


def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Dict							=						1.3
 __lowerCamelCase		:							Optional[int]							=						len(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							List[str]							=						False

 while not completed:
  # Update the gap value for a next comb
  __lowerCamelCase		:							List[Any]							=						int(gap / shrink_factor						)
  if gap <= 1:
   __lowerCamelCase		:							List[str]							=						True

  __lowerCamelCase		:							List[Any]							=						0
  while index + gap < len(SCREAMING_SNAKE_CASE__						):
   if data[index] > data[index + gap]:
    # Swap values
    __lowerCamelCase					,     __lowerCamelCase		:							Tuple							=						data[index + gap], data[index]
    __lowerCamelCase		:							Union[str, Any]							=						False
   index += 1

 return data


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       lowercase_           =	input('Enter numbers separated by a comma:\n').strip()
       lowercase_           =	[int(item) for item in user_input.split(',')]
       print(comb_sort(unsorted))





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        A_					(       nn.Module    ):




      '''simple docstring'''


      __snake_case					       =   42
      __snake_case					       =   jnp.floataa




      def      _snake_case	(				self:       List[Any]				):
       __lowerCamelCase		:							Union[str, Any]							=						self.config.text_config
       __lowerCamelCase		:							List[Any]							=						self.config.vision_config

       __lowerCamelCase		:							Any							=						self.config.projection_dim
       __lowerCamelCase		:							Dict							=						text_config.hidden_size
       __lowerCamelCase		:							str							=						vision_config.hidden_size

       __lowerCamelCase		:							Optional[Any]							=						FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
       __lowerCamelCase		:							int							=						FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__						,      a				).module_class

       __lowerCamelCase		:							Union[str, Any]							=						text_module(a						,      dtype=self.dtype				)
       __lowerCamelCase		:							int							=						vision_module(a						,      dtype=self.dtype				)

       __lowerCamelCase		:							Union[str, Any]							=						nn.Dense(
           self.projection_dim						,      dtype=self.dtype						,      kernel_init=jax.nn.initializers.normal(0.0_2				)						,      use_bias=a						,      )
       __lowerCamelCase		:							int							=						nn.Dense(
           self.projection_dim						,      dtype=self.dtype						,      kernel_init=jax.nn.initializers.normal(0.0_2				)						,      use_bias=a						,      )
       __lowerCamelCase		:							Dict							=						self.param('logit_scale'						,      jax.nn.initializers.ones						,      []				)




      def __call__(				self:       int						,      a:       Union[str, Any]=None						,      a:       str=None						,      a:       Tuple=None						,      a:       str=None						,      a:       List[Any]=None						,      a:       bool = True						,      a:       List[str]=None						,      a:       Any=None						,      a:       Optional[Any]=None						,      ):
       __lowerCamelCase		:							str							=						return_dict if return_dict is not None else self.config.return_dict

       __lowerCamelCase		:							Any							=						self.vision_model(
           pixel_values=a						,      deterministic=a						,      output_attentions=a						,      output_hidden_states=a						,      return_dict=a						,      )

       __lowerCamelCase		:							Union[str, Any]							=						self.text_model(
           input_ids=a						,      attention_mask=a						,      token_type_ids=a						,      position_ids=a						,      deterministic=a						,      output_attentions=a						,      output_hidden_states=a						,      return_dict=a						,      )

       __lowerCamelCase		:							Tuple							=						vision_outputs[1]
       __lowerCamelCase		:							List[Any]							=						self.visual_projection(a				)

       __lowerCamelCase		:							Any							=						text_outputs[1]
       __lowerCamelCase		:							Optional[Any]							=						self.text_projection(a				)

       # normalized features
       __lowerCamelCase		:							str							=						image_embeds / jnp.linalg.norm(a						,      axis=-1						,      keepdims=a				)
       __lowerCamelCase		:							Optional[int]							=						text_embeds / jnp.linalg.norm(a						,      axis=-1						,      keepdims=a				)

       # cosine similarity as logits
       __lowerCamelCase		:							Union[str, Any]							=						jnp.exp(self.logit_scale				)
       __lowerCamelCase		:							Union[str, Any]							=						jnp.matmul(a						,      image_embeds.T				) * logit_scale
       __lowerCamelCase		:							List[str]							=						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=a						,      logits_per_text=a						,      text_embeds=a						,      image_embeds=a						,      text_model_output=a						,      vision_model_output=a						,      )



class        A_					(       __UpperCamelCase    ):




      '''simple docstring'''


      __snake_case					       =   HybridCLIPConfig
      __snake_case					       =   FlaxHybridCLIPModule




      def __init__(				self:       List[Any]						,      a:       HybridCLIPConfig						,      a:       Optional[Tuple] = None						,      a:       int = 0						,      a:       jnp.dtype = jnp.floataa						,      **a:       int						,      ):
       if input_shape is None:
        __lowerCamelCase		:							int							=						((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))

       __lowerCamelCase		:							int							=						self.module_class(config=a						,      dtype=a						,      **a				)
       super().__init__(a						,      a						,      input_shape=a						,      seed=a						,      dtype=a				)




      def      _snake_case	(				self:       Dict						,      a:       jax.random.PRNGKey						,      a:       Tuple						,      a:       FrozenDict = None				):
       # init input tensor
       __lowerCamelCase		:							int							=						jnp.zeros(input_shape[0]						,      dtype='i4'				)
       __lowerCamelCase		:							List[Any]							=						jnp.broadcast_to(jnp.arange(jnp.atleast_ad(a				).shape[-1]				)						,      input_shape[0]				)
       __lowerCamelCase		:							Any							=						jnp.ones_like(a				)
       __lowerCamelCase		:							int							=						jnp.ones_like(a				)

