






"""simple docstring"""



import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',
    datefmt='''%m/%d/%Y %H:%M:%S''',
    level=logging.INFO,
)
A_     =				logging.getLogger(__name__)

A_     =				int(1_0000)  # Hardcoded max length to avoid infinite loop

A_     =				{
    '''gpt2''': (GPTaLMHeadModel, GPTaTokenizer),
    '''ctrl''': (CTRLLMHeadModel, CTRLTokenizer),
    '''openai-gpt''': (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    '''xlnet''': (XLNetLMHeadModel, XLNetTokenizer),
    '''transfo-xl''': (TransfoXLLMHeadModel, TransfoXLTokenizer),
    '''xlm''': (XLMWithLMHeadModel, XLMTokenizer),
    '''gptj''': (GPTJForCausalLM, AutoTokenizer),
    '''bloom''': (BloomForCausalLM, BloomTokenizerFast),
    '''llama''': (LlamaForCausalLM, LlamaTokenizer),
    '''opt''': (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
A_     =				'''In 1991, the remains of Russian Tsar Nicholas II and his family
(except for Alexei and Maria) are discovered.
The voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the
remainder of the story. 1883 Western Siberia,
a young Grigori Rasputin is asked by his father and a group of men to perform magic.
Rasputin has a vision and denounces one of the men as a horse thief. Although his
father initially slaps him for making such an accusation, Rasputin watches as the
man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
with people, even a bishop, begging for his blessing. <eod> </s> <eos>'''




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Dict   )		->str:
						np.random.seed(args.seed   )
						torch.manual_seed(args.seed   )
						if args.n_gpu > 0:
												torch.cuda.manual_seed_all(args.seed   )




def   _lowerCAmelCase		(				UpperCAmelCase__							:       List[str],    UpperCAmelCase__							:       List[str],    UpperCAmelCase__							:       Tuple,    UpperCAmelCase__							:       int   )		->List[Any]:
						if args.temperature > 0.7:
												logger.info("""CTRL typically works better with lower temperatures (and lower top_k)."""   )

						A__    :	List[str]			=	tokenizer.encode(UpperCAmelCase__,    add_special_tokens=UpperCAmelCase__   )
						if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()   ):
												logger.info("""WARNING! You are not starting your generation from a control code so you won't get good results"""   )
						return prompt_text




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Union[str, Any],    UpperCAmelCase__							:       Any,    UpperCAmelCase__							:       Union[str, Any],    UpperCAmelCase__							:       int   )		->List[Any]:
						# kwargs = {"language": None, "mask_token_id": None}

						# Set the language
						A__    :	Optional[Any]			=	hasattr(model.config,    """use_lang_emb"""   ) and model.config.use_lang_emb
						if hasattr(model.config,    """lang2id"""   ) and use_lang_emb:
												A__    :	Optional[Any]			=	model.config.langaid.keys()
												if args.xlm_language in available_languages:
																		A__    :	List[str]			=	args.xlm_language
												else:
																		A__    :	Any			=	None
																		while language not in available_languages:
																								A__    :	str			=	input("""Using XLM. Select language in """ + str(list(UpperCAmelCase__   )   ) + """ >>> """   )

												A__    :	Any			=	model.config.langaid[language]
												# kwargs["language"] = tokenizer.lang2id[language]

						# TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
						# XLM masked-language modeling (MLM) models need masked token
						# is_xlm_mlm = "mlm" in args.model_name_or_path
						# if is_xlm_mlm:
						#     kwargs["mask_token_id"] = tokenizer.mask_token_id

						return prompt_text




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Optional[Any],    UpperCAmelCase__							:       Tuple,    UpperCAmelCase__							:       Any,    UpperCAmelCase__							:       List[Any]   )		->Tuple:
						A__    :	Dict			=	args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
						A__    :	List[str]			=	prefix + prompt_text
						return prompt_text




def   _lowerCAmelCase		(				UpperCAmelCase__							:       str,    UpperCAmelCase__							:       Any,    UpperCAmelCase__							:       str,    UpperCAmelCase__							:       Union[str, Any]   )		->int:
						A__    :	Dict			=	args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
						A__    :	str			=	prefix + prompt_text
						return prompt_text


A_     =				{
    '''ctrl''': prepare_ctrl_input,
    '''xlm''': prepare_xlm_input,
    '''xlnet''': prepare_xlnet_input,
    '''transfo-xl''': prepare_transfoxl_input,
}




