


import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


snake_case							: Dict      =   [
    # tf -> hf
    ('''/''', '''.'''),
    ('''layer_''', '''layers.'''),
    ('''kernel''', '''weight'''),
    ('''beta''', '''bias'''),
    ('''gamma''', '''weight'''),
    ('''pegasus''', '''model'''),
]
snake_case							: Union[str, Any]      =   [
    ('''.output.dense''', '''.fc2'''),
    ('''intermediate.LayerNorm''', '''final_layer_norm'''),
    ('''intermediate.dense''', '''fc1'''),
]

snake_case							: Union[str, Any]      =   (
    INIT_COMMON
    + [
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.out_proj'''),
        ('''attention.self''', '''self_attn'''),
        ('''attention.encdec.LayerNorm''', '''encoder_attn_layer_norm'''),
        ('''attention.encdec_output.dense''', '''encoder_attn.out_proj'''),
        ('''attention.encdec''', '''encoder_attn'''),
        ('''key''', '''k_proj'''),
        ('''value''', '''v_proj'''),
        ('''query''', '''q_proj'''),
        ('''decoder.LayerNorm''', '''decoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

snake_case							: List[str]      =   (
    INIT_COMMON
    + [
        ('''embeddings.word_embeddings''', '''shared.weight'''),
        ('''embeddings.position_embeddings''', '''embed_positions.weight'''),
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.output'''),
        ('''attention.self''', '''self_attn.self'''),
        ('''encoder.LayerNorm''', '''encoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

snake_case							: Union[str, Any]      =   [
    '''encdec/key/bias''',
    '''encdec/query/bias''',
    '''encdec/value/bias''',
    '''self/key/bias''',
    '''self/query/bias''',
    '''self/value/bias''',
    '''encdec_output/dense/bias''',
    '''attention/output/dense/bias''',
]






def 							__lowercase     (				__lowerCAmelCase							:       List[str]      ,					__lowerCAmelCase							:       str     ):
							for tf_name, hf_name in patterns:
														a__    =  k.replace(__lowerCAmelCase      ,					__lowerCAmelCase     )
							return k






def 							__lowercase     (				__lowerCAmelCase							:       dict      ,					__lowerCAmelCase							:       dict     ):
							a__    =  BigBirdPegasusConfig(**__lowerCAmelCase     )
							a__    =  BigBirdPegasusForConditionalGeneration(__lowerCAmelCase     )
							a__    =  torch_model.state_dict()
							a__    =  {}

							# separating decoder weights
							a__    =  {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'     )}
							a__    =  {k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder'     )}

							for k, v in tqdm(decoder_weights.items()      ,					'tf -> hf conversion'     ):
														a__    =  [k.endswith(__lowerCAmelCase     ) for ending in KEYS_TO_IGNORE]
														if any(__lowerCAmelCase     ):
																					continue
														a__    =  DECODER_PATTERNS
														a__    =  rename_state_dict_key(__lowerCAmelCase      ,					__lowerCAmelCase     )
														if new_k not in state_dict:
																					raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'     )
														if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']     ):
																					a__    =  v.T
														a__    =  torch.from_numpy(__lowerCAmelCase     )
														assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

							for k, v in tqdm(remaining_weights.items()      ,					'tf -> hf conversion'     ):
														a__    =  [k.endswith(__lowerCAmelCase     ) for ending in KEYS_TO_IGNORE]
														if any(__lowerCAmelCase     ):
																					continue
														a__    =  REMAINING_PATTERNS
														a__    =  rename_state_dict_key(__lowerCAmelCase      ,					__lowerCAmelCase     )
														if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
																					raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'     )
														if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']     ):
																					a__    =  v.T
														a__    =  torch.from_numpy(__lowerCAmelCase     )
														if k != "pegasus/embeddings/position_embeddings":
																					assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

