"""simple docstring"""


import argparse
import json
from pathlib import Path

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

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


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





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase=False   ,    __lowerCAmelCase=False				)							->					Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__					="backbone." if is_semantic else ""

						lowerCamelCase__					=[]
						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"),
						    ]				)

						if has_lm_head:
												# mask token + shared relative position bias + layernorm
												rename_keys.extend(
												    [
												        ("mask_token", "beit.embeddings.mask_token"),
												        (
												            "rel_pos_bias.relative_position_bias_table",
												            "beit.encoder.relative_position_bias.relative_position_bias_table",
												        ),
												        (
												            "rel_pos_bias.relative_position_index",
												            "beit.encoder.relative_position_bias.relative_position_index",
												        ),
												        ("norm.weight", "layernorm.weight"),
												        ("norm.bias", "layernorm.bias"),
												    ]				)
						elif is_semantic:
												# semantic segmentation classification heads
												rename_keys.extend(
												    [
												        ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
												        ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
												        ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
												        ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.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_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase=False   ,    __lowerCAmelCase=False				)							->					Any:




						'''simple docstring'''



						for i in range(config.num_hidden_layers				):
												lowerCamelCase__					="backbone." if is_semantic else ""
												# queries, keys and values
												lowerCamelCase__					=state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''				)
												lowerCamelCase__					=state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''				)
												lowerCamelCase__					=state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''				)

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

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

												lowerCamelCase__					=gamma_a
												lowerCamelCase__					=gamma_a

												# relative_position bias table + index
												if not has_lm_head:
																		# each layer has its own relative position bias
																		lowerCamelCase__					=state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''				)
																		lowerCamelCase__					=state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''				)

																		lowerCamelCase__					=table
																		lowerCamelCase__					=index





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)							->					str:




						'''simple docstring'''



						lowerCamelCase__					=dct.pop(__lowerCAmelCase				)
						lowerCamelCase__					=val





def 			lowerCamelCase_ (					)							->					int:




						'''simple docstring'''



						lowerCamelCase__					="http://images.cocodataset.org/val2017/000000039769.jpg"
						lowerCamelCase__					=Image.open(requests.get(__lowerCAmelCase   ,    stream=__lowerCAmelCase				).raw				)
						return im





@torch.no_grad()
def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase				)							->					Any:




						'''simple docstring'''



						lowerCamelCase__					=BeitConfig()
						lowerCamelCase__					=False
						lowerCamelCase__					=False
						lowerCamelCase__					="huggingface/label-files"
						# set config parameters based on URL
						if checkpoint_url[-9:-4] == "pt22k":
												# masked image modeling
												lowerCamelCase__					=True
												lowerCamelCase__					=True
												lowerCamelCase__					=True
						elif checkpoint_url[-9:-4] == "ft22k":
												# intermediate fine-tuning on ImageNet-22k
												lowerCamelCase__					=True
												lowerCamelCase__					=2_1841
												lowerCamelCase__					="imagenet-22k-id2label.json"
												lowerCamelCase__					=json.load(open(hf_hub_download(__lowerCAmelCase   ,    __lowerCAmelCase   ,    repo_type="dataset"				)   ,    "r"				)				)
												lowerCamelCase__					={int(__lowerCAmelCase				): v for k, v in idalabel.items()}
												# this dataset contains 21843 labels but the model only has 21841
												# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
												del idalabel[9205]
												del idalabel[1_5027]
												lowerCamelCase__					=idalabel
												lowerCamelCase__					={v: k for k, v in idalabel.items()}
						elif checkpoint_url[-8:-4] == "to1k":
												# fine-tuning on ImageNet-1k
												lowerCamelCase__					=True
												lowerCamelCase__					=1000
												lowerCamelCase__					="imagenet-1k-id2label.json"
												lowerCamelCase__					=json.load(open(hf_hub_download(__lowerCAmelCase   ,    __lowerCAmelCase   ,    repo_type="dataset"				)   ,    "r"				)				)
												lowerCamelCase__					={int(__lowerCAmelCase				): v for k, v in idalabel.items()}
												lowerCamelCase__					=idalabel
												lowerCamelCase__					={v: k for k, v in idalabel.items()}
												if "384" in checkpoint_url:
																		lowerCamelCase__					=384
												if "512" in checkpoint_url:
																		lowerCamelCase__					=512
						elif "ade20k" in checkpoint_url:
												# fine-tuning
												lowerCamelCase__					=True
												lowerCamelCase__					=150
												lowerCamelCase__					="ade20k-id2label.json"
												lowerCamelCase__					=json.load(open(hf_hub_download(__lowerCAmelCase   ,    __lowerCAmelCase   ,    repo_type="dataset"				)   ,    "r"				)				)
												lowerCamelCase__					={int(__lowerCAmelCase				): v for k, v in idalabel.items()}
												lowerCamelCase__					=idalabel
												lowerCamelCase__					={v: k for k, v in idalabel.items()}
												lowerCamelCase__					=640
												lowerCamelCase__					=True
						else:
												raise ValueError("Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'"				)

