





import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
_a						=   logging.get_logger("transformers.models.speecht5")

_a						=   {
    "speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
    "speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
    "speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
    "speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
_a						=   {
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
_a						=   {
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
    "speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
    "speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
    "speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
_a						=   {
    "speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
    "speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
    "speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
    "speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
    "speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
    "speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
    "speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
    "speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
    "speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
    "speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
    "speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
    "speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
_a						=   {
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
_a						=   {
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
_a						=   {
    "encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
    "encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
    "encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
    "encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
    "encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
    "encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
    "encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
    "encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
    "encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
_a						=   {
    "decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
    "decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
    "decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
    "decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
    "decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
    "decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
    "decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
    "decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
    "decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
    "decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
    "decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
    "decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
    "decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
_a						=   {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_a						=   {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_a						=   {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_a						=   []
_a						=   [
    "encoder.version",
    "encoder.layers.*.norm_k.weight",
    "encoder.layers.*.norm_k.bias",
    "decoder.version",
    "decoder.layers.*.norm_k.weight",
    "decoder.layers.*.norm_k.bias",
    "decoder.pos_emb.pe_k",
    "speech_encoder_prenet.embed_positions._float_tensor",
    "text_decoder_prenet.embed_positions._float_tensor",
]
_a						=   IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
_a						=   IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
_a						=   IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   Optional[int]:







	'''simple docstring'''







	for attribute in key.split('''.'''							):
		lowerCamelCase__              =	getattr(__snake_case ,__snake_case							)

	if weight_type is not None:
		lowerCamelCase__              =	getattr(__snake_case ,__snake_case							).shape
	else:
		lowerCamelCase__              =	hf_pointer.shape

	if hf_shape != value.shape:
		raise ValueError(
		    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
		    F' {value.shape} for {full_name}'							)

	if weight_type == "weight":
		lowerCamelCase__              =	value
	elif weight_type == "weight_g":
		lowerCamelCase__              =	value
	elif weight_type == "weight_v":
		lowerCamelCase__              =	value
	elif weight_type == "bias":
		lowerCamelCase__              =	value
	elif weight_type == "running_mean":
		lowerCamelCase__              =	value
	elif weight_type == "running_var":
		lowerCamelCase__              =	value
	elif weight_type == "num_batches_tracked":
		lowerCamelCase__              =	value
	else:
		lowerCamelCase__              =	value

	logger.info(F'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'							)





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Optional[Any]:







	'''simple docstring'''







	for key in ignore_keys:
		if key.endswith('''.*'''							):
			if name.startswith(key[:-1]							):
				return True
		elif ".*." in key:
			lowerCamelCase__       ,			lowerCamelCase__              =	key.split('''.*.'''							)
			if prefix in name and suffix in name:
				return True
		elif key in name:
			return True
	return False





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   List[str]:







	'''simple docstring'''







	lowerCamelCase__              =	[]

	if task == "s2t":
		lowerCamelCase__              =	hf_model.speechta.encoder.prenet.feature_encoder
		lowerCamelCase__              =	MAPPING_S2T
		lowerCamelCase__              =	IGNORE_KEYS_S2T
	elif task == "t2s":
		lowerCamelCase__              =	None
		lowerCamelCase__              =	MAPPING_T2S
		lowerCamelCase__              =	IGNORE_KEYS_T2S
	elif task == "s2s":
		lowerCamelCase__              =	hf_model.speechta.encoder.prenet.feature_encoder
		lowerCamelCase__              =	MAPPING_S2S
		lowerCamelCase__              =	IGNORE_KEYS_S2S
	else:
		raise ValueError(F'Unsupported task: {task}'							)

	for name, value in fairseq_dict.items():
		if should_ignore(__snake_case ,__snake_case							):
			logger.info(F'{name} was ignored'							)
			continue

		lowerCamelCase__              =	False
		if "conv_layers" in name:
			load_conv_layer(
			    __snake_case ,__snake_case ,__snake_case ,__snake_case ,hf_model.config.feat_extract_norm == '''group''' ,)
			lowerCamelCase__              =	True
		else:
			for key, mapped_key in MAPPING.items():
				# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

