


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				(DDIMParallelScheduler,)
					__UpperCAmelCase            =				(("eta", 0.0), ("num_inference_steps", 50))






					def A      (				self ,       **snake_case_						)   ->			List[Any]:







								'''simple docstring'''

								__lowercase 					=   {
								    '''num_train_timesteps''': 1_0_0_0,
								    '''beta_start''': 0.0_0_0_1,
								    '''beta_end''': 0.0_2,
								    '''beta_schedule''': '''linear''',
								    '''clip_sample''': True,
								}

								config.update(**snake_case_						)
								return config






					def A      (				self ,       **snake_case_						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config(**snake_case_						)
								__lowercase 					=   scheduler_class(**snake_case_						)

								__lowercase					, __lowercase 					=   1_0, 0.0

								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter

								scheduler.set_timesteps(snake_case_						)

								for t in scheduler.timesteps:
											__lowercase 					=   model(snake_case_ ,       snake_case_						)
											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						).prev_sample

								return sample






					def A      (				self						)   ->			str:







								'''simple docstring'''

								for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
											self.check_over_configs(num_train_timesteps=snake_case_						)






					def A      (				self						)   ->			str:







								'''simple docstring'''

								for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=snake_case_						)

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config(steps_offset=1						)
								__lowercase 					=   scheduler_class(**snake_case_						)
								scheduler.set_timesteps(5						)
								assert torch.equal(scheduler.timesteps ,       torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]						)						)






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] ,       [0.0_0_2, 0.0_2, 0.2, 2]						):
											self.check_over_configs(beta_start=snake_case_ ,       beta_end=snake_case_						)






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







								'''simple docstring'''

								for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=snake_case_						)






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=snake_case_						)






					def A      (				self						)   ->			int:







								'''simple docstring'''

								for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=snake_case_						)






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								for timestep_spacing in ["trailing", "leading"]:
											self.check_over_configs(timestep_spacing=snake_case_						)






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







								'''simple docstring'''

								for rescale_betas_zero_snr in [True, False]:
											self.check_over_configs(rescale_betas_zero_snr=snake_case_						)






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







								'''simple docstring'''

								self.check_over_configs(thresholding=snake_case_						)
								for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(
														    thresholding=snake_case_ ,       prediction_type=snake_case_ ,       sample_max_value=snake_case_ ,       )






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								for t in [1, 1_0, 4_9]:
											self.check_over_forward(time_step=snake_case_						)






					def A      (				self						)   ->			int:







								'''simple docstring'''

								for t, num_inference_steps in zip([1, 1_0, 5_0] ,       [1_0, 5_0, 5_0_0]						):
											self.check_over_forward(time_step=snake_case_ ,       num_inference_steps=snake_case_						)






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







								'''simple docstring'''

								for t, eta in zip([1, 1_0, 4_9] ,       [0.0, 0.5, 1.0]						):
											self.check_over_forward(time_step=snake_case_ ,       eta=snake_case_						)






					def A      (				self						)   ->			str:







								'''simple docstring'''

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config()
								__lowercase 					=   scheduler_class(**snake_case_						)

								assert torch.sum(torch.abs(scheduler._get_variance(0 ,       0						) - 0.0						)						) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_2_0 ,       4_0_0						) - 0.1_4_7_7_1						)						) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_8_0 ,       9_6_0						) - 0.3_2_4_6_0						)						) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(0 ,       0						) - 0.0						)						) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_8_7 ,       4_8_6						) - 0.0_0_9_7_9						)						) < 1e-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_9_9 ,       9_9_8						) - 0.0_2						)						) < 1e-5






					def A      (				self						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config()
								__lowercase 					=   scheduler_class(**snake_case_						)

								__lowercase					, __lowercase 					=   1_0, 0.0
								scheduler.set_timesteps(snake_case_						)

								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter
								__lowercase 					=   self.dummy_sample_deter + 0.1
								__lowercase 					=   self.dummy_sample_deter - 0.1

								__lowercase 					=   samplea.shape[0]
								__lowercase 					=   torch.stack([samplea, samplea, samplea] ,       dim=0						)
								__lowercase 					=   torch.arange(snake_case_						)[0:3, None].repeat(1 ,       snake_case_						)

								__lowercase 					=   model(samples.flatten(0 ,       1						) ,       timesteps.flatten(0 ,       1						)						)
								__lowercase 					=   scheduler.batch_step_no_noise(snake_case_ ,       timesteps.flatten(0 ,       1						) ,       samples.flatten(0 ,       1						) ,       snake_case_						)

