






"""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


_snake_case							:   Union[str, Any]	  =       logging.get_logger(__name__)




class 							__lowercase(							nn.Module	):
						lowerCAmelCase_		:				HybridCLIPConfig
						lowerCAmelCase_		:				jnp.dtype=							jnp.floataa






						def 		lowerCAmelCase (       self)      ->	List[str]:




											"""simple docstring"""





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






						def __call__(       self     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE = True     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE=None     ,    )      ->	Optional[Any]:




											"""simple docstring"""





											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 							__lowercase(							_UpperCamelCase	):
						lowerCAmelCase_		:				int=							HybridCLIPConfig
						lowerCAmelCase_		:				Optional[Any]=							FlaxHybridCLIPModule






						def __init__(       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = 0     ,    _SCREAMING_SNAKE_CASE = jnp.floataa     ,    **_SCREAMING_SNAKE_CASE     ,    )      ->	List[Any]:




											"""simple docstring"""





											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 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = None)      ->	FrozenDict:




											"""simple docstring"""





											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     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = False     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    )      ->	List[Any]:




											"""simple docstring"""





											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 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE=None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE=False     ,    )      ->	Optional[Any]:




											"""simple docstring"""





											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(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE):
																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 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE=False)      ->	List[Any]:




											"""simple docstring"""





											lowerCamelCase__					={}
											if dropout_rng is not None:
																lowerCamelCase__					=dropout_rng

											def _get_features(_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE):
																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 		lowerCAmelCase (       cls     ,    _SCREAMING_SNAKE_CASE = None     ,    _SCREAMING_SNAKE_CASE = None     ,    *_SCREAMING_SNAKE_CASE     ,    **_SCREAMING_SNAKE_CASE     ,    )      ->	FlaxPreTrainedModel:




											"""simple docstring"""





											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"""



import random




class 							__lowercase:






						def __init__(       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE = 0.01     ,    _SCREAMING_SNAKE_CASE = 10_00     ,    _SCREAMING_SNAKE_CASE = -1     ,    )      ->	None:




											"""simple docstring"""





											lowerCamelCase__					=sample
											if len(self.sample) == 0:
																raise ValueError('Sample data can not be empty')
											lowerCamelCase__					=target
											if len(self.target) == 0:
																raise ValueError('Target data can not be empty')
											if len(self.sample) != len(self.target):
																raise ValueError('Sample data and Target data do not have matching lengths')
											lowerCamelCase__					=learning_rate
											lowerCamelCase__					=epoch_number
											lowerCamelCase__					=bias
											lowerCamelCase__					=len(_SCREAMING_SNAKE_CASE)
											lowerCamelCase__					=len(sample[0])  # number of columns in dataset
											lowerCamelCase__					=[]






						def 		lowerCAmelCase (       self)      ->	None:




											"""simple docstring"""





											for sample in self.sample:
																sample.insert(0     ,    self.bias)

											for _ in range(self.col_sample):
																self.weight.append(random.random())

											self.weight.insert(0     ,    self.bias)

											lowerCamelCase__					=0

											while True:
																lowerCamelCase__					=False
																for i in range(self.number_sample):
																					lowerCamelCase__					=0
																					for j in range(self.col_sample + 1):
																										lowerCamelCase__					=u + self.weight[j] * self.sample[i][j]
																					lowerCamelCase__					=self.sign(_SCREAMING_SNAKE_CASE)
																					if y != self.target[i]:
																										for j in range(self.col_sample + 1):
																															lowerCamelCase__					=(
																															    self.weight[j]
																															    + self.learning_rate
																															    * (self.target[i] - y)
																															    * self.sample[i][j]
																															)
																										lowerCamelCase__					=True
            # print('Epoch: \n',epoch_count)
																lowerCamelCase__					=epoch_count + 1
																# if you want control the epoch or just by error
																if not has_misclassified:
																					print(('\nEpoch:\n', epoch_count))
																					print('------------------------\n')
																					# if epoch_count > self.epoch_number or not error:
																					break






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE)      ->	None:




											"""simple docstring"""





											if len(self.sample) == 0:
																raise ValueError('Sample data can not be empty')
											sample.insert(0     ,    self.bias)
											lowerCamelCase__					=0
											for i in range(self.col_sample + 1):
																lowerCamelCase__					=u + self.weight[i] * sample[i]

											lowerCamelCase__					=self.sign(_SCREAMING_SNAKE_CASE)

											if y == -1:
																print(('Sample: ', sample))
																print('classification: P1')
											else:
																print(('Sample: ', sample))
																print('classification: P2')






						def 		lowerCAmelCase (       self     ,    _SCREAMING_SNAKE_CASE)      ->	int:




											"""simple docstring"""





											return 1 if u >= 0 else -1



_snake_case							:   int	  =       [
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

_snake_case							:   List[str]	  =       [
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						_snake_case							:   int	  =       Perceptron(
						    sample=samples, target=target, learning_rate=0.01, epoch_number=1_000, bias=-1
						)
						network.training()
						print("Finished training perceptron")
						print("Enter values to predict or q to exit")
						while True:
												_snake_case							:   list	  =       []
												for i in range(len(samples[0])):
																		_snake_case							:   List[str]	  =       input("value: ").strip()
																		if user_input == "q":
																								break
																		_snake_case							:   Optional[Any]	  =       float(user_input)
																		sample.insert(i, observation)
												network.sort(sample)