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

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						__lowerCAmelCase     =  dct.pop(_lowerCAmelCase						)
						__lowerCAmelCase     =  val

def  lowercase     (_lowerCAmelCase						):
						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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						__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(
												    [-1.4_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:
												__lowerCAmelCase     =  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:
												__lowerCAmelCase     =  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:
												__lowerCAmelCase     =  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] ,    _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__":
			SCREAMING_SNAKE_CASE_					     =	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.'''
			)
			SCREAMING_SNAKE_CASE_					     =	parser.parse_args()
			convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


"""simple docstring"""




import operator



def     __magic_name__		(					UpperCamelCase     :		list							,		UpperCamelCase     :		bool = False							,		UpperCamelCase     :		list | None = None     )     ->						list:
    a__		=			operator.lt if reverse else operator.gt
    a__		=			solution or []

    if not arr:
        return solution

    a__		=			[arr.pop(0     )]
    for i, item in enumerate(UpperCamelCase     ):
        if _operator(UpperCamelCase							,		sublist[-1]     ):
            sublist.append(UpperCamelCase     )
            arr.pop(UpperCamelCase     )

    #  merging sublist into solution list
    if not solution:
        solution.extend(UpperCamelCase     )
    else:
        while sublist:
            a__		=			sublist.pop(0     )
            for i, xx in enumerate(UpperCamelCase     ):
                if not _operator(UpperCamelCase							,		UpperCamelCase     ):
                    solution.insert(UpperCamelCase							,		UpperCamelCase     )
                    break
            else:
                solution.append(UpperCamelCase     )

    strand_sort(UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase     )
    return solution


if __name__ == "__main__":
    assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
    assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]


