




"""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()
a							      =     logging.get_logger(__name__)






def 		_snake_case	(   _snake_case				:   str							,    _snake_case				:   int				)       ->   str:






      '''simple docstring'''


      _A     =  []
      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				:   int							,    _snake_case				:   Dict				)       ->   Optional[Any]:






      '''simple docstring'''


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

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






def 		_snake_case	(   _snake_case				:   Dict							,    _snake_case				:   int							,    _snake_case				:   int				)       ->   List[Any]:






      '''simple docstring'''


      _A     =  dct.pop(_snake_case				)
      _A     =  val






def 		_snake_case	(   _snake_case				:   Tuple				)       ->   Any:






      '''simple docstring'''


      if "handwritten" in checkpoint_url:
            _A     =  '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:
            _A     =  'https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'
      _A     =  Image.open(requests.get(_snake_case							,    stream=_snake_case				).raw				).convert('RGB'				)
      return im






@torch.no_grad()
def 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   str				)       ->   str:






      '''simple docstring'''


      _A     =  ViTConfig(image_size=3_84							,    qkv_bias=_snake_case				)
      _A     =  TrOCRConfig()

      # size of the architecture
      if "base" in checkpoint_url:
            _A     =  7_68
      elif "large" in checkpoint_url:
            # use ViT-large encoder
            _A     =  10_24
            _A     =  40_96
            _A     =  24
            _A     =  16
            _A     =  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:
            _A     =  False
            _A     =  'relu'
            _A     =  10_24
            _A     =  True
            _A     =  False
            _A     =  False

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

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

      _A     =  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():
            _A     =  state_dict.pop(_snake_case				)
            if key.startswith('decoder'				) and "output_projection" not in key:
                  _A     =  val
            else:
                  _A     =  val

    # load state dict
      model.load_state_dict(_snake_case				)

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

      _A     =  processor(images=prepare_img(_snake_case				)							,    return_tensors='pt'				).pixel_values

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

      _A     =  torch.Size([1, 1, 5_02_65]				)
      if "trocr-base-handwritten" in checkpoint_url:
            _A     =  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:
            _A     =  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:
            _A     =  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:
            _A     =  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]							,    _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__":
       a							      =     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.'''
       )
       a							      =     parser.parse_args()
       convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







"""simple docstring"""

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	UNetaDModel
 UpperCAmelCase       :	KarrasVeScheduler

 def __init__(					self			:			Any       ,				_UpperCAmelCase			:			UNetaDModel       ,				_UpperCAmelCase			:			KarrasVeScheduler						):
       super().__init__()
       self.register_modules(unet=_UpperCAmelCase       ,				scheduler=_UpperCAmelCase						)



 @torch.no_grad()
 def __call__(					self			:			Optional[int]       ,				_UpperCAmelCase			:			int = 1       ,				_UpperCAmelCase			:			int = 50       ,				_UpperCAmelCase			:			Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,				_UpperCAmelCase			:			Optional[str] = "pil"       ,				_UpperCAmelCase			:			bool = True       ,				**_UpperCAmelCase			:			Optional[Any]       ,				):
       _A     =  self.unet.config.sample_size
       _A     =  (batch_size, 3, img_size, img_size)

       _A     =  self.unet

       # sample x_0 ~ N(0, sigma_0^2 * I)
       _A     =  randn_tensor(_UpperCAmelCase       ,				generator=_UpperCAmelCase       ,				device=self.device						) * self.scheduler.init_noise_sigma

       self.scheduler.set_timesteps(_UpperCAmelCase						)

       for t in self.progress_bar(self.scheduler.timesteps						):
             # here sigma_t == t_i from the paper
             _A     =  self.scheduler.schedule[t]
             _A     =  self.scheduler.schedule[t - 1] if t > 0 else 0

             # 1. Select temporarily increased noise level sigma_hat
             # 2. Add new noise to move from sample_i to sample_hat
             _A  ,       _A     =  self.scheduler.add_noise_to_input(_UpperCAmelCase       ,				_UpperCAmelCase       ,				generator=_UpperCAmelCase						)

             # 3. Predict the noise residual given the noise magnitude `sigma_hat`
             # The model inputs and output are adjusted by following eq. (213) in [1].
             _A     =  (sigma_hat / 2) * model((sample_hat + 1) / 2       ,				sigma_hat / 2						).sample

             # 4. Evaluate dx/dt at sigma_hat
             # 5. Take Euler step from sigma to sigma_prev
             _A     =  self.scheduler.step(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)

             if sigma_prev != 0:
                   # 6. Apply 2nd order correction
                   # The model inputs and output are adjusted by following eq. (213) in [1].
                   _A     =  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2       ,				sigma_prev / 2						).sample
                   _A     =  self.scheduler.step_correct(
                       _UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase       ,				step_output.prev_sample       ,				step_output['derivative']       ,				)
             _A     =  step_output.prev_sample

       _A     =  (sample / 2 + 0.5).clamp(0       ,				1						)
       _A     =  sample.cpu().permute(0       ,				2       ,				3       ,				1						).numpy()
       if output_type == "pil":
             _A     =  self.numpy_to_pil(_UpperCAmelCase						)

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_UpperCAmelCase						)

