






"""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


lowercase_								=	logging.get_logger(__name__)


class    snake_case						(				nn.Module		):


   '''simple docstring'''

   A_		:      HybridCLIPConfig
   A_		:      jnp.dtype							=					jnp.floataa


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     __A		     =					self.config.text_config
     __A		     =					self.config.vision_config

     __A		     =					self.config.projection_dim
     __A		     =					text_config.hidden_size
     __A		     =					vision_config.hidden_size

     __A		     =					FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
     __A		     =					FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__,			_lowerCamelCase    ).module_class

     __A		     =					text_module(_lowerCamelCase,			dtype=self.dtype    )
     __A		     =					vision_module(_lowerCamelCase,			dtype=self.dtype    )

     __A		     =					nn.Dense(
         self.projection_dim,			dtype=self.dtype,			kernel_init=jax.nn.initializers.normal(0.02    ),			use_bias=_lowerCamelCase,			)
     __A		     =					nn.Dense(
         self.projection_dim,			dtype=self.dtype,			kernel_init=jax.nn.initializers.normal(0.02    ),			use_bias=_lowerCamelCase,			)
     __A		     =					self.param('''logit_scale''',			jax.nn.initializers.ones,			[]    )






   def __call__(      self					:	Any,			_lowerCamelCase					:	Union[str, Any]=None,			_lowerCamelCase					:	Union[str, Any]=None,			_lowerCamelCase					:	Optional[int]=None,			_lowerCamelCase					:	Any=None,			_lowerCamelCase					:	str=None,			_lowerCamelCase					:	bool = True,			_lowerCamelCase					:	List[str]=None,			_lowerCamelCase					:	List[Any]=None,			_lowerCamelCase					:	Tuple=None,			):

     '''simple docstring'''
     __A		     =					return_dict if return_dict is not None else self.config.return_dict

     __A		     =					self.vision_model(
         pixel_values=_lowerCamelCase,			deterministic=_lowerCamelCase,			output_attentions=_lowerCamelCase,			output_hidden_states=_lowerCamelCase,			return_dict=_lowerCamelCase,			)

     __A		     =					self.text_model(
         input_ids=_lowerCamelCase,			attention_mask=_lowerCamelCase,			token_type_ids=_lowerCamelCase,			position_ids=_lowerCamelCase,			deterministic=_lowerCamelCase,			output_attentions=_lowerCamelCase,			output_hidden_states=_lowerCamelCase,			return_dict=_lowerCamelCase,			)

     __A		     =					vision_outputs[1]
     __A		     =					self.visual_projection(_lowerCamelCase    )

     __A		     =					text_outputs[1]
     __A		     =					self.text_projection(_lowerCamelCase    )

     # normalized features
     __A		     =					image_embeds / jnp.linalg.norm(_lowerCamelCase,			axis=-1,			keepdims=_lowerCamelCase    )
     __A		     =					text_embeds / jnp.linalg.norm(_lowerCamelCase,			axis=-1,			keepdims=_lowerCamelCase    )

     # cosine similarity as logits
     __A		     =					jnp.exp(self.logit_scale    )
     __A		     =					jnp.matmul(_lowerCamelCase,			image_embeds.T    ) * logit_scale
     __A		     =					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=_lowerCamelCase,			logits_per_text=_lowerCamelCase,			text_embeds=_lowerCamelCase,			image_embeds=_lowerCamelCase,			text_model_output=_lowerCamelCase,			vision_model_output=_lowerCamelCase,			)





class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      List[str]							=					HybridCLIPConfig
   A_		:      Tuple							=					FlaxHybridCLIPModule


   def __init__(      self					:	Tuple,			_lowerCamelCase					:	HybridCLIPConfig,			_lowerCamelCase					:	Optional[Tuple] = None,			_lowerCamelCase					:	int = 0,			_lowerCamelCase					:	jnp.dtype = jnp.floataa,			**_lowerCamelCase					:	Dict,			):

     '''simple docstring'''
     if input_shape is None:
       __A		     =					((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))

     __A		     =					self.module_class(config=_lowerCamelCase,			dtype=_lowerCamelCase,			**_lowerCamelCase    )
     super().__init__(_lowerCamelCase,			_lowerCamelCase,			input_shape=_lowerCamelCase,			seed=_lowerCamelCase,			dtype=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any],			_lowerCamelCase					:	jax.random.PRNGKey,			_lowerCamelCase					:	Tuple,			_lowerCamelCase					:	FrozenDict = None    ):

