



'''simple docstring'''





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()
_lowerCAmelCase      						=	logging.get_logger('''transformers.models.speecht5''')

_lowerCAmelCase      						=	{
    '''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''',
}
_lowerCAmelCase      						=	{
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
_lowerCAmelCase      						=	{
    '''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''',
}
_lowerCAmelCase      						=	{
    '''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''',
}
_lowerCAmelCase      						=	{
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
_lowerCAmelCase      						=	{
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
_lowerCAmelCase      						=	{
    '''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''',
}
_lowerCAmelCase      						=	{
    '''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''',
}
_lowerCAmelCase      						=	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_lowerCAmelCase      						=	{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_lowerCAmelCase      						=	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_lowerCAmelCase      						=	[]
_lowerCAmelCase      						=	[
    '''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''',
]
_lowerCAmelCase      						=	IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
_lowerCAmelCase      						=	IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
_lowerCAmelCase      						=	IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    for attribute in key.split("""."""			):
        lowerCAmelCase__ :  Optional[Any]							      =					getattr(UpperCamelCase							,    UpperCamelCase			)

    if weight_type is not None:
        lowerCAmelCase__ :  Optional[int]							      =					getattr(UpperCamelCase							,    UpperCamelCase			).shape
    else:
        lowerCAmelCase__ :  Optional[int]							      =					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__ :  int							      =					value
    elif weight_type == "weight_g":
        lowerCAmelCase__ :  Optional[int]							      =					value
    elif weight_type == "weight_v":
        lowerCAmelCase__ :  Union[str, Any]							      =					value
    elif weight_type == "bias":
        lowerCAmelCase__ :  str							      =					value
    elif weight_type == "running_mean":
        lowerCAmelCase__ :  str							      =					value
    elif weight_type == "running_var":
        lowerCAmelCase__ :  int							      =					value
    elif weight_type == "num_batches_tracked":
        lowerCAmelCase__ :  Dict							      =					value
    else:
        lowerCAmelCase__ :  str							      =					value

    logger.info(f"""{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."""			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





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



    """simple docstring"""





    lowerCAmelCase__ :  Dict							      =					[]

    if task == "s2t":
        lowerCAmelCase__ :  int							      =					hf_model.speechta.encoder.prenet.feature_encoder
        lowerCAmelCase__ :  List[str]							      =					MAPPING_S2T
        lowerCAmelCase__ :  List[str]							      =					IGNORE_KEYS_S2T
    elif task == "t2s":
        lowerCAmelCase__ :  Optional[Any]							      =					None
        lowerCAmelCase__ :  Optional[int]							      =					MAPPING_T2S
        lowerCAmelCase__ :  str							      =					IGNORE_KEYS_T2S
    elif task == "s2s":
        lowerCAmelCase__ :  int							      =					hf_model.speechta.encoder.prenet.feature_encoder
        lowerCAmelCase__ :  Optional[Any]							      =					MAPPING_S2S
        lowerCAmelCase__ :  Optional[int]							      =					IGNORE_KEYS_S2S
    else:
        raise ValueError(f"""Unsupported task: {task}"""			)

    for name, value in fairseq_dict.items():
        if should_ignore(UpperCamelCase							,    UpperCamelCase			):
            logger.info(f"""{name} was ignored"""			)
            continue

        lowerCAmelCase__ :  int							      =					False
        if "conv_layers" in name:
            load_conv_layer(
                UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    hf_model.config.feat_extract_norm == """group"""							,    )
            lowerCAmelCase__ :  str							      =					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__ :  Optional[int]							      =					key.split(""".*."""			)
                    if prefix in name and suffix in name:
                        lowerCAmelCase__ :  Any							      =					suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                if key in name:
                    lowerCAmelCase__ :  List[str]							      =					True
                    if "*" in mapped_key:
                        lowerCAmelCase__ :  int							      =					name.split(UpperCamelCase			)[0].split("""."""			)[-2]
                        lowerCAmelCase__ :  Dict							      =					mapped_key.replace("""*"""							,    UpperCamelCase			)
                    if "weight_g" in name:
                        lowerCAmelCase__ :  int							      =					"""weight_g"""
                    elif "weight_v" in name:
                        lowerCAmelCase__ :  Any							      =					"""weight_v"""
                    elif "bias" in name:
                        lowerCAmelCase__ :  List[Any]							      =					"""bias"""
                    elif "weight" in name:
                        lowerCAmelCase__ :  int							      =					"""weight"""
                    elif "running_mean" in name:
                        lowerCAmelCase__ :  List[Any]							      =					"""running_mean"""
                    elif "running_var" in name:
                        lowerCAmelCase__ :  Any							      =					"""running_var"""
                    elif "num_batches_tracked" in name:
                        lowerCAmelCase__ :  Optional[Any]							      =					"""num_batches_tracked"""
                    else:
                        lowerCAmelCase__ :  Optional[Any]							      =					None
                    set_recursively(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			)
                continue
        if not is_used:
            unused_weights.append(UpperCamelCase			)

