
"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification

def lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       str           =   SwinvaConfig()
							SCREAMING_SNAKE_CASE__						:       str           =   swinva_name.split("""_"""						)

							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   name_split[1]
							if "to" in name_split[3]:
														SCREAMING_SNAKE_CASE__						:       Dict           =   int(name_split[3][-3:]						)
							else:
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   int(name_split[3]						)
							if "to" in name_split[2]:
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   int(name_split[2][-2:]						)
							else:
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   int(name_split[2][6:]						)

							if model_size == "tiny":
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   96
														SCREAMING_SNAKE_CASE__						:       List[str]           =   (2, 2, 6, 2)
														SCREAMING_SNAKE_CASE__						:       str           =   (3, 6, 12, 24)
							elif model_size == "small":
														SCREAMING_SNAKE_CASE__						:       int           =   96
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   (2, 2, 18, 2)
														SCREAMING_SNAKE_CASE__						:       Dict           =   (3, 6, 12, 24)
							elif model_size == "base":
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   128
														SCREAMING_SNAKE_CASE__						:       Any           =   (2, 2, 18, 2)
														SCREAMING_SNAKE_CASE__						:       Any           =   (4, 8, 16, 32)
							else:
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   192
														SCREAMING_SNAKE_CASE__						:       Any           =   (2, 2, 18, 2)
														SCREAMING_SNAKE_CASE__						:       Dict           =   (6, 12, 24, 48)

							if "to" in swinva_name:
														SCREAMING_SNAKE_CASE__						:       str           =   (12, 12, 12, 6)

							if ("22k" in swinva_name) and ("to" not in swinva_name):
														SCREAMING_SNAKE_CASE__						:       Dict           =   21_841
														SCREAMING_SNAKE_CASE__						:       str           =   """huggingface/label-files"""
														SCREAMING_SNAKE_CASE__						:       Any           =   """imagenet-22k-id2label.json"""
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   json.load(open(hf_hub_download(_snake_case   ,_snake_case   ,repo_type="""dataset"""						)   ,"""r"""						)						)
														SCREAMING_SNAKE_CASE__						:       List[str]           =   {int(_snake_case						): v for k, v in idalabel.items()}
														SCREAMING_SNAKE_CASE__						:       Any           =   idalabel
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   {v: k for k, v in idalabel.items()}

							else:
														SCREAMING_SNAKE_CASE__						:       Dict           =   1_000
														SCREAMING_SNAKE_CASE__						:       Any           =   """huggingface/label-files"""
														SCREAMING_SNAKE_CASE__						:       int           =   """imagenet-1k-id2label.json"""
														SCREAMING_SNAKE_CASE__						:       str           =   json.load(open(hf_hub_download(_snake_case   ,_snake_case   ,repo_type="""dataset"""						)   ,"""r"""						)						)
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   {int(_snake_case						): v for k, v in idalabel.items()}
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   idalabel
														SCREAMING_SNAKE_CASE__						:       List[str]           =   {v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE__						:       List[str]           =   img_size
							SCREAMING_SNAKE_CASE__						:       Dict           =   num_classes
							SCREAMING_SNAKE_CASE__						:       Tuple           =   embed_dim
							SCREAMING_SNAKE_CASE__						:       str           =   depths
							SCREAMING_SNAKE_CASE__						:       str           =   num_heads
							SCREAMING_SNAKE_CASE__						:       List[str]           =   window_size

							return config

def lowercase_  (   _snake_case						):
							if "patch_embed.proj" in name:
														SCREAMING_SNAKE_CASE__						:       List[str]           =   name.replace("""patch_embed.proj"""   ,"""embeddings.patch_embeddings.projection"""						)
							if "patch_embed.norm" in name:
														SCREAMING_SNAKE_CASE__						:       str           =   name.replace("""patch_embed.norm"""   ,"""embeddings.norm"""						)
							if "layers" in name:
														SCREAMING_SNAKE_CASE__						:       Tuple           =   """encoder.""" + name
							if "attn.proj" in name:
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   name.replace("""attn.proj"""   ,"""attention.output.dense"""						)
							if "attn" in name:
														SCREAMING_SNAKE_CASE__						:       str           =   name.replace("""attn"""   ,"""attention.self"""						)
							if "norm1" in name:
														SCREAMING_SNAKE_CASE__						:       Tuple           =   name.replace("""norm1"""   ,"""layernorm_before"""						)
							if "norm2" in name:
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   name.replace("""norm2"""   ,"""layernorm_after"""						)
							if "mlp.fc1" in name:
														SCREAMING_SNAKE_CASE__						:       Dict           =   name.replace("""mlp.fc1"""   ,"""intermediate.dense"""						)
							if "mlp.fc2" in name:
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   name.replace("""mlp.fc2"""   ,"""output.dense"""						)
							if "q_bias" in name:
														SCREAMING_SNAKE_CASE__						:       int           =   name.replace("""q_bias"""   ,"""query.bias"""						)
							if "k_bias" in name:
														SCREAMING_SNAKE_CASE__						:       Dict           =   name.replace("""k_bias"""   ,"""key.bias"""						)
							if "v_bias" in name:
														SCREAMING_SNAKE_CASE__						:       str           =   name.replace("""v_bias"""   ,"""value.bias"""						)
							if "cpb_mlp" in name:
														SCREAMING_SNAKE_CASE__						:       Dict           =   name.replace("""cpb_mlp"""   ,"""continuous_position_bias_mlp"""						)
							if name == "norm.weight":
														SCREAMING_SNAKE_CASE__						:       Tuple           =   """layernorm.weight"""
							if name == "norm.bias":
														SCREAMING_SNAKE_CASE__						:       Dict           =   """layernorm.bias"""

