import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase					:   str							=		logging.get_logger(__name__)





def 	__lowerCamelCase							(UpperCAmelCase__     :							List[Any]      , UpperCAmelCase__     :							str=False      , UpperCAmelCase__     :							Union[str, Any]=False  ):
					SCREAMING_SNAKE_CASE    			=       "backbone." if is_semantic else ""

					SCREAMING_SNAKE_CASE    			=       []
					for i in range(config.num_hidden_layers  ):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append((F"{prefix}blocks.{i}.norm1.weight", F"beit.encoder.layer.{i}.layernorm_before.weight")  )
										rename_keys.append((F"{prefix}blocks.{i}.norm1.bias", F"beit.encoder.layer.{i}.layernorm_before.bias")  )
										rename_keys.append(
										    (F"{prefix}blocks.{i}.attn.proj.weight", F"beit.encoder.layer.{i}.attention.output.dense.weight")  )
										rename_keys.append(
										    (F"{prefix}blocks.{i}.attn.proj.bias", F"beit.encoder.layer.{i}.attention.output.dense.bias")  )
										rename_keys.append((F"{prefix}blocks.{i}.norm2.weight", F"beit.encoder.layer.{i}.layernorm_after.weight")  )
										rename_keys.append((F"{prefix}blocks.{i}.norm2.bias", F"beit.encoder.layer.{i}.layernorm_after.bias")  )
										rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.weight", F"beit.encoder.layer.{i}.intermediate.dense.weight")  )
										rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.bias", F"beit.encoder.layer.{i}.intermediate.dense.bias")  )
										rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.weight", F"beit.encoder.layer.{i}.output.dense.weight")  )
										rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.bias", F"beit.encoder.layer.{i}.output.dense.bias")  )

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        (F"{prefix}cls_token", "beit.embeddings.cls_token"),
					        (F"{prefix}patch_embed.proj.weight", "beit.embeddings.patch_embeddings.projection.weight"),
					        (F"{prefix}patch_embed.proj.bias", "beit.embeddings.patch_embeddings.projection.bias"),
					        (F"{prefix}pos_embed", "beit.embeddings.position_embeddings"),
					    ]  )

					if has_lm_head:
										# mask token + layernorm
										rename_keys.extend(
										    [
										        ("mask_token", "beit.embeddings.mask_token"),
										        ("norm.weight", "layernorm.weight"),
										        ("norm.bias", "layernorm.bias"),
										    ]  )
					else:
										# layernorm + classification head
										rename_keys.extend(
										    [
										        ("fc_norm.weight", "beit.pooler.layernorm.weight"),
										        ("fc_norm.bias", "beit.pooler.layernorm.bias"),
										        ("head.weight", "classifier.weight"),
										        ("head.bias", "classifier.bias"),
										    ]  )

					return rename_keys





def 	__lowerCamelCase							(UpperCAmelCase__     :							List[Any]      , UpperCAmelCase__     :							List[str]      , UpperCAmelCase__     :							Any=False      , UpperCAmelCase__     :							Union[str, Any]=False  ):
					for i in range(config.num_hidden_layers  ):
										SCREAMING_SNAKE_CASE    			=       "backbone." if is_semantic else ""
										# queries, keys and values
										SCREAMING_SNAKE_CASE    			=       state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"  )
										SCREAMING_SNAKE_CASE    			=       state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"  )
										SCREAMING_SNAKE_CASE    			=       state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"  )

										SCREAMING_SNAKE_CASE    			=       in_proj_weight[
										    : config.hidden_size, :
										]
										SCREAMING_SNAKE_CASE    			=       q_bias
										SCREAMING_SNAKE_CASE    			=       in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										SCREAMING_SNAKE_CASE    			=       in_proj_weight[
										    -config.hidden_size :, :
										]
										SCREAMING_SNAKE_CASE    			=       v_bias

										# gamma_1 and gamma_2
										# we call them lambda because otherwise they are renamed when using .from_pretrained
										SCREAMING_SNAKE_CASE    			=       state_dict.pop(F"{prefix}blocks.{i}.gamma_1"  )
										SCREAMING_SNAKE_CASE    			=       state_dict.pop(F"{prefix}blocks.{i}.gamma_2"  )

										SCREAMING_SNAKE_CASE    			=       gamma_a
										SCREAMING_SNAKE_CASE    			=       gamma_a





def 	__lowerCamelCase							(UpperCAmelCase__     :							List[Any]      , UpperCAmelCase__     :							List[str]      , UpperCAmelCase__     :							Optional[int]  ):
					SCREAMING_SNAKE_CASE    			=       dct.pop(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       val





