






'''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()
lowerCamelCase_				: Any    	=							logging.get_logger(__name__)







def 	__magic_name__( _A						,      _A					):
						'''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 	__magic_name__( _A						,      _A					):
						'''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 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = dct.pop(_A					)
						UpperCamelCase__         = val







def 	__magic_name__( _A					):
						'''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(_A						,      stream=_A					).raw					).convert("""RGB"""					)
						return im







@torch.no_grad()
def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = ViTConfig(image_size=384						,      qkv_bias=_A					)
						UpperCamelCase__         = TrOCRConfig()

						# size of the architecture
						if "base" in checkpoint_url:
												UpperCamelCase__         = 768
						elif "large" in checkpoint_url:
												# use ViT-large encoder
												UpperCamelCase__         = 1024
												UpperCamelCase__         = 4096
												UpperCamelCase__         = 24
												UpperCamelCase__         = 16
												UpperCamelCase__         = 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:
												UpperCamelCase__         = False
												UpperCamelCase__         = """relu"""
												UpperCamelCase__         = 1024
												UpperCamelCase__         = True
												UpperCamelCase__         = False
												UpperCamelCase__         = False

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

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

						UpperCamelCase__         = create_rename_keys(_A						,      _A					)
						for src, dest in rename_keys:
												rename_key(_A						,      _A						,      _A					)
						read_in_q_k_v(_A						,      _A					)

						# 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(_A					)
												if key.startswith("""decoder"""					) and "output_projection" not in key:
																		UpperCamelCase__         = val
												else:
																		UpperCamelCase__         = val

    # load state dict
						model.load_state_dict(_A					)

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

						UpperCamelCase__         = processor(images=prepare_img(_A					)						,      return_tensors="""pt"""					).pixel_values

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

						UpperCamelCase__         = torch.Size([1, 1, 5_0265]					)
						if "trocr-base-handwritten" in checkpoint_url:
												UpperCamelCase__         = torch.tensor(
												    [-1.4_5_0_2, -4.6_6_8_3, -0.5_3_4_7, -2.9_2_9_1, 9.1_4_3_5, -3.0_5_7_1, 8.9_7_6_4, 1.7_5_6_0, 8.7_3_5_8, -1.5_3_1_1]					)
						elif "trocr-large-handwritten" in checkpoint_url:
												UpperCamelCase__         = torch.tensor(
												    [-2.6_4_3_7, -1.3_1_2_9, -2.2_5_9_6, -5.3_4_5_5, 6.3_5_3_9, 1.7_6_0_4, 5.4_9_9_1, 1.4_7_0_2, 5.6_1_1_3, 2.0_1_7_0]					)
						elif "trocr-base-printed" in checkpoint_url:
												UpperCamelCase__         = torch.tensor(
												    [-5.6_8_1_6, -5.8_3_8_8, 1.1_3_9_8, -6.9_0_3_4, 6.8_5_0_5, -2.4_3_9_3, 1.2_2_8_4, -1.0_2_3_2, -1.9_6_6_1, -3.9_2_1_0]					)
						elif "trocr-large-printed" in checkpoint_url:
												UpperCamelCase__         = torch.tensor(
												    [-6.0_1_6_2, -7.0_9_5_9, 4.4_1_5_5, -5.1_0_6_3, 7.0_4_6_8, -3.1_6_3_1, 2.6_4_6_6, -0.3_0_8_1, -0.8_1_0_6, -1.7_5_3_5]					)

						if "stage1" not in checkpoint_url:
												assert logits.shape == expected_shape, "Shape of logits not as expected"
												assert torch.allclose(logits[0, 0, :10]						,      _A						,      atol=1e-3					), "First elements of logits not as expected"

						Path(_A					).mkdir(exist_ok=_A					)
						print(f"Saving model to {pytorch_dump_folder_path}"					)
						model.save_pretrained(_A					)
						print(f"Saving processor to {pytorch_dump_folder_path}"					)
						processor.save_pretrained(_A					)


if __name__ == "__main__":
					lowerCamelCase_				: Union[str, 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_				: Any    	=							parser.parse_args()
					convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








'''simple docstring'''


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase_				: str    	=							logging.get_logger(__name__)

lowerCamelCase_				: Optional[int]    	=							{
    '''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json''',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							Tuple					     =					"deformable_detr"
			__a							:							str					     =					{
			    "hidden_size": "d_model",
			    "num_attention_heads": "encoder_attention_heads",
			}






