



'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_			=     logging.get_logger(__name__)






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Dict       ):
				SCREAMING_SNAKE_CASE  :						List[str]      					= '''huggingface/label-files'''
				SCREAMING_SNAKE_CASE  :						str      					= '''imagenet-1k-id2label.json'''
				SCREAMING_SNAKE_CASE  :						List[Any]      					= json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	repo_type='''dataset'''       )    ,	'''r'''       )       )
				SCREAMING_SNAKE_CASE  :						List[str]      					= {int(_SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
				SCREAMING_SNAKE_CASE  :						str      					= {v: k for k, v in idalabel.items()}

				SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''std_conv''' if '''bit''' in model_name else False

				# note that when using BiT as backbone for ViT-hybrid checkpoints,
				# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
				# config.conv_layer = "std_conv_same"
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= BitConfig(
				    conv_layer=_SCREAMING_SNAKE_CASE    ,	num_labels=10_00    ,	idalabel=_SCREAMING_SNAKE_CASE    ,	labelaid=_SCREAMING_SNAKE_CASE    ,	)

				return config






def       __lowercase  (_SCREAMING_SNAKE_CASE      :int       ):
				if "stem.conv" in name:
								SCREAMING_SNAKE_CASE  :						Tuple      					= name.replace('''stem.conv'''    ,	'''bit.embedder.convolution'''       )
				if "blocks" in name:
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= name.replace('''blocks'''    ,	'''layers'''       )
				if "head.fc" in name:
								SCREAMING_SNAKE_CASE  :						List[str]      					= name.replace('''head.fc'''    ,	'''classifier.1'''       )
				if name.startswith('''norm'''       ):
								SCREAMING_SNAKE_CASE  :						List[str]      					= '''bit.''' + name
				if "bit" not in name and "classifier" not in name:
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= '''bit.encoder.''' + name

				return name






def       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						Tuple      					= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				SCREAMING_SNAKE_CASE  :						int      					= Image.open(requests.get(_SCREAMING_SNAKE_CASE    ,	stream=_SCREAMING_SNAKE_CASE       ).raw       )
				return im






@torch.no_grad()
def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[int]    ,	_SCREAMING_SNAKE_CASE      :List[Any]    ,	_SCREAMING_SNAKE_CASE      :Tuple=False       ):
				SCREAMING_SNAKE_CASE  :						Any      					= get_config(_SCREAMING_SNAKE_CASE       )

				# load original model from timm
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= create_model(_SCREAMING_SNAKE_CASE    ,	pretrained=_SCREAMING_SNAKE_CASE       )
				timm_model.eval()

				# load state_dict of original model
				SCREAMING_SNAKE_CASE  :						Tuple      					= timm_model.state_dict()
				for key in state_dict.copy().keys():
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= state_dict.pop(_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						Tuple      					= val.squeeze() if '''head''' in key else val

				# load HuggingFace model
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= BitForImageClassification(_SCREAMING_SNAKE_CASE       )
				model.eval()
				model.load_state_dict(_SCREAMING_SNAKE_CASE       )

				# create image processor
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= create_transform(**resolve_data_config({}    ,	model=_SCREAMING_SNAKE_CASE       )       )
				SCREAMING_SNAKE_CASE  :						Dict      					= transform.transforms

				SCREAMING_SNAKE_CASE  :						Tuple      					= {
				    '''bilinear''': PILImageResampling.BILINEAR,
				    '''bicubic''': PILImageResampling.BICUBIC,
				    '''nearest''': PILImageResampling.NEAREST,
				}

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= BitImageProcessor(
				    do_resize=_SCREAMING_SNAKE_CASE    ,	size={'''shortest_edge''': timm_transforms[0].size}    ,	resample=pillow_resamplings[timm_transforms[0].interpolation.value]    ,	do_center_crop=_SCREAMING_SNAKE_CASE    ,	crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}    ,	do_normalize=_SCREAMING_SNAKE_CASE    ,	image_mean=timm_transforms[-1].mean.tolist()    ,	image_std=timm_transforms[-1].std.tolist()    ,	)

				SCREAMING_SNAKE_CASE  :						Optional[int]      					= prepare_img()
				SCREAMING_SNAKE_CASE  :						int      					= transform(_SCREAMING_SNAKE_CASE       ).unsqueeze(0       )
				SCREAMING_SNAKE_CASE  :						str      					= processor(_SCREAMING_SNAKE_CASE    ,	return_tensors='''pt'''       ).pixel_values

				# verify pixel values
				assert torch.allclose(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

				# verify logits
				with torch.no_grad():
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model(_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						List[str]      					= outputs.logits

				print('''Logits:'''    ,	logits[0, :3]       )
				print('''Predicted class:'''    ,	model.config.idalabel[logits.argmax(-1       ).item()]       )
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= timm_model(_SCREAMING_SNAKE_CASE       )
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(_SCREAMING_SNAKE_CASE    ,	outputs.logits    ,	atol=1E-3       )
				print('''Looks ok!'''       )

				if pytorch_dump_folder_path is not None:
								Path(_SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE       )
								print(F'''Saving model {model_name} and processor to {pytorch_dump_folder_path}'''       )
								model.save_pretrained(_SCREAMING_SNAKE_CASE       )
								processor.save_pretrained(_SCREAMING_SNAKE_CASE       )

				if push_to_hub:
								print(F'''Pushing model {model_name} and processor to the hub'''       )
								model.push_to_hub(F'''ybelkada/{model_name}'''       )
								processor.push_to_hub(F'''ybelkada/{model_name}'''       )


if __name__ == "__main__":
	snake_case_			=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default="""resnetv2_50x1_bitm""",
	    type=str,
	    help="""Name of the BiT timm model you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
	)
	parser.add_argument(
	    """--push_to_hub""",
	    action="""store_true""",
	    help="""Whether to push the model to the hub.""",
	)

	snake_case_			=     parser.parse_args()
	convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''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()
snake_case_			=     logging.get_logger(__name__)