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Dict,    UpperCAmelCase__							:       Any   )		->int:
						if length < 0 and max_sequence_length > 0:
												A__    :	Union[str, Any]			=	max_sequence_length
						elif 0 < max_sequence_length < length:
												A__    :	Optional[int]			=	max_sequence_length  # No generation bigger than model size
						elif length < 0:
												A__    :	Tuple			=	MAX_LENGTH  # avoid infinite loop
						return length




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Dict   )		->Optional[int]:
						A__    :	Optional[int]			=	None
						if hasattr(UpperCAmelCase__,    """hidden_size"""   ):
												A__    :	int			=	model_config.hidden_size
						elif hasattr(UpperCAmelCase__,    """n_embed"""   ):
												A__    :	Tuple			=	model_config.n_embed
						elif hasattr(UpperCAmelCase__,    """n_embd"""   ):
												A__    :	int			=	model_config.n_embd

						A__    :	List[str]			=	None
						if hasattr(UpperCAmelCase__,    """num_attention_heads"""   ):
												A__    :	List[str]			=	model_config.num_attention_heads
						elif hasattr(UpperCAmelCase__,    """n_head"""   ):
												A__    :	Optional[int]			=	model_config.n_head

						if embedding_size is None or num_head is None or num_head == 0:
												raise ValueError("""Check the model config"""   )

						A__    :	Any			=	int(embedding_size / num_head   )
						if hasattr(UpperCAmelCase__,    """n_layer"""   ):
												A__    :	Tuple			=	model_config.n_layer
						elif hasattr(UpperCAmelCase__,    """num_hidden_layers"""   ):
												A__    :	List[Any]			=	model_config.num_hidden_layers
						else:
												raise ValueError("""Number of hidden layers couldn't be determined from the model config"""   )

						return num_layer, num_head, num_embedding_size_per_head




def   _lowerCAmelCase		(				UpperCAmelCase__							:       List[Any],    UpperCAmelCase__							:       Union[str, Any],    UpperCAmelCase__							:       Union[str, Any]   )		->List[str]:
						A__	,	A__	,	A__    :	Optional[int]			=	sparse_model_config(model.config   )
						if model.config.model_type == "bloom":
												A__    :	str			=	tuple(
												    (
												        torch.empty(int(num_attention_heads * batch_size   ),    UpperCAmelCase__,    UpperCAmelCase__   )
												        .to(model.dtype   )
												        .to(model.device   ),
												        torch.empty(int(num_attention_heads * batch_size   ),    UpperCAmelCase__,    UpperCAmelCase__   )
												        .to(model.dtype   )
												        .to(model.device   ),
												    )
												    for _ in range(UpperCAmelCase__   )   )
						else:
												A__    :	Optional[int]			=	tuple(
												    (
												        torch.empty(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__   )
												        .to(model.dtype   )
												        .to(model.device   ),
												        torch.empty(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__   )
												        .to(model.dtype   )
												        .to(model.device   ),
												    )
												    for _ in range(UpperCAmelCase__   )   )
						return past_key_values




def   _lowerCAmelCase		(				UpperCAmelCase__							:       str,    UpperCAmelCase__							:       int,    UpperCAmelCase__							:       List[str]   )		->str:
						A__    :	Optional[Any]			=	len(UpperCAmelCase__   )
						A__    :	Optional[Any]			=	tokenizer.batch_encode_plus(UpperCAmelCase__,    return_tensors="""pt"""   )
						A__    :	str			=	dummy_input.to(model.device   )
						if model.config.use_cache:
												A__    :	List[Any]			=	generate_past_key_values(UpperCAmelCase__,    UpperCAmelCase__,    1   )
						A__    :	Optional[Any]			=	torch.cat(
						    [
						        torch.zeros(dummy_input["""attention_mask"""].shape[0],    1   )
						        .to(dummy_input["""attention_mask"""].dtype   )
						        .to(model.device   ),
						        dummy_input["""attention_mask"""],
						    ],    -1,    )
						return dummy_input






class 				__SCREAMING_SNAKE_CASE    (					UpperCamelCase		):
							snake_case_  =				('_optimized', '_default')


							def __init__(      self   :    Union[str, Any]      ,     snake_case   :    str      ,     snake_case   :    Dict				):
													'''simple docstring'''