				if "*" in key:
					lowerCamelCase__       ,			lowerCamelCase__              =	key.split('''.*.'''							)
					if prefix in name and suffix in name:
						lowerCamelCase__              =	suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
				if key in name:
					lowerCamelCase__              =	True
					if "*" in mapped_key:
						lowerCamelCase__              =	name.split(__snake_case							)[0].split('''.'''							)[-2]
						lowerCamelCase__              =	mapped_key.replace('''*''' ,__snake_case							)
					if "weight_g" in name:
						lowerCamelCase__              =	'''weight_g'''
					elif "weight_v" in name:
						lowerCamelCase__              =	'''weight_v'''
					elif "bias" in name:
						lowerCamelCase__              =	'''bias'''
					elif "weight" in name:
						lowerCamelCase__              =	'''weight'''
					elif "running_mean" in name:
						lowerCamelCase__              =	'''running_mean'''
					elif "running_var" in name:
						lowerCamelCase__              =	'''running_var'''
					elif "num_batches_tracked" in name:
						lowerCamelCase__              =	'''num_batches_tracked'''
					else:
						lowerCamelCase__              =	None
					set_recursively(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case							)
				continue
		if not is_used:
			unused_weights.append(__snake_case							)

	logger.warning(F'Unused weights: {unused_weights}'							)





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   Tuple:







	'''simple docstring'''







	lowerCamelCase__              =	full_name.split('''conv_layers.'''							)[-1]
	lowerCamelCase__              =	name.split('''.'''							)
	lowerCamelCase__              =	int(items[0]							)
	lowerCamelCase__              =	int(items[1]							)

	if type_id == 0:
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
				raise ValueError(
				    F'{full_name} has size {value.shape}, but'
				    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'							)
			lowerCamelCase__              =	value
			logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'							)
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
				raise ValueError(
				    F'{full_name} has size {value.shape}, but'
				    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'							)
			lowerCamelCase__              =	value
			logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'							)
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
				raise ValueError(
				    F'{full_name} has size {value.shape}, but'
				    F' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'							)
			lowerCamelCase__              =	value
			logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
				raise ValueError(
				    F'{full_name} has size {value.shape}, but'
				    F' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'							)
			lowerCamelCase__              =	value
			logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
	else:
		unused_weights.append(__snake_case							)





@torch.no_grad()
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case=None ,__snake_case=None ,__snake_case=None ,)       ->   str:







	'''simple docstring'''







	if config_path is not None:
		lowerCamelCase__              =	SpeechTaConfig.from_pretrained(__snake_case							)
	else:
		lowerCamelCase__              =	SpeechTaConfig()

	if task == "s2t":
		lowerCamelCase__              =	config.max_text_positions
		lowerCamelCase__              =	SpeechTaForSpeechToText(__snake_case							)
	elif task == "t2s":
		lowerCamelCase__              =	1876
		lowerCamelCase__              =	600
		lowerCamelCase__              =	config.max_speech_positions
		lowerCamelCase__              =	SpeechTaForTextToSpeech(__snake_case							)
	elif task == "s2s":
		lowerCamelCase__              =	1876
		lowerCamelCase__              =	config.max_speech_positions
		lowerCamelCase__              =	SpeechTaForSpeechToSpeech(__snake_case							)
	else:
		raise ValueError(F'Unknown task name: {task}'							)

	if vocab_path:
		lowerCamelCase__              =	SpeechTaTokenizer(__snake_case ,model_max_length=config.max_text_positions							)

		# Mask token behaves like a normal word, i.e. include the space before it
		lowerCamelCase__              =	AddedToken('''<mask>''' ,lstrip=__snake_case ,rstrip=__snake_case							)
		lowerCamelCase__              =	mask_token
		tokenizer.add_special_tokens({'''mask_token''': mask_token}							)
		tokenizer.add_tokens(['''<ctc_blank>''']							)

	lowerCamelCase__              =	SpeechTaFeatureExtractor()
	lowerCamelCase__              =	SpeechTaProcessor(tokenizer=__snake_case ,feature_extractor=__snake_case							)
	processor.save_pretrained(__snake_case							)

	lowerCamelCase__              =	torch.load(__snake_case							)
	recursively_load_weights(fairseq_checkpoint['''model'''] ,__snake_case ,__snake_case							)

	model.save_pretrained(__snake_case							)

	if repo_id:
		print('''Pushing to the hub...'''							)
		processor.push_to_hub(__snake_case							)
		model.push_to_hub(__snake_case							)


if __name__ == "__main__":
			_a						=   argparse.ArgumentParser()
			parser.add_argument(
			    "--task",
			    default="s2t",
			    type=str,
			    help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
			)
			parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
			parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
			parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
			parser.add_argument(
			    "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
			)
			parser.add_argument(
			    "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
			)

