"""simple docstring"""




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 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




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






class       a_      :




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

							if isinstance(snake_case__  ,						torch.nn.Module     ):
									lowerCAmelCase__        =   (
									    """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"""  ,						snake_case__  ,						standard_warn=snake_case__  ,						)
									lowerCAmelCase__        =   parameters.parameters()

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

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

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

							lowerCAmelCase__        =   list(snake_case__     )
							lowerCAmelCase__        =   [p.clone().detach() for p in parameters]

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

							lowerCAmelCase__        =   None

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

							lowerCAmelCase__        =   model_cls
							lowerCAmelCase__        =   model_config




					@classmethod
					def 						_SCREAMING_SNAKE_CASE						(	cls						:     int  ,						snake_case__						:     Tuple  ,						snake_case__						:     Optional[Any]     ):
							lowerCAmelCase__   ,					lowerCAmelCase__        =   model_cls.load_config(snake_case__  ,						return_unused_kwargs=snake_case__     )
							lowerCAmelCase__        =   model_cls.from_pretrained(snake_case__     )

							lowerCAmelCase__        =   cls(model.parameters()  ,						model_cls=snake_case__  ,						model_config=model.config     )

							ema_model.load_state_dict(snake_case__     )
							return ema_model




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]  ,						snake_case__						:     str     ):
							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__."""     )

							lowerCAmelCase__        =   self.model_cls.from_config(self.model_config     )
							lowerCAmelCase__        =   self.state_dict()
							state_dict.pop("""shadow_params"""  ,						snake_case__     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     int     ):
							lowerCAmelCase__        =   max(0  ,						optimization_step - self.update_after_step - 1     )

							if step <= 0:
									return 0.0

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

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




					@torch.no_grad()
					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Iterable[torch.nn.Parameter]     ):
							if isinstance(snake_case__  ,						torch.nn.Module     ):
									lowerCAmelCase__        =   (
									    """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"""  ,						snake_case__  ,						standard_warn=snake_case__  ,						)
									lowerCAmelCase__        =   parameters.parameters()

							lowerCAmelCase__        =   list(snake_case__     )

							self.optimization_step += 1

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

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

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

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Iterable[torch.nn.Parameter]     ):
							lowerCAmelCase__        =   list(snake_case__     )
							for s_param, param in zip(self.shadow_params  ,						snake_case__     ):
									param.data.copy_(s_param.to(param.device     ).data     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     str=None  ,						snake_case__						:     List[Any]=None     ):
							lowerCAmelCase__        =   [
							    p.to(device=snake_case__  ,						dtype=snake_case__     ) if p.is_floating_point() else p.to(device=snake_case__     )
							    for p in self.shadow_params
							]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							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 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     Iterable[torch.nn.Parameter]     ):
							lowerCAmelCase__        =   [param.detach().cpu().clone() for param in parameters]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     Iterable[torch.nn.Parameter]     ):
							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  ,						snake_case__     ):
									param.data.copy_(c_param.data     )

							# Better memory-wise.
							lowerCAmelCase__        =   None




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     dict     ):
							lowerCAmelCase__        =   copy.deepcopy(snake_case__     )

							lowerCAmelCase__        =   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"""     )

							lowerCAmelCase__        =   state_dict.get("""min_decay"""  ,						self.min_decay     )
							if not isinstance(self.min_decay  ,						snake_case__     ):
									raise ValueError("""Invalid min_decay"""     )

							lowerCAmelCase__        =   state_dict.get("""optimization_step"""  ,						self.optimization_step     )
							if not isinstance(self.optimization_step  ,						snake_case__     ):
									raise ValueError("""Invalid optimization_step"""     )

							lowerCAmelCase__        =   state_dict.get("""update_after_step"""  ,						self.update_after_step     )
							if not isinstance(self.update_after_step  ,						snake_case__     ):
									raise ValueError("""Invalid update_after_step"""     )

							lowerCAmelCase__        =   state_dict.get("""use_ema_warmup"""  ,						self.use_ema_warmup     )
							if not isinstance(self.use_ema_warmup  ,						snake_case__     ):
									raise ValueError("""Invalid use_ema_warmup"""     )

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

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

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





"""simple docstring"""




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       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   0




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32"""     )
							self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
									lowerCAmelCase__        =   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"""     )     )

									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )
									self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							# Ensure we can load the image processor from the feature extractor config
							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
									lowerCAmelCase__        =   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"""     )     )

									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )
									self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase__        =   CLIPConfig()

									# Create a dummy config file with image_proceesor_type
									lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
									lowerCAmelCase__        =   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
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     ).to_dict()

									config_dict.pop("""image_processor_type"""     )
									lowerCAmelCase__        =   CLIPImageProcessor(**snake_case__     )

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

									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )

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

							self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
									json.dump(
									    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}  ,						open(snake_case__  ,						"""w"""     )  ,						)

									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__     )
									self.assertIsInstance(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							with self.assertRaisesRegex(
							    snake_case__  ,						"""clip-base is not a local folder and is not a valid model identifier"""     ):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""clip-base"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							with self.assertRaisesRegex(
							    snake_case__  ,						R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""     ):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__  ,						revision="""aaaaaa"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							with self.assertRaisesRegex(
							    snake_case__  ,						"""hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""  ,						):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							# If remote code is not set, we will time out when asking whether to load the model.
							with self.assertRaises(snake_case__     ):
									lowerCAmelCase__        =   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__     ):
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(
									    """hf-internal-testing/test_dynamic_image_processor"""  ,						trust_remote_code=snake_case__     )

							lowerCAmelCase__        =   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__     )
									lowerCAmelCase__        =   AutoImageProcessor.from_pretrained(snake_case__  ,						trust_remote_code=snake_case__     )
							self.assertEqual(reloaded_image_processor.__class__.__name__  ,						"""NewImageProcessor"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							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:
											lowerCAmelCase__        =   Path(snake_case__     ) / """preprocessor_config.json"""
											lowerCAmelCase__        =   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"""     )     )

											lowerCAmelCase__        =   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__     )
											lowerCAmelCase__        =   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 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):

							class       a_      (     __UpperCamelCase			):
												UpperCamelCase_ :	Tuple			=    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
									lowerCAmelCase__        =   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.
									lowerCAmelCase__        =   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
									lowerCAmelCase__        =   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]




