
import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A		: Union[str, Any]								=     logging.get_logger(__name__)


A		: Any								=     {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

A		: Union[str, Any]								=     {
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

A		: List[str]								=     {'''facebook/blenderbot_small-90M''': 512}




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]       )  ->     int:
						_lowercase					       =					set()
						_lowercase					       =					word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)       )
												_lowercase					       =					char

						_lowercase					       =					set(__lowerCAmelCase       )
						return pairs



class    a_ (			__UpperCamelCase    ):
			a	:			Union[str, Any]					   =							VOCAB_FILES_NAMES
			a	:			str					   =							PRETRAINED_VOCAB_FILES_MAP
			a	:			str					   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a	:			List[Any]					   =							['input_ids', 'attention_mask']






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase="__start__"				,			__UpperCamelCase="__end__"				,			__UpperCamelCase="__unk__"				,			__UpperCamelCase="__null__"				,			**__UpperCamelCase				,			):
									super().__init__(unk_token=__UpperCamelCase				,			bos_token=__UpperCamelCase				,			eos_token=__UpperCamelCase				,			pad_token=__UpperCamelCase				,			**__UpperCamelCase  )

									with open(__UpperCamelCase				,			encoding="""utf-8"""  ) as vocab_handle:
															_lowercase					       =					json.load(__UpperCamelCase  )
									_lowercase					       =					{v: k for k, v in self.encoder.items()}
									with open(__UpperCamelCase				,			encoding="""utf-8"""  ) as merges_handle:
															_lowercase					       =					merges_handle.read().split("""\n"""  )[1:-1]
									_lowercase					       =					[tuple(merge.split()  ) for merge in merges]
									_lowercase					       =					dict(zip(__UpperCamelCase				,			range(len(__UpperCamelCase  )  )  )  )
									_lowercase					       =					{}






			@property
			def    UpperCamelCase_				(      self  ):
									return len(self.encoder  )






			def    UpperCamelCase_				(      self  ):
									return dict(self.encoder				,			**self.added_tokens_encoder  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									if token in self.cache:
															return self.cache[token]
									_lowercase					       =					re.sub("""([.,!?()])"""				,			r""" \1"""				,			__UpperCamelCase  )
									_lowercase					       =					re.sub("""(')"""				,			r""" \1 """				,			__UpperCamelCase  )
									_lowercase					       =					re.sub(r"""\s{2,}"""				,			""" """				,			__UpperCamelCase  )
									if "\n" in token:
															_lowercase					       =					token.replace("""\n"""				,			""" __newln__"""  )

									_lowercase					       =					token.split(""" """  )
									_lowercase					       =					[]
									for token in tokens:
															if not len(__UpperCamelCase  ):
																					continue

															_lowercase					       =					token.lower()
															_lowercase					       =					tuple(__UpperCamelCase  )
															_lowercase					       =					tuple(list(word[:-1]  ) + [word[-1] + """</w>"""]  )
															_lowercase					       =					get_pairs(__UpperCamelCase  )

															if not pairs:
																					words.append(__UpperCamelCase  )
																					continue

															while True:
																					_lowercase					       =					min(__UpperCamelCase				,			key=lambda __UpperCamelCase  : self.bpe_ranks.get(__UpperCamelCase				,			float("""inf"""  )  )  )
																					if bigram not in self.bpe_ranks:
																											break
																					_lowercase       ,						_lowercase					       =					bigram
																					_lowercase					       =					[]
																					_lowercase					       =					0

																					while i < len(__UpperCamelCase  ):
																											try:
																																	_lowercase					       =					word.index(__UpperCamelCase				,			__UpperCamelCase  )
																																	new_word.extend(word[i:j]  )
																																	_lowercase					       =					j
																											except ValueError:
																																	new_word.extend(word[i:]  )
																																	break

