



"""simple docstring"""






import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


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






def 			snake_case_    ( A_   :						int,							A_   :						Tuple		):


       '''simple docstring'''





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

       # cls token, position embeddings and patch embeddings of encoder
       rename_keys.extend(
           [
               ('''encoder.deit.cls_token''', '''encoder.embeddings.cls_token'''),
               ('''encoder.deit.pos_embed''', '''encoder.embeddings.position_embeddings'''),
               ('''encoder.deit.patch_embed.proj.weight''', '''encoder.embeddings.patch_embeddings.projection.weight'''),
               ('''encoder.deit.patch_embed.proj.bias''', '''encoder.embeddings.patch_embeddings.projection.bias'''),
               ('''encoder.deit.norm.weight''', '''encoder.layernorm.weight'''),
               ('''encoder.deit.norm.bias''', '''encoder.layernorm.bias'''),
           ]		)

       return rename_keys






def 			snake_case_    ( A_   :						List[Any],							A_   :						Tuple		):


       '''simple docstring'''





       for i in range(encoder_config.num_hidden_layers		):
              # queries, keys and values (only weights, no biases)
              _lowerCamelCase		:							Any            =					state_dict.pop(F'''encoder.deit.blocks.{i}.attn.qkv.weight'''		)

              _lowerCamelCase		:							Dict            =					in_proj_weight[
                  : encoder_config.hidden_size, :
              ]
              _lowerCamelCase		:							Dict            =					in_proj_weight[
                  encoder_config.hidden_size : encoder_config.hidden_size * 2, :
              ]
              _lowerCamelCase		:							str            =					in_proj_weight[
                  -encoder_config.hidden_size :, :
              ]






def 			snake_case_    ( A_   :						List[str],							A_   :						int,							A_   :						int		):


       '''simple docstring'''





       _lowerCamelCase		:							str            =					dct.pop(A_		)
       _lowerCamelCase		:							str            =					val






def 			snake_case_    ( A_   :						Dict		):


       '''simple docstring'''





       if "handwritten" in checkpoint_url:
              _lowerCamelCase		:							int            =					'''https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'''  # industry
              # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
              # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
              # url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
              # url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
       elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
              _lowerCamelCase		:							List[str]            =					'''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
       _lowerCamelCase		:							Dict            =					Image.open(requests.get(A_,							stream=A_		).raw		).convert('''RGB'''		)
       return im






@torch.no_grad()
def 			snake_case_    ( A_   :						Tuple,							A_   :						Dict		):


       '''simple docstring'''





       _lowerCamelCase		:							Union[str, Any]            =					ViTConfig(image_size=3_84,							qkv_bias=A_		)
       _lowerCamelCase		:							int            =					TrOCRConfig()

       # size of the architecture
       if "base" in checkpoint_url:
              _lowerCamelCase		:							Any            =					7_68
       elif "large" in checkpoint_url:
              # use ViT-large encoder
              _lowerCamelCase		:							str            =					10_24
              _lowerCamelCase		:							List[str]            =					40_96
              _lowerCamelCase		:							Any            =					24
              _lowerCamelCase		:							Union[str, Any]            =					16
              _lowerCamelCase		:							str            =					10_24
       else:
              raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''		)

       # the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
       if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
              _lowerCamelCase		:							Tuple            =					False
              _lowerCamelCase		:							Any            =					'''relu'''
              _lowerCamelCase		:							Optional[Any]            =					10_24
              _lowerCamelCase		:							List[str]            =					True
              _lowerCamelCase		:							Tuple            =					False
              _lowerCamelCase		:							Tuple            =					False

       # load HuggingFace model
       _lowerCamelCase		:							List[Any]            =					ViTModel(A_,							add_pooling_layer=A_		)
       _lowerCamelCase		:							List[str]            =					TrOCRForCausalLM(A_		)
       _lowerCamelCase		:							List[Any]            =					VisionEncoderDecoderModel(encoder=A_,							decoder=A_		)
       model.eval()

       # load state_dict of original model, rename some keys
       _lowerCamelCase		:							Optional[int]            =					torch.hub.load_state_dict_from_url(A_,							map_location='''cpu''',							check_hash=A_		)['''model''']

       _lowerCamelCase		:							Tuple            =					create_rename_keys(A_,							A_		)
       for src, dest in rename_keys:
              rename_key(A_,							A_,							A_		)
       read_in_q_k_v(A_,							A_		)

       # remove parameters we don't need
       del state_dict["encoder.deit.head.weight"]
       del state_dict["encoder.deit.head.bias"]
       del state_dict["decoder.version"]

       # add prefix to decoder keys
       for key, val in state_dict.copy().items():
              _lowerCamelCase		:							Union[str, Any]            =					state_dict.pop(A_		)
              if key.startswith('''decoder'''		) and "output_projection" not in key:
                     _lowerCamelCase		:							Dict            =					val
              else:
                     _lowerCamelCase		:							Dict            =					val

    # load state dict
       model.load_state_dict(A_		)

       # Check outputs on an image
       _lowerCamelCase		:							str            =					ViTImageProcessor(size=encoder_config.image_size		)
       _lowerCamelCase		:							List[str]            =					RobertaTokenizer.from_pretrained('''roberta-large'''		)
       _lowerCamelCase		:							List[str]            =					TrOCRProcessor(A_,							A_		)

       _lowerCamelCase		:							Dict            =					processor(images=prepare_img(A_		),							return_tensors='''pt'''		).pixel_values

       # verify logits
       _lowerCamelCase		:							str            =					torch.tensor([[model.config.decoder.decoder_start_token_id]]		)
       _lowerCamelCase		:							Tuple            =					model(pixel_values=A_,							decoder_input_ids=A_		)
       _lowerCamelCase		:							Optional[Any]            =					outputs.logits

       _lowerCamelCase		:							str            =					torch.Size([1, 1, 5_02_65]		)
       if "trocr-base-handwritten" in checkpoint_url:
              _lowerCamelCase		:							Dict            =					torch.tensor(
                  [-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311]		)
       elif "trocr-large-handwritten" in checkpoint_url:
              _lowerCamelCase		:							Optional[int]            =					torch.tensor(
                  [-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170]		)
       elif "trocr-base-printed" in checkpoint_url:
              _lowerCamelCase		:							Tuple            =					torch.tensor(
                  [-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210]		)
       elif "trocr-large-printed" in checkpoint_url:
              _lowerCamelCase		:							Tuple            =					torch.tensor(
                  [-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535]		)

       if "stage1" not in checkpoint_url:
              assert logits.shape == expected_shape, "Shape of logits not as expected"
              assert torch.allclose(logits[0, 0, :10],							A_,							atol=1E-3		), "First elements of logits not as expected"

       Path(A_		).mkdir(exist_ok=A_		)
       print(F'''Saving model to {pytorch_dump_folder_path}'''		)
       model.save_pretrained(A_		)
       print(F'''Saving processor to {pytorch_dump_folder_path}'''		)
       processor.save_pretrained(A_		)


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

   parser.add_argument(
       '''--checkpoint_url''',
       default='''https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt''',
       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.'''
   )
   lowerCAmelCase__              =							parser.parse_args()
   convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)





