
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__	=							logging.get_logger(__name__)


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		_UpperCamelCase						    =	[]
		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 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				str:

		"""simple docstring"""






		for i in range(encoder_config.num_hidden_layers ):
				# queries, keys and values (only weights, no biases)
				_UpperCamelCase						    =	state_dict.pop(F'encoder.deit.blocks.{i}.attn.qkv.weight' )

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


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		_UpperCamelCase						    =	dct.pop(lowerCAmelCase )
		_UpperCamelCase						    =	val


def 							__A(lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		if "handwritten" in checkpoint_url:
				_UpperCamelCase						    =	"""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						    =	"""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						    =	Image.open(requests.get(lowerCAmelCase						,  stream=lowerCAmelCase ).raw ).convert("""RGB""" )
		return im


@torch.no_grad()
def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				str:

		"""simple docstring"""






		_UpperCamelCase						    =	ViTConfig(image_size=3_8_4						,  qkv_bias=lowerCAmelCase )
		_UpperCamelCase						    =	TrOCRConfig()

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

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

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

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

    # load state dict
		model.load_state_dict(lowerCAmelCase )

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

		_UpperCamelCase						    =	processor(images=prepare_img(lowerCAmelCase )						,  return_tensors="""pt""" ).pixel_values

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

		_UpperCamelCase						    =	torch.Size([1, 1, 5_0_2_6_5] )
		if "trocr-base-handwritten" in checkpoint_url:
				_UpperCamelCase						    =	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						    =	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						    =	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						    =	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]						,  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__":
			lowerCamelCase__	=							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__	=							parser.parse_args()
			convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__	=							logging.get_logger()







@dataclass
class        lowerCAmelCase__      :
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: List[nn.Module]	=		field(default_factory=__lowercase	)
						UpperCamelCase_							: list	=		field(default_factory=__lowercase	)

						def  A_				(			self       ,							a       ,							a       ,							a				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	len(list(m.modules()				)				) == 1 or isinstance(a       ,							nn.Convad				) or isinstance(a       ,							nn.BatchNormad				)
								if has_not_submodules:
										self.traced.append(a				)

						def __call__(			self       ,							a				)					->      Optional[int]:


								'''simple docstring'''

								for m in self.module.modules():
										self.handles.append(m.register_forward_hook(self._forward_hook				)				)
								self.module(a				)
								[x.remove() for x in self.handles]
								return self







						@property
						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								return list(filter(lambda a				: len(list(x.state_dict().keys()				)				) > 0       ,							self.traced				)				)







@dataclass
class        lowerCAmelCase__      :
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: int	=		0
						UpperCamelCase_							: List	=		field(default_factory=__lowercase	)
						UpperCamelCase_							: List	=		field(default_factory=__lowercase	)

						def __call__(			self       ,							a				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	Tracker(self.dest				)(a				).parametrized
								_UpperCamelCase						    =	Tracker(self.src				)(a				).parametrized

								_UpperCamelCase						    =	list(filter(lambda a				: type(a				) not in self.src_skip       ,							a				)				)
								_UpperCamelCase						    =	list(filter(lambda a				: type(a				) not in self.dest_skip       ,							a				)				)

								if len(a				) != len(a				):
										raise Exception(
										    F'Numbers of operations are different. Source module has {len(a				)} operations while'
										    F' destination module has {len(a				)}.'				)

								for dest_m, src_m in zip(a       ,							a				):
										dest_m.load_state_dict(src_m.state_dict()				)
										if self.verbose == 1:
												print(F'Transfered from={src_m} to={dest_m}'				)




def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase = True )		->				Optional[Any]:

		"""simple docstring"""






		print(F'Converting {name}...' )
		with torch.no_grad():
				_UpperCamelCase						    =	timm.create_model(lowerCAmelCase						,  pretrained=lowerCAmelCase ).eval()
				_UpperCamelCase						    =	ResNetForImageClassification(lowerCAmelCase ).eval()
				_UpperCamelCase						    =	ModuleTransfer(src=lowerCAmelCase						,  dest=lowerCAmelCase )
				_UpperCamelCase						    =	torch.randn((1, 3, 2_2_4, 2_2_4) )
				module_transfer(lowerCAmelCase )

		assert torch.allclose(from_model(lowerCAmelCase )						,  our_model(lowerCAmelCase ).logits ), "The model logits don't match the original one."

		_UpperCamelCase						    =	F'resnet{"-".join(name.split("resnet" ) )}'
		print(lowerCAmelCase )

		if push_to_hub:
				our_model.push_to_hub(
				    repo_path_or_name=save_directory / checkpoint_name						,  commit_message="""Add model"""						,  use_temp_dir=lowerCAmelCase						,  )

				# we can use the convnext one
				_UpperCamelCase						    =	AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
				image_processor.push_to_hub(
				    repo_path_or_name=save_directory / checkpoint_name						,  commit_message="""Add image processor"""						,  use_temp_dir=lowerCAmelCase						,  )

				print(F'Pushed {checkpoint_name}' )


def 							__A(lowerCAmelCase						,  lowerCAmelCase = None						,  lowerCAmelCase = True )		->				Tuple:

		"""simple docstring"""






		_UpperCamelCase						    =	"""imagenet-1k-id2label.json"""
		_UpperCamelCase						    =	1_0_0_0
		_UpperCamelCase						    =	(1, num_labels)

		_UpperCamelCase						    =	"""huggingface/label-files"""
		_UpperCamelCase						    =	num_labels
		_UpperCamelCase						    =	json.load(open(hf_hub_download(lowerCAmelCase						,  lowerCAmelCase						,  repo_type="""dataset""" )						,  """r""" ) )
		_UpperCamelCase						    =	{int(lowerCAmelCase ): v for k, v in idalabel.items()}

		_UpperCamelCase						    =	idalabel
		_UpperCamelCase						    =	{v: k for k, v in idalabel.items()}

		_UpperCamelCase						    =	partial(lowerCAmelCase						,  num_labels=lowerCAmelCase						,  idalabel=lowerCAmelCase						,  labelaid=lowerCAmelCase )

		_UpperCamelCase						    =	{
		    """resnet18""": ImageNetPreTrainedConfig(
		        depths=[2, 2, 2, 2]						,  hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]						,  layer_type="""basic""" ),
		    """resnet26""": ImageNetPreTrainedConfig(
		        depths=[2, 2, 2, 2]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		    """resnet34""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 6, 3]						,  hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]						,  layer_type="""basic""" ),
		    """resnet50""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 6, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		    """resnet101""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 2_3, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		    """resnet152""": ImageNetPreTrainedConfig(
		        depths=[3, 8, 3_6, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		}

		if model_name:
				convert_weight_and_push(lowerCAmelCase						,  names_to_config[model_name]						,  lowerCAmelCase						,  lowerCAmelCase )
		else:
				for model_name, config in names_to_config.items():
						convert_weight_and_push(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )
		return config, expected_shape


if __name__ == "__main__":
		lowerCamelCase__	=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--model_name",
		    default=None,
		    type=str,
		    help=(
		        "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
		        " currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
		    ),
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path",
		    default=None,
		    type=Path,
		    required=True,
		    help="Path to the output PyTorch model directory.",
		)
		parser.add_argument(
		    "--push_to_hub",
		    default=True,
		    type=bool,
		    required=False,
		    help="If True, push model and image processor to the hub.",
		)

		lowerCamelCase__	=							parser.parse_args()
		lowerCamelCase__	=							args.pytorch_dump_folder_path
		pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
		convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)



