





import argparse
import json
from pathlib import Path

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

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[Any]		,   __UpperCAmelCase:			Dict=False		,   __UpperCAmelCase:			Optional[Any]=False      )      ->	str:
    UpperCamelCase__							:				Optional[int]          =      '''backbone.''' if is_semantic else ''''''

    UpperCamelCase__							:				Optional[Any]          =      []
    for i in range(config.num_hidden_layers      ):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((f"{prefix}blocks.{i}.norm1.weight", f"beit.encoder.layer.{i}.layernorm_before.weight")      )
        rename_keys.append((f"{prefix}blocks.{i}.norm1.bias", f"beit.encoder.layer.{i}.layernorm_before.bias")      )
        rename_keys.append(
            (f"{prefix}blocks.{i}.attn.proj.weight", f"beit.encoder.layer.{i}.attention.output.dense.weight")      )
        rename_keys.append(
            (f"{prefix}blocks.{i}.attn.proj.bias", f"beit.encoder.layer.{i}.attention.output.dense.bias")      )
        rename_keys.append((f"{prefix}blocks.{i}.norm2.weight", f"beit.encoder.layer.{i}.layernorm_after.weight")      )
        rename_keys.append((f"{prefix}blocks.{i}.norm2.bias", f"beit.encoder.layer.{i}.layernorm_after.bias")      )
        rename_keys.append((f"{prefix}blocks.{i}.mlp.fc1.weight", f"beit.encoder.layer.{i}.intermediate.dense.weight")      )
        rename_keys.append((f"{prefix}blocks.{i}.mlp.fc1.bias", f"beit.encoder.layer.{i}.intermediate.dense.bias")      )
        rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.weight", f"beit.encoder.layer.{i}.output.dense.weight")      )
        rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.bias", f"beit.encoder.layer.{i}.output.dense.bias")      )

    # projection layer + position embeddings
    rename_keys.extend(
        [
            (f"{prefix}cls_token", '''beit.embeddings.cls_token'''),
            (f"{prefix}patch_embed.proj.weight", '''beit.embeddings.patch_embeddings.projection.weight'''),
            (f"{prefix}patch_embed.proj.bias", '''beit.embeddings.patch_embeddings.projection.bias'''),
            (f"{prefix}pos_embed", '''beit.embeddings.position_embeddings'''),
        ]      )

    if has_lm_head:
        # mask token + layernorm
        rename_keys.extend(
            [
                ('''mask_token''', '''beit.embeddings.mask_token'''),
                ('''norm.weight''', '''layernorm.weight'''),
                ('''norm.bias''', '''layernorm.bias'''),
            ]      )
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
                ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
                ('''head.weight''', '''classifier.weight'''),
                ('''head.bias''', '''classifier.bias'''),
            ]      )

    return rename_keys




def 		lowerCAmelCase_    (  __UpperCAmelCase:			str		,   __UpperCAmelCase:			Union[str, Any]		,   __UpperCAmelCase:			Dict=False		,   __UpperCAmelCase:			int=False      )      ->	List[str]:






    for i in range(config.num_hidden_layers      ):
        UpperCamelCase__							:				List[Any]          =      '''backbone.''' if is_semantic else ''''''
        # queries, keys and values
        UpperCamelCase__							:				Tuple          =      state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight"      )
        UpperCamelCase__							:				Any          =      state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias"      )
        UpperCamelCase__							:				Any          =      state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias"      )

        UpperCamelCase__							:				Optional[Any]          =      in_proj_weight[
            : config.hidden_size, :
        ]
        UpperCamelCase__							:				Union[str, Any]          =      q_bias
        UpperCamelCase__							:				Dict          =      in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        UpperCamelCase__							:				Any          =      in_proj_weight[
            -config.hidden_size :, :
        ]
        UpperCamelCase__							:				Any          =      v_bias

        # gamma_1 and gamma_2
        # we call them lambda because otherwise they are renamed when using .from_pretrained
        UpperCamelCase__							:				Dict          =      state_dict.pop(f"{prefix}blocks.{i}.gamma_1"      )
        UpperCamelCase__							:				int          =      state_dict.pop(f"{prefix}blocks.{i}.gamma_2"      )

        UpperCamelCase__							:				List[Any]          =      gamma_a
        UpperCamelCase__							:				List[Any]          =      gamma_a




