





'''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()
lowercase_					= logging.get_logger(__name__)





def lowerCamelCase						(     __lowerCamelCase :    Dict					,       __lowerCamelCase :    int    )						->Union[str, Any]:
    _SCREAMING_SNAKE_CASE									=			[]
    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 lowerCamelCase						(     __lowerCamelCase :    Tuple					,       __lowerCamelCase :    Dict    )						->Dict:
    for i in range(encoder_config.num_hidden_layers    ):
        # queries, keys and values (only weights, no biases)
        _SCREAMING_SNAKE_CASE									=			state_dict.pop(F'encoder.deit.blocks.{i}.attn.qkv.weight'    )

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





def lowerCamelCase						(     __lowerCamelCase :    List[Any]					,       __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    Union[str, Any]    )						->str:
    _SCREAMING_SNAKE_CASE									=			dct.pop(_UpperCAmelCase    )
    _SCREAMING_SNAKE_CASE									=			val





def lowerCamelCase						(     __lowerCamelCase :    Dict    )						->Union[str, Any]:
    if "handwritten" in checkpoint_url:
        _SCREAMING_SNAKE_CASE									=			"""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:
        _SCREAMING_SNAKE_CASE									=			"""https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"""
    _SCREAMING_SNAKE_CASE									=			Image.open(requests.get(_UpperCAmelCase					,       stream=_UpperCAmelCase    ).raw    ).convert("""RGB"""    )
    return im





@torch.no_grad()
def lowerCamelCase						(     __lowerCamelCase :    List[str]					,       __lowerCamelCase :    Any    )						->int:
    _SCREAMING_SNAKE_CASE									=			ViTConfig(image_size=384					,       qkv_bias=_UpperCAmelCase    )
    _SCREAMING_SNAKE_CASE									=			TrOCRConfig()

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

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

    # load state_dict of original model, rename some keys
    _SCREAMING_SNAKE_CASE									=			torch.hub.load_state_dict_from_url(_UpperCAmelCase					,       map_location="""cpu"""					,       check_hash=_UpperCAmelCase    )["""model"""]

    _SCREAMING_SNAKE_CASE									=			create_rename_keys(_UpperCAmelCase					,       _UpperCAmelCase    )
    for src, dest in rename_keys:
        rename_key(_UpperCAmelCase					,       _UpperCAmelCase					,       _UpperCAmelCase    )
    read_in_q_k_v(_UpperCAmelCase					,       _UpperCAmelCase    )

    # 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():
        _SCREAMING_SNAKE_CASE									=			state_dict.pop(_UpperCAmelCase    )
        if key.startswith("""decoder"""    ) and "output_projection" not in key:
            _SCREAMING_SNAKE_CASE									=			val
        else:
            _SCREAMING_SNAKE_CASE									=			val

    # load state dict
    model.load_state_dict(_UpperCAmelCase    )

    # Check outputs on an image
    _SCREAMING_SNAKE_CASE									=			ViTImageProcessor(size=encoder_config.image_size    )
    _SCREAMING_SNAKE_CASE									=			RobertaTokenizer.from_pretrained("""roberta-large"""    )
    _SCREAMING_SNAKE_CASE									=			TrOCRProcessor(_UpperCAmelCase					,       _UpperCAmelCase    )

    _SCREAMING_SNAKE_CASE									=			processor(images=prepare_img(_UpperCAmelCase    )					,       return_tensors="""pt"""    ).pixel_values

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

    _SCREAMING_SNAKE_CASE									=			torch.Size([1, 1, 5_0265]    )
    if "trocr-base-handwritten" in checkpoint_url:
        _SCREAMING_SNAKE_CASE									=			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:
        _SCREAMING_SNAKE_CASE									=			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:
        _SCREAMING_SNAKE_CASE									=			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:
        _SCREAMING_SNAKE_CASE									=			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]					,       _UpperCAmelCase					,       atol=1e-3    ), "First elements of logits not as expected"

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


if __name__ == "__main__":
      lowercase_					= 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."""
      )
      lowercase_					= parser.parse_args()
      convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








import os

try:
 from .build_directory_md import good_file_paths
except ImportError:
 from build_directory_md import good_file_paths  # type: ignore

__snake_case						:Optional[Any]      		=     list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__snake_case						:Any      		=     [file for file in filepaths if file != file.lower()]
if upper_files:
 print(f'{len(upper_files)} files contain uppercase characters:')
 print('''\n'''.join(upper_files) + '''\n''')

__snake_case						:Tuple      		=     [file for file in filepaths if ''' ''' in file]
if space_files:
 print(f'{len(space_files)} files contain space characters:')
 print('''\n'''.join(space_files) + '''\n''')

__snake_case						:Optional[int]      		=     [file for file in filepaths if '''-''' in file]
if hyphen_files:
 print(f'{len(hyphen_files)} files contain hyphen characters:')
 print('''\n'''.join(hyphen_files) + '''\n''')

__snake_case						:Optional[int]      		=     [file for file in filepaths if os.sep not in file]
if nodir_files:
 print(f'{len(nodir_files)} files are not in a directory:')
 print('''\n'''.join(nodir_files) + '''\n''')

__snake_case						:int      		=     len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
 import sys

 sys.exit(bad_files)





