

"""simple docstring"""





from __future__ import annotations

import random

# Maximum size of the population.  Bigger could be faster but is more memory expensive.
__UpperCamelCase    : Optional[int]				 =						2_0_0
# 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.
__UpperCamelCase    : List[str]				 =						5_0
# 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.
__UpperCamelCase    : int				 =						0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	lowerCAmelCase__      :		Tuple    							=      len([g for position, g in enumerate(A_					) if g == main_target[position]]					)
	return (item, float(A_					))

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	lowerCAmelCase__      :		List[Any]    							=      random.randint(0		,	len(A_					) - 1					)
	lowerCAmelCase__      :		Tuple    							=      parent_a[:random_slice] + parent_a[random_slice:]
	lowerCAmelCase__      :		List[str]    							=      parent_a[:random_slice] + parent_a[random_slice:]
	return (child_a, child_a)

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	lowerCAmelCase__      :		Optional[Any]    							=      list(A_					)
	if random.uniform(0		,	1					) < MUTATION_PROBABILITY:
		lowerCAmelCase__      :		Any    							=      random.choice(A_					)
	return "".join(A_					)

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

		lowerCAmelCase__							,lowerCAmelCase__      :		Any    							=      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     __SCREAMING_SNAKE_CASE    ( A_		,	A_		,	A_ = True					):

	# Verify if N_POPULATION is bigger than N_SELECTED
	if N_POPULATION < N_SELECTED:
		lowerCAmelCase__      :		Optional[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.
	lowerCAmelCase__      :		Tuple    							=      sorted({c for c in target if c not in genes}					)
	if not_in_genes_list:
		lowerCAmelCase__      :		Dict    							=      f'{not_in_genes_list} is not in genes list, evolution cannot converge'
		raise ValueError(A_					)

	# Generate random starting population.
	lowerCAmelCase__      :		List[Any]    							=      []
	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.
	lowerCAmelCase__							,lowerCAmelCase__      :		Dict    							=      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.
		lowerCAmelCase__      :		Tuple    							=      [evaluate(A_		,	A_					) for item in population]

		# Check if there is a matching evolution.
		lowerCAmelCase__      :		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.
		lowerCAmelCase__      :		List[Any]    							=      population[: int(N_POPULATION / 3					)]
		population.clear()
		population.extend(A_					)
		# Normalize population score to be between 0 and 1.
		lowerCAmelCase__      :		Optional[Any]    							=      [
		    (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__":
							__UpperCamelCase    : Tuple				 =						(
							    '''This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'''
							)
							__UpperCamelCase    : Optional[Any]				 =						list(
							    ''' ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm'''
							    '''nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\'''
							)
							__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase    : Any				 =						basic(target_str, genes_list)
							print(
							    F'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
							)







"""simple docstring"""





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__UpperCamelCase    : List[Any]				 =						logging.get_logger(__name__)

__UpperCamelCase    : Any				 =						{
    '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
    '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
    '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
    '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
    '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			"t5"
					lowercase__												=			["past_key_values"]
					lowercase__												=			{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}







					def __init__(			self     :							Tuple  ,lowercase_     :							Tuple=3_2_1_2_8  ,lowercase_     :							Union[str, Any]=5_1_2  ,lowercase_     :							Dict=6_4  ,lowercase_     :							Any=2_0_4_8  ,lowercase_     :							Dict=6  ,lowercase_     :							Dict=None  ,lowercase_     :							Dict=8  ,lowercase_     :							Optional[int]=3_2  ,lowercase_     :							List[Any]=1_2_8  ,lowercase_     :							Any=0.1  ,lowercase_     :							Optional[Any]=1E-6  ,lowercase_     :							Any=1.0  ,lowercase_     :							Dict="relu"  ,lowercase_     :							Tuple=True  ,lowercase_     :							List[Any]=True  ,lowercase_     :							Tuple=0  ,lowercase_     :							Optional[Any]=1  ,**lowercase_     :							Any  ,):
						lowerCAmelCase__      :		Tuple    							=      vocab_size
						lowerCAmelCase__      :		List[str]    							=      d_model
						lowerCAmelCase__      :		Union[str, Any]    							=      d_kv
						lowerCAmelCase__      :		List[Any]    							=      d_ff
						lowerCAmelCase__      :		int    							=      num_layers
						lowerCAmelCase__      :		Union[str, Any]    							=      (
						    num_decoder_layers if num_decoder_layers is not None else self.num_layers
						)  # default = symmetry
						lowerCAmelCase__      :		Any    							=      num_heads
						lowerCAmelCase__      :		Optional[int]    							=      relative_attention_num_buckets
						lowerCAmelCase__      :		Union[str, Any]    							=      relative_attention_max_distance
						lowerCAmelCase__      :		List[Any]    							=      dropout_rate
						lowerCAmelCase__      :		str    							=      layer_norm_epsilon
						lowerCAmelCase__      :		int    							=      initializer_factor
						lowerCAmelCase__      :		Dict    							=      feed_forward_proj
						lowerCAmelCase__      :		int    							=      use_cache

						lowerCAmelCase__      :		int    							=      self.feed_forward_proj.split('''-'''						)
						lowerCAmelCase__      :		str    							=      act_info[-1]
						lowerCAmelCase__      :		Tuple    							=      act_info[0] == '''gated'''

						if len(lowercase_						) > 1 and act_info[0] != "gated" or len(lowercase_						) > 2:
							raise ValueError(
							    F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
							    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
							    '''\'gated-gelu\' or \'relu\''''						)

						# for backwards compatibility
						if feed_forward_proj == "gated-gelu":
							lowerCAmelCase__      :		Tuple    							=      '''gelu_new'''

						super().__init__(
						    pad_token_id=lowercase_  ,eos_token_id=lowercase_  ,is_encoder_decoder=lowercase_  ,**lowercase_  ,)







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					@property
					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__      :		int    							=      {
						    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
						    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
						}
						if self.use_past:
							lowerCAmelCase__      :		Union[str, Any]    							=      '''past_encoder_sequence + sequence'''
							lowerCAmelCase__      :		List[Any]    							=      {0: '''batch'''}
							lowerCAmelCase__      :		Union[str, Any]    							=      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
						else:
							lowerCAmelCase__      :		Union[str, Any]    							=      {0: '''batch''', 1: '''decoder_sequence'''}
							lowerCAmelCase__      :		Any    							=      {0: '''batch''', 1: '''decoder_sequence'''}

						if self.use_past:
							self.fill_with_past_key_values_(lowercase_  ,direction='''inputs'''						)

						return common_inputs







					@property
					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						return 1_3




