






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,
)

__a      :       List[Any]				  =   logging.getLogger(__name__)

__a      :       List[str]				  =   {"facebook/bart-base": BartForConditionalGeneration}
__a      :       int				  =   {"facebook/bart-base": BartTokenizer}



def 			_SCREAMING_SNAKE_CASE						( )						->      Optional[Any]:





    """simple docstring"""

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

    __A         =	parser.parse_args()

    return args



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Union[str, Any]			,				__lowercase   :			str="cpu"    )						->      Optional[int]:





    """simple docstring"""

    __A         =	model_dict[model_name].from_pretrained(__lowercase    ).to(__lowercase    )
    __A         =	tokenizer_dict[model_name].from_pretrained(__lowercase    )

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

    return huggingface_model, tokenizer



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			Dict			,				__lowercase   :			Tuple			,				__lowercase   :			Union[str, Any]			,				__lowercase   :			str    )						->      int:





    """simple docstring"""

    model.eval()

    __A         =	None
    __A         =	torch.jit.script(BARTBeamSearchGenerator(__lowercase    )    )

    with torch.no_grad():
        __A         =	"""My friends are cool but they eat too many carbs."""
        __A         =	tokenizer([ARTICLE_TO_SUMMARIZE]			,				max_length=1_0_2_4			,				return_tensors="""pt"""    ).to(model.device    )

        __A         =	model.generate(
            inputs["""input_ids"""]			,				attention_mask=inputs["""attention_mask"""]			,				num_beams=__lowercase			,				max_length=__lowercase			,				early_stopping=__lowercase			,				decoder_start_token_id=model.config.decoder_start_token_id			,				)

        torch.onnx.export(
            __lowercase			,				(
                inputs["""input_ids"""],
                inputs["""attention_mask"""],
                num_beams,
                max_length,
                model.config.decoder_start_token_id,
            )			,				__lowercase			,				opset_version=1_4			,				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=__lowercase			,				)

        logger.info("""Model exported to {}""".format(__lowercase    )    )

        __A         =	remove_dup_initializers(os.path.abspath(__lowercase    )    )

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

        __A         =	onnxruntime.InferenceSession(__lowercase    )
        __A         =	ort_sess.run(
            __lowercase			,				{
                """input_ids""": inputs["""input_ids"""].cpu().numpy(),
                """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
                """num_beams""": np.array(__lowercase    ),
                """max_length""": np.array(__lowercase    ),
                """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						( )						->      List[str]:





    """simple docstring"""

    __A         =	parse_args()
    __A         =	5
    __A         =	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         =	torch.device(args.device    )

    __A    ,	__A         =	load_model_tokenizer(args.model_name_or_path			,				__lowercase    )

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

    model.to(__lowercase    )

    if args.max_length:
        __A         =	args.max_length

    if args.num_beams:
        __A         =	args.num_beams

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

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


if __name__ == "__main__":
  main()












import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			int    )						->      Union[str, Any]:





    """simple docstring"""

    __A         =	checkpoints.load_tax_checkpoint(__lowercase    )
    __A         =	flatten_dict(__lowercase    )
    return flax_params



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]    )						->      List[str]:





    """simple docstring"""

    __A         =	{}

    __A         =	{
        """token_embedder""": """embeddings""",
        """encoder_norm""": """layernorm""",
        """kernel""": """weight""",
        """.out""": """.output""",
        """scale""": """weight""",
        """embedders_0.pos_embedding""": """row_embedder.weight""",
        """embedders_1.pos_embedding""": """column_embedder.weight""",
    }

