
"""simple docstring"""






from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


lowerCamelCase__ :       Optional[Any]						     =			logging.get_logger(__name__)





def __A						(   a_ :							Union[str, Any]		,		a_ :							Any     )->							int:




		'''simple docstring'''






		try:
				with open(a_		,		'''rb'''     ) as flax_state_f:
						SCREAMING_SNAKE_CASE       :     str				= from_bytes(a_		,		flax_state_f.read()     )
		except UnpicklingError as e:
				try:
						with open(a_     ) as f:
								if f.read().startswith('''version'''     ):
										raise OSError(
										    '''You seem to have cloned a repository without having git-lfs installed. Please'''
										    ''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
										    ''' folder you cloned.'''     )
								else:
										raise ValueError from e
				except (UnicodeDecodeError, ValueError):
						raise EnvironmentError(F"Unable to convert {model_file} to Flax deserializable object. "     )

		return load_flax_weights_in_pytorch_model(a_		,		a_     )





def __A						(   a_ :							int		,		a_ :							int     )->							Tuple:




		'''simple docstring'''






		try:
				import torch  # noqa: F401
		except ImportError:
				logger.error(
				    '''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
				    ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
				    ''' instructions.'''     )
				raise

		# check if we have bf16 weights
		SCREAMING_SNAKE_CASE       :     Dict				= flatten_dict(jax.tree_util.tree_map(lambda a_     : x.dtype == jnp.bfloataa		,		a_     )     ).values()
		if any(a_     ):
				# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

				# and bf16 is not fully supported in PT yet.
				logger.warning(
				    '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
				    '''before loading those in PyTorch model.'''     )
				SCREAMING_SNAKE_CASE       :     Optional[int]				= jax.tree_util.tree_map(
				    lambda a_     : params.astype(np.floataa     ) if params.dtype == jnp.bfloataa else params		,		a_     )

		SCREAMING_SNAKE_CASE       :     Dict				= ''''''

		SCREAMING_SNAKE_CASE       :     List[str]				= flatten_dict(a_		,		sep='''.'''     )
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= pt_model.state_dict()

		# keep track of unexpected & missing keys
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= []
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= set(pt_model_dict.keys()     )

		for flax_key_tuple, flax_tensor in flax_state_dict.items():
				SCREAMING_SNAKE_CASE       :     int				= flax_key_tuple.split('''.'''     )

				if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
						SCREAMING_SNAKE_CASE       :     List[Any]				= flax_key_tuple_array[:-1] + ['''weight''']
						SCREAMING_SNAKE_CASE       :     Tuple				= jnp.transpose(a_		,		(3, 2, 0, 1)     )
				elif flax_key_tuple_array[-1] == "kernel":
						SCREAMING_SNAKE_CASE       :     Dict				= flax_key_tuple_array[:-1] + ['''weight''']
						SCREAMING_SNAKE_CASE       :     List[Any]				= flax_tensor.T
				elif flax_key_tuple_array[-1] == "scale":
						SCREAMING_SNAKE_CASE       :     List[str]				= flax_key_tuple_array[:-1] + ['''weight''']

				if "time_embedding" not in flax_key_tuple_array:
						for i, flax_key_tuple_string in enumerate(a_     ):
								SCREAMING_SNAKE_CASE       :     Optional[Any]				= (
								    flax_key_tuple_string.replace('''_0'''		,		'''.0'''     )
								    .replace('''_1'''		,		'''.1'''     )
								    .replace('''_2'''		,		'''.2'''     )
								    .replace('''_3'''		,		'''.3'''     )
								    .replace('''_4'''		,		'''.4'''     )
								    .replace('''_5'''		,		'''.5'''     )
								    .replace('''_6'''		,		'''.6'''     )
								    .replace('''_7'''		,		'''.7'''     )
								    .replace('''_8'''		,		'''.8'''     )
								    .replace('''_9'''		,		'''.9'''     )
								)

				SCREAMING_SNAKE_CASE       :     Optional[int]				= '''.'''.join(a_     )

				if flax_key in pt_model_dict:
						if flax_tensor.shape != pt_model_dict[flax_key].shape:
								raise ValueError(
								    F"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
								    F"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."     )
						else:
								# add weight to pytorch dict
								SCREAMING_SNAKE_CASE       :     List[str]				= np.asarray(a_     ) if not isinstance(a_		,		np.ndarray     ) else flax_tensor
								SCREAMING_SNAKE_CASE       :     Optional[Any]				= torch.from_numpy(a_     )
								# remove from missing keys
								missing_keys.remove(a_     )
				else:
						# weight is not expected by PyTorch model
						unexpected_keys.append(a_     )

		pt_model.load_state_dict(a_     )

