



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

_lowercase: str      			=		(
    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
)

_lowercase: Dict      			=		(
    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
)

_lowercase: 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     a(      A       :    Optional[Any] ,     A       :    Optional[Any]       )		->       Any:
					"""simple docstring"""





					for tf_name, hf_name in patterns:
										a					     =   k.replace(A ,     A       )
					return k






def     a(      A       :    dict ,     A       :    dict       )		->       BigBirdPegasusForConditionalGeneration:
					"""simple docstring"""





					a					     =   BigBirdPegasusConfig(**A       )
					a					     =   BigBirdPegasusForConditionalGeneration(A       )
					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(A       ) for ending in KEYS_TO_IGNORE]
										if any(A       ):
															continue
										a					     =   DECODER_PATTERNS
										a					     =   rename_state_dict_key(A ,     A       )
										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(A       )
										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(A       ) for ending in KEYS_TO_IGNORE]
										if any(A       ):
															continue
										a					     =   REMAINING_PATTERNS
										a					     =   rename_state_dict_key(A ,     A       )
										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(A       )
										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(A ,     strict=A       )
					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     a(      A       :    List[Any]       )		->       Dict:
					"""simple docstring"""





					a					     =   tf.train.list_variables(A       )
					a					     =   {}
					a					     =   ["global_step"]
					for name, shape in tqdm(A ,     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(A ,     A       )
										a					     =   array
					return tf_weights






def     a(      A       :    str ,     A       :    str ,     A       :    dict       )		->       Dict:
					"""simple docstring"""





					a					     =   get_tf_weights_as_numpy(A       )
					a					     =   convert_bigbird_pegasus(A ,     A       )
					torch_model.save_pretrained(A       )


if __name__ == "__main__":
				_lowercase: List[str]      			=		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.")
				_lowercase: Optional[Any]      			=		parser.parse_args()
				_lowercase: int      			=		{}
				convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)





from __future__ import annotations

_lowercase: Tuple      			=		list[list[int]]

# assigning initial values to the grid
_lowercase: Matrix      			=		[
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
_lowercase: Matrix      			=		[
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]






def     a(      A       :    Matrix ,     A       :    int ,     A       :    int ,     A       :    int       )		->       bool:
					"""simple docstring"""





					for i in range(9       ):
										if grid[row][i] == n or grid[i][column] == n:
															return False

					for i in range(3       ):
										for j in range(3       ):
															if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
																				return False

					return True






def     a(      A       :    Matrix       )		->       tuple[int, int] | None:
					"""simple docstring"""





					for i in range(9       ):
										for j in range(9       ):
															if grid[i][j] == 0:
																				return i, j
					return None






def     a(      A       :    Matrix       )		->       Matrix | None:
					"""simple docstring"""





					if location := find_empty_location(A       ):
										a     ,				a					     =   location
					else:
										# If the location is ``None``, then the grid is solved.
										return grid

					for digit in range(1 ,     10       ):
										if is_safe(A ,     A ,     A ,     A       ):
															a					     =   digit

															if sudoku(A       ) is not None:
																				return grid

															a					     =   0

					return None






def     a(      A       :    Matrix       )		->       None:
					"""simple docstring"""





					for row in grid:
										for cell in row:
															print(A ,     end=" "       )
										print()


if __name__ == "__main__":
				# make a copy of grid so that you can compare with the unmodified grid
				for example_grid in (initial_grid, no_solution):
								print("\nExample grid:\n" + "=" * 20)
								print_solution(example_grid)
								print("\nExample grid solution:")
								_lowercase: List[str]      			=		sudoku(example_grid)
								if solution is not None:
												print_solution(solution)
								else:
												print("Cannot find a solution.")
