






'''simple docstring'''







import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCamelCase    :      Union[str, Any]  			=		logging.get_logger(__name__)
def 						A__			(				__lowerCAmelCase		:			int						):
						lowerCamelCase__         =  R"""\w+[.]\d+"""
						lowerCamelCase__         =  re.findall(__lowerCAmelCase   ,    __lowerCAmelCase						)
						for pat in pats:
												lowerCamelCase__         =  key.replace(__lowerCAmelCase   ,    """_""".join(pat.split("""."""						)						)						)
						return key
def 						A__			(				__lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Tuple						):
						lowerCamelCase__         =  pt_tuple_key[:-1] + ("""scale""",)
						if (
						    any("""norm""" in str_ for str_ in pt_tuple_key						)
						    and (pt_tuple_key[-1] == "bias")
						    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
						    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
						):
												lowerCamelCase__         =  pt_tuple_key[:-1] + ("""scale""",)
												return renamed_pt_tuple_key, pt_tensor
						elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
												lowerCamelCase__         =  pt_tuple_key[:-1] + ("""scale""",)
												return renamed_pt_tuple_key, pt_tensor

						# embedding
						if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
												lowerCamelCase__         =  pt_tuple_key[:-1] + ("""embedding""",)
												return renamed_pt_tuple_key, pt_tensor

						# conv layer
						lowerCamelCase__         =  pt_tuple_key[:-1] + ("""kernel""",)
						if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
												lowerCamelCase__         =  pt_tensor.transpose(2   ,    3   ,    1   ,    0						)
												return renamed_pt_tuple_key, pt_tensor

						# linear layer
						lowerCamelCase__         =  pt_tuple_key[:-1] + ("""kernel""",)
						if pt_tuple_key[-1] == "weight":
												lowerCamelCase__         =  pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm weight
						lowerCamelCase__         =  pt_tuple_key[:-1] + ("""weight""",)
						if pt_tuple_key[-1] == "gamma":
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm bias
						lowerCamelCase__         =  pt_tuple_key[:-1] + ("""bias""",)
						if pt_tuple_key[-1] == "beta":
												return renamed_pt_tuple_key, pt_tensor

						return pt_tuple_key, pt_tensor





def 						A__			(				__lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			Union[str, Any]   ,    __lowerCAmelCase		:			Tuple=42						):
						# Step 1: Convert pytorch tensor to numpy
						lowerCamelCase__         =  {k: v.numpy() for k, v in pt_state_dict.items()}

						# Step 2: Since the model is stateless, get random Flax params
						lowerCamelCase__         =  flax_model.init_weights(PRNGKey(__lowerCAmelCase						)						)

						lowerCamelCase__         =  flatten_dict(__lowerCAmelCase						)
						lowerCamelCase__         =  {}

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												lowerCamelCase__         =  rename_key(__lowerCAmelCase						)
												lowerCamelCase__         =  tuple(renamed_pt_key.split("""."""						)						)

												# Correctly rename weight parameters
												lowerCamelCase__       ,			lowerCamelCase__         =  rename_key_and_reshape_tensor(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase						)

												if flax_key in random_flax_state_dict:
																		if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																								raise ValueError(
																								    F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																								    F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''						)

        # also add unexpected weight so that warning is thrown
												lowerCamelCase__         =  jnp.asarray(__lowerCAmelCase						)

						return unflatten_dict(__lowerCAmelCase						)









'''simple docstring'''







import numpy

# List of input, output pairs
UpperCamelCase    :      List[Any]  			=		(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
UpperCamelCase    :      Optional[int]  			=		(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
UpperCamelCase    :      int  			=		[2, 4, 1, 5]
UpperCamelCase    :      int  			=		len(train_data)
UpperCamelCase    :      Dict  			=		0.009
def 						A__			(				__lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			str="train"						):
						return calculate_hypothesis_value(__lowerCAmelCase   ,    __lowerCAmelCase						) - output(
						    __lowerCAmelCase   ,    __lowerCAmelCase						)
def 						A__			(				__lowerCAmelCase		:			Any						):
						lowerCamelCase__         =  0
						for i in range(len(__lowerCAmelCase						) - 1						):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val
def 						A__			(				__lowerCAmelCase		:			Optional[Any]   ,    __lowerCAmelCase		:			Tuple						):
						if data_set == "train":
												return train_data[example_no][1]
						elif data_set == "test":
												return test_data[example_no][1]
						return None
def 						A__			(				__lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			Dict						):
						if data_set == "train":
												return _hypothesis_value(train_data[example_no][0]						)
						elif data_set == "test":
												return _hypothesis_value(test_data[example_no][0]						)
						return None
def 						A__			(				__lowerCAmelCase		:			List[Any]   ,    __lowerCAmelCase		:			Optional[Any]=m						):
						lowerCamelCase__         =  0
						for i in range(__lowerCAmelCase						):
												if index == -1:
																		summation_value += _error(__lowerCAmelCase						)
												else:
																		summation_value += _error(__lowerCAmelCase						) * train_data[i][0][index]
						return summation_value
def 						A__			(				__lowerCAmelCase		:			List[Any]						):
						lowerCamelCase__         =  summation_of_cost_derivative(__lowerCAmelCase   ,    __lowerCAmelCase						) / m
						return cost_derivative_value
def 						A__			(				):
						global parameter_vector
						# Tune these values to set a tolerance value for predicted output
						lowerCamelCase__         =  0.00_0002
						lowerCamelCase__         =  0
						lowerCamelCase__         =  0
						while True:
												j += 1
												lowerCamelCase__         =  [0, 0, 0, 0]
												for i in range(0   ,    len(__lowerCAmelCase						)						):
																		lowerCamelCase__         =  get_cost_derivative(i - 1						)
																		lowerCamelCase__         =  (
																		    parameter_vector[i] - LEARNING_RATE * cost_derivative
																		)
												if numpy.allclose(
												    __lowerCAmelCase   ,    __lowerCAmelCase   ,    atol=__lowerCAmelCase   ,    rtol=__lowerCAmelCase   ,    ):
																		break
												lowerCamelCase__         =  temp_parameter_vector
						print(("""Number of iterations:""", j)						)
def 						A__			(				):
						for i in range(len(__lowerCAmelCase						)						):
												print(("""Actual output value:""", output(__lowerCAmelCase   ,    """test"""						))						)
												print(("""Hypothesis output:""", calculate_hypothesis_value(__lowerCAmelCase   ,    """test"""						))						)


if __name__ == "__main__":
							run_gradient_descent()
							print('\nTesting gradient descent for a linear hypothesis function.\n')
							test_gradient_descent()

