code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  from typing import Optional, Tuple
  
  import flax.linen as nn
  import jax
  import jax.numpy as jnp
  from configuration_hybrid_clip import HybridCLIPConfig
  from flax.core.frozen_dict import FrozenDict
  
  from transformers import FLAX_MODEL_MAPPING, FlaxCLIPVisionModel
  from transformers.modeling_flax_utils import FlaxPreTrainedModel
  from transformers.models.clip.modeling_flax_clip import FlaxCLIPOutput
  from transformers.utils import logging
  
  
  _lowerCamelCase							:		Optional[int]										=						logging.get_logger(__name__)
  
  
  
  
  
  class   lowerCamelCase_							( nn.Module							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				42
  					lowercase__					=				jnp.floataa
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self.config.text_config
  							_snake_case			 = self.config.vision_config
  
  							_snake_case			 = self.config.projection_dim
  							_snake_case			 = text_config.hidden_size
  							_snake_case			 = vision_config.hidden_size
  
  							_snake_case			 = FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
  							_snake_case			 = FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__,						__UpperCamelCase				).module_class
  
  							_snake_case			 = text_module(__UpperCamelCase,						dtype=self.dtype				)
  							_snake_case			 = vision_module(__UpperCamelCase,						dtype=self.dtype				)
  
  							_snake_case			 = nn.Dense(
  							    self.projection_dim,						dtype=self.dtype,						kernel_init=jax.nn.initializers.normal(0.02				),						use_bias=__UpperCamelCase,						)
  							_snake_case			 = nn.Dense(
  							    self.projection_dim,						dtype=self.dtype,						kernel_init=jax.nn.initializers.normal(0.02				),						use_bias=__UpperCamelCase,						)
  							_snake_case			 = self.param("logit_scale",						jax.nn.initializers.ones,						[]				)
  
  
  
  
  
  
  					def __call__(					self,						__UpperCamelCase=None,						__UpperCamelCase=None,						__UpperCamelCase=None,						__UpperCamelCase=None,						__UpperCamelCase=None,						__UpperCamelCase = True,						__UpperCamelCase=None,						__UpperCamelCase=None,						__UpperCamelCase=None,						):
  							_snake_case			 = return_dict if return_dict is not None else self.config.return_dict
  
  							_snake_case			 = self.vision_model(
  							    pixel_values=__UpperCamelCase,						deterministic=__UpperCamelCase,						output_attentions=__UpperCamelCase,						output_hidden_states=__UpperCamelCase,						return_dict=__UpperCamelCase,						)
  
  							_snake_case			 = self.text_model(
  							    input_ids=__UpperCamelCase,						attention_mask=__UpperCamelCase,						token_type_ids=__UpperCamelCase,						position_ids=__UpperCamelCase,						deterministic=__UpperCamelCase,						output_attentions=__UpperCamelCase,						output_hidden_states=__UpperCamelCase,						return_dict=__UpperCamelCase,						)
  
  							_snake_case			 = vision_outputs[1]
  							_snake_case			 = self.visual_projection(__UpperCamelCase				)
  
  							_snake_case			 = text_outputs[1]
  							_snake_case			 = self.text_projection(__UpperCamelCase				)
  
  							# normalized features
  							_snake_case			 = image_embeds / jnp.linalg.norm(__UpperCamelCase,						axis=-1,						keepdims=__UpperCamelCase				)
  							_snake_case			 = text_embeds / jnp.linalg.norm(__UpperCamelCase,						axis=-1,						keepdims=__UpperCamelCase				)
  
  							# cosine similarity as logits
  							_snake_case			 = jnp.exp(self.logit_scale				)
  							_snake_case			 = jnp.matmul(__UpperCamelCase,						image_embeds.T				) * logit_scale
  							_snake_case			 = logits_per_text.T
  
  							if not return_dict:
  									return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
  
  							return FlaxCLIPOutput(
  							    logits_per_image=__UpperCamelCase,						logits_per_text=__UpperCamelCase,						text_embeds=__UpperCamelCase,						image_embeds=__UpperCamelCase,						text_model_output=__UpperCamelCase,						vision_model_output=__UpperCamelCase,						)
  
  
  
  
  
  class   lowerCamelCase_							( UpperCAmelCase_							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				HybridCLIPConfig
  					lowercase__					=				FlaxHybridCLIPModule
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase,						__UpperCamelCase = None,						__UpperCamelCase = 0,						__UpperCamelCase = jnp.floataa,						**__UpperCamelCase,						):
  							if input_shape is None:
  									_snake_case			 = ((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))
  
