


import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
			import transformers
def      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		random.seed(SCREAMING_SNAKE_CASE	)
		np.random.seed(SCREAMING_SNAKE_CASE	)
		torch.manual_seed(SCREAMING_SNAKE_CASE	)
		torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE	)
		# ^^ safe to call this function even if cuda is not available


class 			_a				:

							def __init__(				self:      List[str]       ,		UpperCamelCase_:      Iterable[torch.nn.Parameter]       ,		UpperCamelCase_:      float = 0.9999       ,		UpperCamelCase_:      float = 0.0       ,		UpperCamelCase_:      int = 0       ,		UpperCamelCase_:      bool = False       ,		UpperCamelCase_:      Union[float, int] = 1.0       ,		UpperCamelCase_:      Union[float, int] = 2 / 3       ,		UpperCamelCase_:      Optional[Any] = None       ,		UpperCamelCase_:      Dict[str, Any] = None       ,		**UpperCamelCase_:      List[Any]       ,		)     ->  Tuple:





									"""simple docstring"""







									if isinstance(UpperCamelCase_       ,		torch.nn.Module				):
											lowercase__				= (
											    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
											    '''Please pass the parameters of the module instead.'''
											)
											deprecate(
											    '''passing a `torch.nn.Module` to `ExponentialMovingAverage`'''       ,		'''1.0.0'''       ,		UpperCamelCase_       ,		standard_warn=UpperCamelCase_       ,		)
											lowercase__				= parameters.parameters()

											# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
											lowercase__				= True

									if kwargs.get('''max_value'''       ,		UpperCamelCase_				) is not None:
											lowercase__				= '''The `max_value` argument is deprecated. Please use `decay` instead.'''
											deprecate('''max_value'''       ,		'''1.0.0'''       ,		UpperCamelCase_       ,		standard_warn=UpperCamelCase_				)
											lowercase__				= kwargs['''max_value''']

									if kwargs.get('''min_value'''       ,		UpperCamelCase_				) is not None:
											lowercase__				= '''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
											deprecate('''min_value'''       ,		'''1.0.0'''       ,		UpperCamelCase_       ,		standard_warn=UpperCamelCase_				)
											lowercase__				= kwargs['''min_value''']

									lowercase__				= list(UpperCamelCase_				)
									lowercase__				= [p.clone().detach() for p in parameters]

									if kwargs.get('''device'''       ,		UpperCamelCase_				) is not None:
											lowercase__				= '''The `device` argument is deprecated. Please use `to` instead.'''
											deprecate('''device'''       ,		'''1.0.0'''       ,		UpperCamelCase_       ,		standard_warn=UpperCamelCase_				)
											self.to(device=kwargs['''device''']				)

									lowercase__				= None

									lowercase__				= decay
									lowercase__				= min_decay
									lowercase__				= update_after_step
									lowercase__				= use_ema_warmup
									lowercase__				= inv_gamma
									lowercase__				= power
									lowercase__				= 0
									lowercase__				= None  # set in `step()`

									lowercase__				= model_cls
									lowercase__				= model_config

							@classmethod
							def  lowerCamelCase_					(				cls:      Union[str, Any]       ,		UpperCamelCase_:      List[Any]       ,		UpperCamelCase_:      int				)     ->  "EMAModel":





									"""simple docstring"""







									lowercase__	,						lowercase__				= model_cls.load_config(UpperCamelCase_       ,		return_unused_kwargs=UpperCamelCase_				)
									lowercase__				= model_cls.from_pretrained(UpperCamelCase_				)

									lowercase__				= cls(model.parameters()       ,		model_cls=UpperCamelCase_       ,		model_config=model.config				)

									ema_model.load_state_dict(UpperCamelCase_				)
									return ema_model

							def  lowerCamelCase_					(				self:      Optional[int]       ,		UpperCamelCase_:      Optional[Any]				)     ->  List[str]:





