
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   lowerCamelCase__							(							__lowerCAmelCase    :    Any							):




			"""simple docstring"""


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

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

			if k.startswith("encoder"							):
						lowerCAmelCase_         =      k.replace(".attn"       ,				".self_attn"							)
						lowerCAmelCase_         =      k.replace("norm1"       ,				"self_attn_layer_norm"							)
						lowerCAmelCase_         =      k.replace("norm2"       ,				"final_layer_norm"							)
			elif k.startswith("decoder"							):
						lowerCAmelCase_         =      k.replace("norm1"       ,				"self_attn_layer_norm"							)
						lowerCAmelCase_         =      k.replace("norm2"       ,				"encoder_attn_layer_norm"							)
						lowerCAmelCase_         =      k.replace("norm3"       ,				"final_layer_norm"							)
			return k






def   lowerCamelCase__							(							__lowerCAmelCase    :    Union[str, Any]							):




			"""simple docstring"""


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


_A  =	["START"]






@torch.no_grad()
def   lowerCamelCase__							(							__lowerCAmelCase    :    Union[str, Any]       ,				__lowerCAmelCase    :    str       ,				__lowerCAmelCase    :    Union[str, Any]							):




			"""simple docstring"""


			lowerCAmelCase_         =      torch.load(__a       ,				map_location="cpu"							)
			lowerCAmelCase_         =      model['''model''']
			lowerCAmelCase_         =      BlenderbotConfig.from_json_file(__a							)
			lowerCAmelCase_         =      BlenderbotForConditionalGeneration(__a							)
			lowerCAmelCase_         =      m.model.state_dict().keys()
			lowerCAmelCase_         =      []
			lowerCAmelCase_         =      {}
			for k, v in sd.items():
						if k in IGNORE_KEYS:
									continue

						lowerCAmelCase_         =      rename_state_dict_key(__a							)
						if new_k not in valid_keys:
									failures.append([k, new_k]							)
						else:
									lowerCAmelCase_         =      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)






from random import randint
from tempfile import TemporaryFile

import numpy as np



def   __UpperCAmelCase					(      __a :     Optional[Any]						,__a :     int						,__a :     Any      )     ->	int:

					"""simple docstring"""

					_a      :       int   =						0
					if start < end:
										_a      :       Tuple   =						randint(__a						,__a      )
										_a      :       Tuple   =						a[end]
										_a      :       List[str]   =						a[pivot]
										_a      :       Any   =						temp

										_a ,   _a      :       Optional[int]   =						_in_place_partition(__a						,__a						,__a      )
										count += _in_place_quick_sort(__a						,__a						,p - 1      )
										count += _in_place_quick_sort(__a						,p + 1						,__a      )
					return count



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Tuple						,__a :     Dict      )     ->	Dict:

					"""simple docstring"""

					_a      :       Dict   =						0
					_a      :       Tuple   =						randint(__a						,__a      )
					_a      :       List[Any]   =						a[end]
					_a      :       str   =						a[pivot]
					_a      :       str   =						temp
					_a      :       Dict   =						start - 1
					for index in range(__a						,__a      ):
										count += 1
										if a[index] < a[end]:  # check if current val is less than pivot value
															_a      :       int   =						new_pivot_index + 1
															_a      :       Any   =						a[new_pivot_index]
															_a      :       Optional[int]   =						a[index]
															_a      :       str   =						temp

					_a      :       Union[str, Any]   =						a[new_pivot_index + 1]
					_a      :       Tuple   =						a[end]
					_a      :       Any   =						temp
					return new_pivot_index + 1, count


a__												=						TemporaryFile()
a__												=						100  # 1000 elements are to be sorted


a__						,     a__												=						0, 1  # mean and standard deviation
a__												=						np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a__												=						np.load(outfile)
a__												=						len(M) - 1
a__												=						_in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)







print(z)