  							_snake_case			 = self.module_class(config=__UpperCamelCase,						dtype=__UpperCamelCase,						**__UpperCamelCase				)
  							super().__init__(__UpperCamelCase,						__UpperCamelCase,						input_shape=__UpperCamelCase,						seed=__UpperCamelCase,						dtype=__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase = None				):
  							# init input tensor
  							_snake_case			 = jnp.zeros(input_shape[0],						dtype="i4"				)
  							_snake_case			 = jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__UpperCamelCase				).shape[-1]				),						input_shape[0]				)
  							_snake_case			 = jnp.ones_like(__UpperCamelCase				)
  							_snake_case			 = jnp.ones_like(__UpperCamelCase				)
  
  							_snake_case			 = jax.random.normal(__UpperCamelCase,						input_shape[1]				)
  
  							_snake_case ,					_snake_case			 = jax.random.split(__UpperCamelCase				)
  							_snake_case			 = {"params": params_rng, "dropout": dropout_rng}
  
  							return self.module.init(__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase				)["params"]
  
  
  
  
  
  
  					def __call__(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase=None,						__UpperCamelCase=None,						__UpperCamelCase=None,						__UpperCamelCase = None,						__UpperCamelCase = None,						__UpperCamelCase = False,						__UpperCamelCase = None,						__UpperCamelCase = None,						__UpperCamelCase = None,						):
  							_snake_case			 = output_attentions if output_attentions is not None else self.config.output_attentions
  							_snake_case			 = (
  							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
  							)
  							_snake_case			 = return_dict if return_dict is not None else self.config.return_dict
  
  							if position_ids is None:
  									_snake_case			 = jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__UpperCamelCase				).shape[-1]				),						input_ids.shape				)
  
  							if token_type_ids is None:
  									_snake_case			 = jnp.zeros_like(__UpperCamelCase				)
  
  							if attention_mask is None:
  									_snake_case			 = jnp.ones_like(__UpperCamelCase				)
  
  							# Handle any PRNG if needed
  							_snake_case			 = {}
  							if dropout_rng is not None:
  									_snake_case			 = dropout_rng
  
  							return self.module.apply(
  							    {"params": params or self.params},						jnp.array(__UpperCamelCase,						dtype="i4"				),						jnp.array(__UpperCamelCase,						dtype=jnp.floataa				),						jnp.array(__UpperCamelCase,						dtype="i4"				),						jnp.array(__UpperCamelCase,						dtype="i4"				),						jnp.array(__UpperCamelCase,						dtype="i4"				),						not train,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						rngs=__UpperCamelCase,						)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase=None,						__UpperCamelCase=None,						__UpperCamelCase=None,						__UpperCamelCase = None,						__UpperCamelCase = None,						__UpperCamelCase=False,						):
  							if position_ids is None:
  									_snake_case			 = jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__UpperCamelCase				).shape[-1]				),						input_ids.shape				)
  
  							if token_type_ids is None:
  									_snake_case			 = jnp.zeros_like(__UpperCamelCase				)
  
  							if attention_mask is None:
  									_snake_case			 = jnp.ones_like(__UpperCamelCase				)
  
  							# Handle any PRNG if needed
  							_snake_case			 = {}
  							if dropout_rng is not None:
  									_snake_case			 = dropout_rng
  
  							def _get_features(__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase				):
  									_snake_case			 = module.text_model(
  									    input_ids=__UpperCamelCase,						attention_mask=__UpperCamelCase,						position_ids=__UpperCamelCase,						token_type_ids=__UpperCamelCase,						deterministic=__UpperCamelCase,						)
  									_snake_case			 = text_outputs[1]
  									_snake_case			 = module.text_projection(__UpperCamelCase				)
  									return text_features
  
  							return self.module.apply(
  							    {"params": params or self.params},						jnp.array(__UpperCamelCase,						dtype="i4"				),						jnp.array(__UpperCamelCase,						dtype="i4"				),						jnp.array(__UpperCamelCase,						dtype="i4"				),						jnp.array(__UpperCamelCase,						dtype="i4"				),						not train,						method=_get_features,						rngs=__UpperCamelCase,						)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase = None,						__UpperCamelCase = None,						__UpperCamelCase=False				):
  							_snake_case			 = {}
  							if dropout_rng is not None:
  									_snake_case			 = dropout_rng
  
  							def _get_features(__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase				):
  									_snake_case			 = module.vision_model(pixel_values=__UpperCamelCase,						deterministic=__UpperCamelCase				)
  									_snake_case			 = vision_outputs[1]  # pooled_output
  									_snake_case			 = module.visual_projection(__UpperCamelCase				)
  									return image_features
  
  							return self.module.apply(
  							    {"params": params or self.params},						jnp.array(__UpperCamelCase,						dtype=jnp.floataa				),						not train,						method=_get_features,						rngs=__UpperCamelCase,						)
  
  
  
  
  
  
  					@classmethod
  					def        __SCREAMING_SNAKE_CASE			(					cls,						__UpperCamelCase = None,						__UpperCamelCase = None,						*__UpperCamelCase,						**__UpperCamelCase,						):
  							_snake_case			 = {
  							    argument[len("text_"				) :]: value for argument, value in kwargs.items() if argument.startswith("text_"				)
  							}
  