									"""simple docstring"""







									if self.model_cls is None:
											raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.'''				)

									if self.model_config is None:
											raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.'''				)

									lowercase__				= self.model_cls.from_config(self.model_config				)
									lowercase__				= self.state_dict()
									state_dict.pop('''shadow_params'''       ,		UpperCamelCase_				)

									model.register_to_config(**UpperCamelCase_				)
									self.copy_to(model.parameters()				)
									model.save_pretrained(UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      Tuple       ,		UpperCamelCase_:      int				)     ->  float:





									"""simple docstring"""







									lowercase__				= max(0       ,		optimization_step - self.update_after_step - 1				)

									if step <= 0:
											return 0.0

									if self.use_ema_warmup:
											lowercase__				= 1 - (1 + step / self.inv_gamma) ** -self.power
									else:
											lowercase__				= (1 + step) / (10 + step)

									lowercase__				= min(UpperCamelCase_       ,		self.decay				)
									# make sure decay is not smaller than min_decay
									lowercase__				= max(UpperCamelCase_       ,		self.min_decay				)
									return cur_decay_value

							@torch.no_grad()
							def  lowerCamelCase_					(				self:      int       ,		UpperCamelCase_:      Iterable[torch.nn.Parameter]				)     ->  int:





									"""simple docstring"""







									if isinstance(UpperCamelCase_       ,		torch.nn.Module				):
											lowercase__				= (
											    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
											    '''Please pass the parameters of the module instead.'''
											)
											deprecate(
											    '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'''       ,		'''1.0.0'''       ,		UpperCamelCase_       ,		standard_warn=UpperCamelCase_       ,		)
											lowercase__				= parameters.parameters()

									lowercase__				= list(UpperCamelCase_				)

									self.optimization_step += 1

									# Compute the decay factor for the exponential moving average.
									lowercase__				= self.get_decay(self.optimization_step				)
									lowercase__				= decay
									lowercase__				= 1 - decay

									lowercase__				= contextlib.nullcontext
									if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
											import deepspeed

									for s_param, param in zip(self.shadow_params       ,		UpperCamelCase_				):
											if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
													lowercase__				= deepspeed.zero.GatheredParameters(UpperCamelCase_       ,		modifier_rank=UpperCamelCase_				)

											with context_manager():
													if param.requires_grad:
															s_param.sub_(one_minus_decay * (s_param - param)				)
													else:
															s_param.copy_(UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      Any       ,		UpperCamelCase_:      Iterable[torch.nn.Parameter]				)     ->  None:





									"""simple docstring"""







									lowercase__				= list(UpperCamelCase_				)
									for s_param, param in zip(self.shadow_params       ,		UpperCamelCase_				):
											param.data.copy_(s_param.to(param.device				).data				)

							def  lowerCamelCase_					(				self:      Union[str, Any]       ,		UpperCamelCase_:      Tuple=None       ,		UpperCamelCase_:      Optional[int]=None				)     ->  None:





									"""simple docstring"""







									lowercase__				= [
									    p.to(device=UpperCamelCase_       ,		dtype=UpperCamelCase_				) if p.is_floating_point() else p.to(device=UpperCamelCase_				)
									    for p in self.shadow_params
									]

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





									"""simple docstring"""







									return {
									    "decay": self.decay,
									    "min_decay": self.min_decay,
									    "optimization_step": self.optimization_step,
									    "update_after_step": self.update_after_step,
									    "use_ema_warmup": self.use_ema_warmup,
									    "inv_gamma": self.inv_gamma,
									    "power": self.power,
									    "shadow_params": self.shadow_params,
									}

							def  lowerCamelCase_					(				self:      Union[str, Any]       ,		UpperCamelCase_:      Iterable[torch.nn.Parameter]				)     ->  None:





									"""simple docstring"""







									lowercase__				= [param.detach().cpu().clone() for param in parameters]

							def  lowerCamelCase_					(				self:      str       ,		UpperCamelCase_:      Iterable[torch.nn.Parameter]				)     ->  None:





									"""simple docstring"""







									if self.temp_stored_params is None:
											raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`'''				)
									for c_param, param in zip(self.temp_stored_params       ,		UpperCamelCase_				):
											param.data.copy_(c_param.data				)

