



"""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


__SCREAMING_SNAKE_CASE     :  Tuple										=					logging.get_logger(__name__)


class 							lowerCamelCase_(      nn.Module			):





					'''simple docstring'''



					lowercase__      :				HybridCLIPConfig
					lowercase__      :				jnp.dtype        =       jnp.floataa

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.config.text_config
									_lowerCamelCase					     = self.config.vision_config

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

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

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

									_lowerCamelCase					     = nn.Dense(
									    self.projection_dim ,				dtype=self.dtype ,				kernel_init=jax.nn.initializers.normal(0.0_2      ) ,				use_bias=__SCREAMING_SNAKE_CASE ,				)
									_lowerCamelCase					     = nn.Dense(
									    self.projection_dim ,				dtype=self.dtype ,				kernel_init=jax.nn.initializers.normal(0.0_2      ) ,				use_bias=__SCREAMING_SNAKE_CASE ,				)
									_lowerCamelCase					     = self.param('''logit_scale''' ,				jax.nn.initializers.ones ,				[]      )





					def __call__(			self ,				lowerCamelCase__=None ,				lowerCamelCase__=None ,				lowerCamelCase__=None ,				lowerCamelCase__=None ,				lowerCamelCase__=None ,				lowerCamelCase__ = True ,				lowerCamelCase__=None ,				lowerCamelCase__=None ,				lowerCamelCase__=None ,				):
									_lowerCamelCase					     = return_dict if return_dict is not None else self.config.return_dict

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

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

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

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

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

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


class 							lowerCamelCase_(      lowerCAmelCase_			):





					'''simple docstring'''



					lowercase__      :				Dict        =       HybridCLIPConfig
					lowercase__      :				int        =       FlaxHybridCLIPModule

					def __init__(			self ,				lowerCamelCase__ ,				lowerCamelCase__ = None ,				lowerCamelCase__ = 0 ,				lowerCamelCase__ = jnp.floataa ,				**lowerCamelCase__ ,				):

									if input_shape is None:
													_lowerCamelCase					     = ((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))

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

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ = None      ):
									_lowerCamelCase					     = jnp.zeros(input_shape[0] ,				dtype='''i4'''      )
									_lowerCamelCase					     = jnp.broadcast_to(jnp.arange(jnp.atleast_ad(__SCREAMING_SNAKE_CASE      ).shape[-1]      ) ,				input_shape[0]      )
									_lowerCamelCase					     = jnp.ones_like(__SCREAMING_SNAKE_CASE      )
									_lowerCamelCase					     = jnp.ones_like(__SCREAMING_SNAKE_CASE      )

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

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

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

					def __call__(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__=None ,				lowerCamelCase__=None ,				lowerCamelCase__=None ,				lowerCamelCase__ = None ,				lowerCamelCase__ = None ,				lowerCamelCase__ = False ,				lowerCamelCase__ = None ,				lowerCamelCase__ = None ,				lowerCamelCase__ = None ,				):
									_lowerCamelCase					     = output_attentions if output_attentions is not None else self.config.output_attentions
									_lowerCamelCase					     = (
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									_lowerCamelCase					     = return_dict if return_dict is not None else self.config.return_dict

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

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

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

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

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

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__=None ,				lowerCamelCase__=None ,				lowerCamelCase__=None ,				lowerCamelCase__ = None ,				lowerCamelCase__ = None ,				lowerCamelCase__=False ,				):

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

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

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

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

									def _get_features(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
													_lowerCamelCase					     = module.text_model(
													    input_ids=__SCREAMING_SNAKE_CASE ,				attention_mask=__SCREAMING_SNAKE_CASE ,				position_ids=__SCREAMING_SNAKE_CASE ,				token_type_ids=__SCREAMING_SNAKE_CASE ,				deterministic=__SCREAMING_SNAKE_CASE ,				)
													_lowerCamelCase					     = text_outputs[1]
													_lowerCamelCase					     = module.text_projection(__SCREAMING_SNAKE_CASE      )
													return text_features

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

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ = None ,				lowerCamelCase__ = None ,				lowerCamelCase__=False      ):
									_lowerCamelCase					     = {}
									if dropout_rng is not None:
													_lowerCamelCase					     = dropout_rng

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

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





					@classmethod
					def 	snake_case__					(			cls ,				lowerCamelCase__ = None ,				lowerCamelCase__ = None ,				*lowerCamelCase__ ,				**lowerCamelCase__ ,				):
									_lowerCamelCase					     = {
									    argument[len('''text_'''      ) :]: value for argument, value in kwargs.items() if argument.startswith('''text_'''      )
									}

									_lowerCamelCase					     = {
									    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					     = kwargs_text.pop('''model''' ,				__SCREAMING_SNAKE_CASE      )
									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					     = AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE      )
																	_lowerCamelCase					     = text_config

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

									_lowerCamelCase					     = kwargs_vision.pop('''model''' ,				__SCREAMING_SNAKE_CASE      )
									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					     = AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE      )
																	_lowerCamelCase					     = vision_config

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

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

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

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

									_lowerCamelCase					     = text_model.params

									return model







'''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    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					list:
			seed(1	)
			return [gauss(lowercase					,	lowercase	) for _ in range(lowercase	)]





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					list:
			return [k for k in range(lowercase	) for _ in range(instance_count[k]	)]





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			return sum(lowercase	) / instance_count





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			return instance_count / total_count





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					float:
			__lowerCAmelCase											=				[]  # 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
			__lowerCAmelCase											=				len(lowercase	)  # Number of classes in dataset
			return 1 / (total_count - n_classes) * sum(lowercase	)





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase					,	lowercase	)					->					list:
			__lowerCAmelCase											=				[]
			# 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]	)	):
									__lowerCAmelCase											=				[]  # 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    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			__lowerCAmelCase											=				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


_a					: Optional[int]      =    TypeVar("""num""")





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase					,	lowercase = lambda lowercase	: True					,	lowercase = None					,	)					->					num:
			while True:
						try:
									__lowerCAmelCase											=				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    _lowerCAmelCase						(						)					->					List[str]:
			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
						__lowerCAmelCase											=				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
						__lowerCAmelCase											=				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
						__lowerCAmelCase											=				[]  # An empty list to store instance counts of classes in dataset
						for i in range(lowercase	):
									__lowerCAmelCase											=				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
						__lowerCAmelCase											=				[]
						for a in range(lowercase	):
									__lowerCAmelCase											=				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
						__lowerCAmelCase											=				[
						    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
						__lowerCAmelCase											=				y_generator(lowercase					,	lowercase	)
						print("""Generated Corresponding Ys: \n"""					,	lowercase	)
						print("""-""" * 100	)

						# Calculating the value of actual mean for each class
						__lowerCAmelCase											=				[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
						__lowerCAmelCase											=				[
						    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
						__lowerCAmelCase											=				calculate_variance(lowercase					,	lowercase					,	sum(lowercase	)	)
						print("""Variance: """					,	lowercase	)
						print("""-""" * 100	)

						# Predicting Y values
						# storing predicted Y values in 'pre_indexes' variable
						__lowerCAmelCase											=				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()

