
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_						:     List[Any]	       =							logging.get_logger(__name__)



def 		UpperCamelCase     (       _UpperCAmelCase  :   List[Any] ,			_UpperCAmelCase  :   Optional[int]  )   ->			Any:






						'''simple docstring'''

						_lowercase       :	Union[str, Any]           =   []
						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 		UpperCamelCase     (       _UpperCAmelCase  :   Any ,			_UpperCAmelCase  :   List[Any]  )   ->			Union[str, Any]:






						'''simple docstring'''

						for i in range(encoder_config.num_hidden_layers  ):
												# queries, keys and values (only weights, no biases)
												_lowercase       :	Optional[Any]           =   state_dict.pop(f"""encoder.deit.blocks.{i}.attn.qkv.weight"""  )

												_lowercase       :	Union[str, Any]           =   in_proj_weight[
												    : encoder_config.hidden_size, :
												]
												_lowercase       :	Any           =   in_proj_weight[
												    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
												]
												_lowercase       :	str           =   in_proj_weight[
												    -encoder_config.hidden_size :, :
												]



def 		UpperCamelCase     (       _UpperCAmelCase  :   Union[str, Any] ,			_UpperCAmelCase  :   Dict ,			_UpperCAmelCase  :   Any  )   ->			Dict:






						'''simple docstring'''

						_lowercase       :	Any           =   dct.pop(_UpperCAmelCase  )
						_lowercase       :	str           =   val



def 		UpperCamelCase     (       _UpperCAmelCase  :   Union[str, Any]  )   ->			str:






						'''simple docstring'''

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



@torch.no_grad()
def 		UpperCamelCase     (       _UpperCAmelCase  :   List[Any] ,			_UpperCAmelCase  :   Any  )   ->			Optional[Any]:






						'''simple docstring'''

						_lowercase       :	int           =   ViTConfig(image_size=384 ,			qkv_bias=_UpperCAmelCase  )
						_lowercase       :	Tuple           =   TrOCRConfig()

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

						# load HuggingFace model
						_lowercase       :	Any           =   ViTModel(_UpperCAmelCase ,			add_pooling_layer=_UpperCAmelCase  )
						_lowercase       :	List[Any]           =   TrOCRForCausalLM(_UpperCAmelCase  )
						_lowercase       :	Union[str, Any]           =   VisionEncoderDecoderModel(encoder=_UpperCAmelCase ,			decoder=_UpperCAmelCase  )
						model.eval()

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

						_lowercase       :	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():
												_lowercase       :	Optional[int]           =   state_dict.pop(_UpperCAmelCase  )
												if key.startswith("decoder"  ) and "output_projection" not in key:
																		_lowercase       :	int           =   val
												else:
																		_lowercase       :	Optional[int]           =   val

    # load state dict
						model.load_state_dict(_UpperCAmelCase  )

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

						_lowercase       :	List[Any]           =   processor(images=prepare_img(_UpperCAmelCase  ) ,			return_tensors="pt"  ).pixel_values

						# verify logits
						_lowercase       :	int           =   torch.tensor([[model.config.decoder.decoder_start_token_id]]  )
						_lowercase       :	Optional[Any]           =   model(pixel_values=_UpperCAmelCase ,			decoder_input_ids=_UpperCAmelCase  )
						_lowercase       :	Dict           =   outputs.logits

						_lowercase       :	List[str]           =   torch.Size([1, 1, 5_0265]  )
						if "trocr-base-handwritten" in checkpoint_url:
												_lowercase       :	str           =   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:
												_lowercase       :	int           =   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:
												_lowercase       :	Optional[Any]           =   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:
												_lowercase       :	Any           =   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] ,			_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__":
							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_						:     Optional[Any]	       =							parser.parse_args()
							convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase_						:     Any	       =							logging.get_logger(__name__)




class   __lowercase						(						__snake_case       ):
	_A       =   ["pixel_values"]






	def __init__(self				: Dict      ,	snake_case				: bool = True      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: PILImageResampling = PILImageResampling.BICUBIC      ,	snake_case				: bool = True      ,	snake_case				: Dict[str, int] = None      ,	snake_case				: bool = True      ,	snake_case				: Union[int, float] = 1 / 255      ,	snake_case				: bool = True      ,	snake_case				: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN      ,	snake_case				: Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD      ,	**snake_case				: Optional[int]      ,	) ->			None:
							super().__init__(**snake_case    )
							_lowercase       :	str           =   size if size is not None else {"shortest_edge": 224}
							_lowercase       :	List[str]           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							_lowercase       :	Optional[int]           =   crop_size if crop_size is not None else {"height": 224, "width": 224}
							_lowercase       :	List[Any]           =   get_size_dict(snake_case      ,	param_name="crop_size"    )

