



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,
)

__snake_case       :	Optional[Any]            =logging.getLogger(__name__)

__snake_case       :	List[str]            ={'facebook/bart-base': BartForConditionalGeneration}
__snake_case       :	List[str]            ={'facebook/bart-base': BartTokenizer}






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       Any				=  argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.''')
				parser.add_argument(
				    '''--validation_file''' ,type=lowerCamelCase_ ,default=lowerCamelCase_ ,help='''A csv or a json file containing the validation data.''')
				parser.add_argument(
				    '''--max_length''' ,type=lowerCamelCase_ ,default=5 ,help='''The maximum total input sequence length after tokenization.''' ,)
				parser.add_argument(
				    '''--num_beams''' ,type=lowerCamelCase_ ,default=lowerCamelCase_ ,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=lowerCamelCase_ ,help='''Path to pretrained model or model identifier from huggingface.co/models.''' ,required=lowerCamelCase_ ,)
				parser.add_argument(
				    '''--config_name''' ,type=lowerCamelCase_ ,default=lowerCamelCase_ ,help='''Pretrained config name or path if not the same as model_name''' ,)
				parser.add_argument(
				    '''--device''' ,type=lowerCamelCase_ ,default='''cpu''' ,help='''Device where the model will be run''' ,)
				parser.add_argument('''--output_file_path''' ,type=lowerCamelCase_ ,default=lowerCamelCase_ ,help='''Where to store the final ONNX file.''')

				lowerCAmelCase__  :       Tuple				=  parser.parse_args()

				return args






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Dict="cpu"):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  model_dict[model_name].from_pretrained(lowerCamelCase_).to(lowerCamelCase_)
				lowerCAmelCase__  :       Dict				=  tokenizer_dict[model_name].from_pretrained(lowerCamelCase_)

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

				return huggingface_model, tokenizer






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Union[str, Any] ,lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	Optional[int] ,lowerCamelCase_	:	List[Any]):





				'''simple docstring'''

				model.eval()

				lowerCAmelCase__  :       Optional[int]				=  None
				lowerCAmelCase__  :       List[str]				=  torch.jit.script(BARTBeamSearchGenerator(lowerCamelCase_))

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

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

								torch.onnx.export(
								    lowerCamelCase_ ,(
								        inputs['''input_ids'''],
								        inputs['''attention_mask'''],
								        num_beams,
								        max_length,
								        model.config.decoder_start_token_id,
								    ) ,lowerCamelCase_ ,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=lowerCamelCase_ ,)

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

								lowerCAmelCase__  :       Any				=  remove_dup_initializers(os.path.abspath(lowerCamelCase_))

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

								lowerCAmelCase__  :       List[Any]				=  onnxruntime.InferenceSession(lowerCamelCase_)
								lowerCAmelCase__  :       Optional[int]				=  ort_sess.run(
								    lowerCamelCase_ ,{
								        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
								        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
								        '''num_beams''': np.array(lowerCamelCase_),
								        '''max_length''': np.array(lowerCamelCase_),
								        '''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 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       Dict				=  parse_args()
				lowerCAmelCase__  :       Any				=  5
				lowerCAmelCase__  :       Union[str, Any]				=  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__  :       Tuple				=  torch.device(args.device)

				lowerCAmelCase__  :       Any				=  load_model_tokenizer(args.model_name_or_path ,lowerCamelCase_)

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

				model.to(lowerCamelCase_)

				if args.max_length:
								lowerCAmelCase__  :       List[Any]				=  args.max_length

				if args.num_beams:
								lowerCAmelCase__  :       Optional[int]				=  args.num_beams

				if args.output_file_path:
								lowerCAmelCase__  :       Optional[int]				=  args.output_file_path
				else:
								lowerCAmelCase__  :       int				=  '''BART.onnx'''

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


if __name__ == "__main__":
							main()






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

__snake_case       :	List[str]            ='tiny-wmt19-en-ru'

# Build

# borrowed from a test
__snake_case       :	Any            =[
    'l',
    'o',
    'w',
    'e',
    'r',
    's',
    't',
    'i',
    'd',
    'n',
    'w</w>',
    'r</w>',
    't</w>',
    'lo',
    'low',
    'er</w>',
    'low</w>',
    'lowest</w>',
    'newer</w>',
    'wider</w>',
    '<unk>',
]
__snake_case       :	Union[str, Any]            =dict(zip(vocab, range(len(vocab))))
__snake_case       :	List[Any]            =['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
							__snake_case       :	Optional[Any]            =Path(tmpdirname)
							__snake_case       :	int            =build_dir / VOCAB_FILES_NAMES['src_vocab_file']
							__snake_case       :	str            =build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
							__snake_case       :	Optional[int]            =build_dir / VOCAB_FILES_NAMES['merges_file']
							with open(src_vocab_file, 'w') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(tgt_vocab_file, 'w') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(merges_file, 'w') as fp:
														fp.write('\n'.join(merges))

							__snake_case       :	Union[str, Any]            =FSMTTokenizer(
							    langs=['en', 'ru'],
							    src_vocab_size=len(vocab),
							    tgt_vocab_size=len(vocab),
							    src_vocab_file=src_vocab_file,
							    tgt_vocab_file=tgt_vocab_file,
							    merges_file=merges_file,
							)

__snake_case       :	Dict            =FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

__snake_case       :	Optional[Any]            =FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__snake_case       :	Dict            =tokenizer(['Making tiny model'], return_tensors='pt')
__snake_case       :	Dict            =tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-ru

