





"""simple docstring"""


from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						list:




      """simple docstring"""

      seed(1	)
      return [gauss(lowercase_						,lowercase_	) for _ in range(lowercase_	)]




def     lowercase__			(    lowercase_						,lowercase_	)    ->						list:




      """simple docstring"""

      return [k for k in range(lowercase_	) for _ in range(instance_count[k]	)]




def     lowercase__			(    lowercase_						,lowercase_	)    ->						float:




      """simple docstring"""

      return sum(lowercase_	) / instance_count




def     lowercase__			(    lowercase_						,lowercase_	)    ->						float:




      """simple docstring"""

      return instance_count / total_count




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						float:




      """simple docstring"""

      _UpperCamelCase					:		Dict  =				[]  # An empty list to store all squared differences
      # iterate over number of elements in items
      for i in range(len(lowercase_	)	):
            # for loop iterates over number of elements in inner layer of items
            for j in range(len(items[i]	)	):
                  # appending squared differences to 'squared_diff' list
                  squared_diff.append((items[i][j] - means[i]) ** 2	)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
      _UpperCamelCase					:		Tuple  =				len(lowercase_	)  # Number of classes in dataset
      return 1 / (total_count - n_classes) * sum(lowercase_	)




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_						,lowercase_	)    ->						list:




      """simple docstring"""

      _UpperCamelCase					:		str  =				[]
      # for loop iterates over number of elements in list
      for i in range(len(lowercase_	)	):
            # for loop iterates over number of inner items of each element
            for j in range(len(x_items[i]	)	):
                  _UpperCamelCase					:		Tuple  =				[]  # to store all discriminant values of each item as a list
                  # for loop iterates over number of classes we have in our dataset
                  for k in range(len(lowercase_	)	):
                        # appending values of discriminants for each class to 'temp' list
                        temp.append(
                            x_items[i][j] * (means[k] / variance)
                            - (means[k] ** 2 / (2 * variance))
                            + log(probabilities[k]	)	)
                  # appending discriminant values of each item to 'results' list
                  results.append(lowercase_	)

      return [result.index(max(lowercase_	)	) for result in results]




def     lowercase__			(    lowercase_						,lowercase_	)    ->						float:




      """simple docstring"""

      _UpperCamelCase					:		str  =				sum(1 for i, j in zip(lowercase_						,lowercase_	) if i == j	)
      # percentage of accuracy equals to number of correct predictions divided by number
      # of all data and multiplied by 100
      return (correct / len(lowercase_	)) * 100


lowerCamelCase__      =      TypeVar("num")




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_						,lowercase_ = lambda lowercase_	: True						,lowercase_ = None						,)    ->						num:




      """simple docstring"""

      while True:
            try:
                  _UpperCamelCase					:		Dict  =				input_type(input(lowercase_	).strip() or default	)
                  if condition(lowercase_	):
                        return user_input
                  else:
                        print(F'''{user_input}: {err_msg}'''	)
                        continue
            except ValueError:
                  print(
                      F'''{user_input}: Incorrect input type, expected {input_type.__name__!r}'''	)




def     lowercase__			(    )    ->						Any:




      """simple docstring"""

      while True:
            print(" Linear Discriminant Analysis ".center(50						,"*"	)	)
            print("*" * 50						,"\n"	)
            print("First of all we should specify the number of classes that"	)
            print("we want to generate as training dataset"	)
            # Trying to get number of classes
            _UpperCamelCase					:		List[str]  =				valid_input(
                input_type=lowercase_						,condition=lambda lowercase_	: x > 0						,input_msg="Enter the number of classes (Data Groupings): "						,err_msg="Number of classes should be positive!"						,)

            print("-" * 100	)

            # Trying to get the value of standard deviation
            _UpperCamelCase					:		List[str]  =				valid_input(
                input_type=lowercase_						,condition=lambda lowercase_	: x >= 0						,input_msg=(
                    "Enter the value of standard deviation"
                    "(Default value is 1.0 for all classes): "
                )						,err_msg="Standard deviation should not be negative!"						,default="1.0"						,)

            print("-" * 100	)

            # Trying to get number of instances in classes and theirs means to generate
            # dataset
            _UpperCamelCase					:		Dict  =				[]  # An empty list to store instance counts of classes in dataset
            for i in range(lowercase_	):
                  _UpperCamelCase					:		str  =				valid_input(
                      input_type=lowercase_						,condition=lambda lowercase_	: x > 0						,input_msg=(F'''Enter The number of instances for class_{i+1}: ''')						,err_msg="Number of instances should be positive!"						,)
                  counts.append(lowercase_	)
            print("-" * 100	)

