




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								=					logging.get_logger(__name__)



def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Tuple      ):
     """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 							_A       (						lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: Tuple      ):
     """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 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Tuple      ):
     """simple docstring"""

     lowerCAmelCase__          =			dct.pop(lowerCAmelCase_      )
     lowerCAmelCase__          =			val



def 							_A       (						lowerCAmelCase_					: Dict      ):
     """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(lowerCAmelCase_       ,    stream=lowerCAmelCase_      ).raw      ).convert("RGB"      )
     return im



@torch.no_grad()
def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Optional[int]      ):
     """simple docstring"""

     lowerCAmelCase__          =			ViTConfig(image_size=384       ,    qkv_bias=lowerCAmelCase_      )
     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(lowerCAmelCase_       ,    add_pooling_layer=lowerCAmelCase_      )
     lowerCAmelCase__          =			TrOCRForCausalLM(lowerCAmelCase_      )
     lowerCAmelCase__          =			VisionEncoderDecoderModel(encoder=lowerCAmelCase_       ,    decoder=lowerCAmelCase_      )
     model.eval()

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

     lowerCAmelCase__          =			create_rename_keys(lowerCAmelCase_       ,    lowerCAmelCase_      )
     for src, dest in rename_keys:
          rename_key(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
     read_in_q_k_v(lowerCAmelCase_       ,    lowerCAmelCase_      )

     # 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(lowerCAmelCase_      )
          if key.startswith("decoder"      ) and "output_projection" not in key:
               lowerCAmelCase__          =			val
          else:
               lowerCAmelCase__          =			val

    # load state dict
     model.load_state_dict(lowerCAmelCase_      )

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

     lowerCAmelCase__          =			processor(images=prepare_img(lowerCAmelCase_      )       ,    return_tensors="pt"      ).pixel_values

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

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

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


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






from math import pi, sqrt



def 							_A       (						lowerCAmelCase_					: float      ):
     """simple docstring"""

     if num <= 0:
          raise ValueError("math domain error"      )
     if num > 171.5:
          raise OverflowError("math range error"      )
     elif num - int(lowerCAmelCase_      ) not in (0, 0.5):
          raise NotImplementedError("num must be an integer or a half-integer"      )
     elif num == 0.5:
          return sqrt(lowerCAmelCase_      )
     else:
          return 1.0 if num == 1 else (num - 1) * gamma(num - 1      )



def 							_A       (						):
     """simple docstring"""

     assert gamma(0.5      ) == sqrt(lowerCAmelCase_      )
     assert gamma(1      ) == 1.0
     assert gamma(2      ) == 1.0


if __name__ == "__main__":
     from doctest import testmod

     testmod()
     UpperCamelCase								=					1.0
     while num:
          UpperCamelCase								=					float(input('Gamma of: '))
          print(F"""gamma({num}) = {gamma(num)}""")
          print('\nEnter 0 to exit...')
