'''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


UpperCamelCase__  :						Optional[Any]					=      logging.get_logger(__name__)


class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   str   				= '''linear'''
      UpperCAmelCase_   :   str   				= '''cosine'''
      UpperCAmelCase_   :   Any   				= '''cosine_with_restarts'''
      UpperCAmelCase_   :   Tuple   				= '''polynomial'''
      UpperCAmelCase_   :   List[Any]   				= '''constant'''
      UpperCAmelCase_   :   List[str]   				= '''constant_with_warmup'''
      UpperCAmelCase_   :   Union[str, Any]   				= '''piecewise_constant'''







def __UpperCamelCase( _A       :					Optimizer	,      _A       :					int = -1     ):
   '''simple docstring'''
   return LambdaLR(_A	,      lambda _A     : 1	,      last_epoch=_A     )







def __UpperCamelCase( _A       :					Optimizer	,      _A       :					int	,      _A       :					int = -1     ):
   '''simple docstring'''

   def lr_lambda(_A       :					int     ):
      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 __UpperCamelCase( _A       :					Optimizer	,      _A       :					str	,      _A       :					int = -1     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Optional[Any] 					=	{}
   UpperCAmelCase__				:					List[str] 					=	step_rules.split(''','''     )
   for rule_str in rule_list[:-1]:
      UpperCAmelCase__					,   UpperCAmelCase__				:					List[str] 					=	rule_str.split(''':'''     )
      UpperCAmelCase__				:					int 					=	int(_A     )
      UpperCAmelCase__				:					Any 					=	float(_A     )
      UpperCAmelCase__				:					str 					=	value
   UpperCAmelCase__				:					str 					=	float(rule_list[-1]     )

   def create_rules_function(_A       :					List[str]	,      _A       :					Tuple     ):
      def rule_func(_A       :					int     ) -> float:
         UpperCAmelCase__				:					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

   UpperCAmelCase__				:					int 					=	create_rules_function(_A	,      _A     )

   return LambdaLR(_A	,      _A	,      last_epoch=_A     )







def __UpperCamelCase( _A       :					int	,      _A       :					Dict	,      _A       :					List[Any]	,      _A       :					Optional[int]=-1     ):
   '''simple docstring'''

   def lr_lambda(_A       :					int     ):
      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 __UpperCamelCase( _A       :					Optimizer	,      _A       :					int	,      _A       :					int	,      _A       :					float = 0.5	,      _A       :					int = -1     ):
   '''simple docstring'''

   def lr_lambda(_A       :					Dict     ):
      if current_step < num_warmup_steps:
         return float(_A     ) / float(max(1	,      _A     )     )
      UpperCAmelCase__				:					Any 					=	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 __UpperCamelCase( _A       :					Optimizer	,      _A       :					int	,      _A       :					int	,      _A       :					int = 1	,      _A       :					int = -1     ):
   '''simple docstring'''

   def lr_lambda(_A       :					int     ):
      if current_step < num_warmup_steps:
         return float(_A     ) / float(max(1	,      _A     )     )
      UpperCAmelCase__				:					List[Any] 					=	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 __UpperCamelCase( _A       :					int	,      _A       :					List[Any]	,      _A       :					List[Any]	,      _A       :					Optional[int]=1e-7	,      _A       :					Dict=1.0	,      _A       :					Tuple=-1     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Any 					=	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       :					int     ):
      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:
         UpperCAmelCase__				:					Dict 					=	lr_init - lr_end
         UpperCAmelCase__				:					int 					=	num_training_steps - num_warmup_steps
         UpperCAmelCase__				:					Optional[int] 					=	1 - (current_step - num_warmup_steps) / decay_steps
         UpperCAmelCase__				:					Optional[int] 					=	lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(_A	,      _A	,      _A     )


UpperCamelCase__  :						Optional[Any]					=      {
    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 __UpperCamelCase( _A       :					Union[str, SchedulerType]	,      _A       :					Optimizer	,      _A       :					Optional[str] = None	,      _A       :					Optional[int] = None	,      _A       :					Optional[int] = None	,      _A       :					int = 1	,      _A       :					float = 1.0	,      _A       :					int = -1	,      ):
   '''simple docstring'''
   UpperCAmelCase__				:					List[str] 					=	SchedulerType(_A     )
   UpperCAmelCase__				:					Dict 					=	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     )





'''simple docstring'''

import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


UpperCamelCase__  :						str					=      get_tests_dir('fixtures')


class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	mock.Mock()
         UpperCAmelCase__				:					Dict 					=	500
         UpperCAmelCase__				:					Optional[int] 					=	{}
         UpperCAmelCase__				:					List[str] 					=	HTTPError
         UpperCAmelCase__				:					Tuple 					=	{}