						# size of the architecture
						if "base" in checkpoint_url:
												pass
						elif "large" in checkpoint_url:
												lowerCamelCase__					=1024
												lowerCamelCase__					=4096
												lowerCamelCase__					=24
												lowerCamelCase__					=16
												if "ade20k" in checkpoint_url:
																		lowerCamelCase__					=640
																		lowerCamelCase__					=[7, 11, 15, 23]
						else:
												raise ValueError("Should either find 'base' or 'large' in checkpoint URL"				)

						# load state_dict of original model, remove and rename some keys
						lowerCamelCase__					=torch.hub.load_state_dict_from_url(__lowerCAmelCase   ,    map_location="cpu"   ,    check_hash=__lowerCAmelCase				)
						lowerCamelCase__					=state_dict["model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"]

						lowerCamelCase__					=create_rename_keys(__lowerCAmelCase   ,    has_lm_head=__lowerCAmelCase   ,    is_semantic=__lowerCAmelCase				)
						for src, dest in rename_keys:
												rename_key(__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase				)
						read_in_q_k_v(__lowerCAmelCase   ,    __lowerCAmelCase   ,    has_lm_head=__lowerCAmelCase   ,    is_semantic=__lowerCAmelCase				)
						if is_semantic:
												# add prefix to decoder keys
												for key, val in state_dict.copy().items():
																		lowerCamelCase__					=state_dict.pop(__lowerCAmelCase				)
																		if key.startswith("backbone.fpn"				):
																								lowerCamelCase__					=key.replace("backbone.fpn"   ,    "fpn"				)
																		lowerCamelCase__					=val

    # load HuggingFace model
						if checkpoint_url[-9:-4] == "pt22k":
												lowerCamelCase__					=BeitForMaskedImageModeling(__lowerCAmelCase				)
						elif "ade20k" in checkpoint_url:
												lowerCamelCase__					=BeitForSemanticSegmentation(__lowerCAmelCase				)
						else:
												lowerCamelCase__					=BeitForImageClassification(__lowerCAmelCase				)
						model.eval()
						model.load_state_dict(__lowerCAmelCase				)

						# Check outputs on an image
						if is_semantic:
												lowerCamelCase__					=BeitImageProcessor(size=config.image_size   ,    do_center_crop=__lowerCAmelCase				)
												lowerCamelCase__					=load_dataset("hf-internal-testing/fixtures_ade20k"   ,    split="test"				)
												lowerCamelCase__					=Image.open(ds[0]["file"]				)
						else:
												lowerCamelCase__					=BeitImageProcessor(
												    size=config.image_size   ,    resample=PILImageResampling.BILINEAR   ,    do_center_crop=__lowerCAmelCase				)
												lowerCamelCase__					=prepare_img()

						lowerCamelCase__					=image_processor(images=__lowerCAmelCase   ,    return_tensors="pt"				)
						lowerCamelCase__					=encoding["pixel_values"]

						lowerCamelCase__					=model(__lowerCAmelCase				)
						lowerCamelCase__					=outputs.logits