								__lowercase 					=   torch.sum(torch.abs(snake_case_						)						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_sum.item() - 1_1_4_7.7_9_0_4						) < 1e-2
								assert abs(result_mean.item() - 0.4_9_8_2						) < 1e-3






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







								'''simple docstring'''

								__lowercase 					=   self.full_loop()

								__lowercase 					=   torch.sum(torch.abs(snake_case_						)						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_sum.item() - 1_7_2.0_0_6_7						) < 1e-2
								assert abs(result_mean.item() - 0.2_2_3_9_6_7						) < 1e-3






					def A      (				self						)   ->			str:







								'''simple docstring'''

								__lowercase 					=   self.full_loop(prediction_type='''v_prediction'''						)

								__lowercase 					=   torch.sum(torch.abs(snake_case_						)						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_sum.item() - 5_2.5_3_0_2						) < 1e-2
								assert abs(result_mean.item() - 0.0_6_8_4						) < 1e-3






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   self.full_loop(set_alpha_to_one=snake_case_ ,       beta_start=0.0_1						)
								__lowercase 					=   torch.sum(torch.abs(snake_case_						)						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_sum.item() - 1_4_9.8_2_9_5						) < 1e-2
								assert abs(result_mean.item() - 0.1_9_5_1						) < 1e-3






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								__lowercase 					=   self.full_loop(set_alpha_to_one=snake_case_ ,       beta_start=0.0_1						)
								__lowercase 					=   torch.sum(torch.abs(snake_case_						)						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								assert abs(result_sum.item() - 1_4_9.0_7_8_4						) < 1e-2
								assert abs(result_mean.item() - 0.1_9_4_1						) < 1e-3






import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
	from ...processing_utils import ProcessorMixin
	from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a : Union[str, Any]		=				logging.get_logger(__name__)

a : Any		=				{
    '''google/owlvit-base-patch32''': '''https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json''',
    '''google/owlvit-base-patch16''': '''https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json''',
    '''google/owlvit-large-patch14''': '''https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json''',
}
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				"owlvit_text_model"






					def __init__(				self ,       snake_case_=4_9_4_0_8 ,       snake_case_=5_1_2 ,       snake_case_=2_0_4_8 ,       snake_case_=1_2 ,       snake_case_=8 ,       snake_case_=1_6 ,       snake_case_="quick_gelu" ,       snake_case_=1e-5 ,       snake_case_=0.0 ,       snake_case_=0.0_2 ,       snake_case_=1.0 ,       snake_case_=0 ,       snake_case_=4_9_4_0_6 ,       snake_case_=4_9_4_0_7 ,       **snake_case_ ,       )   ->			int:







								'''simple docstring'''

								super().__init__(pad_token_id=snake_case_ ,       bos_token_id=snake_case_ ,       eos_token_id=snake_case_ ,       **snake_case_						)

								__lowercase 					=   vocab_size
								__lowercase 					=   hidden_size
								__lowercase 					=   intermediate_size
								__lowercase 					=   num_hidden_layers
								__lowercase 					=   num_attention_heads
								__lowercase 					=   max_position_embeddings
								__lowercase 					=   hidden_act
								__lowercase 					=   layer_norm_eps
								__lowercase 					=   attention_dropout
								__lowercase 					=   initializer_range
								__lowercase 					=   initializer_factor






					@classmethod
					def A      (				cls ,       snake_case_ ,       **snake_case_						)   ->			"PretrainedConfig":







								'''simple docstring'''

								cls._set_token_in_kwargs(snake_case_						)

								__lowercase					, __lowercase 					=   cls.get_config_dict(snake_case_ ,       **snake_case_						)

								# get the text config dict if we are loading from OwlViTConfig
								if config_dict.get('''model_type'''						) == "owlvit":
											__lowercase 					=   config_dict['''text_config''']

								if "model_type" in config_dict and hasattr(cls ,       '''model_type'''						) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
											    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'						)

								return cls.from_dict(snake_case_ ,       **snake_case_						)




class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				"owlvit_vision_model"






					def __init__(				self ,       snake_case_=7_6_8 ,       snake_case_=3_0_7_2 ,       snake_case_=1_2 ,       snake_case_=1_2 ,       snake_case_=3 ,       snake_case_=7_6_8 ,       snake_case_=3_2 ,       snake_case_="quick_gelu" ,       snake_case_=1e-5 ,       snake_case_=0.0 ,       snake_case_=0.0_2 ,       snake_case_=1.0 ,       **snake_case_ ,       )   ->			Any:







