


"""simple docstring"""
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


SCREAMING_SNAKE_CASE :    Tuple									=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :    Tuple									=				{
    """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 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				='owlvit_text_model'



		def __init__(self				,       a_=4_94_08				,       a_=5_12				,       a_=20_48				,       a_=12				,       a_=8				,       a_=16				,       a_="quick_gelu"				,       a_=1E-5				,       a_=0.0				,       a_=0.02				,       a_=1.0				,       a_=0				,       a_=4_94_06				,       a_=4_94_07				,       **a_				,       ):





			'''simple docstring'''

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

			__snake_case	:						Any						=				vocab_size
			__snake_case	:						Dict						=				hidden_size
			__snake_case	:						Any						=				intermediate_size
			__snake_case	:						Union[str, Any]						=				num_hidden_layers
			__snake_case	:						int						=				num_attention_heads
			__snake_case	:						Any						=				max_position_embeddings
			__snake_case	:						Any						=				hidden_act
			__snake_case	:						Optional[Any]						=				layer_norm_eps
			__snake_case	:						Optional[int]						=				attention_dropout
			__snake_case	:						Tuple						=				initializer_range
			__snake_case	:						Tuple						=				initializer_factor



		@classmethod
		def 				SCREAMING_SNAKE_CASE  (cls				,       a_				,       **a_					):





			'''simple docstring'''

			cls._set_token_in_kwargs(a_					)

			__snake_case	,							__snake_case	:						Optional[Any]						=				cls.get_config_dict(a_				,       **a_					)

			# get the text config dict if we are loading from OwlViTConfig
			if config_dict.get('''model_type'''					) == "owlvit":
				__snake_case	:						Any						=				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 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				='owlvit_vision_model'



		def __init__(self				,       a_=7_68				,       a_=30_72				,       a_=12				,       a_=12				,       a_=3				,       a_=7_68				,       a_=32				,       a_="quick_gelu"				,       a_=1E-5				,       a_=0.0				,       a_=0.02				,       a_=1.0				,       **a_				,       ):





			'''simple docstring'''

			super().__init__(**a_					)

			__snake_case	:						str						=				hidden_size
			__snake_case	:						List[str]						=				intermediate_size
			__snake_case	:						Dict						=				num_hidden_layers
			__snake_case	:						str						=				num_attention_heads
			__snake_case	:						int						=				num_channels
			__snake_case	:						Union[str, Any]						=				image_size
			__snake_case	:						List[str]						=				patch_size
			__snake_case	:						Dict						=				hidden_act
			__snake_case	:						Optional[Any]						=				layer_norm_eps
			__snake_case	:						str						=				attention_dropout
			__snake_case	:						List[str]						=				initializer_range
			__snake_case	:						str						=				initializer_factor



		@classmethod
		def 				SCREAMING_SNAKE_CASE  (cls				,       a_				,       **a_					):





			'''simple docstring'''

			cls._set_token_in_kwargs(a_					)

			__snake_case	,							__snake_case	:						Optional[int]						=				cls.get_config_dict(a_				,       **a_					)

			# get the vision config dict if we are loading from OwlViTConfig
			if config_dict.get('''model_type'''					) == "owlvit":
				__snake_case	:						Dict						=				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 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				='owlvit'
		lowerCamelCase__				=True



		def __init__(self				,       a_=None				,       a_=None				,       a_=5_12				,       a_=2.6592				,       a_=True				,       **a_				,       ):





			'''simple docstring'''

			super().__init__(**a_					)

			if text_config is None:
				__snake_case	:						Union[str, Any]						=				{}
				logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.'''					)

			if vision_config is None:
				__snake_case	:						Optional[int]						=				{}
				logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.'''					)

			__snake_case	:						str						=				OwlViTTextConfig(**a_					)
			__snake_case	:						Union[str, Any]						=				OwlViTVisionConfig(**a_					)

			__snake_case	:						List[str]						=				projection_dim
			__snake_case	:						Optional[int]						=				logit_scale_init_value
			__snake_case	:						List[str]						=				return_dict
			__snake_case	:						List[Any]						=				1.0



		@classmethod
		def 				SCREAMING_SNAKE_CASE  (cls				,       a_				,       **a_					):





			'''simple docstring'''

			cls._set_token_in_kwargs(a_					)

			__snake_case	,							__snake_case	:						int						=				cls.get_config_dict(a_				,       **a_					)

			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_					)



		@classmethod
		def 				SCREAMING_SNAKE_CASE  (cls				,       a_				,       a_				,       **a_					):





			'''simple docstring'''

			__snake_case	:						str						=				{}
			__snake_case	:						Dict						=				text_config
			__snake_case	:						Optional[Any]						=				vision_config

			return cls.from_dict(a_				,       **a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						str						=				copy.deepcopy(self.__dict__					)
			__snake_case	:						Dict						=				self.text_config.to_dict()
			__snake_case	:						Optional[int]						=				self.vision_config.to_dict()
			__snake_case	:						Tuple						=				self.__class__.model_type
			return output



