






'''simple docstring'''







import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase									=   logging.get_logger(__name__)





def 		_UpperCAmelCase (				__A				: Tuple    ,		__A				: List[str]    ,		__A				: Dict   ):
				a_		:	List[str]									=   os.path.abspath(__A   )
				logger.info(f'Converting TensorFlow checkpoint from {tf_path}'   )
				# Load weights from TF model
				a_		:	List[Any]									=   tf.train.list_variables(__A   )
				a_		:	Union[str, Any]									=   []
				a_		:	Union[str, Any]									=   []
				a_		:	Optional[int]									=   []
				for full_name, shape in init_vars:
								# logger.info(f"Loading TF weight {name} with shape {shape}")
								a_		:	Dict									=   full_name.split('''/'''   )
								if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
												logger.info(f'Skipping non-model layer {full_name}'   )
												continue
								if "optimizer" in full_name:
												logger.info(f'Skipping optimization layer {full_name}'   )
												continue
								if name[0] == "model":
												# ignore initial 'model'
												a_		:	List[Any]									=   name[1:]
								# figure out how many levels deep the name is
								a_		:	Tuple									=   0
								for _name in name:
												if _name.startswith('''layer_with_weights'''   ):
																depth += 1
												else:
																break
								layer_depth.append(__A   )
								# read data
								a_		:	Optional[Any]									=   tf.train.load_variable(__A    ,		__A   )
								names.append('''/'''.join(__A   )   )
								arrays.append(__A   )
				logger.info(f'Read a total of {len(__A   ):,} layers'   )

				# Sanity check
				if len(set(__A   )   ) != 1:
								raise ValueError(f'Found layer names with different depths (layer depth {list(set(__A   )   )})'   )
				a_		:	Any									=   list(set(__A   )   )[0]
				if layer_depth != 1:
								raise ValueError(
								    '''The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'''
								    ''' heads.'''   )

				# convert layers
				logger.info('''Converting weights...'''   )
				for full_name, array in zip(__A    ,		__A   ):
								a_		:	str									=   full_name.split('''/'''   )
								a_		:	int									=   model
								a_		:	List[Any]									=   []
								for i, m_name in enumerate(__A   ):
												if m_name == ".ATTRIBUTES":
																# variable names end with .ATTRIBUTES/VARIABLE_VALUE
																break
												if m_name.startswith('''layer_with_weights'''   ):
																a_		:	Optional[int]									=   int(m_name.split('''-'''   )[-1]   )
																if layer_num <= 2:
																				# embedding layers
																				# layer_num 0: word_embeddings
																				# layer_num 1: position_embeddings
																				# layer_num 2: token_type_embeddings
																				continue
																elif layer_num == 3:
																				# embedding LayerNorm
																				trace.extend(['''embeddings''', '''LayerNorm''']   )
																				a_		:	str									=   getattr(__A    ,		'''embeddings'''   )
																				a_		:	str									=   getattr(__A    ,		'''LayerNorm'''   )
																elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
																				# encoder layers
																				trace.extend(['''encoder''', '''layer''', str(layer_num - 4   )]   )
																				a_		:	str									=   getattr(__A    ,		'''encoder'''   )
																				a_		:	Tuple									=   getattr(__A    ,		'''layer'''   )
																				a_		:	List[str]									=   pointer[layer_num - 4]
																elif layer_num == config.num_hidden_layers + 4:
																				# pooler layer
																				trace.extend(['''pooler''', '''dense''']   )
																				a_		:	List[str]									=   getattr(__A    ,		'''pooler'''   )
																				a_		:	int									=   getattr(__A    ,		'''dense'''   )
												elif m_name == "embeddings":
																trace.append('''embeddings'''   )
																a_		:	Any									=   getattr(__A    ,		'''embeddings'''   )
																if layer_num == 0:
																				trace.append('''word_embeddings'''   )
																				a_		:	int									=   getattr(__A    ,		'''word_embeddings'''   )
																elif layer_num == 1:
																				trace.append('''position_embeddings'''   )
																				a_		:	List[str]									=   getattr(__A    ,		'''position_embeddings'''   )
																elif layer_num == 2:
																				trace.append('''token_type_embeddings'''   )
																				a_		:	Any									=   getattr(__A    ,		'''token_type_embeddings'''   )
																else:
																				raise ValueError(f'Unknown embedding layer with name {full_name}'   )
																trace.append('''weight'''   )
																a_		:	int									=   getattr(__A    ,		'''weight'''   )
												elif m_name == "_attention_layer":
																# self-attention layer
																trace.extend(['''attention''', '''self''']   )
																a_		:	str									=   getattr(__A    ,		'''attention'''   )
																a_		:	List[Any]									=   getattr(__A    ,		'''self'''   )
												elif m_name == "_attention_layer_norm":
																# output attention norm
																trace.extend(['''attention''', '''output''', '''LayerNorm''']   )
																a_		:	Optional[Any]									=   getattr(__A    ,		'''attention'''   )
																a_		:	List[str]									=   getattr(__A    ,		'''output'''   )
																a_		:	str									=   getattr(__A    ,		'''LayerNorm'''   )
												elif m_name == "_attention_output_dense":
																# output attention dense
																trace.extend(['''attention''', '''output''', '''dense''']   )
																a_		:	Optional[int]									=   getattr(__A    ,		'''attention'''   )
																a_		:	int									=   getattr(__A    ,		'''output'''   )
																a_		:	int									=   getattr(__A    ,		'''dense'''   )
												elif m_name == "_output_dense":
																# output dense
																trace.extend(['''output''', '''dense''']   )
																a_		:	Union[str, Any]									=   getattr(__A    ,		'''output'''   )
																a_		:	List[str]									=   getattr(__A    ,		'''dense'''   )
												elif m_name == "_output_layer_norm":
																# output dense
																trace.extend(['''output''', '''LayerNorm''']   )
																a_		:	List[Any]									=   getattr(__A    ,		'''output'''   )
																a_		:	Tuple									=   getattr(__A    ,		'''LayerNorm'''   )
												elif m_name == "_key_dense":
																# attention key
																trace.append('''key'''   )
																a_		:	Dict									=   getattr(__A    ,		'''key'''   )
												elif m_name == "_query_dense":
																# attention query
																trace.append('''query'''   )
																a_		:	List[Any]									=   getattr(__A    ,		'''query'''   )
												elif m_name == "_value_dense":
																# attention value
																trace.append('''value'''   )
																a_		:	List[Any]									=   getattr(__A    ,		'''value'''   )
												elif m_name == "_intermediate_dense":
																# attention intermediate dense
																trace.extend(['''intermediate''', '''dense''']   )
																a_		:	List[str]									=   getattr(__A    ,		'''intermediate'''   )
																a_		:	str									=   getattr(__A    ,		'''dense'''   )
												elif m_name == "_output_layer_norm":
																# output layer norm
																trace.append('''output'''   )
																a_		:	Optional[int]									=   getattr(__A    ,		'''output'''   )
												# weights & biases
												elif m_name in ["bias", "beta"]:
																trace.append('''bias'''   )
																a_		:	List[str]									=   getattr(__A    ,		'''bias'''   )
												elif m_name in ["kernel", "gamma"]:
																trace.append('''weight'''   )
																a_		:	Optional[int]									=   getattr(__A    ,		'''weight'''   )
												else:
																logger.warning(f'Ignored {m_name}'   )
        # for certain layers reshape is necessary
								a_		:	Union[str, Any]									=   '''.'''.join(__A   )
								if re.match(R'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'''    ,		__A   ) or re.match(
								    R'''(\S+)\.attention\.output\.dense\.weight'''    ,		__A   ):
												a_		:	Dict									=   array.reshape(pointer.data.shape   )
								if "kernel" in full_name:
												a_		:	int									=   array.transpose()
								if pointer.shape == array.shape:
												a_		:	Dict									=   torch.from_numpy(__A   )
								else:
												raise ValueError(
												    f'Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'
												    f' {array.shape}'   )
								logger.info(f'Successfully set variable {full_name} to PyTorch layer {trace}'   )
				return model





