


import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


_snake_case					:		int      =    "scheduler_config.json"

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  1
							__UpperCAmelCase   :							Tuple					  =  2
							__UpperCAmelCase   :							Union[str, Any]					  =  3
							__UpperCAmelCase   :							List[Any]					  =  4
							__UpperCAmelCase   :							Tuple					  =  5

@dataclass
class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							jnp.ndarray

class     a		:




							"""simple docstring"""


							__UpperCAmelCase   :							Dict					  =  SCHEDULER_CONFIG_NAME
							__UpperCAmelCase   :							Union[str, Any]					  =  ["dtype"]
							__UpperCAmelCase   :							Tuple					  =  []
							__UpperCAmelCase   :							int					  =  True

							@classmethod
							def    __snake_case (							cls	:  List[str]   ,     lowerCamelCase	:  Dict[str, Any] = None   ,     lowerCamelCase	:  Optional[str] = None   ,     lowerCamelCase	:  List[str]=False   ,     **lowerCamelCase	:  Union[str, Any]   ,     )     ->   List[str]:
														__snake_case			,      __snake_case		:      List[str]										=       cls.load_config(
														    pretrained_model_name_or_path=lowerCamelCase   ,     subfolder=lowerCamelCase   ,     return_unused_kwargs=lowerCamelCase   ,     **lowerCamelCase   ,     )
														__snake_case			,      __snake_case		:      Dict										=       cls.from_config(lowerCamelCase   ,     return_unused_kwargs=lowerCamelCase   ,     **lowerCamelCase    )

														if hasattr(lowerCamelCase   ,     "create_state"    ) and getattr(lowerCamelCase   ,     "has_state"   ,     lowerCamelCase    ):
																					__snake_case		:      Tuple										=       scheduler.create_state()

														if return_unused_kwargs:
																					return scheduler, state, unused_kwargs

														return scheduler, state

							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  Union[str, os.PathLike]   ,     lowerCamelCase	:  bool = False   ,     **lowerCamelCase	:  List[Any]    )     ->   int:
														self.save_config(save_directory=lowerCamelCase   ,     push_to_hub=lowerCamelCase   ,     **lowerCamelCase    )

							@property
							def    __snake_case (							self	:  Tuple    )     ->   List[Any]:
														return self._get_compatibles()






							@classmethod
							def    __snake_case (							cls	:  int    )     ->   Dict:
														__snake_case		:      Tuple										=       list(set([cls.__name__] + cls._compatibles    )    )
														__snake_case		:      int										=       importlib.import_module(__name__.split("."    )[0]    )
														__snake_case		:      Tuple										=       [
														    getattr(lowerCamelCase   ,     lowerCamelCase    ) for c in compatible_classes_str if hasattr(lowerCamelCase   ,     lowerCamelCase    )
														]
														return compatible_classes







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							assert len(__lowerCamelCase     ) >= x.ndim
							return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(__lowerCamelCase     ) - x.ndim)     )							,     __lowerCamelCase     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase=0.9_9_9							,     __lowerCamelCase=jnp.floataa     ):

							def alpha_bar(__lowerCamelCase     ):
														return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2     ) ** 2

							__snake_case		:      List[Any]										=       []
							for i in range(__lowerCamelCase     ):
														__snake_case		:      Dict										=       i / num_diffusion_timesteps
														__snake_case		:      str										=       (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar(__lowerCamelCase     ) / alpha_bar(__lowerCamelCase     )							,     __lowerCamelCase     )     )
							return jnp.array(__lowerCamelCase							,     dtype=__lowerCamelCase     )

@flax.struct.dataclass
class     a		:




							"""simple docstring"""


							__UpperCAmelCase   :							jnp.ndarray
							__UpperCAmelCase   :							jnp.ndarray
							__UpperCAmelCase   :							jnp.ndarray

