






import argparse

import torch

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


logging.set_verbosity_info()
lowerCamelCase       :       Optional[int]        =				logging.get_logger(__name__)

lowerCamelCase       :       int        =				[
    ['''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 					snake_case_						(   lowerCAmelCase_				:  str					):
							if k == "embeddings.weight":
														return "shared.weight"

							for parlai_name, hf_name in PATTERNS:
														__lowercase					:							Tuple			=   k.replace(lowerCAmelCase_				,				lowerCAmelCase_					)

							if k.startswith("""encoder"""					):
														__lowercase					:							Union[str, Any]			=   k.replace(""".attn"""				,				""".self_attn"""					)
														__lowercase					:							Any			=   k.replace("""norm1"""				,				"""self_attn_layer_norm"""					)
														__lowercase					:							Any			=   k.replace("""norm2"""				,				"""final_layer_norm"""					)
							elif k.startswith("""decoder"""					):
														__lowercase					:							Optional[int]			=   k.replace("""norm1"""				,				"""self_attn_layer_norm"""					)
														__lowercase					:							Tuple			=   k.replace("""norm2"""				,				"""encoder_attn_layer_norm"""					)
														__lowercase					:							Tuple			=   k.replace("""norm3"""				,				"""final_layer_norm"""					)
							return k
def 					snake_case_						(   lowerCAmelCase_				:  int					):
							__lowercase					:							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:
														__lowercase					:							Optional[int]			=   sd.pop(lowerCAmelCase_					)
														__lowercase					:							List[Any]			=   k.replace("""layernorm_embedding"""				,				"""layer_norm"""					)
														assert new_k not in sd
														__lowercase					:							Optional[Any]			=   v


lowerCamelCase       :       Any        =				['''START''']
@torch.no_grad()
def 					snake_case_						(   lowerCAmelCase_				:  Union[str, Any]				,				lowerCAmelCase_				:  Optional[Any]				,				lowerCAmelCase_				:  str					):
							__lowercase					:							str			=   torch.load(lowerCAmelCase_				,				map_location="""cpu"""					)
							__lowercase					:							Optional[int]			=   model["""model"""]
							__lowercase					:							Optional[Any]			=   BlenderbotConfig.from_json_file(lowerCAmelCase_					)
							__lowercase					:							str			=   BlenderbotForConditionalGeneration(lowerCAmelCase_					)
							__lowercase					:							Tuple			=   m.model.state_dict().keys()
							__lowercase					:							str			=   []
							__lowercase					:							int			=   {}
							for k, v in sd.items():
														if k in IGNORE_KEYS:
																					continue

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


if __name__ == "__main__":
	lowerCamelCase       :       Optional[Any]        =				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'''
	)
	lowerCamelCase       :       Union[str, Any]        =				parser.parse_args()
	convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)







from __future__ import annotations

import math

lowerCamelCase       :       List[Any]        =				'''2020.9.26'''
lowerCamelCase       :       str        =				'''xcodz-dot, cclaus, dhruvmanila'''
def 					snake_case_						(   lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float					):
							if not all(isinstance(lowerCAmelCase_				,				(float, int)					) for val in locals().values()					):
														__lowercase					:							str			=   F"Input values must either be float or int: {list(locals().values()					)}"
														raise TypeError(lowerCAmelCase_					)
							__lowercase					:							List[Any]			=   ((x * distance) / (z + distance)) * scale
							__lowercase					:							Tuple			=   ((y * distance) / (z + distance)) * scale
							return projected_x, projected_y
def 					snake_case_						(   lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  float					):
							if not isinstance(lowerCAmelCase_				,				lowerCAmelCase_					):
														raise TypeError("""Axis must be a str"""					)
							__lowercase					:							Optional[int]			=   locals()
							del input_variables["axis"]
							if not all(isinstance(lowerCAmelCase_				,				(float, int)					) for val in input_variables.values()					):
														__lowercase					:							List[str]			=   (
														    """Input values except axis must either be float or int: """
														    F"{list(input_variables.values()					)}"
														)
														raise TypeError(lowerCAmelCase_					)
							__lowercase					:							Tuple			=   (angle % 360) / 450 * 180 / math.pi
							if axis == "z":
														__lowercase					:							int			=   x * math.cos(lowerCAmelCase_					) - y * math.sin(lowerCAmelCase_					)
														__lowercase					:							Tuple			=   y * math.cos(lowerCAmelCase_					) + x * math.sin(lowerCAmelCase_					)
														__lowercase					:							Union[str, Any]			=   z
							elif axis == "x":
														__lowercase					:							str			=   y * math.cos(lowerCAmelCase_					) - z * math.sin(lowerCAmelCase_					)
														__lowercase					:							Dict			=   z * math.cos(lowerCAmelCase_					) + y * math.sin(lowerCAmelCase_					)
														__lowercase					:							List[str]			=   x
							elif axis == "y":
														__lowercase					:							List[str]			=   x * math.cos(lowerCAmelCase_					) - z * math.sin(lowerCAmelCase_					)
														__lowercase					:							List[str]			=   z * math.cos(lowerCAmelCase_					) + x * math.sin(lowerCAmelCase_					)
														__lowercase					:							List[Any]			=   y
							else:
														raise ValueError("""not a valid axis, choose one of 'x', 'y', 'z'"""					)

							return new_x, new_y, new_z


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(f'''{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }''')
	print(f'''{rotate(1.0, 2.0, 3.0, "y", 90.0) = }''')