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int		,   __UpperCAmelCase:			Union[str, Any]		,   __UpperCAmelCase:			List[Any]      )      ->	Dict:
    UpperCamelCase__							:				Any          =      dct.pop(SCREAMING_SNAKE_CASE__      )
    UpperCamelCase__							:				Dict          =      val




def 		lowerCAmelCase_    (  )      ->	str:
    UpperCamelCase__							:				Any          =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase__							:				List[Any]          =      Image.open(requests.get(SCREAMING_SNAKE_CASE__		,   stream=SCREAMING_SNAKE_CASE__      ).raw      )
    return im




@torch.no_grad()
def 		lowerCAmelCase_    (  __UpperCAmelCase:			Union[str, Any]		,   __UpperCAmelCase:			Union[str, Any]		,   __UpperCAmelCase:			List[str]=False      )      ->	str:
    UpperCamelCase__							:				Optional[int]          =      False if '''rvlcdip''' in checkpoint_url else True
    UpperCamelCase__							:				List[str]          =      BeitConfig(use_absolute_position_embeddings=SCREAMING_SNAKE_CASE__		,   use_mask_token=SCREAMING_SNAKE_CASE__      )

    # size of the architecture
    if "large" in checkpoint_url or "dit-l" in checkpoint_url:
        UpperCamelCase__							:				List[Any]          =      1024
        UpperCamelCase__							:				Dict          =      4096
        UpperCamelCase__							:				Union[str, Any]          =      24
        UpperCamelCase__							:				Optional[int]          =      16

    # labels
    if "rvlcdip" in checkpoint_url:
        UpperCamelCase__							:				Union[str, Any]          =      16
        UpperCamelCase__							:				Tuple          =      '''huggingface/label-files'''
        UpperCamelCase__							:				Optional[Any]          =      '''rvlcdip-id2label.json'''
        UpperCamelCase__							:				Optional[int]          =      json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__		,   SCREAMING_SNAKE_CASE__		,   repo_type='''dataset'''      )		,   '''r'''      )      )
        UpperCamelCase__							:				List[str]          =      {int(SCREAMING_SNAKE_CASE__      ): v for k, v in idalabel.items()}
        UpperCamelCase__							:				Optional[int]          =      idalabel
        UpperCamelCase__							:				Optional[Any]          =      {v: k for k, v in idalabel.items()}

    # load state_dict of original model, remove and rename some keys
    UpperCamelCase__							:				Optional[Any]          =      torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__		,   map_location='''cpu'''      )['''model''']

    UpperCamelCase__							:				str          =      create_rename_keys(SCREAMING_SNAKE_CASE__		,   has_lm_head=SCREAMING_SNAKE_CASE__      )
    for src, dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE__		,   SCREAMING_SNAKE_CASE__		,   SCREAMING_SNAKE_CASE__      )
    read_in_q_k_v(SCREAMING_SNAKE_CASE__		,   SCREAMING_SNAKE_CASE__		,   has_lm_head=SCREAMING_SNAKE_CASE__      )

    # load HuggingFace model
    UpperCamelCase__							:				str          =      BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE__      ) if has_lm_head else BeitForImageClassification(SCREAMING_SNAKE_CASE__      )
    model.eval()
    model.load_state_dict(SCREAMING_SNAKE_CASE__      )

    # Check outputs on an image
    UpperCamelCase__							:				List[Any]          =      BeitImageProcessor(
        size=config.image_size		,   resample=PILImageResampling.BILINEAR		,   do_center_crop=SCREAMING_SNAKE_CASE__      )
    UpperCamelCase__							:				Any          =      prepare_img()

    UpperCamelCase__							:				Tuple          =      image_processor(images=SCREAMING_SNAKE_CASE__		,   return_tensors='''pt'''      )
    UpperCamelCase__							:				Optional[int]          =      encoding['''pixel_values''']

    UpperCamelCase__							:				Union[str, Any]          =      model(SCREAMING_SNAKE_CASE__      )
    UpperCamelCase__							:				Union[str, Any]          =      outputs.logits

    # verify logits
    UpperCamelCase__							:				str          =      [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8192]
    assert logits.shape == torch.Size(SCREAMING_SNAKE_CASE__      ), "Shape of logits not as expected"