         # Download this model to make sure it's in the cache.
         UpperCAmelCase__				:					Any 					=	ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit'''				)
         # Under the mock environment we get a 500 error when trying to reach the model.
         with mock.patch('''requests.Session.request'''       ,return_value=lowerCamelCase_				) as mock_head:
            UpperCAmelCase__				:					Tuple 					=	ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit'''				)
            # This check we did call the fake head request
            mock_head.assert_called()




      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	ViTImageProcessor.from_pretrained(
             '''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json'''				)




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



         '''simple docstring'''



         with self.assertRaises(lowerCamelCase_				):
            # config is in subfolder, the following should not work without specifying the subfolder
            UpperCAmelCase__				:					int 					=	AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants'''				)

         UpperCAmelCase__				:					Optional[Any] 					=	AutoImageProcessor.from_pretrained(
             '''hf-internal-testing/stable-diffusion-all-variants'''       ,subfolder='''feature_extractor'''				)

         self.assertIsNotNone(lowerCamelCase_				)




@is_staging_test
class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      @classmethod
      def 		lowerCAmelCase__       (					cls				)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	TOKEN
         HfFolder.save_token(lowerCamelCase_				)




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



         '''simple docstring'''



         try:
            delete_repo(token=cls._token       ,repo_id='''test-image-processor'''				)
         except HTTPError:
            pass

         try:
            delete_repo(token=cls._token       ,repo_id='''valid_org/test-image-processor-org'''				)
         except HTTPError:
            pass

         try:
            delete_repo(token=cls._token       ,repo_id='''test-dynamic-image-processor'''				)
         except HTTPError:
            pass




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



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	ViTImageProcessor.from_pretrained(lowerCamelCase_				)
         image_processor.push_to_hub('''test-image-processor'''       ,use_auth_token=self._token				)

         UpperCAmelCase__				:					Any 					=	ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor'''				)
         for k, v in image_processor.__dict__.items():
            self.assertEqual(lowerCamelCase_       ,getattr(lowerCamelCase_       ,lowerCamelCase_				)				)

         # Reset repo
         delete_repo(token=self._token       ,repo_id='''test-image-processor'''				)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
            image_processor.save_pretrained(
                lowerCamelCase_       ,repo_id='''test-image-processor'''       ,push_to_hub=lowerCamelCase_       ,use_auth_token=self._token				)

         UpperCAmelCase__				:					List[Any] 					=	ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor'''				)
         for k, v in image_processor.__dict__.items():
            self.assertEqual(lowerCamelCase_       ,getattr(lowerCamelCase_       ,lowerCamelCase_				)				)




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



         '''simple docstring'''



         UpperCAmelCase__				:					Union[str, Any] 					=	ViTImageProcessor.from_pretrained(lowerCamelCase_				)
         image_processor.push_to_hub('''valid_org/test-image-processor'''       ,use_auth_token=self._token				)

         UpperCAmelCase__				:					Any 					=	ViTImageProcessor.from_pretrained('''valid_org/test-image-processor'''				)
         for k, v in image_processor.__dict__.items():
            self.assertEqual(lowerCamelCase_       ,getattr(lowerCamelCase_       ,lowerCamelCase_				)				)

         # Reset repo
         delete_repo(token=self._token       ,repo_id='''valid_org/test-image-processor'''				)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
            image_processor.save_pretrained(
                lowerCamelCase_       ,repo_id='''valid_org/test-image-processor-org'''       ,push_to_hub=lowerCamelCase_       ,use_auth_token=self._token				)

         UpperCAmelCase__				:					Any 					=	ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org'''				)
         for k, v in image_processor.__dict__.items():
            self.assertEqual(lowerCamelCase_       ,getattr(lowerCamelCase_       ,lowerCamelCase_				)				)




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         CustomImageProcessor.register_for_auto_class()
         UpperCAmelCase__				:					List[str] 					=	CustomImageProcessor.from_pretrained(lowerCamelCase_				)

         image_processor.push_to_hub('''test-dynamic-image-processor'''       ,use_auth_token=self._token				)

         # This has added the proper auto_map field to the config
         self.assertDictEqual(
             image_processor.auto_map       ,{'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''}       ,)

         UpperCAmelCase__				:					Any 					=	AutoImageProcessor.from_pretrained(
             f'''{USER}/test-dynamic-image-processor'''       ,trust_remote_code=lowerCamelCase_				)
         # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
         self.assertEqual(new_image_processor.__class__.__name__       ,'''CustomImageProcessor'''				)