							if "head" in name:
														SCREAMING_SNAKE_CASE__						:       Any           =   name.replace("""head"""   ,"""classifier"""						)
							else:
														SCREAMING_SNAKE_CASE__						:       str           =   """swinv2.""" + name

							return name

def lowercase_  (   _snake_case   ,_snake_case						):
							for key in orig_state_dict.copy().keys():
														SCREAMING_SNAKE_CASE__						:       Any           =   orig_state_dict.pop(_snake_case						)

														if "mask" in key:
																					continue
														elif "qkv" in key:
																					SCREAMING_SNAKE_CASE__						:       int           =   key.split("""."""						)
																					SCREAMING_SNAKE_CASE__						:       Any           =   int(key_split[1]						)
																					SCREAMING_SNAKE_CASE__						:       Optional[int]           =   int(key_split[3]						)
																					SCREAMING_SNAKE_CASE__						:       Any           =   model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																					if "weight" in key:
																												SCREAMING_SNAKE_CASE__						:       List[Any]           =   val[:dim, :]
																												SCREAMING_SNAKE_CASE__						:       Any           =   val[dim : dim * 2, :]
																												SCREAMING_SNAKE_CASE__						:       List[str]           =   val[-dim:, :]
																					else:
																												SCREAMING_SNAKE_CASE__						:       Any           =   val[:dim]
																												SCREAMING_SNAKE_CASE__						:       int           =   val[
																												    dim : dim * 2
																												]
																												SCREAMING_SNAKE_CASE__						:       Optional[int]           =   val[-dim:]
														else:
																					SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   val

							return orig_state_dict

def lowercase_  (   _snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   timm.create_model(_snake_case   ,pretrained=_snake_case						)
							timm_model.eval()

							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   get_swinva_config(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       Dict           =   SwinvaForImageClassification(_snake_case						)
							model.eval()

							SCREAMING_SNAKE_CASE__						:       str           =   convert_state_dict(timm_model.state_dict()   ,_snake_case						)
							model.load_state_dict(_snake_case						)

							SCREAMING_SNAKE_CASE__						:       str           =   """http://images.cocodataset.org/val2017/000000039769.jpg"""

							SCREAMING_SNAKE_CASE__						:       Tuple           =   AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""   ,"""-"""						)						)						)
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   Image.open(requests.get(_snake_case   ,stream=_snake_case						).raw						)
							SCREAMING_SNAKE_CASE__						:       Dict           =   image_processor(images=_snake_case   ,return_tensors="""pt"""						)

							SCREAMING_SNAKE_CASE__						:       int           =   timm_model(inputs["""pixel_values"""]						)
							SCREAMING_SNAKE_CASE__						:       Dict           =   model(**_snake_case						).logits

							assert torch.allclose(_snake_case   ,_snake_case   ,atol=1E-3						)

							print(f'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''						)
							model.save_pretrained(_snake_case						)

							print(f'''Saving image processor to {pytorch_dump_folder_path}'''						)
							image_processor.save_pretrained(_snake_case						)

							model.push_to_hub(
							    repo_path_or_name=Path(_snake_case   ,_snake_case						)   ,organization="""nandwalritik"""   ,commit_message="""Add model"""   ,)


if __name__ == "__main__":
					UpperCAmelCase__      :    List[str]    							= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--swinv2_name',
					    default='swinv2_tiny_patch4_window8_256',
					    type=str,
					    help='Name of the Swinv2 timm model you\'d like to convert.',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
					)

					UpperCAmelCase__      :    int    							= parser.parse_args()
					convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_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,
)

_a      :		Tuple           =   logging.getLogger(__name__)

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





def 							SCREAMING_SNAKE_CASE     (			)       ->      int:
  _lowerCAmelCase						:       int  			=     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						:       Optional[Any]  			=     parser.parse_args()

  return args





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       Union[str, Any]="cpu"	)       ->      str:
  _lowerCAmelCase						:       List[str]  			=     model_dict[model_name].from_pretrained(_lowerCamelCase	).to(_lowerCamelCase	)
  _lowerCAmelCase						:       Optional[Any]  			=     tokenizer_dict[model_name].from_pretrained(_lowerCamelCase	)

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

  return huggingface_model, tokenizer





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       List[str]			,_lowerCamelCase						:       int			,_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       List[str]	)       ->      Tuple:
  model.eval()

  _lowerCAmelCase						:       str  			=     None
  _lowerCAmelCase						:       int  			=     torch.jit.script(BARTBeamSearchGenerator(_lowerCamelCase	)	)

  with torch.no_grad():
    _lowerCAmelCase						:       List[Any]  			=     """My friends are cool but they eat too many carbs."""
    _lowerCAmelCase						:       Union[str, 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						:       List[str]  			=     remove_dup_initializers(os.path.abspath(_lowerCamelCase	)	)

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

    _lowerCAmelCase						:       str  			=     onnxruntime.InferenceSession(_lowerCamelCase	)
    _lowerCAmelCase						:       Union[str, Any]  			=     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     (			)       ->      Any:
  _lowerCAmelCase						:       Any  			=     parse_args()
  _lowerCAmelCase						:       List[Any]  			=     5
  _lowerCAmelCase						:       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()

  _lowerCAmelCase						:       Optional[Any]  			=     torch.device(args.device	)

  _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     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						:       Dict  			=     args.max_length

  if args.num_beams:
    _lowerCAmelCase						:       Dict  			=     args.num_beams

  if args.output_file_path:
    _lowerCAmelCase						:       Any  			=     args.output_file_path
  else:
    _lowerCAmelCase						:       Union[str, Any]  			=     """BART.onnx"""

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


if __name__ == "__main__":
     main()