    Path(SCREAMING_SNAKE_CASE__      ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__      )
    print(f"Saving model to {pytorch_dump_folder_path}"      )
    model.save_pretrained(SCREAMING_SNAKE_CASE__      )
    print(f"Saving image processor to {pytorch_dump_folder_path}"      )
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE__      )

    if push_to_hub:
        if has_lm_head:
            UpperCamelCase__							:				Tuple          =      '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
        else:
            UpperCamelCase__							:				List[Any]          =      '''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
        image_processor.push_to_hub(
            repo_path_or_name=Path(SCREAMING_SNAKE_CASE__		,   SCREAMING_SNAKE_CASE__      )		,   organization='''nielsr'''		,   commit_message='''Add image processor'''		,   use_temp_dir=SCREAMING_SNAKE_CASE__		,   )
        model.push_to_hub(
            repo_path_or_name=Path(SCREAMING_SNAKE_CASE__		,   SCREAMING_SNAKE_CASE__      )		,   organization='''nielsr'''		,   commit_message='''Add model'''		,   use_temp_dir=SCREAMING_SNAKE_CASE__		,   )


if __name__ == "__main__":
  UpperCAmelCase_     							= argparse.ArgumentParser()

  parser.add_argument(
      '--checkpoint_url',
      default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
      type=str,
      help='URL to the original PyTorch checkpoint (.pth file).',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
  )
  parser.add_argument(
      '--push_to_hub',
      action='store_true',
  )
  UpperCAmelCase_     							= parser.parse_args()
  convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


lowercase_       		=  "true"
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	List[Any]=82    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]=16      )							->      Optional[Any]:





    '''simple docstring'''






    set_seed(42      )
    A__          =   RegressionModel()
    A__          =   deepcopy(SCREAMING_SNAKE_CASE__      )
    A__          =   RegressionDataset(length=SCREAMING_SNAKE_CASE__      )
    A__          =   DataLoader(SCREAMING_SNAKE_CASE__    ,			batch_size=SCREAMING_SNAKE_CASE__      )
    model.to(accelerator.device      )
    A__      ,						A__          =   accelerator.prepare(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    return model, ddp_model, dataloader
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Accelerator    ,			SCREAMING_SNAKE_CASE__			:	Tuple=False      )							->      int:





    '''simple docstring'''






    A__          =   AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'      )
    A__          =   load_dataset('glue'    ,			'mrpc'    ,			split='validation'      )

    def tokenize_function(SCREAMING_SNAKE_CASE__			:	List[Any]      ):
        A__          =   tokenizer(examples['sentence1']    ,			examples['sentence2']    ,			truncation=SCREAMING_SNAKE_CASE__    ,			max_length=SCREAMING_SNAKE_CASE__      )
        return outputs

    with accelerator.main_process_first():
        A__          =   dataset.map(
            SCREAMING_SNAKE_CASE__    ,			batched=SCREAMING_SNAKE_CASE__    ,			remove_columns=['idx', 'sentence1', 'sentence2']    ,			)

    A__          =   tokenized_datasets.rename_column('label'    ,			'labels'      )

    def collate_fn(SCREAMING_SNAKE_CASE__			:	Dict      ):
        if use_longest:
            return tokenizer.pad(SCREAMING_SNAKE_CASE__    ,			padding='longest'    ,			return_tensors='pt'      )
        return tokenizer.pad(SCREAMING_SNAKE_CASE__    ,			padding='max_length'    ,			max_length=128    ,			return_tensors='pt'      )

    return DataLoader(SCREAMING_SNAKE_CASE__    ,			shuffle=SCREAMING_SNAKE_CASE__    ,			collate_fn=SCREAMING_SNAKE_CASE__    ,			batch_size=16      )
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Any      )							->      str:





    '''simple docstring'''






    A__          =   Accelerator(dispatch_batches=SCREAMING_SNAKE_CASE__    ,			split_batches=SCREAMING_SNAKE_CASE__      )
    A__          =   get_dataloader(SCREAMING_SNAKE_CASE__    ,			not dispatch_batches      )
    A__          =   AutoModelForSequenceClassification.from_pretrained(
        'hf-internal-testing/mrpc-bert-base-cased'    ,			return_dict=SCREAMING_SNAKE_CASE__      )
    A__      ,						A__          =   accelerator.prepare(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      List[str]:





    '''simple docstring'''