													A__    :	Tuple			=	optimized
													A__    :	Union[str, Any]			=	default


							def __call__(      self   :    int      ,     *snake_case   :    Any      ,     **snake_case   :    str				):
													'''simple docstring'''





													if kwargs["past_key_values"] is None and self._default.config.use_cache:
																			A__    :	List[Any]			=	generate_past_key_values(self._default      ,     kwargs["""input_ids"""].shape[0]      ,     0				)
													kwargs.pop("""position_ids"""      ,     snake_case				)
													for k in list(kwargs.keys()				):
																			if kwargs[k] is None or isinstance(kwargs[k]      ,     snake_case				):
																									kwargs.pop(snake_case				)
													A__    :	int			=	self._optimized(**snake_case				)
													A__    :	Any			=	outputs[0]
													A__    :	Tuple			=	outputs[1]
													A__    :	Any			=	CausalLMOutputWithPast(
													    loss=snake_case      ,     logits=snake_case      ,     past_key_values=snake_case      ,     hidden_states=snake_case      ,     attentions=snake_case      ,     )
													return fixed_output


							def __getattr__(      self   :    Dict      ,     snake_case   :    Optional[int]				):
													'''simple docstring'''





													return getattr(self._default      ,     snake_case				)


							def     _UpperCamelCase							(      self   :    Tuple      ,     snake_case   :    Optional[Any]      ,     snake_case   :    int=None      ,     snake_case   :    Optional[Any]=None      ,     snake_case   :    Union[str, Any]=None      ,     **snake_case   :    Optional[int]				):
													'''simple docstring'''





													return self._default.prepare_inputs_for_generation(
													    snake_case      ,     past_key_values=snake_case      ,     inputs_embeds=snake_case      ,     use_cache=snake_case      ,     **snake_case				)


							def     _UpperCamelCase							(      self   :    Optional[Any]      ,     snake_case   :    Tuple[Tuple[torch.Tensor]]      ,     snake_case   :    torch.Tensor				):
													'''simple docstring'''





													return self._default._reorder_cache(snake_case      ,     snake_case				)




def   _lowerCAmelCase		(				)		->Dict:
						A__    :	Optional[int]			=	argparse.ArgumentParser()
						parser.add_argument(
						    """--model_type""",    default=UpperCAmelCase__,    type=UpperCAmelCase__,    required=UpperCAmelCase__,    help="""Model type selected in the list: """ + """, """.join(MODEL_CLASSES.keys()   ),    )
						parser.add_argument(
						    """--model_name_or_path""",    default=UpperCAmelCase__,    type=UpperCAmelCase__,    required=UpperCAmelCase__,    help="""Path to pre-trained model or shortcut name selected in the list: """ + """, """.join(MODEL_CLASSES.keys()   ),    )

						parser.add_argument("""--prompt""",    type=UpperCAmelCase__,    default=""""""   )
						parser.add_argument("""--length""",    type=UpperCAmelCase__,    default=2_0   )
						parser.add_argument("""--stop_token""",    type=UpperCAmelCase__,    default=UpperCAmelCase__,    help="""Token at which text generation is stopped"""   )

						parser.add_argument(
						    """--temperature""",    type=UpperCAmelCase__,    default=1.0,    help="""temperature of 1.0 has no effect, lower tend toward greedy sampling""",    )
						parser.add_argument(
						    """--repetition_penalty""",    type=UpperCAmelCase__,    default=1.0,    help="""primarily useful for CTRL model; in that case, use 1.2"""   )
						parser.add_argument("""--k""",    type=UpperCAmelCase__,    default=0   )
						parser.add_argument("""--p""",    type=UpperCAmelCase__,    default=0.9   )

						parser.add_argument("""--prefix""",    type=UpperCAmelCase__,    default="""""",    help="""Text added prior to input."""   )
						parser.add_argument("""--padding_text""",    type=UpperCAmelCase__,    default="""""",    help="""Deprecated, the use of `--prefix` is preferred."""   )
						parser.add_argument("""--xlm_language""",    type=UpperCAmelCase__,    default="""""",    help="""Optional language when used with the XLM model."""   )

						parser.add_argument("""--seed""",    type=UpperCAmelCase__,    default=4_2,    help="""random seed for initialization"""   )
						parser.add_argument("""--no_cuda""",    action="""store_true""",    help="""Avoid using CUDA when available"""   )
						parser.add_argument("""--num_return_sequences""",    type=UpperCAmelCase__,    default=1,    help="""The number of samples to generate."""   )
						parser.add_argument(
						    """--fp16""",    action="""store_true""",    help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit""",    )
						parser.add_argument("""--jit""",    action="""store_true""",    help="""Whether or not to use jit trace to accelerate inference"""   )
						A__    :	Any			=	parser.parse_args()

						A__    :	int			=	torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""   )
						A__    :	Dict			=	0 if args.no_cuda else torch.cuda.device_count()

						logger.warning(f'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'   )

						set_seed(UpperCAmelCase__   )