    logger.warning(f"""Unused weights: {unused_weights}"""			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Tuple							      =					full_name.split("""conv_layers."""			)[-1]
    lowerCAmelCase__ :  Dict							      =					name.split("""."""			)
    lowerCAmelCase__ :  str							      =					int(items[0]			)
    lowerCAmelCase__ :  str							      =					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__ :  Union[str, Any]							      =					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__ :  Any							      =					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__ :  List[str]							      =					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__ :  Any							      =					value
            logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""			)
    else:
        unused_weights.append(UpperCamelCase			)
@torch.no_grad()
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase=None							,    UpperCamelCase=None							,    UpperCamelCase=None							,    ):



    """simple docstring"""





    if config_path is not None:
        lowerCAmelCase__ :  str							      =					SpeechTaConfig.from_pretrained(UpperCamelCase			)
    else:
        lowerCAmelCase__ :  Dict							      =					SpeechTaConfig()

    if task == "s2t":
        lowerCAmelCase__ :  Optional[int]							      =					config.max_text_positions
        lowerCAmelCase__ :  int							      =					SpeechTaForSpeechToText(UpperCamelCase			)
    elif task == "t2s":
        lowerCAmelCase__ :  Tuple							      =					1876
        lowerCAmelCase__ :  int							      =					600
        lowerCAmelCase__ :  List[Any]							      =					config.max_speech_positions
        lowerCAmelCase__ :  Dict							      =					SpeechTaForTextToSpeech(UpperCamelCase			)
    elif task == "s2s":
        lowerCAmelCase__ :  List[Any]							      =					1876
        lowerCAmelCase__ :  List[Any]							      =					config.max_speech_positions
        lowerCAmelCase__ :  Dict							      =					SpeechTaForSpeechToSpeech(UpperCamelCase			)
    else:
        raise ValueError(f"""Unknown task name: {task}"""			)

    if vocab_path:
        lowerCAmelCase__ :  Dict							      =					SpeechTaTokenizer(UpperCamelCase							,    model_max_length=config.max_text_positions			)

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

    lowerCAmelCase__ :  str							      =					SpeechTaFeatureExtractor()
    lowerCAmelCase__ :  int							      =					SpeechTaProcessor(tokenizer=UpperCamelCase							,    feature_extractor=UpperCamelCase			)
    processor.save_pretrained(UpperCamelCase			)

    lowerCAmelCase__ :  Dict							      =					torch.load(UpperCamelCase			)
    recursively_load_weights(fairseq_checkpoint["""model"""]							,    UpperCamelCase							,    UpperCamelCase			)

    model.save_pretrained(UpperCamelCase			)

    if repo_id:
        print("""Pushing to the hub..."""			)
        processor.push_to_hub(UpperCamelCase			)
        model.push_to_hub(UpperCamelCase			)


if __name__ == "__main__":
 _lowerCAmelCase      						=	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.'''
 )

 _lowerCAmelCase      						=	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,
 )







'''simple docstring'''





from math import sqrt
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

    lowerCAmelCase__ :  int							      =					True

    # 0 and 1 are none primes.
    if number <= 1:
        lowerCAmelCase__ :  Optional[Any]							      =					False

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

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

    return status
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

    # beginList: contains all natural numbers from 2 up to N
    lowerCAmelCase__ :  List[str]							      =					list(range(2							,    n + 1			)			)

    lowerCAmelCase__ :  str							      =					[]  # this list will be returns.

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

    # filters actual prime numbers.
    lowerCAmelCase__ :  List[Any]							      =					[x for x in begin_list if x != 0]

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

    return ans
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

    lowerCAmelCase__ :  List[str]							      =					[]

    # 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(UpperCamelCase			):
            ans.append(UpperCamelCase			)

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

    return ans
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

    lowerCAmelCase__ :  Optional[Any]							      =					[]  # this list will be returns of the function.

