


'''simple docstring'''

import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


a_  :				Dict						= logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
     import torch_xla.core.xla_model as xm
     import torch_xla.debug.metrics as met





class      snake_case    (    lowercase			):





  """simple docstring"""




  def __init__(    self			,  *UpperCamelCase			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase=None			,  **UpperCamelCase     ):







   """simple docstring"""
   super().__init__(*UpperCamelCase			,  **UpperCamelCase     )
   lowerCamelCase_      =  eval_examples
   lowerCamelCase_      =  post_process_function
   lowerCamelCase_      =  quant_trainer_args
   lowerCamelCase_      =  128  # default number of calibration samples






  def   snake_case		(    self			,  UpperCamelCase=None     ):







   """simple docstring"""
   if calib_dataset is None and self.calib_dataset is None:
    raise ValueError("Trainer: calibration requires an calib_dataset."     )
   lowerCamelCase_      =  calib_dataset if calib_dataset is not None else self.calib_dataset

   lowerCamelCase_      =  self._remove_unused_columns(UpperCamelCase			,  description="Calibration"     )

   return DataLoader(
       UpperCamelCase			,  batch_size=self.args.eval_batch_size			,  collate_fn=self.data_collator			,  drop_last=self.args.dataloader_drop_last			,  num_workers=self.args.dataloader_num_workers			,  pin_memory=self.args.dataloader_pin_memory			,  shuffle=UpperCamelCase			,  )






  def   snake_case		(    self			,  UpperCamelCase=None     ):







   """simple docstring"""
   lowerCamelCase_      =  self.train_dataset if calib_dataset is None else calib_dataset
   lowerCamelCase_      =  self.get_calib_dataloader(UpperCamelCase     )

   lowerCamelCase_      =  self.model
   quant_trainer.configure_model(UpperCamelCase			,  self.quant_trainer_args			,  calib=UpperCamelCase     )
   model.eval()
   quant_trainer.enable_calibration(UpperCamelCase     )

   logger.info("***** Running calibration *****"     )
   logger.info(f'''  Num examples = {self.calib_num}'''     )
   logger.info(f'''  Batch size = {calib_dataloader.batch_size}'''     )

   for step, inputs in enumerate(UpperCamelCase     ):
    # Prediction step
    lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  self.prediction_step(UpperCamelCase			,  UpperCamelCase			,  prediction_loss_only=UpperCamelCase     )
    if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
     break

   quant_trainer.finish_calibration(UpperCamelCase			,  self.quant_trainer_args     )
   lowerCamelCase_      =  model






  def   snake_case		(    self			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase = "eval"     ):







   """simple docstring"""
   lowerCamelCase_      =  self.eval_dataset if eval_dataset is None else eval_dataset
   lowerCamelCase_      =  self.get_eval_dataloader(UpperCamelCase     )
   lowerCamelCase_      =  self.eval_examples if eval_examples is None else eval_examples

   # Temporarily disable metric computation, we will do it in the loop here.
   lowerCamelCase_      =  self.compute_metrics
   lowerCamelCase_      =  None
   lowerCamelCase_      =  self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
   try:
    lowerCamelCase_      =  eval_loop(
        UpperCamelCase			,  description="Evaluation"			,  prediction_loss_only=True if compute_metrics is None else None			,  ignore_keys=UpperCamelCase			,  )
   finally:
    lowerCamelCase_      =  compute_metrics

   if self.post_process_function is not None and self.compute_metrics is not None:
    lowerCamelCase_      =  self.post_process_function(UpperCamelCase			,  UpperCamelCase			,  output.predictions     )
    lowerCamelCase_      =  self.compute_metrics(UpperCamelCase     )

    # Prefix all keys with metric_key_prefix + '_'
    for key in list(metrics.keys()     ):
     if not key.startswith(f'''{metric_key_prefix}_'''     ):
      lowerCamelCase_      =  metrics.pop(UpperCamelCase     )

    self.log(UpperCamelCase     )
   else:
    lowerCamelCase_      =  {}

   if self.args.tpu_metrics_debug or self.args.debug:
    # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
    xm.master_print(met.metrics_report()     )

   lowerCamelCase_      =  self.callback_handler.on_evaluate(self.args			,  self.state			,  self.control			,  UpperCamelCase     )
   return metrics






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=None			,  UpperCamelCase = "test"     ):







   """simple docstring"""
   lowerCamelCase_      =  self.get_test_dataloader(UpperCamelCase     )

   # Temporarily disable metric computation, we will do it in the loop here.
   lowerCamelCase_      =  self.compute_metrics
   lowerCamelCase_      =  None
   lowerCamelCase_      =  self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
   try:
    lowerCamelCase_      =  eval_loop(
        UpperCamelCase			,  description="Prediction"			,  prediction_loss_only=True if compute_metrics is None else None			,  ignore_keys=UpperCamelCase			,  )
   finally:
    lowerCamelCase_      =  compute_metrics

   if self.post_process_function is None or self.compute_metrics is None:
    return output

   lowerCamelCase_      =  self.post_process_function(UpperCamelCase			,  UpperCamelCase			,  output.predictions			,  "predict"     )
   lowerCamelCase_      =  self.compute_metrics(UpperCamelCase     )

