
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 :		List[Any]							      =  logging.get_logger(__name__)






def   lowerCAmelCase_						(					_snake_case      :  str      ,   _snake_case      :  Tuple   )				->		List[Any]:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     []
	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_						(					_snake_case      :  int      ,   _snake_case      :  Optional[Any]   )				->		str:







	'''simple docstring'''


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

		__magic_name__		:		List[Any]	   =     in_proj_weight[
		    : encoder_config.hidden_size, :
		]
		__magic_name__		:		List[str]	   =     in_proj_weight[
		    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
		]
		__magic_name__		:		Optional[int]	   =     in_proj_weight[
		    -encoder_config.hidden_size :, :
		]






def   lowerCAmelCase_						(					_snake_case      :  Any      ,   _snake_case      :  str      ,   _snake_case      :  Tuple   )				->		Union[str, Any]:







	'''simple docstring'''


	__magic_name__		:		List[Any]	   =     dct.pop(_snake_case   )
	__magic_name__		:		Union[str, Any]	   =     val






def   lowerCAmelCase_						(					_snake_case      :  Dict   )				->		Tuple:







	'''simple docstring'''


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






@torch.no_grad()
def   lowerCAmelCase_						(					_snake_case      :  Optional[Any]      ,   _snake_case      :  Union[str, Any]   )				->		List[Any]:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     ViTConfig(image_size=384      ,   qkv_bias=_snake_case   )
	__magic_name__		:		Any	   =     TrOCRConfig()

	# size of the architecture
	if "base" in checkpoint_url:
		__magic_name__		:		List[str]	   =     768
	elif "large" in checkpoint_url:
		# use ViT-large encoder
		__magic_name__		:		str	   =     1024
		__magic_name__		:		Optional[int]	   =     4096
		__magic_name__		:		Tuple	   =     24
		__magic_name__		:		Union[str, Any]	   =     16
		__magic_name__		:		List[Any]	   =     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:
		__magic_name__		:		List[Any]	   =     False
		__magic_name__		:		Any	   =     "relu"
		__magic_name__		:		int	   =     1024
		__magic_name__		:		Union[str, Any]	   =     True
		__magic_name__		:		int	   =     False
		__magic_name__		:		str	   =     False

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

	# load state_dict of original model, rename some keys
	__magic_name__		:		List[str]	   =     torch.hub.load_state_dict_from_url(_snake_case      ,   map_location="cpu"      ,   check_hash=_snake_case   )["model"]

	__magic_name__		:		List[Any]	   =     create_rename_keys(_snake_case      ,   _snake_case   )
	for src, dest in rename_keys:
		rename_key(_snake_case      ,   _snake_case      ,   _snake_case   )
	read_in_q_k_v(_snake_case      ,   _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():
		__magic_name__		:		Optional[int]	   =     state_dict.pop(_snake_case   )
		if key.startswith("decoder"   ) and "output_projection" not in key:
			__magic_name__		:		Optional[Any]	   =     val
		else:
			__magic_name__		:		int	   =     val

    # load state dict
	model.load_state_dict(_snake_case   )

	# Check outputs on an image
	__magic_name__		:		Tuple	   =     ViTImageProcessor(size=encoder_config.image_size   )
	__magic_name__		:		List[Any]	   =     RobertaTokenizer.from_pretrained("roberta-large"   )
	__magic_name__		:		List[Any]	   =     TrOCRProcessor(_snake_case      ,   _snake_case   )

	__magic_name__		:		Tuple	   =     processor(images=prepare_img(_snake_case   )      ,   return_tensors="pt"   ).pixel_values

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

	__magic_name__		:		Dict	   =     torch.Size([1, 1, 50265]   )
	if "trocr-base-handwritten" in checkpoint_url:
		__magic_name__		:		List[str]	   =     torch.tensor(
		    [-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311]   )
	elif "trocr-large-handwritten" in checkpoint_url:
		__magic_name__		:		Union[str, Any]	   =     torch.tensor(
		    [-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170]   )
	elif "trocr-base-printed" in checkpoint_url:
		__magic_name__		:		Optional[int]	   =     torch.tensor(
		    [-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210]   )
	elif "trocr-large-printed" in checkpoint_url:
		__magic_name__		:		int	   =     torch.tensor(
		    [-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535]   )

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

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


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


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case :		Dict							      =  logging.get_logger(__name__)

snake_case :		List[Any]							      =  {
    "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
    "YituTech/conv-bert-medium-small": (
        "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
    ),
    "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    'convbert'







			def __init__(				self						,   _a=30_522						,   _a=768						,   _a=12						,   _a=12						,   _a=3_072						,   _a="gelu"						,   _a=0.1						,   _a=0.1						,   _a=512						,   _a=2						,   _a=0.02						,   _a=1e-12						,   _a=1						,   _a=0						,   _a=2						,   _a=768						,   _a=2						,   _a=9						,   _a=1						,   _a=None						,   **_a						,   ):
				super().__init__(
				    pad_token_id=_a						,   bos_token_id=_a						,   eos_token_id=_a						,   **_a						,   )

				__magic_name__		:		Tuple	   =     vocab_size
				__magic_name__		:		List[Any]	   =     hidden_size
				__magic_name__		:		Union[str, Any]	   =     num_hidden_layers
				__magic_name__		:		List[Any]	   =     num_attention_heads
				__magic_name__		:		str	   =     intermediate_size
				__magic_name__		:		Any	   =     hidden_act
				__magic_name__		:		List[Any]	   =     hidden_dropout_prob
				__magic_name__		:		Optional[int]	   =     attention_probs_dropout_prob
				__magic_name__		:		Tuple	   =     max_position_embeddings
				__magic_name__		:		str	   =     type_vocab_size
				__magic_name__		:		List[str]	   =     initializer_range
				__magic_name__		:		Tuple	   =     layer_norm_eps
				__magic_name__		:		List[Any]	   =     embedding_size
				__magic_name__		:		List[Any]	   =     head_ratio
				__magic_name__		:		str	   =     conv_kernel_size
				__magic_name__		:		Dict	   =     num_groups
				__magic_name__		:		str	   =     classifier_dropout







class       _snake_case					(     snake_case				):







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				if self.task == "multiple-choice":
					__magic_name__		:		Dict	   =     {0: "batch", 1: "choice", 2: "sequence"}
				else:
					__magic_name__		:		Dict	   =     {0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("token_type_ids", dynamic_axis),
				    ]		)