       __lowerCamelCase		:							Optional[Any]							=						jax.random.normal(a						,      input_shape[1]				)

       __lowerCamelCase					,     __lowerCamelCase		:							List[Any]							=						jax.random.split(a				)
       __lowerCamelCase		:							Tuple							=						{'params': params_rng, 'dropout': dropout_rng}

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




      def __call__(				self:       Tuple						,      a:       Optional[Any]						,      a:       Dict						,      a:       Optional[int]=None						,      a:       Union[str, Any]=None						,      a:       int=None						,      a:       dict = None						,      a:       jax.random.PRNGKey = None						,      a:       bool = False						,      a:       Optional[bool] = None						,      a:       Optional[bool] = None						,      a:       Optional[bool] = None						,      ):
       __lowerCamelCase		:							str							=						output_attentions if output_attentions is not None else self.config.output_attentions
       __lowerCamelCase		:							Dict							=						(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       __lowerCamelCase		:							int							=						return_dict if return_dict is not None else self.config.return_dict

       if position_ids is None:
        __lowerCamelCase		:							Optional[int]							=						jnp.broadcast_to(jnp.arange(jnp.atleast_ad(a				).shape[-1]				)						,      input_ids.shape				)

       if token_type_ids is None:
        __lowerCamelCase		:							str							=						jnp.zeros_like(a				)

       if attention_mask is None:
        __lowerCamelCase		:							str							=						jnp.ones_like(a				)

       # Handle any PRNG if needed
       __lowerCamelCase		:							Union[str, Any]							=						{}
       if dropout_rng is not None:
        __lowerCamelCase		:							Any							=						dropout_rng

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




      def      _snake_case	(				self:       Tuple						,      a:       Tuple						,      a:       List[Any]=None						,      a:       Dict=None						,      a:       int=None						,      a:       dict = None						,      a:       jax.random.PRNGKey = None						,      a:       Tuple=False						,      ):
       if position_ids is None:
        __lowerCamelCase		:							str							=						jnp.broadcast_to(jnp.arange(jnp.atleast_ad(a				).shape[-1]				)						,      input_ids.shape				)

       if token_type_ids is None:
        __lowerCamelCase		:							Union[str, Any]							=						jnp.zeros_like(a				)

       if attention_mask is None:
        __lowerCamelCase		:							int							=						jnp.ones_like(a				)

       # Handle any PRNG if needed
       __lowerCamelCase		:							Optional[int]							=						{}
       if dropout_rng is not None:
        __lowerCamelCase		:							List[str]							=						dropout_rng

       def _get_features(a:       str						,      a:       Any						,      a:       Optional[int]						,      a:       Optional[int]						,      a:       Union[str, Any]						,      a:       Optional[int]				):
        __lowerCamelCase		:							Tuple							=						module.text_model(
            input_ids=a						,      attention_mask=a						,      position_ids=a						,      token_type_ids=a						,      deterministic=a						,      )
        __lowerCamelCase		:							int							=						text_outputs[1]
        __lowerCamelCase		:							Optional[Any]							=						module.text_projection(a				)
        return text_features

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




      def      _snake_case	(				self:       int						,      a:       Optional[int]						,      a:       dict = None						,      a:       jax.random.PRNGKey = None						,      a:       str=False				):
       __lowerCamelCase		:							Dict							=						{}
       if dropout_rng is not None:
        __lowerCamelCase		:							List[str]							=						dropout_rng

       def _get_features(a:       str						,      a:       Tuple						,      a:       Any				):
        __lowerCamelCase		:							Dict							=						module.vision_model(pixel_values=a						,      deterministic=a				)
        __lowerCamelCase		:							int							=						vision_outputs[1]  # pooled_output
        __lowerCamelCase		:							int							=						module.visual_projection(a				)
        return image_features

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




      @classmethod
      def      _snake_case	(				cls:       str						,      a:       str = None						,      a:       str = None						,      *a:       Optional[Any]						,      **a:       List[str]						,      ):
       __lowerCamelCase		:							List[Any]							=						{
           argument[len('text_'				) :]: value for argument, value in kwargs.items() if argument.startswith('text_'				)
       }

       __lowerCamelCase		:							Optional[Any]							=						{
           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
       __lowerCamelCase		:							Any							=						kwargs_text.pop('model'						,      a				)
       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

         __lowerCamelCase		:							Optional[int]							=						AutoConfig.from_pretrained(a				)
         __lowerCamelCase		:							Any							=						text_config

        __lowerCamelCase		:							Dict							=						FlaxAutoModel.from_pretrained(a						,      *a						,      **a				)

       __lowerCamelCase		:							Any							=						kwargs_vision.pop('model'						,      a				)
       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

         __lowerCamelCase		:							Any							=						AutoConfig.from_pretrained(a				)
         __lowerCamelCase		:							int							=						vision_config

        __lowerCamelCase		:							str							=						FlaxAutoModel.from_pretrained(a						,      *a						,      **a				)

       # instantiate config with corresponding kwargs
       __lowerCamelCase		:							Optional[int]							=						kwargs.pop('dtype'						,      jnp.floataa				)
       __lowerCamelCase		:							List[Any]							=						HybridCLIPConfig.from_text_vision_configs(text_model.config						,      vision_model.config						,      **a				)

       # init model
       __lowerCamelCase		:							List[Any]							=						cls(a						,      *a						,      dtype=a						,      **a				)

       if vision_config.model_type == "clip":
        __lowerCamelCase		:							Any							=						vision_model.params['vision_model']
        __lowerCamelCase		:							Optional[Any]							=						vision_model.params['visual_projection']['kernel']
       else:
        __lowerCamelCase		:							Optional[int]							=						vision_model.params

       __lowerCamelCase		:							List[str]							=						text_model.params

       return model