						# Initialize the model and tokenizer
						try:
												A__    :	str			=	args.model_type.lower()
												A__	,	A__    :	int			=	MODEL_CLASSES[args.model_type]
						except KeyError:
												raise KeyError("""the model {} you specified is not supported. You are welcome to add it and open a PR :)"""   )

						A__    :	List[Any]			=	tokenizer_class.from_pretrained(args.model_name_or_path   )
						if tokenizer.pad_token is None:
												A__    :	Tuple			=	tokenizer.eos_token
						A__    :	Tuple			=	model_class.from_pretrained(args.model_name_or_path   )
						model.to(args.device   )

						if args.fpaa:
												model.half()
						A__    :	str			=	getattr(model.config,    """max_position_embeddings""",    0   )
						A__    :	Tuple			=	adjust_length_to_model(args.length,    max_sequence_length=UpperCAmelCase__   )
						logger.info(UpperCAmelCase__   )

						A__    :	Union[str, Any]			=	args.prompt if args.prompt else input("""Model prompt >>> """   )

						# Different models need different input formatting and/or extra arguments
						A__    :	List[Any]			=	args.model_type in PREPROCESSING_FUNCTIONS.keys()
						if requires_preprocessing:
												A__    :	Union[str, Any]			=	PREPROCESSING_FUNCTIONS.get(args.model_type   )
												A__    :	int			=	prepare_input(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__   )

												if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
																		A__    :	str			=	{"""add_space_before_punct_symbol""": True}
												else:
																		A__    :	List[str]			=	{}

												A__    :	Optional[int]			=	tokenizer.encode(
												    UpperCAmelCase__,    add_special_tokens=UpperCAmelCase__,    return_tensors="""pt""",    **UpperCAmelCase__   )
						else:
												A__    :	Dict			=	args.prefix if args.prefix else args.padding_text
												A__    :	Optional[Any]			=	tokenizer.encode(prefix + prompt_text,    add_special_tokens=UpperCAmelCase__,    return_tensors="""pt"""   )
						A__    :	Dict			=	encoded_prompt.to(args.device   )

						if encoded_prompt.size()[-1] == 0:
												A__    :	List[str]			=	None
						else:
												A__    :	Dict			=	encoded_prompt

						if args.jit:
												A__    :	Optional[Any]			=	["""enable jit"""]
												A__    :	Union[str, Any]			=	prepare_jit_inputs(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__   )
												torch._C._jit_set_texpr_fuser_enabled(UpperCAmelCase__   )
												A__    :	List[Any]			=	False
												if hasattr(UpperCAmelCase__,    """forward"""   ):
																		A__    :	Union[str, Any]			=	inspect.signature(model.forward   )
												else:
																		A__    :	Optional[int]			=	inspect.signature(model.__call__   )
												A__    :	int			=	tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(UpperCAmelCase__,    UpperCAmelCase__   ) is not None   )
												A__    :	Tuple			=	torch.jit.trace(UpperCAmelCase__,    UpperCAmelCase__,    strict=UpperCAmelCase__   )
												A__    :	List[Any]			=	torch.jit.freeze(traced_model.eval()   )
												traced_model(*UpperCAmelCase__   )
												traced_model(*UpperCAmelCase__   )

												A__    :	Optional[int]			=	_ModelFallbackWrapper(UpperCAmelCase__,    UpperCAmelCase__   )

						A__    :	Optional[int]			=	model.generate(
						    input_ids=UpperCAmelCase__,    max_length=args.length + len(encoded_prompt[0]   ),    temperature=args.temperature,    top_k=args.k,    top_p=args.p,    repetition_penalty=args.repetition_penalty,    do_sample=UpperCAmelCase__,    num_return_sequences=args.num_return_sequences,    )

						# Remove the batch dimension when returning multiple sequences
						if len(output_sequences.shape   ) > 2:
												output_sequences.squeeze_()

						A__    :	List[str]			=	[]

						for generated_sequence_idx, generated_sequence in enumerate(UpperCAmelCase__   ):
												print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='   )
												A__    :	List[Any]			=	generated_sequence.tolist()

												# Decode text
												A__    :	List[Any]			=	tokenizer.decode(UpperCAmelCase__,    clean_up_tokenization_spaces=UpperCAmelCase__   )

												# Remove all text after the stop token
												A__    :	Union[str, Any]			=	text[: text.find(args.stop_token   ) if args.stop_token else None]

												# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
												A__    :	List[str]			=	(
												    prompt_text + text[len(tokenizer.decode(encoded_prompt[0],    clean_up_tokenization_spaces=UpperCAmelCase__   )   ) :]
												)

												generated_sequences.append(UpperCAmelCase__   )
												print(UpperCAmelCase__   )

						return generated_sequences


if __name__ == "__main__":
						main()