							@classmethod
							def    __snake_case (							cls	:  Union[str, Any]   ,     lowerCamelCase	:  int    )     ->   List[Any]:
														__snake_case		:      Dict										=       scheduler.config

														if config.trained_betas is not None:
																					__snake_case		:      Dict										=       jnp.asarray(config.trained_betas   ,     dtype=scheduler.dtype    )
														elif config.beta_schedule == "linear":
																					__snake_case		:      Optional[int]										=       jnp.linspace(config.beta_start   ,     config.beta_end   ,     config.num_train_timesteps   ,     dtype=scheduler.dtype    )
														elif config.beta_schedule == "scaled_linear":
																					# this schedule is very specific to the latent diffusion model.
																					__snake_case		:      str										=       (
																					    jnp.linspace(
																					        config.beta_start**0.5   ,     config.beta_end**0.5   ,     config.num_train_timesteps   ,     dtype=scheduler.dtype    )
																					    ** 2
																					)
														elif config.beta_schedule == "squaredcos_cap_v2":
																					# Glide cosine schedule
																					__snake_case		:      Optional[Any]										=       betas_for_alpha_bar(config.num_train_timesteps   ,     dtype=scheduler.dtype    )
														else:
																					raise NotImplementedError(
																					    F'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'    )

														__snake_case		:      Any										=       1.0 - betas

														__snake_case		:      int										=       jnp.cumprod(lowerCamelCase   ,     axis=0    )

														return cls(
														    alphas=lowerCamelCase   ,     betas=lowerCamelCase   ,     alphas_cumprod=lowerCamelCase   ,     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      List[Any]										=       state.alphas_cumprod

							__snake_case		:      str										=       alphas_cumprod[timesteps] ** 0.5
							__snake_case		:      Dict										=       sqrt_alpha_prod.flatten()
							__snake_case		:      str										=       broadcast_to_shape_from_left(__lowerCamelCase							,     original_samples.shape     )

							__snake_case		:      Tuple										=       (1 - alphas_cumprod[timesteps]) ** 0.5
							__snake_case		:      str										=       sqrt_one_minus_alpha_prod.flatten()
							__snake_case		:      Tuple										=       broadcast_to_shape_from_left(__lowerCamelCase							,     original_samples.shape     )

							return sqrt_alpha_prod, sqrt_one_minus_alpha_prod







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case			,      __snake_case		:      Union[str, Any]										=       get_sqrt_alpha_prod(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     )
							__snake_case		:      List[str]										=       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case			,      __snake_case		:      Dict										=       get_sqrt_alpha_prod(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     )
							__snake_case		:      Optional[int]										=       sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
							return velocity





import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							List[str]					  =  ["image_processor", "tokenizer"]
							__UpperCAmelCase   :							str					  =  "OwlViTImageProcessor"
							__UpperCAmelCase   :							Dict					  =  ("CLIPTokenizer", "CLIPTokenizerFast")

							def __init__(							self	:  str   ,     lowerCamelCase	:  Any=None   ,     lowerCamelCase	:  Any=None   ,     **lowerCamelCase	:  Union[str, Any]    )     ->   List[Any]:
														__snake_case		:      List[Any]										=       None
														if "feature_extractor" in kwargs:
																					warnings.warn(
																					    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																					    " instead."   ,     lowerCamelCase   ,     )
																					__snake_case		:      List[Any]										=       kwargs.pop("feature_extractor"    )

														__snake_case		:      str										=       image_processor if image_processor is not None else feature_extractor
														if image_processor is None:
																					raise ValueError("You need to specify an `image_processor`."    )
														if tokenizer is None:
																					raise ValueError("You need to specify a `tokenizer`."    )

														super().__init__(lowerCamelCase   ,     lowerCamelCase    )

							def __call__(							self	:  Union[str, Any]   ,     lowerCamelCase	:  Tuple=None   ,     lowerCamelCase	:  int=None   ,     lowerCamelCase	:  Union[str, Any]=None   ,     lowerCamelCase	:  List[str]="max_length"   ,     lowerCamelCase	:  Dict="np"   ,     **lowerCamelCase	:  str    )     ->   List[Any]:

														if text is None and query_images is None and images is None:
																					raise ValueError(
																					    "You have to specify at least one text or query image or image. All three cannot be none."    )

														if text is not None:
																					if isinstance(lowerCamelCase   ,     lowerCamelCase    ) or (isinstance(lowerCamelCase   ,     lowerCamelCase    ) and not isinstance(text[0]   ,     lowerCamelCase    )):
																												__snake_case		:      Union[str, Any]										=       [self.tokenizer(lowerCamelCase   ,     padding=lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     **lowerCamelCase    )]

																					elif isinstance(lowerCamelCase   ,     lowerCamelCase    ) and isinstance(text[0]   ,     lowerCamelCase    ):
																												__snake_case		:      Tuple										=       []

																												# Maximum number of queries across batch
																												__snake_case		:      str										=       max([len(lowerCamelCase    ) for t in text]    )