def 		_UpperCAmelCase (				__A				: Any    ,		__A				: str    ,		__A				: Tuple   ):
				# Instantiate model
				logger.info(f'Loading model based on config from {config_path}...'   )
				a_		:	Optional[int]									=   BertConfig.from_json_file(__A   )
				a_		:	Tuple									=   BertModel(__A   )

				# Load weights from checkpoint
				logger.info(f'Loading weights from checkpoint {tf_checkpoint_path}...'   )
				load_tfa_weights_in_bert(__A    ,		__A    ,		__A   )

				# Save pytorch-model
				logger.info(f'Saving PyTorch model to {pytorch_dump_path}...'   )
				torch.save(model.state_dict()    ,		__A   )


if __name__ == "__main__":
		__lowerCAmelCase									=   argparse.ArgumentParser()
		parser.add_argument(
		    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow 2.x checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    type=str,
		    required=True,
		    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
		)
		parser.add_argument(
		    '--pytorch_dump_path',
		    type=str,
		    required=True,
		    help='Path to the output PyTorch model (must include filename).',
		)
		__lowerCAmelCase									=   parser.parse_args()
		convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)












'''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,
)

__lowerCAmelCase									=   logging.getLogger(__name__)

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





def 		_UpperCAmelCase (				):
				a_		:	int									=   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.'''   )

				a_		:	Tuple									=   parser.parse_args()

				return args





def 		_UpperCAmelCase (				__A				: List[str]    ,		__A				: Dict="cpu"   ):
				a_		:	str									=   model_dict[model_name].from_pretrained(__A   ).to(__A   )
				a_		:	str									=   tokenizer_dict[model_name].from_pretrained(__A   )

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

				return huggingface_model, tokenizer





def 		_UpperCAmelCase (				__A				: int    ,		__A				: Dict    ,		__A				: str    ,		__A				: Optional[Any]    ,		__A				: Any   ):
				model.eval()

				a_		:	Union[str, Any]									=   None
				a_		:	str									=   torch.jit.script(BARTBeamSearchGenerator(__A   )   )

				with torch.no_grad():
								a_		:	Any									=   '''My friends are cool but they eat too many carbs.'''
								a_		:	Dict									=   tokenizer([ARTICLE_TO_SUMMARIZE]    ,		max_length=10_24    ,		return_tensors='''pt'''   ).to(model.device   )

								a_		:	Dict									=   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   )   )

								a_		:	Any									=   remove_dup_initializers(os.path.abspath(__A   )   )

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

								a_		:	int									=   onnxruntime.InferenceSession(__A   )
								a_		:	Optional[Any]									=   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 		_UpperCAmelCase (				):
				a_		:	int									=   parse_args()
				a_		:	List[str]									=   5
				a_		:	List[str]									=   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()

				a_		:	Any									=   torch.device(args.device   )

				a_					,      a_		:	Optional[Any]									=   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:
								a_		:	Dict									=   args.max_length

				if args.num_beams:
								a_		:	str									=   args.num_beams

				if args.output_file_path:
								a_		:	str									=   args.output_file_path
				else:
								a_		:	Union[str, Any]									=   '''BART.onnx'''

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


if __name__ == "__main__":
		main()




