





'''simple docstring'''






import argparse

import torch

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


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

snake_case_     =	[
    ["""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(  UpperCamelCase__    : str		)		->  int:
    if k == "embeddings.weight":
        return "shared.weight"

    for parlai_name, hf_name in PATTERNS:
        A       :       str							=						k.replace(UpperCamelCase__    ,       UpperCamelCase__		)

    if k.startswith('''encoder'''		):
        A       :       Dict							=						k.replace('''.attn'''    ,       '''.self_attn'''		)
        A       :       Dict							=						k.replace('''norm1'''    ,       '''self_attn_layer_norm'''		)
        A       :       Dict							=						k.replace('''norm2'''    ,       '''final_layer_norm'''		)
    elif k.startswith('''decoder'''		):
        A       :       Any							=						k.replace('''norm1'''    ,       '''self_attn_layer_norm'''		)
        A       :       int							=						k.replace('''norm2'''    ,       '''encoder_attn_layer_norm'''		)
        A       :       str							=						k.replace('''norm3'''    ,       '''final_layer_norm'''		)
    return k
def 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  str:
    A       :       Optional[Any]							=						[
        '''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       :       Dict							=						sd.pop(UpperCamelCase__		)
        A       :       Optional[int]							=						k.replace('''layernorm_embedding'''    ,       '''layer_norm'''		)
        assert new_k not in sd
        A       :       int							=						v


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

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


if __name__ == "__main__":
     snake_case_     =	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"""
     )
     snake_case_     =	parser.parse_args()
     convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)







'''simple docstring'''






def 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  None:
    A       :       List[Any]							=						generate_pascal_triangle(UpperCamelCase__		)
    for row_idx in range(UpperCamelCase__		):
        # Print left spaces
        for _ in range(num_rows - row_idx - 1		):
            print(end=''' '''		)
        # Print row values
        for col_idx in range(row_idx + 1		):
            if col_idx != row_idx:
                print(triangle[row_idx][col_idx]    ,       end=''' '''		)
            else:
                print(triangle[row_idx][col_idx]    ,       end=''''''		)
        print()
def 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  list[list[int]]:

    if not isinstance(UpperCamelCase__    ,       UpperCamelCase__		):
        raise TypeError('''The input value of \'num_rows\' should be \'int\''''		)

    if num_rows == 0:
        return []
    elif num_rows < 0:
        raise ValueError(
            '''The input value of \'num_rows\' should be greater than or equal to 0'''		)

    A       :       list[list[int]]							=						[]
    for current_row_idx in range(UpperCamelCase__		):
        A       :       Optional[int]							=						populate_current_row(UpperCamelCase__    ,       UpperCamelCase__		)
        triangle.append(UpperCamelCase__		)
    return triangle
def 							_lowerCamelCase(  UpperCamelCase__    : list[list[int]]    ,       UpperCamelCase__    : int		)		->  list[int]:
    A       :       Dict							=						[-1] * (current_row_idx + 1)
    # first and last elements of current row are equal to 1
    A,	A       :       Union[str, Any]							=						1, 1
    for current_col_idx in range(1    ,       UpperCamelCase__		):
        calculate_current_element(
            UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)
    return current_row
def 							_lowerCamelCase(  UpperCamelCase__    : list[list[int]]    ,       UpperCamelCase__    : list[int]    ,       UpperCamelCase__    : int    ,       UpperCamelCase__    : int    ,       )		->  None:
    A       :       Dict							=						triangle[current_row_idx - 1][current_col_idx - 1]
    A       :       Any							=						triangle[current_row_idx - 1][current_col_idx]
    A       :       Optional[int]							=						above_to_left_elt + above_to_right_elt
def 							_lowerCamelCase(  UpperCamelCase__    : int		)		->  list[list[int]]:

    if not isinstance(UpperCamelCase__    ,       UpperCamelCase__		):
        raise TypeError('''The input value of \'num_rows\' should be \'int\''''		)

    if num_rows == 0:
        return []
    elif num_rows < 0:
        raise ValueError(
            '''The input value of \'num_rows\' should be greater than or equal to 0'''		)

    A       :       list[list[int]]							=						[[1]]

    for row_index in range(1    ,       UpperCamelCase__		):
        A       :       str							=						[0] + result[-1] + [0]
        A       :       Optional[Any]							=						row_index + 1
        # Calculate the number of distinct elements in a row
        A       :       str							=						sum(divmod(UpperCamelCase__    ,       2		)		)
        A       :       Optional[int]							=						[
            temp_row[i - 1] + temp_row[i] for i in range(1    ,       distinct_elements + 1		)
        ]
        A       :       Any							=						row_first_half[: (row_index + 1) // 2]
        row_second_half.reverse()
        A       :       Any							=						row_first_half + row_second_half
        result.append(UpperCamelCase__		)

    return result
def 							_lowerCamelCase(  )		->  None:
    from collections.abc import Callable
    from timeit import timeit

    def benchmark_a_function(UpperCamelCase__    : Callable    ,       UpperCamelCase__    : int		) -> None:
        A       :       Dict							=						F'''{func.__name__}({value})'''
        A       :       List[Any]							=						timeit(F'''__main__.{call}'''    ,       setup='''import __main__'''		)
        # print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
        print(F'''{call:38} -- {timing:.4f} seconds'''		)

    for value in range(15		):  # (1, 7, 14):
        for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
            benchmark_a_function(UpperCamelCase__    ,       UpperCamelCase__		)
        print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()