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 1E-4



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = -1				,       a_ = -1				,       a_ = None				,       ):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				super().generate_dummy_inputs(
			    processor.tokenizer				,       batch_size=a_				,       seq_length=a_				,       framework=a_					)
			__snake_case	:						Any						=				super().generate_dummy_inputs(
			    processor.image_processor				,       batch_size=a_				,       framework=a_					)
			return {**text_input_dict, **image_input_dict}



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return 14









"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


SCREAMING_SNAKE_CASE :    List[str]									=				logging.get_logger(__name__)  # pylint: disable=invalid-name



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		def __init__(self				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       ):





			'''simple docstring'''

			super().__init__()
			self.register_modules(
			    vae=a_				,       text_encoder=a_				,       tokenizer=a_				,       unet=a_				,       scheduler=a_				,       safety_checker=a_				,       feature_extractor=a_				,       )



		def 				SCREAMING_SNAKE_CASE  (self				,       a_ = "auto"					):





			'''simple docstring'''

			if slice_size == "auto":
				# half the attention head size is usually a good trade-off between
				# speed and memory
				__snake_case	:						Optional[int]						=				self.unet.config.attention_head_dim // 2
			self.unet.set_attention_slice(a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self.enable_attention_slicing(a_					)



		@torch.no_grad()
		def __call__(self				,       a_				,       a_ = 5_12				,       a_ = 5_12				,       a_ = 50				,       a_ = 7.5				,       a_ = None				,       a_ = 1				,       a_ = 0.0				,       a_ = None				,       a_ = None				,       a_ = "pil"				,       a_ = True				,       a_ = None				,       a_ = 1				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

			if isinstance(a_				,       a_					):
				__snake_case	:						Any						=				1
			elif isinstance(a_				,       a_					):
				__snake_case	:						Any						=				len(a_					)
			else:
				raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(a_					)}"""					)

			if height % 8 != 0 or width % 8 != 0:
				raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""					)

			if (callback_steps is None) or (
			    callback_steps is not None and (not isinstance(a_				,       a_					) or callback_steps <= 0)
			):
				raise ValueError(
				    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
				    f""" {type(a_					)}."""					)

			# get prompt text embeddings
			__snake_case	:						int						=				self.tokenizer(
			    a_				,       padding='''max_length'''				,       max_length=self.tokenizer.model_max_length				,       return_tensors='''pt'''				,       )
			__snake_case	:						int						=				text_inputs.input_ids

			if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
				__snake_case	:						List[Any]						=				self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]					)
				logger.warning(
				    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
				    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""					)
				__snake_case	:						Optional[int]						=				text_input_ids[:, : self.tokenizer.model_max_length]

			if text_embeddings is None:
				__snake_case	:						Optional[int]						=				self.text_encoder(text_input_ids.to(self.device					)					)[0]

			# duplicate text embeddings for each generation per prompt, using mps friendly method
			__snake_case	,							__snake_case	,							__snake_case	:						Union[str, Any]						=				text_embeddings.shape
			__snake_case	:						Optional[int]						=				text_embeddings.repeat(1				,       a_				,       1					)
			__snake_case	:						Dict						=				text_embeddings.view(bs_embed * num_images_per_prompt				,       a_				,       -1					)

			# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
			# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
			# corresponds to doing no classifier free guidance.
			__snake_case	:						Dict						=				guidance_scale > 1.0
			# get unconditional embeddings for classifier free guidance
			if do_classifier_free_guidance:
				__snake_case	:						List[str]
				if negative_prompt is None:
					__snake_case	:						List[Any]						=				['''''']
				elif type(a_					) is not type(a_					):
					raise TypeError(
					    f"""`negative_prompt` should be the same type to `prompt`, but got {type(a_					)} !="""
					    f""" {type(a_					)}."""					)
				elif isinstance(a_				,       a_					):
					__snake_case	:						List[str]						=				[negative_prompt]
				elif batch_size != len(a_					):
					raise ValueError(
					    f"""`negative_prompt`: {negative_prompt} has batch size {len(a_					)}, but `prompt`:"""
					    f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
					    ''' the batch size of `prompt`.'''					)
				else:
					__snake_case	:						Optional[int]						=				negative_prompt

				__snake_case	:						Optional[int]						=				text_input_ids.shape[-1]
				__snake_case	:						List[Any]						=				self.tokenizer(
				    a_				,       padding='''max_length'''				,       max_length=a_				,       truncation=a_				,       return_tensors='''pt'''				,       )
				__snake_case	:						str						=				self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]

				# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
				__snake_case	:						str						=				uncond_embeddings.shape[1]
				__snake_case	:						int						=				uncond_embeddings.repeat(a_				,       a_				,       1					)
				__snake_case	:						int						=				uncond_embeddings.view(batch_size * num_images_per_prompt				,       a_				,       -1					)

				# For classifier free guidance, we need to do two forward passes.
				# Here we concatenate the unconditional and text embeddings into a single batch
				# to avoid doing two forward passes
				__snake_case	:						Any						=				torch.cat([uncond_embeddings, text_embeddings]					)

