




"""simple docstring"""



def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_		)		->						list[int]:
 if hue < 0 or hue > 3_60:
  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__			=							value * saturation
 A__			=							hue / 60
 A__			=							chroma * (1 - abs(hue_section % 2 - 1		))
 A__			=							value - chroma

 if hue_section >= 0 and hue_section <= 1:
  A__			=							round(2_55 * (chroma + match_value)		)
  A__			=							round(2_55 * (second_largest_component + match_value)		)
  A__			=							round(2_55 * (match_value)		)
 elif hue_section > 1 and hue_section <= 2:
  A__			=							round(2_55 * (second_largest_component + match_value)		)
  A__			=							round(2_55 * (chroma + match_value)		)
  A__			=							round(2_55 * (match_value)		)
 elif hue_section > 2 and hue_section <= 3:
  A__			=							round(2_55 * (match_value)		)
  A__			=							round(2_55 * (chroma + match_value)		)
  A__			=							round(2_55 * (second_largest_component + match_value)		)
 elif hue_section > 3 and hue_section <= 4:
  A__			=							round(2_55 * (match_value)		)
  A__			=							round(2_55 * (second_largest_component + match_value)		)
  A__			=							round(2_55 * (chroma + match_value)		)
 elif hue_section > 4 and hue_section <= 5:
  A__			=							round(2_55 * (second_largest_component + match_value)		)
  A__			=							round(2_55 * (match_value)		)
  A__			=							round(2_55 * (chroma + match_value)		)
 else:
  A__			=							round(2_55 * (chroma + match_value)		)
  A__			=							round(2_55 * (match_value)		)
  A__			=							round(2_55 * (second_largest_component + match_value)		)

 return [red, green, blue]







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_		)		->						list[float]:
 if red < 0 or red > 2_55:
  raise Exception("red should be between 0 and 255"		)

 if green < 0 or green > 2_55:
  raise Exception("green should be between 0 and 255"		)

 if blue < 0 or blue > 2_55:
  raise Exception("blue should be between 0 and 255"		)

 A__			=							red / 2_55
 A__			=							green / 2_55
 A__			=							blue / 2_55
 A__			=							max(lowercase_     ,					lowercase_     ,					lowercase_		)
 A__			=							value - min(lowercase_     ,					lowercase_     ,					lowercase_		)
 A__			=							0 if value == 0 else chroma / value

 if chroma == 0:
  A__			=							0.0
 elif value == float_red:
  A__			=							60 * (0 + (float_green - float_blue) / chroma)
 elif value == float_green:
  A__			=							60 * (2 + (float_blue - float_red) / chroma)
 else:
  A__			=							60 * (4 + (float_red - float_green) / chroma)

 A__			=							(hue + 3_60) % 3_60

 return [hue, saturation, value]







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						bool:
 A__			=							abs(hsv_a[0] - hsv_a[0]		) < 0.2
 A__			=							abs(hsv_a[1] - hsv_a[1]		) < 0.0_0_2
 A__			=							abs(hsv_a[2] - hsv_a[2]		) < 0.0_0_2

 return check_hue and check_saturation and check_value











"""simple docstring"""



import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_     ,					lowercase_		)		->						Union[str, Any]:
 # Initialise PyTorch model
 A__			=							json.load(open(lowercase_     ,					"r"		)		)
 A__			=							GPTNeoConfig(
     hidden_size=config_json["n_embd"]     ,					num_layers=config_json["n_layer"]     ,					num_heads=config_json["n_head"]     ,					attention_types=config_json["attention_types"]     ,					max_position_embeddings=config_json["n_positions"]     ,					resid_dropout=config_json["res_dropout"]     ,					embed_dropout=config_json["embed_dropout"]     ,					attention_dropout=config_json["attn_dropout"]     ,					)
 print(f"""Building PyTorch model from configuration: {config}"""		)
 A__			=							GPTNeoForCausalLM(lowercase_		)

 # Load weights from tf checkpoint
 load_tf_weights_in_gpt_neo(lowercase_     ,					lowercase_     ,					lowercase_		)

 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""		)
 model.save_pretrained(lowercase_		)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE							=			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--config_file",
          default=None,
          type=str,
          required=True,
          help=(
              "The config json file corresponding to the pre-trained mesh-tf model. \n"
              "This specifies the model architecture."
          ),
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      SCREAMING_SNAKE_CASE							=			parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