							a__    =  mapping['model.embed_positions.weight']
							a__    =  mapping.pop('model.embed_positions.weight'     )
							a__   ,      a__    =  torch_model.load_state_dict(__lowerCAmelCase      ,					strict=__lowerCAmelCase     )
							a__    =  [
							    k
							    for k in missing
							    if k
							    not in [
							        'final_logits_bias',
							        'model.encoder.embed_tokens.weight',
							        'model.decoder.embed_tokens.weight',
							        'lm_head.weight',
							    ]
							]
							assert unexpected_missing == [], F'no matches found for the following torch keys {unexpected_missing}'
							assert extra == [], F'no matches found for the following tf keys {extra}'
							return torch_model






def 							__lowercase     (				__lowerCAmelCase							:       Dict     ):
							a__    =  tf.train.list_variables(__lowerCAmelCase     )
							a__    =  {}
							a__    =  ['global_step']
							for name, shape in tqdm(__lowerCAmelCase      ,					desc='converting tf checkpoint to dict'     ):
														a__    =  any(pat in name for pat in ignore_name     )
														if skip_key:
																					continue
														a__    =  tf.train.load_variable(__lowerCAmelCase      ,					__lowerCAmelCase     )
														a__    =  array
							return tf_weights






def 							__lowercase     (				__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       dict     ):
							a__    =  get_tf_weights_as_numpy(__lowerCAmelCase     )
							a__    =  convert_bigbird_pegasus(__lowerCAmelCase      ,					__lowerCAmelCase     )
							torch_model.save_pretrained(__lowerCAmelCase     )


if __name__ == "__main__":
						snake_case							: Tuple      =   argparse.ArgumentParser()
						parser.add_argument('''--tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
						parser.add_argument('''--save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
						snake_case							: Dict      =   parser.parse_args()
						snake_case							: List[Any]      =   {}
						convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)








def 							__lowercase     (				__lowerCAmelCase							:       int     ):
							a__    =  generate_pascal_triangle(__lowerCAmelCase     )
							for row_idx in range(__lowerCAmelCase     ):
														# 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 							__lowercase     (				__lowerCAmelCase							:       int     ):

							if not isinstance(__lowerCAmelCase      ,					__lowerCAmelCase     ):
														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__    =  []
							for current_row_idx in range(__lowerCAmelCase     ):
														a__    =  populate_current_row(__lowerCAmelCase      ,					__lowerCAmelCase     )
														triangle.append(__lowerCAmelCase     )
							return triangle






def 							__lowercase     (				__lowerCAmelCase							:       list[list[int]]      ,					__lowerCAmelCase							:       int     ):
							a__    =  [-1] * (current_row_idx + 1)
							# first and last elements of current row are equal to 1
							a__   ,      a__    =  1, 1
							for current_col_idx in range(1      ,					__lowerCAmelCase     ):
														calculate_current_element(
														    __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase     )
							return current_row






def 							__lowercase     (				__lowerCAmelCase							:       list[list[int]]      ,					__lowerCAmelCase							:       list[int]      ,					__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       int      ,					):
							a__    =  triangle[current_row_idx - 1][current_col_idx - 1]
							a__    =  triangle[current_row_idx - 1][current_col_idx]
							a__    =  above_to_left_elt + above_to_right_elt






def 							__lowercase     (				__lowerCAmelCase							:       int     ):

							if not isinstance(__lowerCAmelCase      ,					__lowerCAmelCase     ):
														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__    =  [[1]]

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

							return result






def 							__lowercase     (				):
							from collections.abc import Callable
							from timeit import timeit

							def benchmark_a_function(__lowerCAmelCase							:       Callable      ,					__lowerCAmelCase							:       int     ) -> None:
														a__    =  F'{func.__name__}({value})'
														a__    =  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(1_5     ):  # (1, 7, 14):
														for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
																					benchmark_a_function(__lowerCAmelCase      ,					__lowerCAmelCase     )
														print()


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()