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       "http://images.cocodataset.org/val2017/000000039769.jpg"
					SCREAMING_SNAKE_CASE    			=       Image.open(requests.get(UpperCAmelCase__      , stream=UpperCAmelCase__  ).raw  )
					return im





@torch.no_grad()
def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[Any]      , UpperCAmelCase__     :							str      , UpperCAmelCase__     :							Dict=False  ):
					SCREAMING_SNAKE_CASE    			=       False if "rvlcdip" in checkpoint_url else True
					SCREAMING_SNAKE_CASE    			=       BeitConfig(use_absolute_position_embeddings=UpperCAmelCase__      , use_mask_token=UpperCAmelCase__  )

					# size of the architecture
					if "large" in checkpoint_url or "dit-l" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       1_0_2_4
										SCREAMING_SNAKE_CASE    			=       4_0_9_6
										SCREAMING_SNAKE_CASE    			=       2_4
										SCREAMING_SNAKE_CASE    			=       1_6

					# labels
					if "rvlcdip" in checkpoint_url:
										SCREAMING_SNAKE_CASE    			=       1_6
										SCREAMING_SNAKE_CASE    			=       "huggingface/label-files"
										SCREAMING_SNAKE_CASE    			=       "rvlcdip-id2label.json"
										SCREAMING_SNAKE_CASE    			=       json.load(open(hf_hub_download(UpperCAmelCase__      , UpperCAmelCase__      , repo_type="dataset"  )      , "r"  )  )
										SCREAMING_SNAKE_CASE    			=       {int(UpperCAmelCase__  ): v for k, v in idalabel.items()}
										SCREAMING_SNAKE_CASE    			=       idalabel
										SCREAMING_SNAKE_CASE    			=       {v: k for k, v in idalabel.items()}

					# load state_dict of original model, remove and rename some keys
					SCREAMING_SNAKE_CASE    			=       torch.hub.load_state_dict_from_url(UpperCAmelCase__      , map_location="cpu"  )["model"]

					SCREAMING_SNAKE_CASE    			=       create_rename_keys(UpperCAmelCase__      , has_lm_head=UpperCAmelCase__  )
					for src, dest in rename_keys:
										rename_key(UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__  )
					read_in_q_k_v(UpperCAmelCase__      , UpperCAmelCase__      , has_lm_head=UpperCAmelCase__  )

					# load HuggingFace model
					SCREAMING_SNAKE_CASE    			=       BeitForMaskedImageModeling(UpperCAmelCase__  ) if has_lm_head else BeitForImageClassification(UpperCAmelCase__  )
					model.eval()
					model.load_state_dict(UpperCAmelCase__  )

					# Check outputs on an image
					SCREAMING_SNAKE_CASE    			=       BeitImageProcessor(
					    size=config.image_size      , resample=PILImageResampling.BILINEAR      , do_center_crop=UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       prepare_img()

					SCREAMING_SNAKE_CASE    			=       image_processor(images=UpperCAmelCase__      , return_tensors="pt"  )
					SCREAMING_SNAKE_CASE    			=       encoding["pixel_values"]

					SCREAMING_SNAKE_CASE    			=       model(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       outputs.logits

					# verify logits
					SCREAMING_SNAKE_CASE    			=       [1, 1_6] if "rvlcdip" in checkpoint_url else [1, 1_9_6, 8_1_9_2]
					assert logits.shape == torch.Size(UpperCAmelCase__  ), "Shape of logits not as expected"

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

					if push_to_hub:
										if has_lm_head:
															SCREAMING_SNAKE_CASE    			=       "dit-base" if "base" in checkpoint_url else "dit-large"
										else:
															SCREAMING_SNAKE_CASE    			=       "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
										image_processor.push_to_hub(
										    repo_path_or_name=Path(UpperCAmelCase__      , UpperCAmelCase__  )      , organization="nielsr"      , commit_message="Add image processor"      , use_temp_dir=UpperCAmelCase__      , )
										model.push_to_hub(
										    repo_path_or_name=Path(UpperCAmelCase__      , UpperCAmelCase__  )      , organization="nielsr"      , commit_message="Add model"      , use_temp_dir=UpperCAmelCase__      , )


if __name__ == "__main__":
					_lowerCamelCase					:   Tuple							=		argparse.ArgumentParser()