							_lowercase       :	Optional[int]           =   do_resize
							_lowercase       :	List[str]           =   size
							_lowercase       :	Tuple           =   resample
							_lowercase       :	Union[str, Any]           =   do_center_crop
							_lowercase       :	List[str]           =   crop_size
							_lowercase       :	Tuple           =   do_rescale
							_lowercase       :	Union[str, Any]           =   rescale_factor
							_lowercase       :	str           =   do_normalize
							_lowercase       :	List[Any]           =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							_lowercase       :	List[Any]           =   image_std if image_std is not None else IMAGENET_DEFAULT_STD






	def       _a(self				: str      ,	snake_case				: np.ndarray      ,	snake_case				: Dict[str, int]      ,	snake_case				: PILImageResampling = PILImageResampling.BICUBIC      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: str      ,	) ->			np.ndarray:
							_lowercase       :	Tuple           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
							if "shortest_edge" in size:
													_lowercase       :	List[str]           =   int((256 / 224) * size["shortest_edge"]    )
													_lowercase       :	Dict           =   get_resize_output_image_size(snake_case      ,	size=snake_case      ,	default_to_square=snake_case    )
													_lowercase       :	int           =   {"height": output_size[0], "width": output_size[1]}
							if "height" not in size_dict or "width" not in size_dict:
													raise ValueError(
													    F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"""    )
							return resize(
							    snake_case      ,	size=(size_dict["height"], size_dict["width"])      ,	resample=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Any      ,	snake_case				: np.ndarray      ,	snake_case				: Dict[str, int]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: Any      ,	) ->			np.ndarray:
							_lowercase       :	Tuple           =   get_size_dict(snake_case    )
							if "height" not in size or "width" not in size:
													raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}"""    )
							return center_crop(snake_case      ,	size=(size["height"], size["width"])      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Dict      ,	snake_case				: np.ndarray      ,	snake_case				: Union[int, float]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: List[str]      ,	) ->			np.ndarray:
							return rescale(snake_case      ,	scale=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: str      ,	snake_case				: np.ndarray      ,	snake_case				: Union[float, List[float]]      ,	snake_case				: Union[float, List[float]]      ,	snake_case				: Optional[Union[str, ChannelDimension]] = None      ,	**snake_case				: Optional[int]      ,	) ->			np.ndarray:
							return normalize(snake_case      ,	mean=snake_case      ,	std=snake_case      ,	data_format=snake_case      ,	**snake_case    )






	def       _a(self				: Any      ,	snake_case				: ImageInput      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Dict[str, int]] = None      ,	snake_case				: PILImageResampling = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Dict[str, int]] = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[float] = None      ,	snake_case				: Optional[bool] = None      ,	snake_case				: Optional[Union[float, Iterable[float]]] = None      ,	snake_case				: Optional[Union[float, Iterable[float]]] = None      ,	snake_case				: Optional[TensorType] = None      ,	snake_case				: ChannelDimension = ChannelDimension.FIRST      ,	**snake_case				: Tuple      ,	) ->			BatchFeature:
							_lowercase       :	Optional[Any]           =   do_resize if do_resize is not None else self.do_resize
							_lowercase       :	Any           =   resample if resample is not None else self.resample
							_lowercase       :	Optional[Any]           =   do_center_crop if do_center_crop is not None else self.do_center_crop
							_lowercase       :	Union[str, Any]           =   do_rescale if do_rescale is not None else self.do_rescale
							_lowercase       :	Optional[Any]           =   rescale_factor if rescale_factor is not None else self.rescale_factor
							_lowercase       :	str           =   do_normalize if do_normalize is not None else self.do_normalize
							_lowercase       :	Dict           =   image_mean if image_mean is not None else self.image_mean
							_lowercase       :	Dict           =   image_std if image_std is not None else self.image_std

							_lowercase       :	Optional[int]           =   size if size is not None else self.size
							_lowercase       :	Dict           =   get_size_dict(snake_case      ,	default_to_square=snake_case    )
							_lowercase       :	Dict           =   crop_size if crop_size is not None else self.crop_size
							_lowercase       :	Optional[int]           =   get_size_dict(snake_case      ,	param_name="crop_size"    )

							_lowercase       :	Dict           =   make_list_of_images(snake_case    )

							if not valid_images(snake_case    ):
													raise ValueError(
													    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
													    "torch.Tensor, tf.Tensor or jax.ndarray."    )

							if do_resize and size is None:
													raise ValueError("Size must be specified if do_resize is True."    )

							if do_center_crop and crop_size is None:
													raise ValueError("Crop size must be specified if do_center_crop is True."    )

							if do_rescale and rescale_factor is None:
													raise ValueError("Rescale factor must be specified if do_rescale is True."    )

							if do_normalize and (image_mean is None or image_std is None):
													raise ValueError("Image mean and std must be specified if do_normalize is True."    )

							# All transformations expect numpy arrays.
							_lowercase       :	Dict           =   [to_numpy_array(snake_case    ) for image in images]

							if do_resize:
													_lowercase       :	List[Any]           =   [self.resize(snake_case      ,	snake_case      ,	snake_case    ) for image in images]

							if do_center_crop:
													_lowercase       :	Union[str, Any]           =   [self.center_crop(snake_case      ,	snake_case    ) for image in images]

							if do_rescale:
													_lowercase       :	List[str]           =   [self.rescale(snake_case      ,	snake_case    ) for image in images]

							if do_normalize:
													_lowercase       :	str           =   [self.normalize(snake_case      ,	snake_case      ,	snake_case    ) for image in images]

							_lowercase       :	Any           =   [to_channel_dimension_format(snake_case      ,	snake_case    ) for image in images]

							_lowercase       :	str           =   {"pixel_values": images}
							return BatchFeature(data=snake_case      ,	tensor_type=snake_case    )




