_case_				:  int       =			float(current_step - num_warmup_steps					) / float(max(1	,    num_training_steps - num_warmup_steps					)					)
        if progress >= 1.0:
            return 0.0
        return max(0.0	,    0.5 * (1.0 + math.cos(math.pi * ((float(_a					) * progress) % 1.0)					))					)

    return LambdaLR(_a	,    _a	,    _a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a=1E-7	,    _a=1.0	,    _a=-1					):
    snake_case_				:  List[str]       =			optimizer.defaults['''lr''']
    if not (lr_init > lr_end):
        raise ValueError(f"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"					)

    def lr_lambda(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1	,    _a					)					)
        elif current_step > num_training_steps:
            return lr_end / lr_init  # as LambdaLR multiplies by lr_init
        else:
            snake_case_				:  Tuple       =			lr_init - lr_end
            snake_case_				:  List[str]       =			num_training_steps - num_warmup_steps
            snake_case_				:  str       =			1 - (current_step - num_warmup_steps) / decay_steps
            snake_case_				:  Optional[int]       =			lr_range * pct_remaining**power + lr_end
            return decay / lr_init  # as LambdaLR multiplies by lr_init

    return LambdaLR(_a	,    _a	,    _a					)


lowercase__					:						Dict												=			{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}






def __lowercase    (							_a	,    _a	,    _a = None	,    _a = None	,    _a = None	,    _a = 1	,    _a = 1.0	,    _a = -1	,    ):
    snake_case_				:  Union[str, Any]       =			SchedulerType(_a					)
    snake_case_				:  Any       =			TYPE_TO_SCHEDULER_FUNCTION[name]
    if name == SchedulerType.CONSTANT:
        return schedule_func(_a	,    last_epoch=_a					)

    if name == SchedulerType.PIECEWISE_CONSTANT:
        return schedule_func(_a	,    step_rules=_a	,    last_epoch=_a					)

    # All other schedulers require `num_warmup_steps`
    if num_warmup_steps is None:
        raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument."					)

    if name == SchedulerType.CONSTANT_WITH_WARMUP:
        return schedule_func(_a	,    num_warmup_steps=_a	,    last_epoch=_a					)

    # All other schedulers require `num_training_steps`
    if num_training_steps is None:
        raise ValueError(f"{name} requires `num_training_steps`, please provide that argument."					)

    if name == SchedulerType.COSINE_WITH_RESTARTS:
        return schedule_func(
            _a	,    num_warmup_steps=_a	,    num_training_steps=_a	,    num_cycles=_a	,    last_epoch=_a	,    )

    if name == SchedulerType.POLYNOMIAL:
        return schedule_func(
            _a	,    num_warmup_steps=_a	,    num_training_steps=_a	,    power=_a	,    last_epoch=_a	,    )

    return schedule_func(
        _a	,    num_warmup_steps=_a	,    num_training_steps=_a	,    last_epoch=_a					)





