

'''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()
_UpperCamelCase						:  int							       =				logging.get_logger(__name__)


def       snake_case   (	snake_case      : Optional[int]					,		snake_case      : Optional[Any]			) ->  Union[str, Any]:


    """simple docstring"""


    lowerCAmelCase 						=							[]
    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   (	snake_case      : List[Any]					,		snake_case      : Any			) ->  Dict:


    """simple docstring"""


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

        lowerCAmelCase 						=							in_proj_weight[
            : encoder_config.hidden_size, :
        ]
        lowerCAmelCase 						=							in_proj_weight[
            encoder_config.hidden_size : encoder_config.hidden_size * 2, :
        ]
        lowerCAmelCase 						=							in_proj_weight[
            -encoder_config.hidden_size :, :
        ]


def       snake_case   (	snake_case      : Tuple					,		snake_case      : Union[str, Any]					,		snake_case      : Optional[int]			) ->  Union[str, Any]:


    """simple docstring"""


    lowerCAmelCase 						=							dct.pop(snake_case			)
    lowerCAmelCase 						=							val


def       snake_case   (	snake_case      : List[str]			) ->  int:


    """simple docstring"""


    if "handwritten" in checkpoint_url:
        lowerCAmelCase 						=							'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 						=							'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 						=							Image.open(requests.get(snake_case					,		stream=snake_case			).raw			).convert('RGB'			)
    return im


@torch.no_grad()
def       snake_case   (	snake_case      : Optional[int]					,		snake_case      : Union[str, Any]			) ->  List[str]:


    """simple docstring"""


    lowerCAmelCase 						=							ViTConfig(image_size=384					,		qkv_bias=snake_case			)
    lowerCAmelCase 						=							TrOCRConfig()

    # size of the architecture
    if "base" in checkpoint_url:
        lowerCAmelCase 						=							768
    elif "large" in checkpoint_url:
        # use ViT-large encoder
        lowerCAmelCase 						=							1024
        lowerCAmelCase 						=							4096
        lowerCAmelCase 						=							24
        lowerCAmelCase 						=							16
        lowerCAmelCase 						=							1024
    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 						=							False
        lowerCAmelCase 						=							'relu'
        lowerCAmelCase 						=							1024
        lowerCAmelCase 						=							True
        lowerCAmelCase 						=							False
        lowerCAmelCase 						=							False

    # load HuggingFace model
    lowerCAmelCase 						=							ViTModel(snake_case					,		add_pooling_layer=snake_case			)
    lowerCAmelCase 						=							TrOCRForCausalLM(snake_case			)
    lowerCAmelCase 						=							VisionEncoderDecoderModel(encoder=snake_case					,		decoder=snake_case			)
    model.eval()

    # load state_dict of original model, rename some keys
    lowerCAmelCase 						=							torch.hub.load_state_dict_from_url(snake_case					,		map_location='cpu'					,		check_hash=snake_case			)['model']

    lowerCAmelCase 						=							create_rename_keys(snake_case					,		snake_case			)
    for src, dest in rename_keys:
        rename_key(snake_case					,		snake_case					,		snake_case			)
    read_in_q_k_v(snake_case					,		snake_case			)

    # 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 						=							state_dict.pop(snake_case			)
        if key.startswith('decoder'			) and "output_projection" not in key:
            lowerCAmelCase 						=							val
        else:
            lowerCAmelCase 						=							val

    # load state dict
    model.load_state_dict(snake_case			)

    # Check outputs on an image
    lowerCAmelCase 						=							ViTImageProcessor(size=encoder_config.image_size			)
    lowerCAmelCase 						=							RobertaTokenizer.from_pretrained('roberta-large'			)
    lowerCAmelCase 						=							TrOCRProcessor(snake_case					,		snake_case			)

    lowerCAmelCase 						=							processor(images=prepare_img(snake_case			)					,		return_tensors='pt'			).pixel_values

    # verify logits
    lowerCAmelCase 						=							torch.tensor([[model.config.decoder.decoder_start_token_id]]			)
    lowerCAmelCase 						=							model(pixel_values=snake_case					,		decoder_input_ids=snake_case			)
    lowerCAmelCase 						=							outputs.logits

    lowerCAmelCase 						=							torch.Size([1, 1, 50265]			)
    if "trocr-base-handwritten" in checkpoint_url:
        lowerCAmelCase 						=							torch.tensor(
            [-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311]			)
    elif "trocr-large-handwritten" in checkpoint_url:
        lowerCAmelCase 						=							torch.tensor(
            [-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170]			)
    elif "trocr-base-printed" in checkpoint_url:
        lowerCAmelCase 						=							torch.tensor(
            [-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210]			)
    elif "trocr-large-printed" in checkpoint_url:
        lowerCAmelCase 						=							torch.tensor(
            [-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535]			)

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

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


if __name__ == "__main__":
     _UpperCamelCase						:  Optional[int]							       =				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."
     )
     _UpperCamelCase						:  List[str]							       =				parser.parse_args()
     convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_UpperCamelCase						:  Optional[int]							       =				{
    "configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
    "processing_vision_text_dual_encoder": ["VisionTextDualEncoderProcessor"],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase						:  Dict							       =				["VisionTextDualEncoderModel"]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase						:  int							       =				["FlaxVisionTextDualEncoderModel"]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase						:  Tuple							       =				["TFVisionTextDualEncoderModel"]


if TYPE_CHECKING:
     from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
     from .processing_vision_text_dual_encoder import VisionTextDualEncoderProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_vision_text_dual_encoder import VisionTextDualEncoderModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_vision_text_dual_encoder import TFVisionTextDualEncoderModel


else:
     import sys

     _UpperCamelCase						:  Optional[int]							       =				_LazyModule(__name__, globals()["__file__"], _import_structure)