    __A         =	{
        """query""": """attention.query""",
        """key""": """attention.key""",
        """value""": """attention.value""",
        """output.dense""": """output""",
        """encoder_decoder_attention.o""": """encoder_decoder_attention.attention.o""",
        """pre_self_attention_layer_norm""": """self_attention.layer_norm""",
        """pre_cross_attention_layer_norm""": """encoder_decoder_attention.layer_norm""",
        """mlp.""": """mlp.DenseReluDense.""",
        """pre_mlp_layer_norm""": """mlp.layer_norm""",
        """self_attention.o""": """self_attention.attention.o""",
        """decoder.embeddings.embedding""": """decoder.embed_tokens.weight""",
        """decoder.relpos_bias.rel_embedding""": """decoder.layer.0.self_attention.attention.relative_attention_bias.weight""",
        """decoder.decoder_norm.weight""": """decoder.final_layer_norm.weight""",
        """decoder.logits_dense.weight""": """decoder.lm_head.weight""",
    }

    for key in flax_dict.keys():
        if "target" in key:
            # remove the first prefix from the key
            __A         =	""".""".join(key[1:]    )

            # rename the key
            for old, new in CONVERSION_MAPPING.items():
                __A         =	new_key.replace(__lowercase			,				__lowercase    )

            if "decoder" in new_key:
                for old, new in DECODER_CONVERSION_MAPPING.items():
                    __A         =	new_key.replace(__lowercase			,				__lowercase    )

            if "layers" in new_key and "decoder" not in new_key:
                # use regex to replace the layer number
                __A         =	re.sub(R"""layers_(\d+)"""			,				R"""layer.\1"""			,				__lowercase    )
                __A         =	new_key.replace("""encoder"""			,				"""encoder.encoder"""    )

            elif "layers" in new_key and "decoder" in new_key:
                # use regex to replace the layer number
                __A         =	re.sub(R"""layers_(\d+)"""			,				R"""layer.\1"""			,				__lowercase    )

            __A         =	flax_dict[key]

    __A         =	{}
    # convert converted_dict into torch format
    for key in converted_dict.keys():
        if ("embed_tokens" not in key) and ("embedder" not in key):
            __A         =	torch.from_numpy(converted_dict[key].T    )
        else:
            __A         =	torch.from_numpy(converted_dict[key]    )

    return converted_torch_dict



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			List[Any]			,				__lowercase   :			Dict=False			,				__lowercase   :			List[str]=False    )						->      Any:





    """simple docstring"""

    __A         =	get_flax_param(__lowercase    )

    if not use_large:
        __A         =	PixaStructVisionConfig()
        __A         =	PixaStructTextConfig()
    else:
        __A         =	PixaStructVisionConfig(
            hidden_size=1_5_3_6			,				d_ff=3_9_6_8			,				num_attention_heads=2_4			,				num_hidden_layers=1_8    )
        __A         =	PixaStructTextConfig(hidden_size=1_5_3_6			,				d_ff=3_9_6_8			,				num_heads=2_4			,				num_layers=1_8    )
    __A         =	PixaStructConfig(
        vision_config=encoder_config.to_dict()			,				text_config=decoder_config.to_dict()			,				is_vqa=__lowercase    )

    __A         =	PixaStructForConditionalGeneration(__lowercase    )

    __A         =	rename_and_convert_flax_params(__lowercase    )
    model.load_state_dict(__lowercase    )

    __A         =	AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""    )
    __A         =	PixaStructImageProcessor()
    __A         =	PixaStructProcessor(image_processor=__lowercase			,				tokenizer=__lowercase    )

    if use_large:
        __A         =	4_0_9_6

    __A         =	True

    # mkdir if needed
    os.makedirs(__lowercase			,				exist_ok=__lowercase    )

    model.save_pretrained(__lowercase    )
    processor.save_pretrained(__lowercase    )

    print("""Model saved in {}""".format(__lowercase    )    )


if __name__ == "__main__":
  __a      :       Union[str, Any]				  =   argparse.ArgumentParser()
  parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--use_large", action="store_true", help="Use large model.")
  parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
  __a      :       Dict				  =   parser.parse_args()

  convert_pixastruct_original_pytorch_checkpoint_to_hf(
      args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
  )