            # An empty list to store values of user-entered means of classes
            _UpperCamelCase					:		str  =				[]
            for a in range(lowercase_	):
                  _UpperCamelCase					:		Tuple  =				valid_input(
                      input_type=lowercase_						,input_msg=(F'''Enter the value of mean for class_{a+1}: ''')						,err_msg="This is an invalid value."						,)
                  user_means.append(lowercase_	)
            print("-" * 100	)

            print("Standard deviation: "						,lowercase_	)
            # print out the number of instances in classes in separated line
            for i, count in enumerate(lowercase_						,1	):
                  print(F'''Number of instances in class_{i} is: {count}'''	)
            print("-" * 100	)

            # print out mean values of classes separated line
            for i, user_mean in enumerate(lowercase_						,1	):
                  print(F'''Mean of class_{i} is: {user_mean}'''	)
            print("-" * 100	)

            # Generating training dataset drawn from gaussian distribution
            _UpperCamelCase					:		Union[str, Any]  =				[
                gaussian_distribution(user_means[j]						,lowercase_						,counts[j]	)
                for j in range(lowercase_	)
            ]
            print("Generated Normal Distribution: \n"						,lowercase_	)
            print("-" * 100	)

            # Generating Ys to detecting corresponding classes
            _UpperCamelCase					:		Any  =				y_generator(lowercase_						,lowercase_	)
            print("Generated Corresponding Ys: \n"						,lowercase_	)
            print("-" * 100	)

            # Calculating the value of actual mean for each class
            _UpperCamelCase					:		List[Any]  =				[calculate_mean(counts[k]						,x[k]	) for k in range(lowercase_	)]
            # for loop iterates over number of elements in 'actual_means' list and print
            # out them in separated line
            for i, actual_mean in enumerate(lowercase_						,1	):
                  print(F'''Actual(Real) mean of class_{i} is: {actual_mean}'''	)
            print("-" * 100	)

            # Calculating the value of probabilities for each class
            _UpperCamelCase					:		Union[str, Any]  =				[
                calculate_probabilities(counts[i]						,sum(lowercase_	)	) for i in range(lowercase_	)
            ]

            # for loop iterates over number of elements in 'probabilities' list and print
            # out them in separated line
            for i, probability in enumerate(lowercase_						,1	):
                  print(F'''Probability of class_{i} is: {probability}'''	)
            print("-" * 100	)

            # Calculating the values of variance for each class
            _UpperCamelCase					:		List[str]  =				calculate_variance(lowercase_						,lowercase_						,sum(lowercase_	)	)
            print("Variance: "						,lowercase_	)
            print("-" * 100	)

            # Predicting Y values
            # storing predicted Y values in 'pre_indexes' variable
            _UpperCamelCase					:		Optional[int]  =				predict_y_values(lowercase_						,lowercase_						,lowercase_						,lowercase_	)
            print("-" * 100	)

            # Calculating Accuracy of the model
            print(F'''Accuracy: {accuracy(lowercase_						,lowercase_	)}'''	)
            print("-" * 100	)
            print(" DONE ".center(100						,"+"	)	)

            if input("Press any key to restart or 'q' for quit: "	).strip().lower() == "q":
                  print("\n" + "GoodBye!".center(100						,"-"	) + "\n"	)
                  break
            system("cls" if name == "nt" else "clear"	)  # noqa: S605


if __name__ == "__main__":
  main()







"""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__      =      logging.get_logger(__name__)



class    __SCREAMING_SNAKE_CASE			(  nn.Module      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:HybridCLIPConfig
  SCREAMING_SNAKE_CASE__							:jnp.dtype		   =    jnp.floataa

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> Tuple:
        _UpperCamelCase					:		List[str]  =				self.config.text_config
        _UpperCamelCase					:		Union[str, Any]  =				self.config.vision_config

        _UpperCamelCase					:		Optional[int]  =				self.config.projection_dim
        _UpperCamelCase					:		List[Any]  =				text_config.hidden_size
        _UpperCamelCase					:		Optional[Any]  =				vision_config.hidden_size

        _UpperCamelCase					:		str  =				FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
        _UpperCamelCase					:		int  =				FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__    ,      __a		).module_class

        _UpperCamelCase					:		str  =				text_module(__a    ,      dtype=self.dtype		)
        _UpperCamelCase					:		List[Any]  =				vision_module(__a    ,      dtype=self.dtype		)

