

import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
a__												=						logging.get_logger(__name__)

a__												=						[
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]



def   __UpperCAmelCase					(      __a :     Any      )     ->	Union[str, Any]:

					"""simple docstring"""

					if k == "embeddings.weight":
										return "shared.weight"

					for parlai_name, hf_name in PATTERNS:
										_a      :       List[Any]   =						k.replace(__a						,__a      )

					if k.startswith('''encoder'''      ):
										_a      :       Dict   =						k.replace('''.attn'''						,'''.self_attn'''      )
										_a      :       Optional[int]   =						k.replace('''norm1'''						,'''self_attn_layer_norm'''      )
										_a      :       Union[str, Any]   =						k.replace('''norm2'''						,'''final_layer_norm'''      )
					elif k.startswith('''decoder'''      ):
										_a      :       int   =						k.replace('''norm1'''						,'''self_attn_layer_norm'''      )
										_a      :       Any   =						k.replace('''norm2'''						,'''encoder_attn_layer_norm'''      )
										_a      :       List[Any]   =						k.replace('''norm3'''						,'''final_layer_norm'''      )
					return k



def   __UpperCAmelCase					(      __a :     Union[str, Any]      )     ->	List[Any]:

					"""simple docstring"""

					_a      :       List[str]   =						[
					    '''model.encoder.layernorm_embedding.weight''',
					    '''model.encoder.layernorm_embedding.bias''',
					    '''model.decoder.layernorm_embedding.weight''',
					    '''model.decoder.layernorm_embedding.bias''',
					]
					for k in keys:
										_a      :       List[str]   =						sd.pop(__a      )
										_a      :       Any   =						k.replace('''layernorm_embedding'''						,'''layer_norm'''      )
										assert new_k not in sd
										_a      :       str   =						v


a__												=						['''START''']



@torch.no_grad()
def   __UpperCAmelCase					(      __a :     Union[str, Any]						,__a :     str						,__a :     Union[str, Any]      )     ->	str:

					"""simple docstring"""

					_a      :       Optional[Any]   =						torch.load(__a						,map_location='''cpu'''      )
					_a      :       Optional[int]   =						model['''model''']
					_a      :       Tuple   =						BlenderbotConfig.from_json_file(__a      )
					_a      :       Tuple   =						BlenderbotForConditionalGeneration(__a      )
					_a      :       Union[str, Any]   =						m.model.state_dict().keys()
					_a      :       str   =						[]
					_a      :       Optional[int]   =						{}
					for k, v in sd.items():
										if k in IGNORE_KEYS:
															continue

										_a      :       str   =						rename_state_dict_key(__a      )
										if new_k not in valid_keys:
															failures.append([k, new_k]      )
										else:
															_a      :       int   =						v
					if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
										rename_layernorm_keys(__a      )
					m.model.load_state_dict(__a						,strict=__a      )
					m.half()
					m.save_pretrained(__a      )


if __name__ == "__main__":
			a__												=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
			parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
			parser.add_argument(
			    '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
			)
			a__												=						parser.parse_args()
			convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

a__												=						'''Usage of script: script_name <size_of_canvas:int>'''

a__												=						[0] * 100 + [1] * 10
random.shuffle(choice)



def   __UpperCAmelCase					(      __a :     int      )     ->	list[list[bool]]:

					"""simple docstring"""

					_a      :       int   =						[[False for i in range(__a      )] for j in range(__a      )]
					return canvas



def   __UpperCAmelCase					(      __a :     list[list[bool]]      )     ->	None:

					"""simple docstring"""

					for i, row in enumerate(__a      ):
										for j, _ in enumerate(__a      ):
															_a      :       Optional[int]   =						bool(random.getrandbits(1      )      )



def   __UpperCAmelCase					(      __a :     list[list[bool]]      )     ->	list[list[bool]]:

					"""simple docstring"""

					_a      :       Any   =						np.array(__a      )
					_a      :       Optional[int]   =						np.array(create_canvas(current_canvas.shape[0]      )      )
					for r, row in enumerate(__a      ):
										for c, pt in enumerate(__a      ):
															_a      :       Tuple   =						__judge_point(
															    __a						,current_canvas[r - 1 : r + 2, c - 1 : c + 2]      )

					_a      :       List[str]   =						next_gen_canvas
					del next_gen_canvas  # cleaning memory as we move on.
					_a      :       list[list[bool]]   =						current_canvas.tolist()
					return return_canvas



def   __UpperCAmelCase					(      __a :     bool						,__a :     list[list[bool]]      )     ->	bool:

					"""simple docstring"""

					_a      :       Optional[Any]   =						0
					_a      :       str   =						0
					# finding dead or alive neighbours count.
					for i in neighbours:
										for status in i:
															if status:
																				alive += 1
															else:
																				dead += 1

    # handling duplicate entry for focus pt.
					if pt:
										alive -= 1
					else:
										dead -= 1

					# running the rules of game here.
					_a      :       Optional[int]   =						pt
					if pt:
										if alive < 2:
															_a      :       Dict   =						False
										elif alive == 2 or alive == 3:
															_a      :       Optional[Any]   =						True
										elif alive > 3:
															_a      :       str   =						False
					else:
										if alive == 3:
															_a      :       int   =						True

					return state


if __name__ == "__main__":
			if len(sys.argv) != 2:
						raise Exception(usage_doc)

			a__												=						int(sys.argv[1])
			# main working structure of this module.
			a__												=						create_canvas(canvas_size)
			seed(c)
			a__						,     a__												=						plt.subplots()
			fig.show()
			a__												=						ListedColormap(['''w''', '''k'''])
			try:
						while True:
									a__												=						run(c)
									ax.matshow(c, cmap=cmap)
									fig.canvas.draw()
									ax.cla()
			except KeyboardInterrupt:
						# do nothing.
						pass



