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





def 	__lowerCamelCase							(UpperCAmelCase__     :							Union[str, Any]      , UpperCAmelCase__     :							Optional[int]  ):
					SCREAMING_SNAKE_CASE    			=       []
					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 	__lowerCamelCase							(UpperCAmelCase__     :							Union[str, Any]      , UpperCAmelCase__     :							Any  ):
					for i in range(encoder_config.num_hidden_layers  ):
										# queries, keys and values (only weights, no biases)
										SCREAMING_SNAKE_CASE    			=       state_dict.pop(F"encoder.deit.blocks.{i}.attn.qkv.weight"  )

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





def 	__lowerCamelCase							(UpperCAmelCase__     :							Dict      , UpperCAmelCase__     :							Optional[Any]      , UpperCAmelCase__     :							Optional[Any]  ):
					SCREAMING_SNAKE_CASE    			=       dct.pop(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       val





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





@torch.no_grad()
def 	__lowerCamelCase							(UpperCAmelCase__     :							List[Any]      , UpperCAmelCase__     :							Union[str, Any]  ):
					SCREAMING_SNAKE_CASE    			=       ViTConfig(image_size=3_8_4      , qkv_bias=UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       TrOCRConfig()

					# size of the architecture
					if "base" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       7_6_8
					elif "large" in checkpoint_url:
										# use ViT-large encoder
										SCREAMING_SNAKE_CASE    			=       1_0_2_4
										SCREAMING_SNAKE_CASE    			=       4_0_9_6
										SCREAMING_SNAKE_CASE    			=       2_4
										SCREAMING_SNAKE_CASE    			=       1_6
										SCREAMING_SNAKE_CASE    			=       1_0_2_4
					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:
										SCREAMING_SNAKE_CASE    			=       False
										SCREAMING_SNAKE_CASE    			=       "relu"
										SCREAMING_SNAKE_CASE    			=       1_0_2_4
										SCREAMING_SNAKE_CASE    			=       True
										SCREAMING_SNAKE_CASE    			=       False
										SCREAMING_SNAKE_CASE    			=       False

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

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

					SCREAMING_SNAKE_CASE    			=       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():
										SCREAMING_SNAKE_CASE    			=       state_dict.pop(UpperCAmelCase__  )
										if key.startswith("decoder"  ) and "output_projection" not in key:
															SCREAMING_SNAKE_CASE    			=       val
										else:
															SCREAMING_SNAKE_CASE    			=       val

    # load state dict
					model.load_state_dict(UpperCAmelCase__  )

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

					SCREAMING_SNAKE_CASE    			=       processor(images=prepare_img(UpperCAmelCase__  )      , return_tensors="pt"  ).pixel_values

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

					SCREAMING_SNAKE_CASE    			=       torch.Size([1, 1, 5_0_2_6_5]  )
					if "trocr-base-handwritten" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       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:
										SCREAMING_SNAKE_CASE    			=       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:
										SCREAMING_SNAKE_CASE    			=       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:
										SCREAMING_SNAKE_CASE    			=       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, :1_0]      , 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__":
							_lowerCamelCase					:   Any							=		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.'''
							)
							_lowerCamelCase					:   List[str]							=		parser.parse_args()
							convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





def 	__lowerCamelCase							(UpperCAmelCase__     :							int  ):
					assert isinstance(UpperCAmelCase__      , UpperCAmelCase__  ), F"The input value of [n={number}] is not an integer"

					if number == 1:
										return 2
					elif number < 1:
										SCREAMING_SNAKE_CASE    			=       F"The input value of [n={number}] has to be > 0"
										raise ValueError(UpperCAmelCase__  )
					else:
										SCREAMING_SNAKE_CASE    			=       sylvester(number - 1  )
										SCREAMING_SNAKE_CASE    			=       num - 1
										SCREAMING_SNAKE_CASE    			=       num
										return lower * upper + 1


if __name__ == "__main__":
							print(f"""The 8th number in Sylvester's sequence: {sylvester(8)}""")