			_a						=   parser.parse_args()
			convert_speechta_checkpoint(
			    args.task,
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.config_path,
			    args.vocab_path,
			    args.push_to_hub,
			)










from math import sqrt





def lowerCAmelCase__(__snake_case							)       ->   bool:







	'''simple docstring'''







	assert isinstance(__snake_case ,__snake_case							) and (
	    number >= 0
	), "'number' must been an int and positive"

	lowerCamelCase__              =	True

	# 0 and 1 are none primes.
	if number <= 1:
		lowerCamelCase__              =	False

	for divisor in range(2 ,int(round(sqrt(__snake_case							)							)							) + 1							):
		# if 'number' divisible by 'divisor' then sets 'status'
		# of false and break up the loop.
		if number % divisor == 0:
			lowerCamelCase__              =	False
			break

    # precondition
	assert isinstance(__snake_case ,__snake_case							), "'status' must been from type bool"

	return status





def lowerCAmelCase__(__snake_case							)       ->   Any:







	'''simple docstring'''







	assert isinstance(__snake_case ,__snake_case							) and (n > 2), "'N' must been an int and > 2"

	# beginList: contains all natural numbers from 2 up to N
	lowerCamelCase__              =	list(range(2 ,n + 1							)							)

	lowerCamelCase__              =	[]  # this list will be returns.

	# actual sieve of erathostenes
	for i in range(len(__snake_case							)							):
		for j in range(i + 1 ,len(__snake_case							)							):
			if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
				lowerCamelCase__              =	0

    # filters actual prime numbers.
	lowerCamelCase__              =	[x for x in begin_list if x != 0]

	# precondition
	assert isinstance(__snake_case ,__snake_case							), "'ans' must been from type list"

	return ans





def lowerCAmelCase__(__snake_case							)       ->   Optional[Any]:







	'''simple docstring'''







	assert isinstance(__snake_case ,__snake_case							) and (n > 2), "'N' must been an int and > 2"

	lowerCamelCase__              =	[]

	# iterates over all numbers between 2 up to N+1
	# if a number is prime then appends to list 'ans'
	for number in range(2 ,n + 1							):
		if is_prime(__snake_case							):
			ans.append(__snake_case							)

    # precondition
	assert isinstance(__snake_case ,__snake_case							), "'ans' must been from type list"

	return ans





def lowerCAmelCase__(__snake_case							)       ->   List[str]:







	'''simple docstring'''







	assert isinstance(__snake_case ,__snake_case							) and number >= 0, "'number' must been an int and >= 0"

	lowerCamelCase__              =	[]  # this list will be returns of the function.

	# potential prime number factors.

	lowerCamelCase__              =	2

	lowerCamelCase__              =	number

	if number == 0 or number == 1:
		ans.append(__snake_case							)

	# if 'number' not prime then builds the prime factorization of 'number'
	elif not is_prime(__snake_case							):
		while quotient != 1:
			if is_prime(__snake_case							) and (quotient % factor == 0):
				ans.append(__snake_case							)
				quotient /= factor
			else:
				factor += 1

	else:
		ans.append(__snake_case							)

	# precondition
	assert isinstance(__snake_case ,__snake_case							), "'ans' must been from type list"

	return ans





def lowerCAmelCase__(__snake_case							)       ->   List[Any]:







	'''simple docstring'''







	assert isinstance(__snake_case ,__snake_case							) and (
	    number >= 0
	), "'number' bust been an int and >= 0"

	lowerCamelCase__              =	0

	# prime factorization of 'number'
	lowerCamelCase__              =	prime_factorization(__snake_case							)

	lowerCamelCase__              =	max(__snake_case							)

	# precondition
	assert isinstance(__snake_case ,__snake_case							), "'ans' must been from type int"

	return ans





def lowerCAmelCase__(__snake_case							)       ->   Dict:







	'''simple docstring'''







	assert isinstance(__snake_case ,__snake_case							) and (
	    number >= 0
	), "'number' bust been an int and >= 0"

	lowerCamelCase__              =	0

	# prime factorization of 'number'
	lowerCamelCase__              =	prime_factorization(__snake_case							)

	lowerCamelCase__              =	min(__snake_case							)