     '''simple docstring'''
     # init input tensor
     __A		     =					jnp.zeros(input_shape[0],			dtype='''i4'''    )
     __A		     =					jnp.broadcast_to(jnp.arange(jnp.atleast_ad(_lowerCamelCase    ).shape[-1]    ),			input_shape[0]    )
     __A		     =					jnp.ones_like(_lowerCamelCase    )
     __A		     =					jnp.ones_like(_lowerCamelCase    )

     __A		     =					jax.random.normal(_lowerCamelCase,			input_shape[1]    )

     __A     ,       __A		     =					jax.random.split(_lowerCamelCase    )
     __A		     =					{'''params''': params_rng, '''dropout''': dropout_rng}

     return self.module.init(_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase    )["params"]


   def __call__(      self					:	Tuple,			_lowerCamelCase					:	str,			_lowerCamelCase					:	Optional[int],			_lowerCamelCase					:	Any=None,			_lowerCamelCase					:	Dict=None,			_lowerCamelCase					:	List[str]=None,			_lowerCamelCase					:	dict = None,			_lowerCamelCase					:	jax.random.PRNGKey = None,			_lowerCamelCase					:	bool = False,			_lowerCamelCase					:	Optional[bool] = None,			_lowerCamelCase					:	Optional[bool] = None,			_lowerCamelCase					:	Optional[bool] = None,			):

     '''simple docstring'''
     __A		     =					output_attentions if output_attentions is not None else self.config.output_attentions
     __A		     =					(
         output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
     )
     __A		     =					return_dict if return_dict is not None else self.config.return_dict

     if position_ids is None:
       __A		     =					jnp.broadcast_to(jnp.arange(jnp.atleast_ad(_lowerCamelCase    ).shape[-1]    ),			input_ids.shape    )

     if token_type_ids is None:
       __A		     =					jnp.zeros_like(_lowerCamelCase    )

     if attention_mask is None:
       __A		     =					jnp.ones_like(_lowerCamelCase    )

     # Handle any PRNG if needed
     __A		     =					{}
     if dropout_rng is not None:
       __A		     =					dropout_rng

     return self.module.apply(
         {'''params''': params or self.params},			jnp.array(_lowerCamelCase,			dtype='''i4'''    ),			jnp.array(_lowerCamelCase,			dtype=jnp.floataa    ),			jnp.array(_lowerCamelCase,			dtype='''i4'''    ),			jnp.array(_lowerCamelCase,			dtype='''i4'''    ),			jnp.array(_lowerCamelCase,			dtype='''i4'''    ),			not train,			_lowerCamelCase,			_lowerCamelCase,			_lowerCamelCase,			rngs=_lowerCamelCase,			)


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple,			_lowerCamelCase					:	Dict,			_lowerCamelCase					:	Optional[Any]=None,			_lowerCamelCase					:	List[str]=None,			_lowerCamelCase					:	Optional[int]=None,			_lowerCamelCase					:	dict = None,			_lowerCamelCase					:	jax.random.PRNGKey = None,			_lowerCamelCase					:	List[str]=False,			):

     '''simple docstring'''
     if position_ids is None:
       __A		     =					jnp.broadcast_to(jnp.arange(jnp.atleast_ad(_lowerCamelCase    ).shape[-1]    ),			input_ids.shape    )

     if token_type_ids is None:
       __A		     =					jnp.zeros_like(_lowerCamelCase    )

     if attention_mask is None:
       __A		     =					jnp.ones_like(_lowerCamelCase    )

     # Handle any PRNG if needed
     __A		     =					{}
     if dropout_rng is not None:
       __A		     =					dropout_rng

     def _get_features(_lowerCamelCase					:	Optional[int],			_lowerCamelCase					:	int,			_lowerCamelCase					:	str,			_lowerCamelCase					:	Dict,			_lowerCamelCase					:	Dict,			_lowerCamelCase					:	Tuple    ):
       __A		     =					module.text_model(
           input_ids=_lowerCamelCase,			attention_mask=_lowerCamelCase,			position_ids=_lowerCamelCase,			token_type_ids=_lowerCamelCase,			deterministic=_lowerCamelCase,			)
       __A		     =					text_outputs[1]
       __A		     =					module.text_projection(_lowerCamelCase    )
       return text_features

     return self.module.apply(
         {'''params''': params or self.params},			jnp.array(_lowerCamelCase,			dtype='''i4'''    ),			jnp.array(_lowerCamelCase,			dtype='''i4'''    ),			jnp.array(_lowerCamelCase,			dtype='''i4'''    ),			jnp.array(_lowerCamelCase,			dtype='''i4'''    ),			not train,			method=_get_features,			rngs=_lowerCamelCase,			)