									# Better memory-wise.
									lowercase__				= None


							def  lowerCamelCase_					(				self:      Dict       ,		UpperCamelCase_:      dict				)     ->  None:





									"""simple docstring"""







									lowercase__				= copy.deepcopy(UpperCamelCase_				)

									lowercase__				= state_dict.get('''decay'''       ,		self.decay				)
									if self.decay < 0.0 or self.decay > 1.0:
											raise ValueError('''Decay must be between 0 and 1'''				)

									lowercase__				= state_dict.get('''min_decay'''       ,		self.min_decay				)
									if not isinstance(self.min_decay       ,		UpperCamelCase_				):
											raise ValueError('''Invalid min_decay'''				)

									lowercase__				= state_dict.get('''optimization_step'''       ,		self.optimization_step				)
									if not isinstance(self.optimization_step       ,		UpperCamelCase_				):
											raise ValueError('''Invalid optimization_step'''				)

									lowercase__				= state_dict.get('''update_after_step'''       ,		self.update_after_step				)
									if not isinstance(self.update_after_step       ,		UpperCamelCase_				):
											raise ValueError('''Invalid update_after_step'''				)

									lowercase__				= state_dict.get('''use_ema_warmup'''       ,		self.use_ema_warmup				)
									if not isinstance(self.use_ema_warmup       ,		UpperCamelCase_				):
											raise ValueError('''Invalid use_ema_warmup'''				)

									lowercase__				= state_dict.get('''inv_gamma'''       ,		self.inv_gamma				)
									if not isinstance(self.inv_gamma       ,		(float, int)				):
											raise ValueError('''Invalid inv_gamma'''				)

									lowercase__				= state_dict.get('''power'''       ,		self.power				)
									if not isinstance(self.power       ,		(float, int)				):
											raise ValueError('''Invalid power'''				)

									lowercase__				= state_dict.get('''shadow_params'''       ,		UpperCamelCase_				)
									if shadow_params is not None:
											lowercase__				= shadow_params
											if not isinstance(self.shadow_params       ,		UpperCamelCase_				):
													raise ValueError('''shadow_params must be a list'''				)
											if not all(isinstance(UpperCamelCase_       ,		torch.Tensor				) for p in self.shadow_params				):
													raise ValueError('''shadow_params must all be Tensors'''				)










import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402






class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =0





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          _lowercase              =AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32')
          self.assertIsInstance(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          with tempfile.TemporaryDirectory() as tmpdirname:
             _lowercase              =Path(snake_case) / 'preprocessor_config.json'
             _lowercase              =Path(snake_case) / 'config.json'
             json.dump(
                 {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'},      open(snake_case,      'w'),      )
             json.dump({'model_type': 'clip'},      open(snake_case,      'w'))

             _lowercase              =AutoImageProcessor.from_pretrained(snake_case)
             self.assertIsInstance(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          with tempfile.TemporaryDirectory() as tmpdirname:
             _lowercase              =Path(snake_case) / 'preprocessor_config.json'
             _lowercase              =Path(snake_case) / 'config.json'
             json.dump(
                 {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'},      open(snake_case,      'w'),      )
             json.dump({'model_type': 'clip'},      open(snake_case,      'w'))

             _lowercase              =AutoImageProcessor.from_pretrained(snake_case)
             self.assertIsInstance(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          with tempfile.TemporaryDirectory() as tmpdirname:
             _lowercase              =CLIPConfig()

             # Create a dummy config file with image_proceesor_type
             _lowercase              =Path(snake_case) / 'preprocessor_config.json'
             _lowercase              =Path(snake_case) / 'config.json'
             json.dump(
                 {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'},      open(snake_case,      'w'),      )
             json.dump({'model_type': 'clip'},      open(snake_case,      'w'))

             # remove image_processor_type to make sure config.json alone is enough to load image processor locally
             _lowercase              =AutoImageProcessor.from_pretrained(snake_case).to_dict()

             config_dict.pop('image_processor_type')
             _lowercase              =CLIPImageProcessor(**snake_case)

