
from collections import OrderedDict
from typing import Mapping

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


snake_case :		int							      =  logging.get_logger(__name__)

snake_case :		List[Any]							      =  {
    "xlm-mlm-en-2048": "https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json",
    "xlm-mlm-ende-1024": "https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json",
    "xlm-mlm-enfr-1024": "https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json",
    "xlm-mlm-enro-1024": "https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json",
    "xlm-mlm-tlm-xnli15-1024": "https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json",
    "xlm-mlm-xnli15-1024": "https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json",
    "xlm-clm-enfr-1024": "https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json",
    "xlm-clm-ende-1024": "https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json",
    "xlm-mlm-17-1280": "https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json",
    "xlm-mlm-100-1280": "https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json",
}





class       _snake_case					(     lowerCamelCase__				):
			UpperCamelCase__          =    'xlm'
			UpperCamelCase__          =    {
			    'hidden_size': 'emb_dim',
			    'num_attention_heads': 'n_heads',
			    'num_hidden_layers': 'n_layers',
			    'n_words': 'vocab_size',  # For backward compatibility
			}







			def __init__(				self						,   _a=30_145						,   _a=2_048						,   _a=12						,   _a=16						,   _a=0.1						,   _a=0.1						,   _a=True						,   _a=False						,   _a=False						,   _a=False						,   _a=1						,   _a=True						,   _a=512						,   _a=2_048**-0.5						,   _a=1e-12						,   _a=0.02						,   _a=0						,   _a=1						,   _a=2						,   _a=3						,   _a=5						,   _a=True						,   _a="first"						,   _a=True						,   _a=None						,   _a=True						,   _a=0.1						,   _a=5						,   _a=5						,   _a=0						,   _a=0						,   _a=2						,   _a=0						,   **_a						,   ):
				__magic_name__		:		int	   =     vocab_size
				__magic_name__		:		Union[str, Any]	   =     emb_dim
				__magic_name__		:		List[str]	   =     n_layers
				__magic_name__		:		Tuple	   =     n_heads
				__magic_name__		:		int	   =     dropout
				__magic_name__		:		str	   =     attention_dropout
				__magic_name__		:		List[str]	   =     gelu_activation
				__magic_name__		:		Dict	   =     sinusoidal_embeddings
				__magic_name__		:		Any	   =     causal
				__magic_name__		:		Dict	   =     asm
				__magic_name__		:		Any	   =     n_langs
				__magic_name__		:		str	   =     use_lang_emb
				__magic_name__		:		Optional[int]	   =     layer_norm_eps
				__magic_name__		:		Union[str, Any]	   =     bos_index
				__magic_name__		:		Any	   =     eos_index
				__magic_name__		:		str	   =     pad_index
				__magic_name__		:		Any	   =     unk_index
				__magic_name__		:		Union[str, Any]	   =     mask_index
				__magic_name__		:		Union[str, Any]	   =     is_encoder
				__magic_name__		:		Any	   =     max_position_embeddings
				__magic_name__		:		Union[str, Any]	   =     embed_init_std
				__magic_name__		:		int	   =     init_std
				__magic_name__		:		int	   =     summary_type
				__magic_name__		:		Optional[Any]	   =     summary_use_proj
				__magic_name__		:		List[str]	   =     summary_activation
				__magic_name__		:		List[Any]	   =     summary_proj_to_labels
				__magic_name__		:		str	   =     summary_first_dropout
				__magic_name__		:		Optional[Any]	   =     start_n_top
				__magic_name__		:		List[Any]	   =     end_n_top
				__magic_name__		:		int	   =     mask_token_id
				__magic_name__		:		Tuple	   =     lang_id

				if "n_words" in kwargs:
					__magic_name__		:		Tuple	   =     kwargs["n_words"]

				super().__init__(pad_token_id=lowercase__						,   bos_token_id=lowercase__						,   **lowercase__		)







class       _snake_case					(     lowerCamelCase__				):







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				if self.task == "multiple-choice":
					__magic_name__		:		List[Any]	   =     {0: "batch", 1: "choice", 2: "sequence"}
				else:
					__magic_name__		:		Optional[Any]	   =     {0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("token_type_ids", dynamic_axis),
				    ]		)



'''simple docstring'''






import colorsys

from PIL import Image  # type: ignore


def 		_A     (    A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = x
 __lowercase							  = y
 for step in range(A__      ):  # noqa: B007
  __lowercase							  = a * a - b * b + x
  __lowercase							  = 2 * a * b + y
  __lowercase							  = a_new

  # divergence happens for all complex number with an absolute value
  # greater than 4
  if a * a + b * b > 4:
   break
 return step / (max_step - 1)


def 		_A     (    A__      ):






 """simple docstring"""



 if distance == 1:
  return (0, 0, 0)
 else:
  return (255, 255, 255)


def 		_A     (    A__      ):






 """simple docstring"""



 if distance == 1:
  return (0, 0, 0)
 else:
  return tuple(round(i * 255      ) for i in colorsys.hsv_to_rgb(A__     ,				1     ,				1      )      )


def 		_A     (    A__ = 800     ,				A__ = 600     ,				A__ = -0.6     ,				A__ = 0     ,				A__ = 3.2     ,				A__ = 50     ,				A__ = True     ,				):






 """simple docstring"""



 __lowercase							  = Image.new('''RGB'''     ,				(image_width, image_height)      )
 __lowercase							  = img.load()

 # loop through the image-coordinates
 for image_x in range(A__      ):
  for image_y in range(A__      ):
   # determine the figure-coordinates based on the image-coordinates
   __lowercase							  = figure_width / image_width * image_height
   __lowercase							  = figure_center_x + (image_x / image_width - 0.5) * figure_width
   __lowercase							  = figure_center_y + (image_y / image_height - 0.5) * figure_height

   __lowercase							  = get_distance(A__     ,				A__     ,				A__      )

   # color the corresponding pixel based on the selected coloring-function
   if use_distance_color_coding:
    __lowercase							  = get_color_coded_rgb(A__      )
   else:
    __lowercase							  = get_black_and_white_rgb(A__      )

 return img


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # colored version, full figure
 lowerCAmelCase__											=	get_image()

 # uncomment for colored version, different section, zoomed in
 # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
 # figure_width = 0.8)

 # uncomment for black and white version, full figure
 # img = get_image(use_distance_color_coding = False)

 # uncomment to save the image
 # img.save("mandelbrot.png")

 img.show()