        _UpperCamelCase					:		List[str]  =				nn.Dense(
            self.projection_dim    ,      dtype=self.dtype    ,      kernel_init=jax.nn.initializers.normal(0.02		)    ,      use_bias=__a    ,      )
        _UpperCamelCase					:		int  =				nn.Dense(
            self.projection_dim    ,      dtype=self.dtype    ,      kernel_init=jax.nn.initializers.normal(0.02		)    ,      use_bias=__a    ,      )
        _UpperCamelCase					:		int  =				self.param("logit_scale"    ,      jax.nn.initializers.ones    ,      []		)


  def __call__(						self      :				Any    ,      __a      :				List[str]=None    ,      __a      :				Optional[int]=None    ,      __a      :				Tuple=None    ,      __a      :				Tuple=None    ,      __a      :				str=None    ,      __a      :				bool = True    ,      __a      :				Dict=None    ,      __a      :				Any=None    ,      __a      :				Any=None    ,      )						-> List[str]:
        _UpperCamelCase					:		List[str]  =				return_dict if return_dict is not None else self.config.return_dict

        _UpperCamelCase					:		Any  =				self.vision_model(
            pixel_values=__a    ,      deterministic=__a    ,      output_attentions=__a    ,      output_hidden_states=__a    ,      return_dict=__a    ,      )

        _UpperCamelCase					:		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    ,      )

        _UpperCamelCase					:		str  =				vision_outputs[1]
        _UpperCamelCase					:		Dict  =				self.visual_projection(__a		)

        _UpperCamelCase					:		Dict  =				text_outputs[1]
        _UpperCamelCase					:		Dict  =				self.text_projection(__a		)

        # normalized features
        _UpperCamelCase					:		Optional[int]  =				image_embeds / jnp.linalg.norm(__a    ,      axis=-1    ,      keepdims=__a		)
        _UpperCamelCase					:		Tuple  =				text_embeds / jnp.linalg.norm(__a    ,      axis=-1    ,      keepdims=__a		)

        # cosine similarity as logits
        _UpperCamelCase					:		Dict  =				jnp.exp(self.logit_scale		)
        _UpperCamelCase					:		Union[str, Any]  =				jnp.matmul(__a    ,      image_embeds.T		) * logit_scale
        _UpperCamelCase					:		List[Any]  =				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    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Tuple		   =    HybridCLIPConfig
  SCREAMING_SNAKE_CASE__							:List[Any]		   =    FlaxHybridCLIPModule

  def __init__(						self      :				Tuple    ,      __a      :				HybridCLIPConfig    ,      __a      :				Optional[Tuple] = None    ,      __a      :				int = 0    ,      __a      :				jnp.dtype = jnp.floataa    ,      **__a      :				Tuple    ,      )						-> List[str]:
        if input_shape is None:
              _UpperCamelCase					:		str  =				((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))

        _UpperCamelCase					:		List[Any]  =				self.module_class(config=__a    ,      dtype=__a    ,      **__a		)
        super().__init__(__a    ,      __a    ,      input_shape=__a    ,      seed=__a    ,      dtype=__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]    ,      __a      :				jax.random.PRNGKey    ,      __a      :				Tuple    ,      __a      :				FrozenDict = None		)						-> FrozenDict:
        # init input tensor
        _UpperCamelCase					:		List[str]  =				jnp.zeros(input_shape[0]    ,      dtype="i4"		)
        _UpperCamelCase					:		List[Any]  =				jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__a		).shape[-1]		)    ,      input_shape[0]		)
        _UpperCamelCase					:		Union[str, Any]  =				jnp.ones_like(__a		)
        _UpperCamelCase					:		str  =				jnp.ones_like(__a		)

        _UpperCamelCase					:		Optional[int]  =				jax.random.normal(__a    ,      input_shape[1]		)

        _UpperCamelCase,     _UpperCamelCase					:		Optional[Any]  =				jax.random.split(__a		)
        _UpperCamelCase					:		Tuple  =				{"params": params_rng, "dropout": dropout_rng}

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

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

        if position_ids is None:
              _UpperCamelCase					:		Union[str, Any]  =				jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__a		).shape[-1]		)    ,      input_ids.shape		)

        if token_type_ids is None:
              _UpperCamelCase					:		str  =				jnp.zeros_like(__a		)

        if attention_mask is None:
              _UpperCamelCase					:		Optional[int]  =				jnp.ones_like(__a		)