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

             _lowercase              =AutoImageProcessor.from_pretrained(snake_case)

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

          self.assertIsInstance(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          with tempfile.TemporaryDirectory() as tmpdirname:
             _lowercase              =Path(snake_case) / 'preprocessor_config.json'
             json.dump(
                 {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'},      open(snake_case,      'w'),      )

             _lowercase              =AutoImageProcessor.from_pretrained(snake_case)
             self.assertIsInstance(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          with self.assertRaisesRegex(
              snake_case,      'clip-base is not a local folder and is not a valid model identifier'):
             _lowercase              =AutoImageProcessor.from_pretrained('clip-base')





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          with self.assertRaisesRegex(
              snake_case,      r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
             _lowercase              =AutoImageProcessor.from_pretrained(snake_case,      revision='aaaaaa')





       def 				UpperCamelCase__						(				self	:List[Any]):



          """simple docstring"""

          with self.assertRaisesRegex(
              snake_case,      'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.',      ):
             _lowercase              =AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model')





       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

          with self.assertRaises(snake_case):
             _lowercase              =AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor')
          # If remote code is disabled, we can't load this config.
          with self.assertRaises(snake_case):
             _lowercase              =AutoImageProcessor.from_pretrained(
                 'hf-internal-testing/test_dynamic_image_processor',      trust_remote_code=snake_case)

          _lowercase              =AutoImageProcessor.from_pretrained(
              'hf-internal-testing/test_dynamic_image_processor',      trust_remote_code=snake_case)
          self.assertEqual(image_processor.__class__.__name__,      'NewImageProcessor')

          # Test image processor can be reloaded.
          with tempfile.TemporaryDirectory() as tmp_dir:
             image_processor.save_pretrained(snake_case)
             _lowercase              =AutoImageProcessor.from_pretrained(snake_case,      trust_remote_code=snake_case)
          self.assertEqual(reloaded_image_processor.__class__.__name__,      'NewImageProcessor')





       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

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

             with tempfile.TemporaryDirectory() as tmpdirname:
                _lowercase              =Path(snake_case) / 'preprocessor_config.json'
                _lowercase              =Path(snake_case) / 'config.json'
                json.dump(
                    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'},      open(snake_case,      'w'),      )
                json.dump({'model_type': 'clip'},      open(snake_case,      'w'))

                _lowercase              =CustomImageProcessor.from_pretrained(snake_case)

             # Now that the config is registered, it can be used as any other config with the auto-API
             with tempfile.TemporaryDirectory() as tmp_dir:
                image_processor.save_pretrained(snake_case)
                _lowercase              =AutoImageProcessor.from_pretrained(snake_case)
                self.assertIsInstance(snake_case,      snake_case)

          finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          class 					SCREAMING_SNAKE_CASE_  (       _a ):
                 """simple docstring"""


                 __lowerCAmelCase    :		Union[str, Any]					=True

          try:
             AutoConfig.register('custom',      snake_case)
             AutoImageProcessor.register(snake_case,      snake_case)
             # If remote code is not set, the default is to use local
             _lowercase              =AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor')
             self.assertEqual(image_processor.__class__.__name__,      'NewImageProcessor')
             self.assertTrue(image_processor.is_local)

             # If remote code is disabled, we load the local one.
             _lowercase              =AutoImageProcessor.from_pretrained(
                 'hf-internal-testing/test_dynamic_image_processor',      trust_remote_code=snake_case)
             self.assertEqual(image_processor.__class__.__name__,      'NewImageProcessor')
             self.assertTrue(image_processor.is_local)

             # If remote is enabled, we load from the Hub
             _lowercase              =AutoImageProcessor.from_pretrained(
                 'hf-internal-testing/test_dynamic_image_processor',      trust_remote_code=snake_case)
             self.assertEqual(image_processor.__class__.__name__,      'NewImageProcessor')
             self.assertTrue(not hasattr(snake_case,      'is_local'))

          finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]