	# precondition
	assert isinstance(__snake_case ,__snake_case							), "'ans' must been from type int"

	return ans





def lowerCAmelCase__(__snake_case							)       ->   List[Any]:







	'''simple docstring'''







	assert isinstance(__snake_case ,__snake_case							), "'number' must been an int"
	assert isinstance(number % 2 == 0 ,__snake_case							), "compare bust been from type bool"

	return number % 2 == 0





def lowerCAmelCase__(__snake_case							)       ->   List[str]:







	'''simple docstring'''







	assert isinstance(__snake_case ,__snake_case							), "'number' must been an int"
	assert isinstance(number % 2 != 0 ,__snake_case							), "compare bust been from type bool"

	return number % 2 != 0





def lowerCAmelCase__(__snake_case							)       ->   List[Any]:







	'''simple docstring'''







	assert (
	    isinstance(__snake_case ,__snake_case							) and (number > 2) and is_even(__snake_case							)
	), "'number' must been an int, even and > 2"

	lowerCamelCase__              =	[]  # this list will returned

	# creates a list of prime numbers between 2 up to 'number'
	lowerCamelCase__              =	get_prime_numbers(__snake_case							)
	lowerCamelCase__              =	len(__snake_case							)

	# run variable for while-loops.
	lowerCamelCase__              =	0
	lowerCamelCase__              =	None

	# exit variable. for break up the loops
	lowerCamelCase__              =	True

	while i < len_pn and loop:
		lowerCamelCase__              =	i + 1

		while j < len_pn and loop:
			if prime_numbers[i] + prime_numbers[j] == number:
				lowerCamelCase__              =	False
				ans.append(prime_numbers[i]							)
				ans.append(prime_numbers[j]							)

			j += 1

		i += 1

	# precondition
	assert (
	    isinstance(__snake_case ,__snake_case							)
	    and (len(__snake_case							) == 2)
	    and (ans[0] + ans[1] == number)
	    and is_prime(ans[0]							)
	    and is_prime(ans[1]							)
	), "'ans' must contains two primes. And sum of elements must been eq 'number'"

	return ans





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   str:







	'''simple docstring'''







	assert (
	    isinstance(__snake_case ,__snake_case							)
	    and isinstance(__snake_case ,__snake_case							)
	    and (numbera >= 0)
	    and (numbera >= 0)
	), "'number1' and 'number2' must been positive integer."

	lowerCamelCase__              =	0

	while numbera != 0:
		lowerCamelCase__              =	numbera % numbera
		lowerCamelCase__              =	numbera
		lowerCamelCase__              =	rest

	# precondition
	assert isinstance(__snake_case ,__snake_case							) and (
	    numbera >= 0
	), "'number' must been from type int and positive"

	return numbera





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Any:







	'''simple docstring'''







	assert (
	    isinstance(__snake_case ,__snake_case							)
	    and isinstance(__snake_case ,__snake_case							)
	    and (numbera >= 1)
	    and (numbera >= 1)
	), "'number1' and 'number2' must been positive integer."

	lowerCamelCase__              =	1  # actual answer that will be return.

	# for kgV (x,1)
	if numbera > 1 and numbera > 1:
		# builds the prime factorization of 'number1' and 'number2'
		lowerCamelCase__              =	prime_factorization(__snake_case							)
		lowerCamelCase__              =	prime_factorization(__snake_case							)

	elif numbera == 1 or numbera == 1:
		lowerCamelCase__              =	[]
		lowerCamelCase__              =	[]
		lowerCamelCase__              =	max(__snake_case ,__snake_case							)

	lowerCamelCase__              =	0
	lowerCamelCase__              =	0

	lowerCamelCase__              =	[]  # captured numbers int both 'primeFac1' and 'primeFac2'

	# iterates through primeFac1
	for n in prime_fac_a:
		if n not in done:
			if n in prime_fac_a:
				lowerCamelCase__              =	prime_fac_a.count(__snake_case							)
				lowerCamelCase__              =	prime_fac_a.count(__snake_case							)

				for _ in range(max(__snake_case ,__snake_case							)							):
					ans *= n

			else:
				lowerCamelCase__              =	prime_fac_a.count(__snake_case							)

				for _ in range(__snake_case							):
					ans *= n

			done.append(__snake_case							)

    # iterates through primeFac2
	for n in prime_fac_a:
		if n not in done:
			lowerCamelCase__              =	prime_fac_a.count(__snake_case							)

			for _ in range(__snake_case							):
				ans *= n

			done.append(__snake_case							)