        # Handle any PRNG if needed
        _UpperCamelCase					:		Optional[int]  =				{}
        if dropout_rng is not None:
              _UpperCamelCase					:		List[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       __SCREAMING_SNAKE_CASE (						self      :				Dict    ,      __a      :				Any    ,      __a      :				List[str]=None    ,      __a      :				Union[str, Any]=None    ,      __a      :				Any=None    ,      __a      :				dict = None    ,      __a      :				jax.random.PRNGKey = None    ,      __a      :				List[str]=False    ,      )						-> str:
        if position_ids is None:
              _UpperCamelCase					:		Union[str, Any]  =				jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__a		).shape[-1]		)    ,      input_ids.shape		)

        if token_type_ids is None:
              _UpperCamelCase					:		Tuple  =				jnp.zeros_like(__a		)

        if attention_mask is None:
              _UpperCamelCase					:		List[str]  =				jnp.ones_like(__a		)

        # Handle any PRNG if needed
        _UpperCamelCase					:		List[str]  =				{}
        if dropout_rng is not None:
              _UpperCamelCase					:		Dict  =				dropout_rng

        def _get_features(__a      :				Optional[int]    ,      __a      :				Optional[Any]    ,      __a      :				List[Any]    ,      __a      :				Optional[int]    ,      __a      :				Union[str, Any]    ,      __a      :				List[Any]		):
              _UpperCamelCase					:		List[Any]  =				module.text_model(
                  input_ids=__a    ,      attention_mask=__a    ,      position_ids=__a    ,      token_type_ids=__a    ,      deterministic=__a    ,      )
              _UpperCamelCase					:		Tuple  =				text_outputs[1]
              _UpperCamelCase					:		Tuple  =				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       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]    ,      __a      :				int    ,      __a      :				dict = None    ,      __a      :				jax.random.PRNGKey = None    ,      __a      :				Optional[Any]=False		)						-> Tuple:
        _UpperCamelCase					:		List[str]  =				{}
        if dropout_rng is not None:
              _UpperCamelCase					:		Tuple  =				dropout_rng

        def _get_features(__a      :				int    ,      __a      :				List[str]    ,      __a      :				Optional[Any]		):
              _UpperCamelCase					:		Optional[int]  =				module.vision_model(pixel_values=__a    ,      deterministic=__a		)
              _UpperCamelCase					:		Optional[int]  =				vision_outputs[1]  # pooled_output
              _UpperCamelCase					:		str  =				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       __SCREAMING_SNAKE_CASE (						cls      :				List[str]    ,      __a      :				str = None    ,      __a      :				str = None    ,      *__a      :				List[Any]    ,      **__a      :				Tuple    ,      )						-> FlaxPreTrainedModel:
        _UpperCamelCase					:		Union[str, Any]  =				{
            argument[len("text_"		) :]: value for argument, value in kwargs.items() if argument.startswith("text_"		)
        }

        _UpperCamelCase					:		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
        _UpperCamelCase					:		int  =				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

                    _UpperCamelCase					:		Any  =				AutoConfig.from_pretrained(__a		)
                    _UpperCamelCase					:		int  =				text_config

              _UpperCamelCase					:		Optional[Any]  =				FlaxAutoModel.from_pretrained(__a    ,      *__a    ,      **__a		)

        _UpperCamelCase					:		str  =				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

                    _UpperCamelCase					:		Dict  =				AutoConfig.from_pretrained(__a		)
                    _UpperCamelCase					:		str  =				vision_config

              _UpperCamelCase					:		int  =				FlaxAutoModel.from_pretrained(__a    ,      *__a    ,      **__a		)

        # instantiate config with corresponding kwargs
        _UpperCamelCase					:		Optional[int]  =				kwargs.pop("dtype"    ,      jnp.floataa		)
        _UpperCamelCase					:		Optional[int]  =				HybridCLIPConfig.from_text_vision_configs(text_model.config    ,      vision_model.config    ,      **__a		)

        # init model
        _UpperCamelCase					:		List[str]  =				cls(__a    ,      *__a    ,      dtype=__a    ,      **__a		)

        if vision_config.model_type == "clip":
              _UpperCamelCase					:		Tuple  =				vision_model.params["vision_model"]
              _UpperCamelCase					:		Tuple  =				vision_model.params["visual_projection"]["kernel"]
        else:
              _UpperCamelCase					:		Tuple  =				vision_model.params

        _UpperCamelCase					:		str  =				text_model.params

        return model
