



"""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:   Tuple								=		logging.get_logger(__name__)

def       _snake_case						(						UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		Optional[int]						):
 UpperCAmelCase	:					int	=	[]
 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						(						UpperCamelCase						:		Dict			,		UpperCamelCase						:		Optional[Any]						):
 for i in range(encoder_config.num_hidden_layers						):
  # queries, keys and values (only weights, no biases)
  UpperCAmelCase	:					Optional[int]	=	state_dict.pop(F"encoder.deit.blocks.{i}.attn.qkv.weight"						)

  UpperCAmelCase	:					str	=	in_proj_weight[
      : encoder_config.hidden_size, :
  ]
  UpperCAmelCase	:					Dict	=	in_proj_weight[
      encoder_config.hidden_size : encoder_config.hidden_size * 2, :
  ]
  UpperCAmelCase	:					Optional[int]	=	in_proj_weight[
      -encoder_config.hidden_size :, :
  ]

def       _snake_case						(						UpperCamelCase						:		str			,		UpperCamelCase						:		Dict			,		UpperCamelCase						:		int						):
 UpperCAmelCase	:					Optional[int]	=	dct.pop(UpperCamelCase						)
 UpperCAmelCase	:					str	=	val

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

@torch.no_grad()
def       _snake_case						(						UpperCamelCase						:		Dict			,		UpperCamelCase						:		int						):
 UpperCAmelCase	:					Dict	=	ViTConfig(image_size=384			,		qkv_bias=UpperCamelCase						)
 UpperCAmelCase	:					List[Any]	=	TrOCRConfig()

 # size of the architecture
 if "base" in checkpoint_url:
  UpperCAmelCase	:					List[Any]	=	768
 elif "large" in checkpoint_url:
  # use ViT-large encoder
  UpperCAmelCase	:					Tuple	=	1024
  UpperCAmelCase	:					Any	=	4096
  UpperCAmelCase	:					List[str]	=	24
  UpperCAmelCase	:					str	=	16
  UpperCAmelCase	:					Optional[Any]	=	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:
  UpperCAmelCase	:					Optional[Any]	=	False
  UpperCAmelCase	:					int	=	"""relu"""
  UpperCAmelCase	:					Dict	=	1024
  UpperCAmelCase	:					List[str]	=	True
  UpperCAmelCase	:					List[str]	=	False
  UpperCAmelCase	:					Any	=	False

 # load HuggingFace model
 UpperCAmelCase	:					Any	=	ViTModel(UpperCamelCase			,		add_pooling_layer=UpperCamelCase						)
 UpperCAmelCase	:					Any	=	TrOCRForCausalLM(UpperCamelCase						)
 UpperCAmelCase	:					Dict	=	VisionEncoderDecoderModel(encoder=UpperCamelCase			,		decoder=UpperCamelCase						)
 model.eval()

 # load state_dict of original model, rename some keys
 UpperCAmelCase	:					str	=	torch.hub.load_state_dict_from_url(UpperCamelCase			,		map_location="""cpu"""			,		check_hash=UpperCamelCase						)["""model"""]

 UpperCAmelCase	:					str	=	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():
  UpperCAmelCase	:					Any	=	state_dict.pop(UpperCamelCase						)
  if key.startswith("""decoder"""						) and "output_projection" not in key:
   UpperCAmelCase	:					Union[str, Any]	=	val
  else:
   UpperCAmelCase	:					List[str]	=	val

    # load state dict
 model.load_state_dict(UpperCamelCase						)

 # Check outputs on an image
 UpperCAmelCase	:					Optional[Any]	=	ViTImageProcessor(size=encoder_config.image_size						)
 UpperCAmelCase	:					List[str]	=	RobertaTokenizer.from_pretrained("""roberta-large"""						)
 UpperCAmelCase	:					Union[str, Any]	=	TrOCRProcessor(UpperCamelCase			,		UpperCamelCase						)

 UpperCAmelCase	:					Tuple	=	processor(images=prepare_img(UpperCamelCase						)			,		return_tensors="""pt"""						).pixel_values

 # verify logits
 UpperCAmelCase	:					Dict	=	torch.tensor([[model.config.decoder.decoder_start_token_id]]						)
 UpperCAmelCase	:					Dict	=	model(pixel_values=UpperCamelCase			,		decoder_input_ids=UpperCamelCase						)
 UpperCAmelCase	:					List[Any]	=	outputs.logits

 UpperCAmelCase	:					Optional[int]	=	torch.Size([1, 1, 50265]						)
 if "trocr-base-handwritten" in checkpoint_url:
  UpperCAmelCase	:					Optional[Any]	=	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:
  UpperCAmelCase	:					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:
  UpperCAmelCase	:					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:
  UpperCAmelCase	:					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]			,		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__":
      A:   List[str]								=		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:   List[str]								=		parser.parse_args()
      convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser

def       _snake_case						(						):
 UpperCAmelCase	:					List[str]	=	ArgumentParser("""Accelerate CLI tool"""			,		usage="""accelerate <command> [<args>]"""			,		allow_abbrev=UpperCamelCase						)
 UpperCAmelCase	:					Dict	=	parser.add_subparsers(help="""accelerate command helpers"""						)

 # Register commands
 get_config_parser(subparsers=UpperCamelCase						)
 env_command_parser(subparsers=UpperCamelCase						)
 launch_command_parser(subparsers=UpperCamelCase						)
 tpu_command_parser(subparsers=UpperCamelCase						)
 test_command_parser(subparsers=UpperCamelCase						)

 # Let's go
 UpperCAmelCase	:					Optional[int]	=	parser.parse_args()

 if not hasattr(UpperCamelCase			,		"""func"""						):
  parser.print_help()
  exit(1						)

 # Run
 args.func(UpperCamelCase						)


if __name__ == "__main__":
      main()