  							_snake_case			 = {
  							    argument[len("vision_"				) :]: value for argument, value in kwargs.items() if argument.startswith("vision_"				)
  							}
  
  							# remove text, vision kwargs from kwargs
  							for key in kwargs_text.keys():
  									del kwargs["text_" + key]
  							for key in kwargs_vision.keys():
  									del kwargs["vision_" + key]
  
  							# Load and initialize the text and vision model
  							_snake_case			 = kwargs_text.pop("model",						__UpperCamelCase				)
  							if text_model is None:
  									assert (
  									    text_model_name_or_path is not None
  									), "If `model` is not defined as an argument, a `text_model_name_or_path` has to be defined"
  									from transformers import FlaxAutoModel
  
  									if "config" not in kwargs_text:
  											from transformers import AutoConfig
  
  											_snake_case			 = AutoConfig.from_pretrained(__UpperCamelCase				)
  											_snake_case			 = text_config
  
  									_snake_case			 = FlaxAutoModel.from_pretrained(__UpperCamelCase,						*__UpperCamelCase,						**__UpperCamelCase				)
  
  							_snake_case			 = kwargs_vision.pop("model",						__UpperCamelCase				)
  							if vision_model is None:
  									assert (
  									    vision_model_name_or_path is not None
  									), "If `model` is not defined as an argument, a `vision_model_name_or_path` has to be defined"
  									from transformers import FlaxAutoModel
  
  									if "config" not in kwargs_vision:
  											from transformers import AutoConfig
  
  											_snake_case			 = AutoConfig.from_pretrained(__UpperCamelCase				)
  											_snake_case			 = vision_config
  
  									_snake_case			 = FlaxAutoModel.from_pretrained(__UpperCamelCase,						*__UpperCamelCase,						**__UpperCamelCase				)
  
  							# instantiate config with corresponding kwargs
  							_snake_case			 = kwargs.pop("dtype",						jnp.floataa				)
  							_snake_case			 = HybridCLIPConfig.from_text_vision_configs(text_model.config,						vision_model.config,						**__UpperCamelCase				)
  
  							# init model
  							_snake_case			 = cls(__UpperCamelCase,						*__UpperCamelCase,						dtype=__UpperCamelCase,						**__UpperCamelCase				)
  
  							if vision_config.model_type == "clip":
  									_snake_case			 = vision_model.params["vision_model"]
  									_snake_case			 = vision_model.params["visual_projection"]["kernel"]
  							else:
  									_snake_case			 = vision_model.params
  
  							_snake_case			 = text_model.params
  
  							return model
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  from __future__ import annotations
  
  from collections.abc import Iterable, Iterator
  from typing import Any, Generic, TypeVar
  
  _lowerCamelCase							:		List[str]										=						TypeVar('T', bound=bool)
  
  
  
  
  
  class   lowerCamelCase_							( Generic[T]							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase				):
  							_snake_case			 = value
  							_snake_case			 = None
  							_snake_case			 = None
  
  
  
  
  
  
  					@property
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							return self._value
  
  
  
  
  
  
  					@staticmethod
  					def        __SCREAMING_SNAKE_CASE			(					__UpperCamelCase,						__UpperCamelCase				):
  							if not roota:
  									return roota
  
  							if not roota:
  									return roota
  
  							if roota.value > roota.value:
  									_snake_case ,					_snake_case			 = roota, roota
  
  							_snake_case			 = roota
  							_snake_case			 = roota.right
  							_snake_case			 = roota.left
  							_snake_case			 = SkewNode.merge(__UpperCamelCase,						__UpperCamelCase				)
  
  							return result
  
  
  
  
  
  class   lowerCamelCase_							( Generic[T]							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase = ()				):
  							_snake_case			 = None
  							if data:
  									for item in data:
  											self.insert(__UpperCamelCase				)
  
  
  
  
  
  
  					def __bool__(					self				):
  							return self._root is not None
  
  
  
  
  
  
  					def __iter__(					self				):
  							_snake_case			 = []
  							while self:
  									result.append(self.pop()				)
  
  							# Pushing items back to the heap not to clear it.
  							for item in result:
  									self.insert(__UpperCamelCase				)
  
  							return iter(__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case			 = SkewNode.merge(self._root,						SkewNode(__UpperCamelCase				)				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self.top()
  							_snake_case			 = (
  							    SkewNode.merge(self._root.left,						self._root.right				) if self._root else None
  							)
  
  							return result
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							if not self._root:
  									raise IndexError("Can't get top element for the empty heap."				)
  							return self._root.value
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = None
  
  
  if __name__ == "__main__":
  						import doctest
  
  						doctest.testmod()
  
  
  
  
  
  