								'''simple docstring'''

								super().__init__(**snake_case_						)

								__lowercase 					=   hidden_size
								__lowercase 					=   intermediate_size
								__lowercase 					=   num_hidden_layers
								__lowercase 					=   num_attention_heads
								__lowercase 					=   num_channels
								__lowercase 					=   image_size
								__lowercase 					=   patch_size
								__lowercase 					=   hidden_act
								__lowercase 					=   layer_norm_eps
								__lowercase 					=   attention_dropout
								__lowercase 					=   initializer_range
								__lowercase 					=   initializer_factor






					@classmethod
					def A      (				cls ,       snake_case_ ,       **snake_case_						)   ->			"PretrainedConfig":







								'''simple docstring'''

								cls._set_token_in_kwargs(snake_case_						)

								__lowercase					, __lowercase 					=   cls.get_config_dict(snake_case_ ,       **snake_case_						)

								# get the vision config dict if we are loading from OwlViTConfig
								if config_dict.get('''model_type'''						) == "owlvit":
											__lowercase 					=   config_dict['''vision_config''']

								if "model_type" in config_dict and hasattr(cls ,       '''model_type'''						) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
											    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'						)

								return cls.from_dict(snake_case_ ,       **snake_case_						)




class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				"owlvit"
					__UpperCAmelCase            =				True






					def __init__(				self ,       snake_case_=None ,       snake_case_=None ,       snake_case_=5_1_2 ,       snake_case_=2.6_5_9_2 ,       snake_case_=True ,       **snake_case_ ,       )   ->			Optional[Any]:







								'''simple docstring'''

								super().__init__(**snake_case_						)

								if text_config is None:
											__lowercase 					=   {}
											logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.'''						)

								if vision_config is None:
											__lowercase 					=   {}
											logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.'''						)

								__lowercase 					=   OwlViTTextConfig(**snake_case_						)
								__lowercase 					=   OwlViTVisionConfig(**snake_case_						)

								__lowercase 					=   projection_dim
								__lowercase 					=   logit_scale_init_value
								__lowercase 					=   return_dict
								__lowercase 					=   1.0






					@classmethod
					def A      (				cls ,       snake_case_ ,       **snake_case_						)   ->			"PretrainedConfig":







								'''simple docstring'''

								cls._set_token_in_kwargs(snake_case_						)

								__lowercase					, __lowercase 					=   cls.get_config_dict(snake_case_ ,       **snake_case_						)

								if "model_type" in config_dict and hasattr(cls ,       '''model_type'''						) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
											    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'						)

								return cls.from_dict(snake_case_ ,       **snake_case_						)






					@classmethod
					def A      (				cls ,       snake_case_ ,       snake_case_ ,       **snake_case_						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   {}
								__lowercase 					=   text_config
								__lowercase 					=   vision_config

								return cls.from_dict(snake_case_ ,       **snake_case_						)






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   copy.deepcopy(self.__dict__						)
								__lowercase 					=   self.text_config.to_dict()
								__lowercase 					=   self.vision_config.to_dict()
								__lowercase 					=   self.__class__.model_type
								return output




class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					@property
					def A      (				self						)   ->			Mapping[str, Mapping[int, str]]:







								'''simple docstring'''

								return OrderedDict(
								    [
								        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
								        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
								        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
								    ]						)






					@property
					def A      (				self						)   ->			Mapping[str, Mapping[int, str]]:







								'''simple docstring'''

								return OrderedDict(
								    [
								        ('''logits_per_image''', {0: '''batch'''}),
								        ('''logits_per_text''', {0: '''batch'''}),
								        ('''text_embeds''', {0: '''batch'''}),
								        ('''image_embeds''', {0: '''batch'''}),
								    ]						)






					@property
					def A      (				self						)   ->			float:







								'''simple docstring'''

								return 1e-4






					def A      (				self ,       snake_case_ ,       snake_case_ = -1 ,       snake_case_ = -1 ,       snake_case_ = None ,       )   ->			Mapping[str, Any]:







								'''simple docstring'''

								__lowercase 					=   super().generate_dummy_inputs(
								    processor.tokenizer ,       batch_size=snake_case_ ,       seq_length=snake_case_ ,       framework=snake_case_						)
								__lowercase 					=   super().generate_dummy_inputs(
								    processor.image_processor ,       batch_size=snake_case_ ,       framework=snake_case_						)
								return {**text_input_dict, **image_input_dict}






					@property
					def A      (				self						)   ->			int:







								'''simple docstring'''

								return 1_4


