


'''simple docstring'''
import argparse

import torch

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


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

UpperCamelCase__    					=      [
    ['''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 					a__       (					lowerCAmelCase__						)    ->	int:
    if k == "embeddings.weight":
        return "shared.weight"

    for parlai_name, hf_name in PATTERNS:
        UpperCAmelCase__			:		Optional[int]													=				k.replace(lowerCAmelCase__  ,					lowerCAmelCase__						)

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





def 					a__       (					lowerCAmelCase__						)    ->	List[str]:
    UpperCAmelCase__			:		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:
        UpperCAmelCase__			:		Any													=				sd.pop(lowerCAmelCase__						)
        UpperCAmelCase__			:		Dict													=				k.replace('''layernorm_embedding'''  ,					'''layer_norm'''						)
        assert new_k not in sd
        UpperCAmelCase__			:		Optional[Any]													=				v


UpperCamelCase__    					=      ['''START''']





@torch.no_grad()
def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	str:
    UpperCAmelCase__			:		List[Any]													=				torch.load(lowerCAmelCase__  ,					map_location='''cpu'''						)
    UpperCAmelCase__			:		str													=				model['''model''']
    UpperCAmelCase__			:		Tuple													=				BlenderbotConfig.from_json_file(lowerCAmelCase__						)
    UpperCAmelCase__			:		str													=				BlenderbotForConditionalGeneration(lowerCAmelCase__						)
    UpperCAmelCase__			:		List[Any]													=				m.model.state_dict().keys()
    UpperCAmelCase__			:		int													=				[]
    UpperCAmelCase__			:		Dict													=				{}
    for k, v in sd.items():
        if k in IGNORE_KEYS:
            continue

        UpperCAmelCase__			:		List[str]													=				rename_state_dict_key(lowerCAmelCase__						)
        if new_k not in valid_keys:
            failures.append([k, new_k]						)
        else:
            UpperCAmelCase__			:		List[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__":
  UpperCamelCase__    					=      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'''
  )
  UpperCamelCase__    					=      parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)




'''simple docstring'''
# Algorithm for the pigeonhole sorting







def 					a__       (					lowerCAmelCase__						)    ->	Optional[Any]:
    UpperCAmelCase__			:		Any													=				min(lowerCAmelCase__						)  # min() finds the minimum value
    UpperCAmelCase__			:		Optional[int]													=				max(lowerCAmelCase__						)  # max() finds the maximum value

    UpperCAmelCase__			:		int													=				max_val - min_val + 1  # size is difference of max and min values plus one

    # list of pigeonholes of size equal to the variable size
    UpperCAmelCase__			:		Any													=				[0] * size

    # Populate the pigeonholes.
    for x in a:
        assert isinstance(lowerCAmelCase__  ,					lowerCAmelCase__						), "integers only please"
        holes[x - min_val] += 1

    # Putting the elements back into the array in an order.
    UpperCAmelCase__			:		Optional[int]													=				0
    for count in range(lowerCAmelCase__						):
        while holes[count] > 0:
            holes[count] -= 1
            UpperCAmelCase__			:		Dict													=				count + min_val
            i += 1





def 					a__       (					)    ->	Union[str, Any]:
    UpperCAmelCase__			:		List[str]													=				[8, 3, 2, 7, 4, 6, 8]
    pigeonhole_sort(lowerCAmelCase__						)
    print('''Sorted order is:'''  ,					''' '''.join(lowerCAmelCase__						)						)


if __name__ == "__main__":
  main()
