

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_         =					logging.get_logger(__name__)

snake_case_         =					{
    "Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json",
    "Salesforce/blip-vqa-capfit-large": (
        "https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json"
    ),
    "Salesforce/blip-image-captioning-base": (
        "https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json"
    ),
    "Salesforce/blip-image-captioning-large": (
        "https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json"
    ),
    "Salesforce/blip-itm-base-coco": "https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json",
    "Salesforce/blip-itm-large-coco": "https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json",
    "Salesforce/blip-itm-base-flikr": "https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json",
    "Salesforce/blip-itm-large-flikr": (
        "https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json"
    ),
}


class  SCREAMING_SNAKE_CASE__   (__snake_case	):
    __lowerCamelCase		:			Dict								=	"""blip_text_model"""





    def __init__( self		,  a=3_0524		,  a=768		,  a=768		,  a=3072		,  a=768		,  a=12		,  a=8		,  a=512		,  a="gelu"		,  a=1e-12		,  a=0.0		,  a=0.0		,  a=0.02		,  a=3_0522		,  a=2		,  a=0		,  a=102		,  a=True		,  a=True		,  **a		,  ):
         super().__init__(
             pad_token_id=a__		,  bos_token_id=a__		,  eos_token_id=a__		,  sep_token_id=a__		,  **a__		,  )

         lowercase__       :  Any =			vocab_size
         lowercase__       :  List[str] =			hidden_size
         lowercase__       :  Any =			encoder_hidden_size
         lowercase__       :  List[Any] =			intermediate_size
         lowercase__       :  Any =			projection_dim
         lowercase__       :  List[Any] =			hidden_dropout_prob
         lowercase__       :  Any =			num_hidden_layers
         lowercase__       :  Dict =			num_attention_heads
         lowercase__       :  Dict =			max_position_embeddings
         lowercase__       :  Optional[Any] =			layer_norm_eps
         lowercase__       :  Optional[Any] =			hidden_act
         lowercase__       :  Any =			initializer_range
         lowercase__       :  List[str] =			attention_probs_dropout_prob
         lowercase__       :  Union[str, Any] =			is_decoder
         lowercase__       :  int =			use_cache





    @classmethod
    def  snake_case_							( cls		,  a		,  **a):
         cls._set_token_in_kwargs(a__)

         lowercase__ ,					lowercase__       :  Optional[Any] =			cls.get_config_dict(a__		,  **a__)

         # get the text config dict if we are loading from BlipConfig
         if config_dict.get('model_type') == "blip":
              lowercase__       :  Dict =			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(a__		,  **a__)



class  SCREAMING_SNAKE_CASE__   (__snake_case	):
    __lowerCamelCase		:			Any								=	"""blip_vision_model"""





    def __init__( self		,  a=768		,  a=3072		,  a=512		,  a=12		,  a=12		,  a=384		,  a=16		,  a="gelu"		,  a=1e-5		,  a=0.0		,  a=1e-10		,  **a		,  ):
         super().__init__(**a__)

         lowercase__       :  int =			hidden_size
         lowercase__       :  str =			intermediate_size
         lowercase__       :  Tuple =			projection_dim
         lowercase__       :  List[str] =			num_hidden_layers
         lowercase__       :  Optional[Any] =			num_attention_heads
         lowercase__       :  int =			patch_size
         lowercase__       :  List[Any] =			image_size
         lowercase__       :  str =			initializer_range
         lowercase__       :  Union[str, Any] =			attention_dropout
         lowercase__       :  Dict =			layer_norm_eps
         lowercase__       :  str =			hidden_act





    @classmethod
    def  snake_case_							( cls		,  a		,  **a):
         cls._set_token_in_kwargs(a__)

         lowercase__ ,					lowercase__       :  List[str] =			cls.get_config_dict(a__		,  **a__)

         # get the vision config dict if we are loading from BlipConfig
         if config_dict.get('model_type') == "blip":
              lowercase__       :  int =			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(a__		,  **a__)



class  SCREAMING_SNAKE_CASE__   (__snake_case	):
    __lowerCamelCase		:			Optional[Any]								=	"""blip"""
    __lowerCamelCase		:			int								=	True





    def __init__( self		,  a=None		,  a=None		,  a=512		,  a=2.6_592		,  a=256		,  **a		,  ):
         super().__init__(**a__)

         if text_config is None:
              lowercase__       :  Tuple =			{}
              logger.info('`text_config` is `None`. Initializing the `BlipTextConfig` with default values.')

         if vision_config is None:
              lowercase__       :  Tuple =			{}
              logger.info('`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.')

         lowercase__       :  List[Any] =			BlipTextConfig(**a__)
         lowercase__       :  Dict =			BlipVisionConfig(**a__)

         lowercase__       :  Dict =			self.vision_config.hidden_size

         lowercase__       :  List[str] =			projection_dim
         lowercase__       :  List[Any] =			logit_scale_init_value
         lowercase__       :  Any =			1.0
         lowercase__       :  Optional[Any] =			0.02
         lowercase__       :  List[Any] =			image_text_hidden_size