   # Prefix all keys with metric_key_prefix + '_'
   for key in list(metrics.keys()     ):
    if not key.startswith(f'''{metric_key_prefix}_'''     ):
     lowerCamelCase_      =  metrics.pop(UpperCamelCase     )

   return PredictionOutput(predictions=predictions.predictions			,  label_ids=predictions.label_ids			,  metrics=UpperCamelCase     )







  def   snake_case		(    self			,  UpperCamelCase="./"     ):







   """simple docstring"""
   lowerCamelCase_      =  self.eval_dataset
   lowerCamelCase_      =  self.get_eval_dataloader(UpperCamelCase     )

   lowerCamelCase_      =  next(iter(UpperCamelCase     )     )

   # saving device - to make it consistent
   lowerCamelCase_      =  torch.device("cuda" if torch.cuda.is_available() else "cpu"     )

   # convert to tuple
   lowerCamelCase_      =  tuple(v.to(UpperCamelCase     ) for k, v in batch.items()     )

   logger.info("Converting model to be onnx compatible"     )
   from pytorch_quantization.nn import TensorQuantizer

   lowerCamelCase_      =  True

   lowerCamelCase_      =  self.model.to(UpperCamelCase     )

   model.eval()
   model.float()

   lowerCamelCase_      =  model.module if hasattr(UpperCamelCase			,  "module"     ) else model
   quant_trainer.configure_model(UpperCamelCase			,  self.quant_trainer_args     )

   lowerCamelCase_      =  os.path.join(UpperCamelCase			,  "model.onnx"     )
   logger.info(f'''exporting model to {output_model_file}'''     )

   lowerCamelCase_      =  {0: "batch_size", 1: "seq_len"}

   torch.onnx.export(
       UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  export_params=UpperCamelCase			,  opset_version=13			,  do_constant_folding=UpperCamelCase			,  input_names=["input_ids", "attention_mask", "token_type_ids"]			,  output_names=["output_start_logits", "output_end_logits"]			,  dynamic_axes={
           "input_ids": axes,
           "attention_mask": axes,
           "token_type_ids": axes,
           "output_start_logits": axes,
           "output_end_logits": axes,
       }			,  verbose=UpperCamelCase			,  )
   logger.info("onnx export finished"     )







'''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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
a_  :				int						= logging.get_logger(__name__)





def __snake_case					(      UpperCAmelCase_						:						List[Any]					,				UpperCAmelCase_						:						Tuple=False		):
 lowerCamelCase_      =  []
 for i in range(config.num_hidden_layers		):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')		)
  rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')		)
  rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')		)
  rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')		)
  rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')		)
  rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')		)
  rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')		)
  rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')		)
  rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')		)
  rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')		)

 # projection layer + position embeddings
 rename_keys.extend(
     [
         ("cls_token", "vit.embeddings.cls_token"),
         ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
         ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
         ("pos_embed", "vit.embeddings.position_embeddings"),
     ]		)

 if base_model:
  # layernorm + pooler
  rename_keys.extend(
      [
          ("norm.weight", "layernorm.weight"),
          ("norm.bias", "layernorm.bias"),
          ("pre_logits.fc.weight", "pooler.dense.weight"),
          ("pre_logits.fc.bias", "pooler.dense.bias"),
      ]		)

  # if just the base model, we should remove "vit" from all keys that start with "vit"
  lowerCamelCase_      =  [(pair[0], pair[1][4:]) if pair[1].startswith("vit"		) else pair for pair in rename_keys]
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ("norm.weight", "vit.layernorm.weight"),
          ("norm.bias", "vit.layernorm.bias"),
          ("head.weight", "classifier.weight"),
          ("head.bias", "classifier.bias"),
      ]		)

 return rename_keys





def __snake_case					(      UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						Dict=False		):
 for i in range(config.num_hidden_layers		):
  if base_model:
   lowerCamelCase_      =  ""
  else:
   lowerCamelCase_      =  "vit."
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  lowerCamelCase_      =  state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''		)
  lowerCamelCase_      =  state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''		)
  # next, add query, keys and values (in that order) to the state dict
  lowerCamelCase_      =  in_proj_weight[
      : config.hidden_size, :
  ]
  lowerCamelCase_      =  in_proj_bias[: config.hidden_size]
  lowerCamelCase_      =  in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  lowerCamelCase_      =  in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  lowerCamelCase_      =  in_proj_weight[
      -config.hidden_size :, :
  ]
  lowerCamelCase_      =  in_proj_bias[-config.hidden_size :]





def __snake_case					(      UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  ["head.weight", "head.bias"]
 for k in ignore_keys:
  state_dict.pop(UpperCAmelCase_					,				UpperCAmelCase_		)





def __snake_case					(      UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						int		):
 lowerCamelCase_      =  dct.pop(UpperCAmelCase_		)
 lowerCamelCase_      =  val





def __snake_case					(      ):
 lowerCamelCase_      =  "http://images.cocodataset.org/val2017/000000039769.jpg"
 lowerCamelCase_      =  Image.open(requests.get(UpperCAmelCase_					,				stream=UpperCAmelCase_		).raw		)
 return im