    # potential prime number factors.

    lowerCAmelCase__ :  Dict							      =					2

    lowerCAmelCase__ :  Dict							      =					number

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

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

    else:
        ans.append(UpperCamelCase			)

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

    return ans
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

    lowerCAmelCase__ :  Optional[int]							      =					0

    # prime factorization of 'number'
    lowerCAmelCase__ :  List[str]							      =					prime_factorization(UpperCamelCase			)

    lowerCAmelCase__ :  Any							      =					max(UpperCamelCase			)

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

    return ans
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

    lowerCAmelCase__ :  List[Any]							      =					0

    # prime factorization of 'number'
    lowerCAmelCase__ :  List[str]							      =					prime_factorization(UpperCamelCase			)

    lowerCAmelCase__ :  Optional[int]							      =					min(UpperCamelCase			)

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

    return ans
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

    return number % 2 == 0
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

    return number % 2 != 0
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

    lowerCAmelCase__ :  Dict							      =					[]  # this list will returned

    # creates a list of prime numbers between 2 up to 'number'
    lowerCAmelCase__ :  Dict							      =					get_prime_numbers(UpperCamelCase			)
    lowerCAmelCase__ :  Optional[Any]							      =					len(UpperCamelCase			)

    # run variable for while-loops.
    lowerCAmelCase__ :  List[str]							      =					0
    lowerCAmelCase__ :  List[Any]							      =					None

    # exit variable. for break up the loops
    lowerCAmelCase__ :  Any							      =					True

    while i < len_pn and loop:
        lowerCAmelCase__ :  List[Any]							      =					i + 1

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

            j += 1

        i += 1

    # precondition
    assert (
        isinstance(UpperCamelCase							,    UpperCamelCase			)
        and (len(UpperCamelCase			) == 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    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





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

    lowerCAmelCase__ :  int							      =					0

    while numbera != 0:
        lowerCAmelCase__ :  Any							      =					numbera % numbera
        lowerCAmelCase__ :  str							      =					numbera
        lowerCAmelCase__ :  List[str]							      =					rest

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

    return numbera
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





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

    lowerCAmelCase__ :  int							      =					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__ :  int							      =					prime_factorization(UpperCamelCase			)
        lowerCAmelCase__ :  Any							      =					prime_factorization(UpperCamelCase			)

    elif numbera == 1 or numbera == 1:
        lowerCAmelCase__ :  Optional[Any]							      =					[]
        lowerCAmelCase__ :  Dict							      =					[]
        lowerCAmelCase__ :  List[str]							      =					max(UpperCamelCase							,    UpperCamelCase			)

    lowerCAmelCase__ :  Tuple							      =					0
    lowerCAmelCase__ :  str							      =					0

    lowerCAmelCase__ :  List[Any]							      =					[]  # 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__ :  int							      =					prime_fac_a.count(UpperCamelCase			)
                lowerCAmelCase__ :  Any							      =					prime_fac_a.count(UpperCamelCase			)

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

            else:
                lowerCAmelCase__ :  Any							      =					prime_fac_a.count(UpperCamelCase			)

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

            done.append(UpperCamelCase			)

    # iterates through primeFac2
    for n in prime_fac_a:
        if n not in done:
            lowerCAmelCase__ :  Optional[int]							      =					prime_fac_a.count(UpperCamelCase			)

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

            done.append(UpperCamelCase			)

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

    return ans
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

    lowerCAmelCase__ :  Optional[Any]							      =					0
    lowerCAmelCase__ :  Tuple							      =					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(UpperCamelCase			):
            ans += 1

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

    return ans
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





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

    lowerCAmelCase__ :  Dict							      =					p_number_a + 1  # jump to the next number

    lowerCAmelCase__ :  List[Any]							      =					[]  # this list will be returns.

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

    while number < p_number_a:
        ans.append(UpperCamelCase			)

        number += 1

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

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

    # 'ans' contains not 'pNumber1' and 'pNumber2' !
    return ans
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

    lowerCAmelCase__ :  List[Any]							      =					[]  # will be returned.

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

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

    return ans
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

    lowerCAmelCase__ :  Optional[int]							      =					get_divisors(UpperCamelCase			)

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

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



    """simple docstring"""





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

    # build the greatest common divisor of numerator and denominator.
    lowerCAmelCase__ :  int							      =					gcd(abs(UpperCamelCase			)							,    abs(UpperCamelCase			)			)

    # precondition
    assert (
        isinstance(UpperCamelCase							,    UpperCamelCase			)
        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    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

    lowerCAmelCase__ :  str							      =					1  # this will be return.

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

    return ans




def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





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

    lowerCAmelCase__ :  List[Any]							      =					0
    lowerCAmelCase__ :  Any							      =					1
    lowerCAmelCase__ :  Optional[Any]							      =					1  # this will be return

    for _ in range(n - 1			):
        lowerCAmelCase__ :  Dict							      =					ans
        ans += fiba
        lowerCAmelCase__ :  str							      =					tmp

    return ans