    # precondition
	assert isinstance(__snake_case ,__snake_case							) and (
	    ans >= 0
	), "'ans' must been from type int and positive"

	return ans





def lowerCAmelCase__(__snake_case							)       ->   Union[str, Any]:







	'''simple docstring'''







	assert isinstance(__snake_case ,__snake_case							) and (n >= 0), "'number' must been a positive int"

	lowerCamelCase__              =	0
	lowerCamelCase__              =	2  # this variable holds the answer

	while index < n:
		index += 1

		ans += 1  # counts to the next number

		# if ans not prime then
		# runs to the next prime number.
		while not is_prime(__snake_case							):
			ans += 1

    # precondition
	assert isinstance(__snake_case ,__snake_case							) and is_prime(
	    __snake_case							), "'ans' must been a prime number and from type int"

	return ans





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Dict:







	'''simple docstring'''







	assert (
	    is_prime(__snake_case							) and is_prime(__snake_case							) and (p_number_a < p_number_a)
	), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"

	lowerCamelCase__              =	p_number_a + 1  # jump to the next number

	lowerCamelCase__              =	[]  # this list will be returns.

	# if number is not prime then
	# fetch the next prime number.
	while not is_prime(__snake_case							):
		number += 1

	while number < p_number_a:
		ans.append(__snake_case							)

		number += 1

		# fetch the next prime number.
		while not is_prime(__snake_case							):
			number += 1

    # precondition
	assert (
	    isinstance(__snake_case ,__snake_case							)
	    and ans[0] != p_number_a
	    and ans[len(__snake_case							) - 1] != p_number_a
	), "'ans' must been a list without the arguments"

	# 'ans' contains not 'pNumber1' and 'pNumber2' !
	return ans





def lowerCAmelCase__(__snake_case							)       ->   Tuple:







	'''simple docstring'''







	assert isinstance(__snake_case ,__snake_case							) and (n >= 1), "'n' must been int and >= 1"

	lowerCamelCase__              =	[]  # will be returned.

	for divisor in range(1 ,n + 1							):
		if n % divisor == 0:
			ans.append(__snake_case							)

    # precondition
	assert ans[0] == 1 and ans[len(__snake_case							) - 1] == n, "Error in function getDivisiors(...)"

	return ans





def lowerCAmelCase__(__snake_case							)       ->   Optional[Any]:







	'''simple docstring'''







	assert isinstance(__snake_case ,__snake_case							) and (
	    number > 1
	), "'number' must been an int and >= 1"

	lowerCamelCase__              =	get_divisors(__snake_case							)

	# precondition
	assert (
	    isinstance(__snake_case ,__snake_case							)
	    and (divisors[0] == 1)
	    and (divisors[len(__snake_case							) - 1] == number)
	), "Error in help-function getDivisiors(...)"

	# summed all divisors up to 'number' (exclusive), hence [:-1]
	return sum(divisors[:-1]							) == number





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Tuple:







	'''simple docstring'''







	assert (
	    isinstance(__snake_case ,__snake_case							)
	    and isinstance(__snake_case ,__snake_case							)
	    and (denominator != 0)
	), "The arguments must been from type int and 'denominator' != 0"

	# build the greatest common divisor of numerator and denominator.
	lowerCamelCase__              =	gcd(abs(__snake_case							) ,abs(__snake_case							)							)

	# precondition
	assert (
	    isinstance(__snake_case ,__snake_case							)
	    and (numerator % gcd_of_fraction == 0)
	    and (denominator % gcd_of_fraction == 0)
	), "Error in function gcd(...,...)"

	return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)





def lowerCAmelCase__(__snake_case							)       ->   Optional[int]:







	'''simple docstring'''







	assert isinstance(__snake_case ,__snake_case							) and (n >= 0), "'n' must been a int and >= 0"

	lowerCamelCase__              =	1  # this will be return.

	for factor in range(1 ,n + 1							):
		ans *= factor

	return ans





def lowerCAmelCase__(__snake_case							)       ->   Optional[Any]:







	'''simple docstring'''







	assert isinstance(__snake_case ,__snake_case							) and (n >= 0), "'n' must been an int and >= 0"

	lowerCamelCase__              =	0
	lowerCamelCase__              =	1
	lowerCamelCase__              =	1  # this will be return

	for _ in range(n - 1							):
		lowerCamelCase__              =	ans
		ans += fiba
		lowerCamelCase__              =	tmp

	return ans