    A__          =   []
    for batch in dataloader:
        A__      ,						A__          =   batch.values()
        with torch.no_grad():
            A__          =   model(SCREAMING_SNAKE_CASE__      )
            A__      ,						A__          =   accelerator.gather_for_metrics((logit, target)      )
            logits_and_targets.append((logit, target)      )
    A__      ,						A__          =   [], []
    for logit, targ in logits_and_targets:
        logits.append(SCREAMING_SNAKE_CASE__      )
        targs.append(SCREAMING_SNAKE_CASE__      )
    A__      ,						A__          =   torch.cat(SCREAMING_SNAKE_CASE__      ), torch.cat(SCREAMING_SNAKE_CASE__      )
    return logits, targs
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Accelerator    ,			SCREAMING_SNAKE_CASE__			:	int=82    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]=False    ,			SCREAMING_SNAKE_CASE__			:	Any=False    ,			SCREAMING_SNAKE_CASE__			:	Tuple=16      )							->      List[Any]:





    '''simple docstring'''






    A__      ,						A__      ,						A__          =   get_basic_setup(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    A__      ,						A__          =   generate_predictions(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    assert (
        len(SCREAMING_SNAKE_CASE__      ) == num_samples
    ), f'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(SCREAMING_SNAKE_CASE__      )}'
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	bool = False    ,			SCREAMING_SNAKE_CASE__			:	bool = False      )							->      str:





    '''simple docstring'''






    A__          =   evaluate.load('glue'    ,			'mrpc'      )
    A__      ,						A__          =   get_mrpc_setup(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    # First do baseline
    A__      ,						A__      ,						A__          =   setup['no']
    model.to(SCREAMING_SNAKE_CASE__      )
    model.eval()
    for batch in dataloader:
        batch.to(SCREAMING_SNAKE_CASE__      )
        with torch.inference_mode():
            A__          =   model(**SCREAMING_SNAKE_CASE__      )
        A__          =   outputs.logits.argmax(dim=-1      )
        metric.add_batch(predictions=SCREAMING_SNAKE_CASE__    ,			references=batch['labels']      )
    A__          =   metric.compute()

    # Then do distributed
    A__      ,						A__      ,						A__          =   setup['ddp']
    model.eval()
    for batch in dataloader:
        with torch.inference_mode():
            A__          =   model(**SCREAMING_SNAKE_CASE__      )
        A__          =   outputs.logits.argmax(dim=-1      )
        A__          =   batch['labels']
        A__      ,						A__          =   accelerator.gather_for_metrics((preds, references)      )
        metric.add_batch(predictions=SCREAMING_SNAKE_CASE__    ,			references=SCREAMING_SNAKE_CASE__      )
    A__          =   metric.compute()

    for key in "accuracy f1".split():
        assert math.isclose(
            baseline[key]    ,			distributed[key]      ), f'Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'
def 						_snake_case(    )							->      Optional[Any]:





    '''simple docstring'''






    A__          =   Accelerator(split_batches=SCREAMING_SNAKE_CASE__    ,			dispatch_batches=SCREAMING_SNAKE_CASE__      )
    if accelerator.is_local_main_process:
        datasets.utils.logging.set_verbosity_warning()
        transformers.utils.logging.set_verbosity_warning()
    else:
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()
    # These are a bit slower so they should only be ran on the GPU or TPU
    if torch.cuda.is_available() or is_tpu_available():
        if accelerator.is_local_main_process:
            print('**Testing gather_for_metrics**'      )
        for split_batches in [True, False]:
            for dispatch_batches in [True, False]:
                if accelerator.is_local_main_process:
                    print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'      )
                test_mrpc(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
                accelerator.state._reset_state()
    if accelerator.is_local_main_process:
        print('**Test torch metrics**'      )
    for split_batches in [True, False]:
        for dispatch_batches in [True, False]:
            A__          =   Accelerator(split_batches=SCREAMING_SNAKE_CASE__    ,			dispatch_batches=SCREAMING_SNAKE_CASE__      )
            if accelerator.is_local_main_process:
                print(f'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'      )
            test_torch_metrics(SCREAMING_SNAKE_CASE__    ,			99      )
            accelerator.state._reset_state()
    if accelerator.is_local_main_process:
        print('**Test last batch is not dropped when perfectly divisible**'      )
    A__          =   Accelerator()
    test_torch_metrics(SCREAMING_SNAKE_CASE__    ,			512      )
    accelerator.state._reset_state()
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	List[Any]      )							->      Union[str, Any]:





    '''simple docstring'''






    main()


if __name__ == "__main__":
     main()