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Dict    ,	_SCREAMING_SNAKE_CASE      :int       ):
				SCREAMING_SNAKE_CASE  :						Dict      					= []
				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  (_SCREAMING_SNAKE_CASE      :List[Any]    ,	_SCREAMING_SNAKE_CASE      :Tuple       ):
				for i in range(encoder_config.num_hidden_layers       ):
								# queries, keys and values (only weights, no biases)
								SCREAMING_SNAKE_CASE  :						str      					= state_dict.pop(F'''encoder.deit.blocks.{i}.attn.qkv.weight'''       )

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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				SCREAMING_SNAKE_CASE  :						List[str]      					= dct.pop(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= val






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]       ):
				if "handwritten" in checkpoint_url:
								SCREAMING_SNAKE_CASE  :						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:
								SCREAMING_SNAKE_CASE  :						Union[str, 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'''
				SCREAMING_SNAKE_CASE  :						List[Any]      					= Image.open(requests.get(_SCREAMING_SNAKE_CASE    ,	stream=_SCREAMING_SNAKE_CASE       ).raw       ).convert('''RGB'''       )
				return im






@torch.no_grad()
def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= ViTConfig(image_size=3_84    ,	qkv_bias=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Any      					= TrOCRConfig()

				# size of the architecture
				if "base" in checkpoint_url:
								SCREAMING_SNAKE_CASE  :						List[Any]      					= 7_68
				elif "large" in checkpoint_url:
								# use ViT-large encoder
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= 10_24
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 40_96
								SCREAMING_SNAKE_CASE  :						List[str]      					= 24
								SCREAMING_SNAKE_CASE  :						Any      					= 16
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 10_24
				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  :						Optional[Any]      					= False
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= '''relu'''
								SCREAMING_SNAKE_CASE  :						Tuple      					= 10_24
								SCREAMING_SNAKE_CASE  :						int      					= True
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= False
								SCREAMING_SNAKE_CASE  :						Any      					= False

				# load HuggingFace model
				SCREAMING_SNAKE_CASE  :						List[Any]      					= ViTModel(_SCREAMING_SNAKE_CASE    ,	add_pooling_layer=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						int      					= TrOCRForCausalLM(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Any      					= VisionEncoderDecoderModel(encoder=_SCREAMING_SNAKE_CASE    ,	decoder=_SCREAMING_SNAKE_CASE       )
				model.eval()

				# load state_dict of original model, rename some keys
				SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE    ,	map_location='''cpu'''    ,	check_hash=_SCREAMING_SNAKE_CASE       )['''model''']

				SCREAMING_SNAKE_CASE  :						int      					= create_rename_keys(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				for src, dest in rename_keys:
								rename_key(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				read_in_q_k_v(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

				# 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  :						List[Any]      					= state_dict.pop(_SCREAMING_SNAKE_CASE       )
								if key.startswith('''decoder'''       ) and "output_projection" not in key:
												SCREAMING_SNAKE_CASE  :						Tuple      					= val
								else:
												SCREAMING_SNAKE_CASE  :						int      					= val

    # load state dict
				model.load_state_dict(_SCREAMING_SNAKE_CASE       )

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

				SCREAMING_SNAKE_CASE  :						Tuple      					= processor(images=prepare_img(_SCREAMING_SNAKE_CASE       )    ,	return_tensors='''pt'''       ).pixel_values

				# verify logits
				SCREAMING_SNAKE_CASE  :						str      					= torch.tensor([[model.config.decoder.decoder_start_token_id]]       )
				SCREAMING_SNAKE_CASE  :						Dict      					= model(pixel_values=_SCREAMING_SNAKE_CASE    ,	decoder_input_ids=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						int      					= outputs.logits

				SCREAMING_SNAKE_CASE  :						Any      					= torch.Size([1, 1, 5_02_65]       )
				if "trocr-base-handwritten" in checkpoint_url:
								SCREAMING_SNAKE_CASE  :						Optional[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:
								SCREAMING_SNAKE_CASE  :						Any      					= 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  :						str      					= 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  :						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]    ,	_SCREAMING_SNAKE_CASE    ,	atol=1E-3       ), "First elements of logits not as expected"

				Path(_SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE       )
				print(F'''Saving model to {pytorch_dump_folder_path}'''       )
				model.save_pretrained(_SCREAMING_SNAKE_CASE       )
				print(F'''Saving processor to {pytorch_dump_folder_path}'''       )
				processor.save_pretrained(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
	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."""
	)
	snake_case_			=     parser.parse_args()
	convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