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Union[str, Any],			_lowerCamelCase					:	Tuple,			_lowerCamelCase					:	dict = None,			_lowerCamelCase					:	jax.random.PRNGKey = None,			_lowerCamelCase					:	int=False    ):

     '''simple docstring'''
     __A		     =					{}
     if dropout_rng is not None:
       __A		     =					dropout_rng

     def _get_features(_lowerCamelCase					:	Any,			_lowerCamelCase					:	Any,			_lowerCamelCase					:	Optional[Any]    ):
       __A		     =					module.vision_model(pixel_values=_lowerCamelCase,			deterministic=_lowerCamelCase    )
       __A		     =					vision_outputs[1]  # pooled_output
       __A		     =					module.visual_projection(_lowerCamelCase    )
       return image_features

     return self.module.apply(
         {'''params''': params or self.params},			jnp.array(_lowerCamelCase,			dtype=jnp.floataa    ),			not train,			method=_get_features,			rngs=_lowerCamelCase,			)






   @classmethod
   def 							_SCREAMING_SNAKE_CASE							(      cls					:	Dict,			_lowerCamelCase					:	str = None,			_lowerCamelCase					:	str = None,			*_lowerCamelCase					:	Optional[int],			**_lowerCamelCase					:	int,			):

     '''simple docstring'''
     __A		     =					{
         argument[len('''text_'''    ) :]: value for argument, value in kwargs.items() if argument.startswith('''text_'''    )
     }

     __A		     =					{
         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
     __A		     =					kwargs_text.pop('''model''',			_lowerCamelCase    )
     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

         __A		     =					AutoConfig.from_pretrained(_lowerCamelCase    )
         __A		     =					text_config

       __A		     =					FlaxAutoModel.from_pretrained(_lowerCamelCase,			*_lowerCamelCase,			**_lowerCamelCase    )

     __A		     =					kwargs_vision.pop('''model''',			_lowerCamelCase    )
     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

         __A		     =					AutoConfig.from_pretrained(_lowerCamelCase    )
         __A		     =					vision_config

       __A		     =					FlaxAutoModel.from_pretrained(_lowerCamelCase,			*_lowerCamelCase,			**_lowerCamelCase    )

     # instantiate config with corresponding kwargs
     __A		     =					kwargs.pop('''dtype''',			jnp.floataa    )
     __A		     =					HybridCLIPConfig.from_text_vision_configs(text_model.config,			vision_model.config,			**_lowerCamelCase    )

     # init model
     __A		     =					cls(_lowerCamelCase,			*_lowerCamelCase,			dtype=_lowerCamelCase,			**_lowerCamelCase    )

     if vision_config.model_type == "clip":
       __A		     =					vision_model.params['''vision_model''']
       __A		     =					vision_model.params['''visual_projection''']['''kernel''']
     else:
       __A		     =					vision_model.params

     __A		     =					text_model.params

     return model





'''simple docstring'''

from .balanced_parentheses import balanced_parentheses
from .stack import Stack



def  _lowerCAmelCase				(				__a      )  ->      int:
     '''simple docstring'''







     return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(__a			,      -1      )



def  _lowerCAmelCase				(				__a      )  ->      str:
     '''simple docstring'''







     if not balanced_parentheses(__a      ):
          raise ValueError("""Mismatched parentheses"""      )
     _UpperCamelCase  :Stack[str]       =Stack()
     _UpperCamelCase  :Optional[int]       =[]
     for char in expression_str:
          if char.isalpha() or char.isdigit():
               postfix.append(__a      )
          elif char == "(":
               stack.push(__a      )
          elif char == ")":
               while not stack.is_empty() and stack.peek() != "(":
                    postfix.append(stack.pop()      )
               stack.pop()
          else:
               while not stack.is_empty() and precedence(__a      ) <= precedence(stack.peek()      ):
                    postfix.append(stack.pop()      )
               stack.push(__a      )
     while not stack.is_empty():
          postfix.append(stack.pop()      )
     return " ".join(__a      )


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      _lowerCamelCase :				Tuple														= """a+b*(c^d-e)^(f+g*h)-i"""

      print("""Infix to Postfix Notation demonstration:\n""")
      print("""Infix notation: """ + expression)
      print("""Postfix notation: """ + infix_to_postfix(expression))