			def __init__(   self							:						List[Any] ,      lowercase							:						List[Any]=True ,      lowercase							:						Tuple=None ,      lowercase							:						Tuple=3 ,      lowercase							:						List[str]=3_0_0 ,      lowercase							:						List[Any]=1_0_2_4 ,      lowercase							:						List[Any]=6 ,      lowercase							:						Tuple=1_0_2_4 ,      lowercase							:						Union[str, Any]=8 ,      lowercase							:						Optional[Any]=6 ,      lowercase							:						Tuple=1_0_2_4 ,      lowercase							:						Any=8 ,      lowercase							:						List[str]=0.0 ,      lowercase							:						Any=True ,      lowercase							:						Union[str, Any]="relu" ,      lowercase							:						Dict=2_5_6 ,      lowercase							:						Optional[int]=0.1 ,      lowercase							:						Optional[int]=0.0 ,      lowercase							:						List[str]=0.0 ,      lowercase							:						Any=0.0_2 ,      lowercase							:						Optional[int]=1.0 ,      lowercase							:						Tuple=True ,      lowercase							:						Optional[int]=False ,      lowercase							:						Any="sine" ,      lowercase							:						List[str]="resnet50" ,      lowercase							:						List[str]=True ,      lowercase							:						Optional[int]=False ,      lowercase							:						int=4 ,      lowercase							:						str=4 ,      lowercase							:						Dict=4 ,      lowercase							:						int=False ,      lowercase							:						List[Any]=3_0_0 ,      lowercase							:						List[Any]=False ,      lowercase							:						Dict=1 ,      lowercase							:						int=5 ,      lowercase							:						List[Any]=2 ,      lowercase							:						List[str]=1 ,      lowercase							:						Tuple=1 ,      lowercase							:						Dict=5 ,      lowercase							:						Dict=2 ,      lowercase							:						Dict=0.1 ,      lowercase							:						List[str]=0.2_5 ,      lowercase							:						Tuple=False ,      **lowercase							:						str ,      )							->    str:

									'''simple docstring'''







									if backbone_config is not None and use_timm_backbone:
															raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""			)

									if not use_timm_backbone:
															if backbone_config is None:
																					logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""			)
																					UpperCamelCase__         = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]			)
															elif isinstance(lowercase ,      lowercase			):
																					UpperCamelCase__         = backbone_config.get("""model_type"""			)
																					UpperCamelCase__         = CONFIG_MAPPING[backbone_model_type]
																					UpperCamelCase__         = config_class.from_dict(lowercase			)
									UpperCamelCase__         = use_timm_backbone
									UpperCamelCase__         = backbone_config
									UpperCamelCase__         = num_channels
									UpperCamelCase__         = num_queries
									UpperCamelCase__         = max_position_embeddings
									UpperCamelCase__         = d_model
									UpperCamelCase__         = encoder_ffn_dim
									UpperCamelCase__         = encoder_layers
									UpperCamelCase__         = encoder_attention_heads
									UpperCamelCase__         = decoder_ffn_dim
									UpperCamelCase__         = decoder_layers
									UpperCamelCase__         = decoder_attention_heads
									UpperCamelCase__         = dropout
									UpperCamelCase__         = attention_dropout
									UpperCamelCase__         = activation_dropout
									UpperCamelCase__         = activation_function
									UpperCamelCase__         = init_std
									UpperCamelCase__         = init_xavier_std
									UpperCamelCase__         = encoder_layerdrop
									UpperCamelCase__         = auxiliary_loss
									UpperCamelCase__         = position_embedding_type
									UpperCamelCase__         = backbone
									UpperCamelCase__         = use_pretrained_backbone
									UpperCamelCase__         = dilation
									# deformable attributes
									UpperCamelCase__         = num_feature_levels
									UpperCamelCase__         = encoder_n_points
									UpperCamelCase__         = decoder_n_points
									UpperCamelCase__         = two_stage
									UpperCamelCase__         = two_stage_num_proposals
									UpperCamelCase__         = with_box_refine
									if two_stage is True and with_box_refine is False:
															raise ValueError("""If two_stage is True, with_box_refine must be True."""			)
									# Hungarian matcher
									UpperCamelCase__         = class_cost
									UpperCamelCase__         = bbox_cost
									UpperCamelCase__         = giou_cost
									# Loss coefficients
									UpperCamelCase__         = mask_loss_coefficient
									UpperCamelCase__         = dice_loss_coefficient
									UpperCamelCase__         = bbox_loss_coefficient
									UpperCamelCase__         = giou_loss_coefficient
									UpperCamelCase__         = eos_coefficient
									UpperCamelCase__         = focal_alpha
									UpperCamelCase__         = disable_custom_kernels
									super().__init__(is_encoder_decoder=lowercase ,      **lowercase			)






			@property
			def 				A      (   self							:						str			)							->    int:

									'''simple docstring'''







									return self.encoder_attention_heads






			@property
			def 				A      (   self							:						Tuple			)							->    int:

									'''simple docstring'''







									return self.d_model







			def 				A      (   self							:						Dict			)							->    Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__         = copy.deepcopy(self.__dict__			)
									if self.backbone_config is not None:
															UpperCamelCase__         = self.backbone_config.to_dict()
									UpperCamelCase__         = self.__class__.model_type
									return output