																											if word[i] == first and i < len(__UpperCamelCase  ) - 1 and word[i + 1] == second:
																																	new_word.append(first + second  )
																																	i += 2
																											else:
																																	new_word.append(word[i]  )
																																	i += 1
																					_lowercase					       =					tuple(__UpperCamelCase  )
																					_lowercase					       =					new_word
																					if len(__UpperCamelCase  ) == 1:
																											break
																					else:
																											_lowercase					       =					get_pairs(__UpperCamelCase  )
															_lowercase					       =					"""@@ """.join(__UpperCamelCase  )
															_lowercase					       =					word[:-4]

															_lowercase					       =					word
															words.append(__UpperCamelCase  )
									return " ".join(__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					[]

									_lowercase					       =					re.findall(r"""\S+\n?"""				,			__UpperCamelCase  )

									for token in words:
															split_tokens.extend(list(self.bpe(__UpperCamelCase  ).split(""" """  )  )  )
									return split_tokens






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					token.lower()
									return self.encoder.get(__UpperCamelCase				,			self.encoder.get(self.unk_token  )  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									return self.decoder.get(__UpperCamelCase				,			self.unk_token  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					""" """.join(__UpperCamelCase  ).replace("""@@ """				,			""""""  ).strip()
									return out_string






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase = None  ):
									if not os.path.isdir(__UpperCamelCase  ):
															logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
															return
									_lowercase					       =					os.path.join(
									    __UpperCamelCase				,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )
									_lowercase					       =					os.path.join(
									    __UpperCamelCase				,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]  )

									with open(__UpperCamelCase				,			"""w"""				,			encoding="""utf-8"""  ) as f:
															f.write(json.dumps(self.encoder				,			indent=2				,			sort_keys=__UpperCamelCase				,			ensure_ascii=__UpperCamelCase  ) + """\n"""  )

									_lowercase					       =					0
									with open(__UpperCamelCase				,			"""w"""				,			encoding="""utf-8"""  ) as writer:
															writer.write("""#version: 0.2\n"""  )
															for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,			key=lambda __UpperCamelCase  : kv[1]  ):
																					if index != token_index:
																											logger.warning(
																											    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																											    """ Please check that the tokenizer is not corrupted!"""  )
																											_lowercase					       =					token_index
																					writer.write(""" """.join(__UpperCamelCase  ) + """\n"""  )
																					index += 1

									return vocab_file, merge_file

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,
)

__magic_name__  							=	logging.getLogger(__name__)

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





def 						SCREAMING_SNAKE_CASE__     (   ):
    snake_case__    =					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."				)

    snake_case__    =					parser.parse_args()

    return args





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase="cpu"				):
    snake_case__    =					model_dict[model_name].from_pretrained(__lowerCAmelCase				).to(__lowerCAmelCase				)
    snake_case__    =					tokenizer_dict[model_name].from_pretrained(__lowerCAmelCase				)

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

    return huggingface_model, tokenizer





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				):
    model.eval()

    snake_case__    =					None
    snake_case__    =					torch.jit.script(BARTBeamSearchGenerator(__lowerCAmelCase				)				)

    with torch.no_grad():
        snake_case__    =					"My friends are cool but they eat too many carbs."
        snake_case__    =					tokenizer([ARTICLE_TO_SUMMARIZE]     ,		max_length=1_024     ,		return_tensors="pt"				).to(model.device				)

        snake_case__    =					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				)				)

        snake_case__    =					remove_dup_initializers(os.path.abspath(__lowerCAmelCase				)				)

        logger.info("Deduplicated and optimized model written to {}".format(__lowerCAmelCase				)				)

        snake_case__    =					onnxruntime.InferenceSession(__lowerCAmelCase				)
        snake_case__    =					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 						SCREAMING_SNAKE_CASE__     (   ):
    snake_case__    =					parse_args()
    snake_case__    =					5
    snake_case__    =					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()

    snake_case__    =					torch.device(args.device				)

    snake_case__    ,       snake_case__    =					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:
        snake_case__    =					args.max_length

    if args.num_beams:
        snake_case__    =					args.num_beams

    if args.output_file_path:
        snake_case__    =					args.output_file_path
    else:
        snake_case__    =					"BART.onnx"

    logger.info("Exporting model to ONNX"				)
    export_and_validate_model(__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase				)


if __name__ == "__main__":
     main()