																												# Pad all batch samples to max number of text queries
																												for t in text:
																																			if len(lowerCamelCase    ) != max_num_queries:
																																										__snake_case		:      Dict										=       t + [" "] * (max_num_queries - len(lowerCamelCase    ))

																																			__snake_case		:      int										=       self.tokenizer(lowerCamelCase   ,     padding=lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     **lowerCamelCase    )
																																			encodings.append(lowerCamelCase    )
																					else:
																												raise TypeError("Input text should be a string, a list of strings or a nested list of strings"    )

																					if return_tensors == "np":
																												__snake_case		:      Any										=       np.concatenate([encoding["input_ids"] for encoding in encodings]   ,     axis=0    )
																												__snake_case		:      Tuple										=       np.concatenate([encoding["attention_mask"] for encoding in encodings]   ,     axis=0    )

																					elif return_tensors == "jax" and is_flax_available():
																												import jax.numpy as jnp

																												__snake_case		:      List[Any]										=       jnp.concatenate([encoding["input_ids"] for encoding in encodings]   ,     axis=0    )
																												__snake_case		:      Any										=       jnp.concatenate([encoding["attention_mask"] for encoding in encodings]   ,     axis=0    )

																					elif return_tensors == "pt" and is_torch_available():
																												import torch

																												__snake_case		:      int										=       torch.cat([encoding["input_ids"] for encoding in encodings]   ,     dim=0    )
																												__snake_case		:      int										=       torch.cat([encoding["attention_mask"] for encoding in encodings]   ,     dim=0    )

																					elif return_tensors == "tf" and is_tf_available():
																												import tensorflow as tf

																												__snake_case		:      int										=       tf.stack([encoding["input_ids"] for encoding in encodings]   ,     axis=0    )
																												__snake_case		:      Dict										=       tf.stack([encoding["attention_mask"] for encoding in encodings]   ,     axis=0    )

																					else:
																												raise ValueError("Target return tensor type could not be returned"    )

																					__snake_case		:      Any										=       BatchEncoding()
																					__snake_case		:      Tuple										=       input_ids
																					__snake_case		:      int										=       attention_mask

														if query_images is not None:
																					__snake_case		:      List[Any]										=       BatchEncoding()
																					__snake_case		:      Union[str, Any]										=       self.image_processor(
																					    lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     **lowerCamelCase    ).pixel_values
																					__snake_case		:      str										=       query_pixel_values

														if images is not None:
																					__snake_case		:      Optional[int]										=       self.image_processor(lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     **lowerCamelCase    )

														if text is not None and images is not None:
																					__snake_case		:      List[str]										=       image_features.pixel_values
																					return encoding
														elif query_images is not None and images is not None:
																					__snake_case		:      int										=       image_features.pixel_values
																					return encoding
														elif text is not None or query_images is not None:
																					return encoding
														else:
																					return BatchEncoding(data=dict(**lowerCamelCase    )   ,     tensor_type=lowerCamelCase    )

							def    __snake_case (							self	:  Dict   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  Union[str, Any]    )     ->   str:
														return self.image_processor.post_process(*lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Union[str, Any]   ,     *lowerCamelCase	:  str   ,     **lowerCamelCase	:  List[str]    )     ->   Tuple:
														return self.image_processor.post_process_object_detection(*lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Optional[Any]   ,     *lowerCamelCase	:  Optional[Any]   ,     **lowerCamelCase	:  Optional[Any]    )     ->   Any:
														return self.image_processor.post_process_image_guided_detection(*lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  List[Any]   ,     *lowerCamelCase	:  Tuple   ,     **lowerCamelCase	:  Optional[int]    )     ->   str:
														return self.tokenizer.batch_decode(*lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Union[str, Any]   ,     *lowerCamelCase	:  Tuple   ,     **lowerCamelCase	:  List[Any]    )     ->   Tuple:
														return self.tokenizer.decode(*lowerCamelCase   ,     **lowerCamelCase    )

							@property
							def    __snake_case (							self	:  Any    )     ->   Dict:
														warnings.warn(
														    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."   ,     lowerCamelCase   ,     )
														return self.image_processor_class






							@property
							def    __snake_case (							self	:  List[str]    )     ->   Union[str, Any]:
														warnings.warn(
														    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."   ,     lowerCamelCase   ,     )
														return self.image_processor