			# get the initial random noise unless the user supplied it

			# Unlike in other pipelines, latents need to be generated in the target device
			# for 1-to-1 results reproducibility with the CompVis implementation.
			# However this currently doesn't work in `mps`.
			__snake_case	:						Any						=				(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
			__snake_case	:						Dict						=				(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
			__snake_case	:						Dict						=				text_embeddings.dtype
			if latents is None:
				if self.device.type == "mps":
					# randn does not exist on mps
					__snake_case	:						Union[str, Any]						=				torch.randn(
					    a_				,       generator=a_				,       device='''cpu'''				,       dtype=a_					).to(self.device					)
					__snake_case	:						Tuple						=				torch.randn(a_				,       generator=a_				,       device='''cpu'''				,       dtype=a_					).to(
					    self.device					)
				else:
					__snake_case	:						Dict						=				torch.randn(
					    a_				,       generator=a_				,       device=self.device				,       dtype=a_					)
					__snake_case	:						Dict						=				torch.randn(a_				,       generator=a_				,       device=self.device				,       dtype=a_					)
			else:
				if latents_reference.shape != latents_shape:
					raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""					)
				__snake_case	:						Union[str, Any]						=				latents_reference.to(self.device					)
				__snake_case	:						Dict						=				latents.to(self.device					)

			# This is the key part of the pipeline where we
			# try to ensure that the generated images w/ the same seed
			# but different sizes actually result in similar images
			__snake_case	:						int						=				(latents_shape[3] - latents_shape_reference[3]) // 2
			__snake_case	:						Tuple						=				(latents_shape[2] - latents_shape_reference[2]) // 2
			__snake_case	:						Optional[int]						=				latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
			__snake_case	:						Union[str, Any]						=				latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
			__snake_case	:						int						=				0 if dx < 0 else dx
			__snake_case	:						Union[str, Any]						=				0 if dy < 0 else dy
			__snake_case	:						str						=				max(-dx				,       0					)
			__snake_case	:						Tuple						=				max(-dy				,       0					)
			# import pdb
			# pdb.set_trace()
			__snake_case	:						Any						=				latents_reference[:, :, dy : dy + h, dx : dx + w]

			# set timesteps
			self.scheduler.set_timesteps(a_					)

			# Some schedulers like PNDM have timesteps as arrays
			# It's more optimized to move all timesteps to correct device beforehand
			__snake_case	:						Optional[Any]						=				self.scheduler.timesteps.to(self.device					)

			# scale the initial noise by the standard deviation required by the scheduler
			__snake_case	:						List[Any]						=				latents * self.scheduler.init_noise_sigma

			# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
			# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
			# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
			# and should be between [0, 1]
			__snake_case	:						Any						=				'''eta''' in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
			__snake_case	:						Tuple						=				{}
			if accepts_eta:
				__snake_case	:						List[str]						=				eta

			for i, t in enumerate(self.progress_bar(a_					)					):
				# expand the latents if we are doing classifier free guidance
				__snake_case	:						str						=				torch.cat([latents] * 2					) if do_classifier_free_guidance else latents
				__snake_case	:						Tuple						=				self.scheduler.scale_model_input(a_				,       a_					)

				# predict the noise residual
				__snake_case	:						int						=				self.unet(a_				,       a_				,       encoder_hidden_states=a_					).sample

				# perform guidance
				if do_classifier_free_guidance:
					__snake_case	,							__snake_case	:						Tuple						=				noise_pred.chunk(2					)
					__snake_case	:						str						=				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

				# compute the previous noisy sample x_t -> x_t-1
				__snake_case	:						Optional[Any]						=				self.scheduler.step(a_				,       a_				,       a_				,       **a_					).prev_sample

				# call the callback, if provided
				if callback is not None and i % callback_steps == 0:
					callback(a_				,       a_				,       a_					)

			__snake_case	:						Union[str, Any]						=				1 / 0.1_8215 * latents
			__snake_case	:						Optional[Any]						=				self.vae.decode(a_					).sample

			__snake_case	:						Union[str, Any]						=				(image / 2 + 0.5).clamp(0				,       1					)

			# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
			__snake_case	:						Union[str, Any]						=				image.cpu().permute(0				,       2				,       3				,       1					).float().numpy()

			if self.safety_checker is not None:
				__snake_case	:						Optional[int]						=				self.feature_extractor(self.numpy_to_pil(a_					)				,       return_tensors='''pt'''					).to(
				    self.device					)
				__snake_case	,							__snake_case	:						List[Any]						=				self.safety_checker(
				    images=a_				,       clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype					)					)
			else:
				__snake_case	:						Union[str, Any]						=				None

			if output_type == "pil":
				__snake_case	:						Union[str, Any]						=				self.numpy_to_pil(a_					)

			if not return_dict:
				return (image, has_nsfw_concept)

			return StableDiffusionPipelineOutput(images=a_				,       nsfw_content_detected=a_					)





