


'''simple docstring'''






import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format='''%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s''',
    datefmt='''%Y-%m-%d %H:%M:%S''',
    level=os.environ.get('''LOGLEVEL''', '''INFO''').upper(),
    stream=sys.stdout,
)

__UpperCAmelCase       							=				logging.getLogger(__name__)

__UpperCAmelCase       							=				{'''facebook/bart-base''': BartForConditionalGeneration}
__UpperCAmelCase       							=				{'''facebook/bart-base''': BartTokenizer}


def    _snake_case  (	)		->       Any:
							lowerCAmelCase__			  =      argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.'''			)
							parser.add_argument(
							    '''--validation_file'''					,       type=A					,       default=A					,       help='''A csv or a json file containing the validation data.'''			)
							parser.add_argument(
							    '''--max_length'''					,       type=A					,       default=5					,       help='''The maximum total input sequence length after tokenization.'''					,       )
							parser.add_argument(
							    '''--num_beams'''					,       type=A					,       default=A					,       help=(
							        '''Number of beams to use for evaluation. This argument will be '''
							        '''passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'''
							    )					,       )
							parser.add_argument(
							    '''--model_name_or_path'''					,       type=A					,       help='''Path to pretrained model or model identifier from huggingface.co/models.'''					,       required=A					,       )
							parser.add_argument(
							    '''--config_name'''					,       type=A					,       default=A					,       help='''Pretrained config name or path if not the same as model_name'''					,       )
							parser.add_argument(
							    '''--device'''					,       type=A					,       default='''cpu'''					,       help='''Device where the model will be run'''					,       )
							parser.add_argument('''--output_file_path'''					,       type=A					,       default=A					,       help='''Where to store the final ONNX file.'''			)

							lowerCAmelCase__			  =      parser.parse_args()

							return args


def    _snake_case  (	A					,       A="cpu"			)		->       int:
							lowerCAmelCase__			  =      model_dict[model_name].from_pretrained(A			).to(A			)
							lowerCAmelCase__			  =      tokenizer_dict[model_name].from_pretrained(A			)

							if model_name in ["facebook/bart-base"]:
														lowerCAmelCase__			  =      0
														lowerCAmelCase__			  =      None
														lowerCAmelCase__			  =      0

							return huggingface_model, tokenizer


def    _snake_case  (	A					,       A					,       A					,       A					,       A			)		->       Union[str, Any]:
							model.eval()

							lowerCAmelCase__			  =      None
							lowerCAmelCase__			  =      torch.jit.script(BARTBeamSearchGenerator(A			)			)

							with torch.no_grad():
														lowerCAmelCase__			  =      '''My friends are cool but they eat too many carbs.'''
														lowerCAmelCase__			  =      tokenizer([ARTICLE_TO_SUMMARIZE]					,       max_length=1024					,       return_tensors='''pt'''			).to(model.device			)

														lowerCAmelCase__			  =      model.generate(
														    inputs['''input_ids''']					,       attention_mask=inputs['''attention_mask''']					,       num_beams=A					,       max_length=A					,       early_stopping=A					,       decoder_start_token_id=model.config.decoder_start_token_id					,       )

														torch.onnx.export(
														    A					,       (
														        inputs['''input_ids'''],
														        inputs['''attention_mask'''],
														        num_beams,
														        max_length,
														        model.config.decoder_start_token_id,
														    )					,       A					,       opset_version=14					,       input_names=['''input_ids''', '''attention_mask''', '''num_beams''', '''max_length''', '''decoder_start_token_id''']					,       output_names=['''output_ids''']					,       dynamic_axes={
														        '''input_ids''': {0: '''batch''', 1: '''seq'''},
														        '''output_ids''': {0: '''batch''', 1: '''seq_out'''},
														    }					,       example_outputs=A					,       )

														logger.info('''Model exported to {}'''.format(A			)			)

														lowerCAmelCase__			  =      remove_dup_initializers(os.path.abspath(A			)			)

														logger.info('''Deduplicated and optimized model written to {}'''.format(A			)			)

														lowerCAmelCase__			  =      onnxruntime.InferenceSession(A			)
														lowerCAmelCase__			  =      ort_sess.run(
														    A					,       {
														        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
														        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
														        '''num_beams''': np.array(A			),
														        '''max_length''': np.array(A			),
														        '''decoder_start_token_id''': np.array(model.config.decoder_start_token_id			),
														    }					,       )

														np.testing.assert_allclose(summary_ids.cpu().numpy()					,       ort_out[0]					,       rtol=1E-3					,       atol=1E-3			)

														logger.info('''Model outputs from torch and ONNX Runtime are similar.'''			)
														logger.info('''Success.'''			)


def    _snake_case  (	)		->       List[str]:
							lowerCAmelCase__			  =      parse_args()
							lowerCAmelCase__			  =      5
							lowerCAmelCase__			  =      4

							# Make one log on every process with the configuration for debugging.
							logging.basicConfig(
							    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''					,       datefmt='''%m/%d/%Y %H:%M:%S'''					,       level=logging.INFO					,       )

							logger.setLevel(logging.INFO			)
							transformers.utils.logging.set_verbosity_error()

							lowerCAmelCase__			  =      torch.device(args.device			)

							lowerCAmelCase__  ,   lowerCAmelCase__			  =      load_model_tokenizer(args.model_name_or_path					,       A			)

							if model.config.decoder_start_token_id is None:
														raise ValueError('''Make sure that `config.decoder_start_token_id` is correctly defined'''			)

							model.to(A			)

							if args.max_length:
														lowerCAmelCase__			  =      args.max_length

							if args.num_beams:
														lowerCAmelCase__			  =      args.num_beams

							if args.output_file_path:
														lowerCAmelCase__			  =      args.output_file_path
							else:
														lowerCAmelCase__			  =      '''BART.onnx'''

							logger.info('''Exporting model to ONNX'''			)
							export_and_validate_model(A					,       A					,       A					,       A					,       A			)


if __name__ == "__main__":
		main()



'''simple docstring'''






from __future__ import annotations

__UpperCAmelCase       							=				list[list[int]]

# assigning initial values to the grid
__UpperCAmelCase       							=				[
    [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
__UpperCAmelCase       							=				[
    [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    _snake_case  (	A					,       A					,       A					,       A			)		->       bool:
							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    _snake_case  (	A			)		->       tuple[int, int] | None:
							for i in range(9			):
														for j in range(9			):
																					if grid[i][j] == 0:
																												return i, j
							return None


def    _snake_case  (	A			)		->       Matrix | None:
							if location := find_empty_location(A			):
														lowerCAmelCase__  ,   lowerCAmelCase__			  =      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			):
																					lowerCAmelCase__			  =      digit

																					if sudoku(A			) is not None:
																												return grid

																					lowerCAmelCase__			  =      0

							return None


def    _snake_case  (	A			)		->       None:
							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:''')
				__UpperCAmelCase       							=				sudoku(example_grid)
				if solution is not None:
						print_solution(solution)
				else:
						print('''Cannot find a solution.''')