@torch.no_grad()
def __snake_case					(      UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						Optional[int]		):
 lowerCamelCase_      =  ViTConfig()
 lowerCamelCase_      =  False
 # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
 if vit_name[-5:] == "in21k":
  lowerCamelCase_      =  True
  lowerCamelCase_      =  int(vit_name[-12:-10]		)
  lowerCamelCase_      =  int(vit_name[-9:-6]		)
 else:
  lowerCamelCase_      =  1000
  lowerCamelCase_      =  "huggingface/label-files"
  lowerCamelCase_      =  "imagenet-1k-id2label.json"
  lowerCamelCase_      =  json.load(open(hf_hub_download(UpperCAmelCase_					,				UpperCAmelCase_					,				repo_type="dataset"		)					,				"r"		)		)
  lowerCamelCase_      =  {int(UpperCAmelCase_		): v for k, v in idalabel.items()}
  lowerCamelCase_      =  idalabel
  lowerCamelCase_      =  {v: k for k, v in idalabel.items()}
  lowerCamelCase_      =  int(vit_name[-6:-4]		)
  lowerCamelCase_      =  int(vit_name[-3:]		)
 # size of the architecture
 if "deit" in vit_name:
  if vit_name[9:].startswith("tiny"		):
   lowerCamelCase_      =  192
   lowerCamelCase_      =  768
   lowerCamelCase_      =  12
   lowerCamelCase_      =  3
  elif vit_name[9:].startswith("small"		):
   lowerCamelCase_      =  384
   lowerCamelCase_      =  1536
   lowerCamelCase_      =  12
   lowerCamelCase_      =  6
  else:
   pass
 else:
  if vit_name[4:].startswith("small"		):
   lowerCamelCase_      =  768
   lowerCamelCase_      =  2304
   lowerCamelCase_      =  8
   lowerCamelCase_      =  8
  elif vit_name[4:].startswith("base"		):
   pass
  elif vit_name[4:].startswith("large"		):
   lowerCamelCase_      =  1024
   lowerCamelCase_      =  4096
   lowerCamelCase_      =  24
   lowerCamelCase_      =  16
  elif vit_name[4:].startswith("huge"		):
   lowerCamelCase_      =  1280
   lowerCamelCase_      =  5120
   lowerCamelCase_      =  32
   lowerCamelCase_      =  16

    # load original model from timm
 lowerCamelCase_      =  timm.create_model(UpperCAmelCase_					,				pretrained=UpperCAmelCase_		)
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 lowerCamelCase_      =  timm_model.state_dict()
 if base_model:
  remove_classification_head_(UpperCAmelCase_		)
 lowerCamelCase_      =  create_rename_keys(UpperCAmelCase_					,				UpperCAmelCase_		)
 for src, dest in rename_keys:
  rename_key(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		)
 read_in_q_k_v(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		)

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  lowerCamelCase_      =  ViTModel(UpperCAmelCase_		).eval()
 else:
  lowerCamelCase_      =  ViTForImageClassification(UpperCAmelCase_		).eval()
 model.load_state_dict(UpperCAmelCase_		)

 # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
 if "deit" in vit_name:
  lowerCamelCase_      =  DeiTImageProcessor(size=config.image_size		)
 else:
  lowerCamelCase_      =  ViTImageProcessor(size=config.image_size		)
 lowerCamelCase_      =  image_processor(images=prepare_img()					,				return_tensors="pt"		)
 lowerCamelCase_      =  encoding["pixel_values"]
 lowerCamelCase_      =  model(UpperCAmelCase_		)

 if base_model:
  lowerCamelCase_      =  timm_model.forward_features(UpperCAmelCase_		)
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(UpperCAmelCase_					,				outputs.pooler_output					,				atol=1E-3		)
 else:
  lowerCamelCase_      =  timm_model(UpperCAmelCase_		)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(UpperCAmelCase_					,				outputs.logits					,				atol=1E-3		)

 Path(UpperCAmelCase_		).mkdir(exist_ok=UpperCAmelCase_		)
 print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''		)
 model.save_pretrained(UpperCAmelCase_		)
 print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
 image_processor.save_pretrained(UpperCAmelCase_		)


if __name__ == "__main__":
     a_  :				Union[str, Any]						= argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--vit_name""",
         default="""vit_base_patch16_224""",
         type=str,
         help="""Name of the ViT 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."""
     )

     a_  :				List[str]						= parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)



