




import argparse

import torch

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


logging.set_verbosity_info()
lowerCamelCase__ :   List[str]					=							logging.get_logger(__name__)

lowerCamelCase__ :   Tuple					=							[
    ['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_							(      __UpperCAmelCase						:	List[Any]							)					-> List[Any]:
      if k == "embeddings.weight":
            return "shared.weight"

      for parlai_name, hf_name in PATTERNS:
            SCREAMING_SNAKE_CASE_							=			k.replace(A__			,			A__							)

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

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Any							)					-> Dict:
      SCREAMING_SNAKE_CASE_							=			[
          "model.encoder.layernorm_embedding.weight",
          "model.encoder.layernorm_embedding.bias",
          "model.decoder.layernorm_embedding.weight",
          "model.decoder.layernorm_embedding.bias",
      ]
      for k in keys:
            SCREAMING_SNAKE_CASE_							=			sd.pop(A__							)
            SCREAMING_SNAKE_CASE_							=			k.replace('layernorm_embedding'			,			'layer_norm'							)
            assert new_k not in sd
            SCREAMING_SNAKE_CASE_							=			v


lowerCamelCase__ :   Dict					=							['START']

@torch.no_grad()
def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Union[str, Any]			,			__UpperCAmelCase						:	int			,			__UpperCAmelCase						:	Union[str, Any]							)					-> str:
      SCREAMING_SNAKE_CASE_							=			torch.load(A__			,			map_location='cpu'							)
      SCREAMING_SNAKE_CASE_							=			model["model"]
      SCREAMING_SNAKE_CASE_							=			BlenderbotConfig.from_json_file(A__							)
      SCREAMING_SNAKE_CASE_							=			BlenderbotForConditionalGeneration(A__							)
      SCREAMING_SNAKE_CASE_							=			m.model.state_dict().keys()
      SCREAMING_SNAKE_CASE_							=			[]
      SCREAMING_SNAKE_CASE_							=			{}
      for k, v in sd.items():
            if k in IGNORE_KEYS:
                  continue

            SCREAMING_SNAKE_CASE_							=			rename_state_dict_key(A__							)
            if new_k not in valid_keys:
                  failures.append([k, new_k]							)
            else:
                  SCREAMING_SNAKE_CASE_							=			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__":
   lowerCamelCase__ :   Optional[int]					=							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__ :   Optional[int]					=							parser.parse_args()
   convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)







"""simple docstring"""
from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt

def 				snake_case      (   A__	,A__	,A__						):
			if not arr:
						return None, None, 0
			if low == high:
						return low, high, arr[low]

			UpperCAmelCase_     : Dict			     =					(low + high) // 2
			UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Any			     =					max_subarray(A__	,A__	,A__						)
			UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Union[str, Any]			     =					max_subarray(A__	,mid + 1	,A__						)
			UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					max_cross_sum(A__	,A__	,A__	,A__						)
			if left_sum >= right_sum and left_sum >= cross_sum:
						return left_low, left_high, left_sum
			elif right_sum >= left_sum and right_sum >= cross_sum:
						return right_low, right_high, right_sum
			return cross_left, cross_right, cross_sum

def 				snake_case      (   A__	,A__	,A__	,A__						):
			UpperCAmelCase_     ,	UpperCAmelCase_     : str			     =					float("-inf"						), -1
			UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					float("-inf"						), -1

			UpperCAmelCase_     : int | float			     =					0
			for i in range(A__	,low - 1	,-1						):
						summ += arr[i]
						if summ > left_sum:
									UpperCAmelCase_     : str			     =					summ
									UpperCAmelCase_     : Any			     =					i

			UpperCAmelCase_     : Dict			     =					0
			for i in range(mid + 1	,high + 1						):
						summ += arr[i]
						if summ > right_sum:
									UpperCAmelCase_     : List[Any]			     =					summ
									UpperCAmelCase_     : Optional[Any]			     =					i

			return max_left, max_right, (left_sum + right_sum)

def 				snake_case      (   A__						):
			UpperCAmelCase_     : str			     =					[randint(1	,A__						) for _ in range(A__						)]
			UpperCAmelCase_     : str			     =					time.time()
			max_subarray(A__	,0	,input_size - 1						)
			UpperCAmelCase_     : int			     =					time.time()
			return end - start

def 				snake_case      (   ):
			UpperCAmelCase_     : int			     =					[10, 1_00, 10_00, 1_00_00, 5_00_00, 10_00_00, 20_00_00, 30_00_00, 40_00_00, 50_00_00]
			UpperCAmelCase_     : List[str]			     =					[time_max_subarray(A__						) for input_size in input_sizes]
			print("No of Inputs\t\tTime Taken"						)
			for input_size, runtime in zip(A__	,A__						):
						print(A__	,"\t\t"	,A__						)
			plt.plot(A__	,A__						)
			plt.xlabel("Number of Inputs"						)
			plt.ylabel("Time taken in seconds"						)
			plt.show()


if __name__ == "__main__":
			from doctest import testmod

			testmod()