					parser.add_argument(
					    '''--checkpoint_url''',
					    default='''https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth''',
					    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.'''
					)
					parser.add_argument(
					    '''--push_to_hub''',
					    action='''store_true''',
					)
					_lowerCamelCase					:   Any							=		parser.parse_args()
					convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)





import numpy as np





def 	__lowerCamelCase							(UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							float = 1e-12      , UpperCAmelCase__     :							int = 1_0_0      , ):
					assert np.shape(UpperCAmelCase__  )[0] == np.shape(UpperCAmelCase__  )[1]
					# Ensure proper dimensionality.
					assert np.shape(UpperCAmelCase__  )[0] == np.shape(UpperCAmelCase__  )[0]
					# Ensure inputs are either both complex or both real
					assert np.iscomplexobj(UpperCAmelCase__  ) == np.iscomplexobj(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       np.iscomplexobj(UpperCAmelCase__  )
					if is_complex:
										# Ensure complex input_matrix is Hermitian
										assert np.array_equal(UpperCAmelCase__      , input_matrix.conj().T  )

					# Set convergence to False. Will define convergence when we exceed max_iterations
					# or when we have small changes from one iteration to next.

					SCREAMING_SNAKE_CASE    			=       False
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       0
					SCREAMING_SNAKE_CASE    			=       1e12

					while not convergence:
										# Multiple matrix by the vector.
										SCREAMING_SNAKE_CASE    			=       np.dot(UpperCAmelCase__      , UpperCAmelCase__  )
										# Normalize the resulting output vector.
										SCREAMING_SNAKE_CASE    			=       w / np.linalg.norm(UpperCAmelCase__  )
										# Find rayleigh quotient
										# (faster than usual b/c we know vector is normalized already)
										SCREAMING_SNAKE_CASE    			=       vector.conj().T if is_complex else vector.T
										SCREAMING_SNAKE_CASE    			=       np.dot(UpperCAmelCase__      , np.dot(UpperCAmelCase__      , UpperCAmelCase__  )  )

										# Check convergence.
										SCREAMING_SNAKE_CASE    			=       np.abs(lambda_ - lambda_previous  ) / lambda_
										iterations += 1

										if error <= error_tol or iterations >= max_iterations:
															SCREAMING_SNAKE_CASE    			=       True

										SCREAMING_SNAKE_CASE    			=       lambda_

					if is_complex:
										SCREAMING_SNAKE_CASE    			=       np.real(lambda_  )

					return lambda_, vector





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE    			=       np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]  )
					SCREAMING_SNAKE_CASE    			=       np.array([4_1, 4, 2_0]  )
					SCREAMING_SNAKE_CASE    			=       real_input_matrix.astype(np.complexaaa  )
					SCREAMING_SNAKE_CASE    			=       np.triu(1j * complex_input_matrix      , 1  )
					complex_input_matrix += imag_matrix
					complex_input_matrix += -1 * imag_matrix.T
					SCREAMING_SNAKE_CASE    			=       np.array([4_1, 4, 2_0]  ).astype(np.complexaaa  )

					for problem_type in ["real", "complex"]:
										if problem_type == "real":
															SCREAMING_SNAKE_CASE    			=       real_input_matrix
															SCREAMING_SNAKE_CASE    			=       real_vector
										elif problem_type == "complex":
															SCREAMING_SNAKE_CASE    			=       complex_input_matrix
															SCREAMING_SNAKE_CASE    			=       complex_vector

										# Our implementation.
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       power_iteration(UpperCAmelCase__      , UpperCAmelCase__  )

										# Numpy implementation.

										# Get eigenvalues and eigenvectors using built-in numpy
										# eigh (eigh used for symmetric or hermetian matrices).
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       np.linalg.eigh(UpperCAmelCase__  )
										# Last eigenvalue is the maximum one.
										SCREAMING_SNAKE_CASE    			=       eigen_values[-1]
										# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
										SCREAMING_SNAKE_CASE    			=       eigen_vectors[:, -1]

										# Check our implementation and numpy gives close answers.
										assert np.abs(eigen_value - eigen_value_max  ) <= 1e-6
										# Take absolute values element wise of each eigenvector.
										# as they are only unique to a minus sign.
										assert np.linalg.norm(np.abs(UpperCAmelCase__  ) - np.abs(UpperCAmelCase__  )  ) <= 1e-6


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_power_iteration()