"""simple docstring"""



def   _lowerCAmelCase		(				UpperCAmelCase__							:       float,    UpperCAmelCase__							:       float,    UpperCAmelCase__							:       float   )		->list[int]:
						if hue < 0 or hue > 3_6_0:
												raise Exception("""hue should be between 0 and 360"""   )

						if saturation < 0 or saturation > 1:
												raise Exception("""saturation should be between 0 and 1"""   )

						if value < 0 or value > 1:
												raise Exception("""value should be between 0 and 1"""   )

						A__    :	int			=	value * saturation
						A__    :	Any			=	hue / 6_0
						A__    :	Any			=	chroma * (1 - abs(hue_section % 2 - 1   ))
						A__    :	Optional[Any]			=	value - chroma

						if hue_section >= 0 and hue_section <= 1:
												A__    :	Dict			=	round(2_5_5 * (chroma + match_value)   )
												A__    :	Optional[Any]			=	round(2_5_5 * (second_largest_component + match_value)   )
												A__    :	Any			=	round(2_5_5 * (match_value)   )
						elif hue_section > 1 and hue_section <= 2:
												A__    :	Optional[int]			=	round(2_5_5 * (second_largest_component + match_value)   )
												A__    :	Union[str, Any]			=	round(2_5_5 * (chroma + match_value)   )
												A__    :	Union[str, Any]			=	round(2_5_5 * (match_value)   )
						elif hue_section > 2 and hue_section <= 3:
												A__    :	Tuple			=	round(2_5_5 * (match_value)   )
												A__    :	List[str]			=	round(2_5_5 * (chroma + match_value)   )
												A__    :	Union[str, Any]			=	round(2_5_5 * (second_largest_component + match_value)   )
						elif hue_section > 3 and hue_section <= 4:
												A__    :	Any			=	round(2_5_5 * (match_value)   )
												A__    :	Dict			=	round(2_5_5 * (second_largest_component + match_value)   )
												A__    :	List[Any]			=	round(2_5_5 * (chroma + match_value)   )
						elif hue_section > 4 and hue_section <= 5:
												A__    :	Any			=	round(2_5_5 * (second_largest_component + match_value)   )
												A__    :	Dict			=	round(2_5_5 * (match_value)   )
												A__    :	Tuple			=	round(2_5_5 * (chroma + match_value)   )
						else:
												A__    :	Tuple			=	round(2_5_5 * (chroma + match_value)   )
												A__    :	Optional[int]			=	round(2_5_5 * (match_value)   )
												A__    :	str			=	round(2_5_5 * (second_largest_component + match_value)   )

						return [red, green, blue]




def   _lowerCAmelCase		(				UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int   )		->list[float]:
						if red < 0 or red > 2_5_5:
												raise Exception("""red should be between 0 and 255"""   )

						if green < 0 or green > 2_5_5:
												raise Exception("""green should be between 0 and 255"""   )

						if blue < 0 or blue > 2_5_5:
												raise Exception("""blue should be between 0 and 255"""   )

						A__    :	int			=	red / 2_5_5
						A__    :	List[str]			=	green / 2_5_5
						A__    :	Optional[int]			=	blue / 2_5_5
						A__    :	Optional[int]			=	max(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__   )
						A__    :	Tuple			=	value - min(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__   )
						A__    :	Any			=	0 if value == 0 else chroma / value

						if chroma == 0:
												A__    :	Optional[Any]			=	0.0
						elif value == float_red:
												A__    :	Optional[Any]			=	6_0 * (0 + (float_green - float_blue) / chroma)
						elif value == float_green:
												A__    :	Tuple			=	6_0 * (2 + (float_blue - float_red) / chroma)
						else:
												A__    :	int			=	6_0 * (4 + (float_red - float_green) / chroma)

						A__    :	Optional[int]			=	(hue + 3_6_0) % 3_6_0

						return [hue, saturation, value]




def   _lowerCAmelCase		(				UpperCAmelCase__							:       list[float],    UpperCAmelCase__							:       list[float]   )		->bool:
						A__    :	Optional[Any]			=	abs(hsv_a[0] - hsv_a[0]   ) < 0.2
						A__    :	str			=	abs(hsv_a[1] - hsv_a[1]   ) < 0.002
						A__    :	Optional[int]			=	abs(hsv_a[2] - hsv_a[2]   ) < 0.002

						return check_hue and check_saturation and check_value