    @classmethod
    def  snake_case_							( cls		,  a		,  a		,  **a):
         return cls(text_config=text_config.to_dict()		,  vision_config=vision_config.to_dict()		,  **a__)





    def  snake_case_							( self):
         lowercase__       :  int =			copy.deepcopy(self.__dict__)
         lowercase__       :  Any =			self.text_config.to_dict()
         lowercase__       :  Union[str, Any] =			self.vision_config.to_dict()
         lowercase__       :  int =			self.__class__.model_type
         return output





import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			(IPNDMScheduler,)
				lowerCAmelCase					=			(('num_inference_steps', 5_0),)

				def   _UpperCAmelCase       (    self					, **a__ )						->					List[str]:
					A             =					{"""num_train_timesteps""": 1000}
					config.update(**a__ )
					return config

				def   _UpperCAmelCase       (    self					, a__=0					, **a__ )						->					Tuple:
					A             =					dict(self.forward_default_kwargs )
					A             =					kwargs.pop("""num_inference_steps"""					, a__ )
					A             =					self.dummy_sample
					A             =					0.1 * sample
					A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

					for scheduler_class in self.scheduler_classes:
						A             =					self.get_scheduler_config(**a__ )
						A             =					scheduler_class(**a__ )
						scheduler.set_timesteps(a__ )
						# copy over dummy past residuals
						A             =					dummy_past_residuals[:]

						if time_step is None:
							A             =					scheduler.timesteps[len(scheduler.timesteps ) // 2]

						with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(a__ )
							A             =					scheduler_class.from_pretrained(a__ )
							new_scheduler.set_timesteps(a__ )
							# copy over dummy past residuals
							A             =					dummy_past_residuals[:]

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

						assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

						assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

				def   _UpperCAmelCase       (    self )						->					Any:
					pass

				def   _UpperCAmelCase       (    self					, a__=0					, **a__ )						->					int:
					A             =					dict(self.forward_default_kwargs )
					A             =					kwargs.pop("""num_inference_steps"""					, a__ )
					A             =					self.dummy_sample
					A             =					0.1 * sample
					A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

					for scheduler_class in self.scheduler_classes:
						A             =					self.get_scheduler_config()
						A             =					scheduler_class(**a__ )
						scheduler.set_timesteps(a__ )

						# copy over dummy past residuals (must be after setting timesteps)
						A             =					dummy_past_residuals[:]

						if time_step is None:
							A             =					scheduler.timesteps[len(scheduler.timesteps ) // 2]

						with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(a__ )
							A             =					scheduler_class.from_pretrained(a__ )
							# copy over dummy past residuals
							new_scheduler.set_timesteps(a__ )

							# copy over dummy past residual (must be after setting timesteps)
							A             =					dummy_past_residuals[:]

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

						assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

						assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

				def   _UpperCAmelCase       (    self					, **a__ )						->					List[Any]:
					A             =					self.scheduler_classes[0]
					A             =					self.get_scheduler_config(**a__ )
					A             =					scheduler_class(**a__ )

					A             =					10
					A             =					self.dummy_model()
					A             =					self.dummy_sample_deter
					scheduler.set_timesteps(a__ )

					for i, t in enumerate(scheduler.timesteps ):
						A             =					model(a__					, a__ )
						A             =					scheduler.step(a__					, a__					, a__ ).prev_sample

					for i, t in enumerate(scheduler.timesteps ):
						A             =					model(a__					, a__ )
						A             =					scheduler.step(a__					, a__					, a__ ).prev_sample

					return sample

				def   _UpperCAmelCase       (    self )						->					Any:
					A             =					dict(self.forward_default_kwargs )

					A             =					kwargs.pop("""num_inference_steps"""					, a__ )

					for scheduler_class in self.scheduler_classes:
						A             =					self.get_scheduler_config()
						A             =					scheduler_class(**a__ )

						A             =					self.dummy_sample
						A             =					0.1 * sample

						if num_inference_steps is not None and hasattr(a__					, """set_timesteps""" ):
							scheduler.set_timesteps(a__ )
						elif num_inference_steps is not None and not hasattr(a__					, """set_timesteps""" ):
							A             =					num_inference_steps

						# copy over dummy past residuals (must be done after set_timesteps)
						A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
						A             =					dummy_past_residuals[:]

						A             =					scheduler.timesteps[5]
						A             =					scheduler.timesteps[6]

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

						self.assertEqual(output_a.shape					, sample.shape )
						self.assertEqual(output_a.shape					, output_a.shape )

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

						self.assertEqual(output_a.shape					, sample.shape )
						self.assertEqual(output_a.shape					, output_a.shape )

				def   _UpperCAmelCase       (    self )						->					Tuple:
					for timesteps in [100, 1000]:
						self.check_over_configs(num_train_timesteps=a__					, time_step=a__ )

				def   _UpperCAmelCase       (    self )						->					Any:
					for t, num_inference_steps in zip([1, 5, 10]					, [10, 50, 100] ):
						self.check_over_forward(num_inference_steps=a__					, time_step=a__ )

				def   _UpperCAmelCase       (    self )						->					int:
					A             =					self.full_loop()
					A             =					torch.mean(torch.abs(a__ ) )

					assert abs(result_mean.item() - 254_0529 ) < 10