		# re-transform missing_keys to list
		SCREAMING_SNAKE_CASE       :     int				= list(a_     )

		if len(a_     ) > 0:
				logger.warning(
				    '''Some weights of the Flax model were not used when initializing the PyTorch model'''
				    F" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
				    F" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
				    ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
				    F" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
				    ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
				    ''' FlaxBertForSequenceClassification model).'''     )
		if len(a_     ) > 0:
				logger.warning(
				    F"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
				    F" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
				    ''' use it for predictions and inference.'''     )

		return pt_model







"""simple docstring"""






from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
lowerCamelCase__ :       Optional[Any]						     =			200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
lowerCamelCase__ :       Optional[int]						     =			50
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
lowerCamelCase__ :       Optional[Any]						     =			0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1000))





def __A						(   a_ :							str		,		a_ :							str     )->							tuple[str, float]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= len([g for position, g in enumerate(a_     ) if g == main_target[position]]     )
		return (item, float(a_     ))





def __A						(   a_ :							str		,		a_ :							str     )->							tuple[str, str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= random.randint(0		,		len(a_     ) - 1     )
		SCREAMING_SNAKE_CASE       :     str				= parent_a[:random_slice] + parent_a[random_slice:]
		SCREAMING_SNAKE_CASE       :     Dict				= parent_a[:random_slice] + parent_a[random_slice:]
		return (child_a, child_a)





def __A						(   a_ :							str		,		a_ :							list[str]     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Any				= list(a_     )
		if random.uniform(0		,		1     ) < MUTATION_PROBABILITY:
				SCREAMING_SNAKE_CASE       :     Any				= random.choice(a_     )
		return "".join(a_     )





def __A						(   a_ :							tuple[str, float]		,		a_ :							list[tuple[str, float]]		,		a_ :							list[str]		,		)->							list[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= []
		# Generate more children proportionally to the fitness score.
		SCREAMING_SNAKE_CASE       :     List[str]				= int(parent_a[1] * 1_00     ) + 1
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 10 if child_n >= 10 else child_n
		for _ in range(a_     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= population_score[random.randint(0		,		a_     )][0]

				SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Tuple				= crossover(parent_a[0]		,		a_     )
				# Append new string to the population list.
				pop.append(mutate(a_		,		a_     )     )
				pop.append(mutate(a_		,		a_     )     )
		return pop





def __A						(   a_ :							str		,		a_ :							list[str]		,		a_ :							bool = True     )->							tuple[int, int, str]:




		'''simple docstring'''






		if N_POPULATION < N_SELECTED:
				SCREAMING_SNAKE_CASE       :     List[Any]				= F"{N_POPULATION} must be bigger than {N_SELECTED}"
				raise ValueError(a_     )
		# Verify that the target contains no genes besides the ones inside genes variable.
		SCREAMING_SNAKE_CASE       :     List[str]				= sorted({c for c in target if c not in genes}     )
		if not_in_genes_list:
				SCREAMING_SNAKE_CASE       :     str				= F"{not_in_genes_list} is not in genes list, evolution cannot converge"
				raise ValueError(a_     )

		# Generate random starting population.
		SCREAMING_SNAKE_CASE       :     Tuple				= []
		for _ in range(a_     ):
				population.append(''''''.join([random.choice(a_     ) for i in range(len(a_     )     )]     )     )

		# Just some logs to know what the algorithms is doing.
		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[str]				= 0, 0

		# This loop will end when we find a perfect match for our target.
		while True:
				generation += 1
				total_population += len(a_     )

				# Random population created. Now it's time to evaluate.

				# Adding a bit of concurrency can make everything faster,
				#
				# import concurrent.futures
				# population_score: list[tuple[str, float]] = []
				# with concurrent.futures.ThreadPoolExecutor(
				#                                   max_workers=NUM_WORKERS) as executor:
				#     futures = {executor.submit(evaluate, item) for item in population}
				#     concurrent.futures.wait(futures)
				#     population_score = [item.result() for item in futures]
				#
				# but with a simple algorithm like this, it will probably be slower.
				# We just need to call evaluate for every item inside the population.
				SCREAMING_SNAKE_CASE       :     int				= [evaluate(a_		,		a_     ) for item in population]

				# Check if there is a matching evolution.
				SCREAMING_SNAKE_CASE       :     List[Any]				= sorted(a_		,		key=lambda a_     : x[1]		,		reverse=a_     )
				if population_score[0][0] == target:
						return (generation, total_population, population_score[0][0])

				# Print the best result every 10 generation.
				# Just to know that the algorithm is working.
				if debug and generation % 10 == 0:
						print(
						    F"\nGeneration: {generation}"
						    F"\nTotal Population:{total_population}"
						    F"\nBest score: {population_score[0][1]}"
						    F"\nBest string: {population_score[0][0]}"     )

				# Flush the old population, keeping some of the best evolutions.
				# Keeping this avoid regression of evolution.
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= population[: int(N_POPULATION / 3     )]
				population.clear()
				population.extend(a_     )
				# Normalize population score to be between 0 and 1.
				SCREAMING_SNAKE_CASE       :     Optional[int]				= [
				    (item, score / len(a_     )) for item, score in population_score
				]

				# This is selection
				for i in range(a_     ):
						population.extend(select(population_score[int(a_     )]		,		a_		,		a_     )     )
						# Check if the population has already reached the maximum value and if so,
						# break the cycle.  If this check is disabled, the algorithm will take
						# forever to compute large strings, but will also calculate small strings in
						# a far fewer generations.
						if len(a_     ) > N_POPULATION:
								break


if __name__ == "__main__":
					lowerCamelCase__ :       Dict						     =			(
					    "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!"
					)
					lowerCamelCase__ :       int						     =			list(
					    " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"
					    "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\"
					)
					lowerCamelCase__     , lowerCamelCase__     , lowerCamelCase__ :       Dict						     =			basic(target_str, genes_list)
					print(
					    f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
					)





