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()
__SCREAMING_SNAKE_CASE						:   Dict         =			logging.get_logger(__name__)
def  snake_case						(__lowercase ,      __lowercase						)	->	Union[str, Any]:

						'''simple docstring'''



						_snake_case						:					Tuple    =   []
						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  snake_case						(__lowercase ,      __lowercase						)	->	Dict:

						'''simple docstring'''



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

												_snake_case						:					Any    =   in_proj_weight[
												    : encoder_config.hidden_size, :
												]
												_snake_case						:					Dict    =   in_proj_weight[
												    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
												]
												_snake_case						:					Tuple    =   in_proj_weight[
												    -encoder_config.hidden_size :, :
												]
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase						)	->	Optional[int]:

						'''simple docstring'''



						_snake_case						:					Optional[int]    =   dct.pop(__lowercase						)
						_snake_case						:					Union[str, Any]    =   val
def  snake_case						(__lowercase						)	->	Dict:

						'''simple docstring'''



						if "handwritten" in checkpoint_url:
												_snake_case						:					Optional[int]    =   "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:
												_snake_case						:					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"
						_snake_case						:					Tuple    =   Image.open(requests.get(__lowercase ,      stream=__lowercase						).raw						).convert("RGB"						)
						return im
@torch.no_grad()
def  snake_case						(__lowercase ,      __lowercase						)	->	Optional[Any]:

						'''simple docstring'''



						_snake_case						:					List[Any]    =   ViTConfig(image_size=384 ,      qkv_bias=__lowercase						)
						_snake_case						:					Tuple    =   TrOCRConfig()

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

						# load HuggingFace model
						_snake_case						:					str    =   ViTModel(__lowercase ,      add_pooling_layer=__lowercase						)
						_snake_case						:					str    =   TrOCRForCausalLM(__lowercase						)
						_snake_case						:					Tuple    =   VisionEncoderDecoderModel(encoder=__lowercase ,      decoder=__lowercase						)
						model.eval()

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

						_snake_case						:					Any    =   create_rename_keys(__lowercase ,      __lowercase						)
						for src, dest in rename_keys:
												rename_key(__lowercase ,      __lowercase ,      __lowercase						)
						read_in_q_k_v(__lowercase ,      __lowercase						)

						# 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():
												_snake_case						:					Optional[int]    =   state_dict.pop(__lowercase						)
												if key.startswith("decoder"						) and "output_projection" not in key:
																		_snake_case						:					List[Any]    =   val
												else:
																		_snake_case						:					Dict    =   val

    # load state dict
						model.load_state_dict(__lowercase						)

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

						_snake_case						:					List[Any]    =   processor(images=prepare_img(__lowercase						) ,      return_tensors="pt"						).pixel_values

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

						_snake_case						:					int    =   torch.Size([1, 1, 50_265]						)
						if "trocr-base-handwritten" in checkpoint_url:
												_snake_case						:					Optional[Any]    =   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:
												_snake_case						:					Optional[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:
												_snake_case						:					Any    =   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:
												_snake_case						:					str    =   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] ,      __lowercase ,      atol=1e-3						), "First elements of logits not as expected"

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


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:   List[str]         =			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.'
				)
				__SCREAMING_SNAKE_CASE						:   Dict         =			parser.parse_args()
				convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def  snake_case						(__lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					int    =   args.pruning_method
						_snake_case						:					List[Any]    =   args.threshold

						_snake_case						:					Optional[Any]    =   args.model_name_or_path.rstrip("/"						)
						_snake_case						:					List[str]    =   args.target_model_path

						print(F"""Load fine-pruned model from {model_name_or_path}"""						)
						_snake_case						:					List[Any]    =   torch.load(os.path.join(__lowercase ,      "pytorch_model.bin"						)						)
						_snake_case						:					List[str]    =   {}

						for name, tensor in model.items():
												if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
																		_snake_case						:					Tuple    =   tensor
																		print(F"""Copied layer {name}"""						)
												elif "classifier" in name or "qa_output" in name:
																		_snake_case						:					Optional[int]    =   tensor
																		print(F"""Copied layer {name}"""						)
												elif "bias" in name:
																		_snake_case						:					List[Any]    =   tensor
																		print(F"""Copied layer {name}"""						)
												else:
																		if pruning_method == "magnitude":
																								_snake_case						:					Tuple    =   MagnitudeBinarizer.apply(inputs=__lowercase ,      threshold=__lowercase						)
																								_snake_case						:					List[str]    =   tensor * mask
																								print(F"""Pruned layer {name}"""						)
																		elif pruning_method == "topK":
																								if "mask_scores" in name:
																														continue
																								_snake_case						:					Optional[Any]    =   name[:-6]
																								_snake_case						:					Any    =   model[F"""{prefix_}mask_scores"""]
																								_snake_case						:					Tuple    =   TopKBinarizer.apply(__lowercase ,      __lowercase						)
																								_snake_case						:					Optional[Any]    =   tensor * mask
																								print(F"""Pruned layer {name}"""						)
																		elif pruning_method == "sigmoied_threshold":
																								if "mask_scores" in name:
																														continue
																								_snake_case						:					int    =   name[:-6]
																								_snake_case						:					List[Any]    =   model[F"""{prefix_}mask_scores"""]
																								_snake_case						:					List[str]    =   ThresholdBinarizer.apply(__lowercase ,      __lowercase ,      __lowercase						)
																								_snake_case						:					List[str]    =   tensor * mask
																								print(F"""Pruned layer {name}"""						)
																		elif pruning_method == "l0":
																								if "mask_scores" in name:
																														continue
																								_snake_case						:					int    =   name[:-6]
																								_snake_case						:					Any    =   model[F"""{prefix_}mask_scores"""]
																								_snake_case    ,_snake_case						:					Union[str, Any]    =   -0.1, 1.1
																								_snake_case						:					Dict    =   torch.sigmoid(__lowercase						)
																								_snake_case						:					List[str]    =   s * (r - l) + l
																								_snake_case						:					Tuple    =   s_bar.clamp(min=0.0 ,      max=1.0						)
																								_snake_case						:					Union[str, Any]    =   tensor * mask
																								print(F"""Pruned layer {name}"""						)
																		else:
																								raise ValueError("Unknown pruning method"						)

						if target_model_path is None:
												_snake_case						:					Any    =   os.path.join(
												    os.path.dirname(__lowercase						) ,      F"""bertarized_{os.path.basename(__lowercase						)}"""						)

						if not os.path.isdir(__lowercase						):
												shutil.copytree(__lowercase ,      __lowercase						)
												print(F"""\nCreated folder {target_model_path}"""						)

						torch.save(__lowercase ,      os.path.join(__lowercase ,      "pytorch_model.bin"						)						)
						print("\nPruned model saved! See you later!"						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:   List[Any]         =			argparse.ArgumentParser()

				parser.add_argument(
				    '--pruning_method',
				    choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
				    type=str,
				    required=True,
				    help=(
				        'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
				        ' sigmoied_threshold = Soft movement pruning)'
				    ),
				)
				parser.add_argument(
				    '--threshold',
				    type=float,
				    required=False,
				    help=(
				        'For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
				        'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
				        'Not needed for `l0`'
				    ),
				)
				parser.add_argument(
				    '--model_name_or_path',
				    type=str,
				    required=True,
				    help='Folder containing the model that was previously fine-pruned',
				)
				parser.add_argument(
				    '--target_model_path',
				    default=None,
				    type=str,
				    required=False,
				    help='Folder containing the model that was previously fine-pruned',
				)

				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			parser.parse_args()

				main(args)