
"""simple docstring"""






from typing import Mapping

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


lowerCamelCase__ :       Dict						     =			logging.get_logger(__name__)

lowerCamelCase__ :       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 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

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





	def __init__(						self    :Optional[Any]		, lowerCamelCase_    :Dict=3_21_28		, lowerCamelCase_    :Optional[Any]=5_12		, lowerCamelCase_    :str=64		, lowerCamelCase_    :int=20_48		, lowerCamelCase_    :Tuple=6		, lowerCamelCase_    :int=None		, lowerCamelCase_    :int=8		, lowerCamelCase_    :Optional[Any]=32		, lowerCamelCase_    :List[Any]=1_28		, lowerCamelCase_    :Dict=0.1		, lowerCamelCase_    :Tuple=1E-6		, lowerCamelCase_    :Dict=1.0		, lowerCamelCase_    :Tuple="relu"		, lowerCamelCase_    :int=True		, lowerCamelCase_    :List[Any]=True		, lowerCamelCase_    :Optional[Any]=0		, lowerCamelCase_    :Any=1		, **lowerCamelCase_    :Tuple		, )							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= vocab_size
			SCREAMING_SNAKE_CASE       :     Dict				= d_model
			SCREAMING_SNAKE_CASE       :     List[str]				= d_kv
			SCREAMING_SNAKE_CASE       :     Tuple				= d_ff
			SCREAMING_SNAKE_CASE       :     str				= num_layers
			SCREAMING_SNAKE_CASE       :     List[Any]				= (
			    num_decoder_layers if num_decoder_layers is not None else self.num_layers
			)  # default = symmetry
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= num_heads
			SCREAMING_SNAKE_CASE       :     Any				= relative_attention_num_buckets
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= relative_attention_max_distance
			SCREAMING_SNAKE_CASE       :     str				= dropout_rate
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= layer_norm_epsilon
			SCREAMING_SNAKE_CASE       :     Tuple				= initializer_factor
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= feed_forward_proj
			SCREAMING_SNAKE_CASE       :     str				= use_cache

			SCREAMING_SNAKE_CASE       :     Any				= self.feed_forward_proj.split('''-'''					)
			SCREAMING_SNAKE_CASE       :     List[str]				= act_info[-1]
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= act_info[0] == '''gated'''

			if len(lowerCamelCase_					) > 1 and act_info[0] != "gated" or len(lowerCamelCase_					) > 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":
					SCREAMING_SNAKE_CASE       :     Dict				= '''gelu_new'''

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





class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	@property
	def   __lowerCAmelCase (						self    :List[Any]					)							->			Mapping[str, Mapping[int, str]]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= {
			    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
			    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
			}
			if self.use_past:
					SCREAMING_SNAKE_CASE       :     List[Any]				= '''past_encoder_sequence + sequence'''
					SCREAMING_SNAKE_CASE       :     List[Any]				= {0: '''batch'''}
					SCREAMING_SNAKE_CASE       :     Dict				= {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
			else:
					SCREAMING_SNAKE_CASE       :     Optional[int]				= {0: '''batch''', 1: '''decoder_sequence'''}
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= {0: '''batch''', 1: '''decoder_sequence'''}

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

			return common_inputs







	@property
	def   __lowerCAmelCase (						self    :Optional[int]					)							->			int:







			'''simple docstring'''




			return 13







"""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}'''
					)





