


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			:  Optional[int]     =logging.get_logger(__name__)



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

								lowerCAmelCase_ :      List[str]    				=				in_proj_weight[
								    : encoder_config.hidden_size, :
								]
								lowerCAmelCase_ :      Tuple    				=				in_proj_weight[
								    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
								]
								lowerCAmelCase_ :      List[Any]    				=				in_proj_weight[
								    -encoder_config.hidden_size :, :
								]



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> Optional[Any]:
				lowerCAmelCase_ :      str    				=				dct.pop(lowerCAmelCase_ )
				lowerCAmelCase_ :      Union[str, Any]    				=				val



def 			lowerCAmelCase (		lowerCAmelCase_ )-> int:
				if "handwritten" in checkpoint_url:
								lowerCAmelCase_ :      Optional[Any]    				=				'''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_ :      Dict    				=				'''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_ :      int    				=				Image.open(requests.get(lowerCAmelCase_ ,   stream=lowerCAmelCase_ ).raw ).convert('''RGB''' )
				return im



@torch.no_grad()
def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> str:
				lowerCAmelCase_ :      Any    				=				ViTConfig(image_size=384 ,   qkv_bias=lowerCAmelCase_ )
				lowerCAmelCase_ :      Optional[Any]    				=				TrOCRConfig()

				# size of the architecture
				if "base" in checkpoint_url:
								lowerCAmelCase_ :      Optional[Any]    				=				768
				elif "large" in checkpoint_url:
								# use ViT-large encoder
								lowerCAmelCase_ :      Tuple    				=				1_024
								lowerCAmelCase_ :      Tuple    				=				4_096
								lowerCAmelCase_ :      Dict    				=				24
								lowerCAmelCase_ :      int    				=				16
								lowerCAmelCase_ :      List[str]    				=				1_024
				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_ :      int    				=				False
								lowerCAmelCase_ :      List[str]    				=				'''relu'''
								lowerCAmelCase_ :      Optional[int]    				=				1_024
								lowerCAmelCase_ :      Optional[Any]    				=				True
								lowerCAmelCase_ :      int    				=				False
								lowerCAmelCase_ :      str    				=				False

				# load HuggingFace model
				lowerCAmelCase_ :      Dict    				=				ViTModel(lowerCAmelCase_ ,   add_pooling_layer=lowerCAmelCase_ )
				lowerCAmelCase_ :      str    				=				TrOCRForCausalLM(lowerCAmelCase_ )
				lowerCAmelCase_ :      str    				=				VisionEncoderDecoderModel(encoder=lowerCAmelCase_ ,   decoder=lowerCAmelCase_ )
				model.eval()

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

				lowerCAmelCase_ :      str    				=				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_ :      Tuple    				=				state_dict.pop(lowerCAmelCase_ )
								if key.startswith('''decoder''' ) and "output_projection" not in key:
												lowerCAmelCase_ :      Tuple    				=				val
								else:
												lowerCAmelCase_ :      List[Any]    				=				val

    # load state dict
				model.load_state_dict(lowerCAmelCase_ )

				# Check outputs on an image
				lowerCAmelCase_ :      List[str]    				=				ViTImageProcessor(size=encoder_config.image_size )
				lowerCAmelCase_ :      str    				=				RobertaTokenizer.from_pretrained('''roberta-large''' )
				lowerCAmelCase_ :      Dict    				=				TrOCRProcessor(lowerCAmelCase_ ,   lowerCAmelCase_ )

				lowerCAmelCase_ :      Union[str, Any]    				=				processor(images=prepare_img(lowerCAmelCase_ ) ,   return_tensors='''pt''' ).pixel_values

				# verify logits
				lowerCAmelCase_ :      Any    				=				torch.tensor([[model.config.decoder.decoder_start_token_id]] )
				lowerCAmelCase_ :      Tuple    				=				model(pixel_values=lowerCAmelCase_ ,   decoder_input_ids=lowerCAmelCase_ )
				lowerCAmelCase_ :      Tuple    				=				outputs.logits

				lowerCAmelCase_ :      Optional[Any]    				=				torch.Size([1, 1, 50_265] )
				if "trocr-base-handwritten" in checkpoint_url:
								lowerCAmelCase_ :      int    				=				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:
								lowerCAmelCase_ :      List[str]    				=				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:
								lowerCAmelCase_ :      Optional[int]    				=				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:
								lowerCAmelCase_ :      Optional[int]    				=				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] ,   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__":
						_UpperCAmelCase			:  List[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."""
						)
						_UpperCAmelCase			:  Optional[Any]     =parser.parse_args()
						convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



from __future__ import annotations



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   )-> tuple:
				if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
								raise ValueError('''You cannot supply more or less than 2 values''' )
				elif electron_conc < 0:
								raise ValueError('''Electron concentration cannot be negative in a semiconductor''' )
				elif hole_conc < 0:
								raise ValueError('''Hole concentration cannot be negative in a semiconductor''' )
				elif intrinsic_conc < 0:
								raise ValueError(
								    '''Intrinsic concentration cannot be negative in a semiconductor''' )
				elif electron_conc == 0:
								return (
								    "electron_conc",
								    intrinsic_conc**2 / hole_conc,
								)
				elif hole_conc == 0:
								return (
								    "hole_conc",
								    intrinsic_conc**2 / electron_conc,
								)
				elif intrinsic_conc == 0:
								return (
								    "intrinsic_conc",
								    (electron_conc * hole_conc) ** 0.5,
								)
				else:
								return (-1, -1)


if __name__ == "__main__":
						import doctest

						doctest.testmod()