						# verify logits
						lowerCamelCase__					=torch.Size([1, 1000]				)
						if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"				):
												lowerCamelCase__					=torch.Size([1, 196, 8192]				)
						elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"				):
												lowerCamelCase__					=torch.Size([1, 196, 8192]				)
						elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"				):
												lowerCamelCase__					=torch.Size([1, 2_1841]				)
												lowerCamelCase__					=torch.tensor([2.2288, 2.4671, 0.7395]				)
												lowerCamelCase__					=2397
						elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"				):
												lowerCamelCase__					=torch.Size([1, 2_1841]				)
												lowerCamelCase__					=torch.tensor([1.6881, -0.2787, 0.5901]				)
												lowerCamelCase__					=2396
						elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"				):
												lowerCamelCase__					=torch.tensor([0.1241, 0.0798, -0.6569]				)
												lowerCamelCase__					=285
						elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"				):
												lowerCamelCase__					=torch.tensor([-1.2385, -1.0987, -1.0108]				)
												lowerCamelCase__					=281
						elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"				):
												lowerCamelCase__					=torch.tensor([-1.5303, -0.9484, -0.3147]				)
												lowerCamelCase__					=761
						elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"				):
												lowerCamelCase__					=torch.tensor([0.4610, -0.0928, 0.2086]				)
												lowerCamelCase__					=761
						elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"				):
												lowerCamelCase__					=torch.tensor([-0.4804, 0.6257, -0.1837]				)
												lowerCamelCase__					=761
						elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"				):
												lowerCamelCase__					=torch.tensor([[-0.5122, 0.5117, -0.2113]]				)
												lowerCamelCase__					=761
						elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"				):
												lowerCamelCase__					=torch.tensor([-0.3062, 0.7261, 0.4852]				)
												lowerCamelCase__					=761
						elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"				):
												lowerCamelCase__					=(1, 150, 160, 160)
												lowerCamelCase__					=torch.tensor(
												    [
												        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
												        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
												        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
												    ]				)
						elif checkpoint_url[:-4].endswith("beit_large_patch16_640_pt22k_ft22ktoade20k"				):
												lowerCamelCase__					=(1, 150, 160, 160)
												lowerCamelCase__					=torch.tensor(
												    [
												        [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
												        [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
												        [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
												    ]				)
						else:
												raise ValueError("Can't verify logits as model is not supported"				)

						assert logits.shape == expected_shape, "Shape of logits not as expected"
						if not has_lm_head:
												if is_semantic:
																		assert torch.allclose(
																		    logits[0, :3, :3, :3]   ,    __lowerCAmelCase   ,    atol=1e-3				), "First elements of logits not as expected"
												else:
																		print("Predicted class idx:"   ,    logits.argmax(-1				).item()				)
																		assert torch.allclose(
																		    logits[0, :3]   ,    __lowerCAmelCase   ,    atol=1e-3				), "First elements of logits not as expected"
																		assert logits.argmax(-1				).item() == expected_class_idx, "Predicted class index not as expected"

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


if __name__ == "__main__":
	a							=argparse.ArgumentParser()

	parser.add_argument(
	    '--checkpoint_url',
	    default='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.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.'
	)
	a							=parser.parse_args()
	convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










"""simple docstring"""



def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str							)->						str:






     '''simple docstring'''



     UpperCAmelCase__		:  Tuple				=				word.upper()
     UpperCAmelCase__		:  Union[str, Any]				=				0
     UpperCAmelCase__		:  Optional[Any]				=				{}
     UpperCAmelCase__		:  List[Any]				=				[]

     for letter in word:
          if letter not in letter_nums:
               UpperCAmelCase__		:  Tuple				=				str(snake_case							)
               next_num += 1
          word_pattern.append(letter_nums[letter]							)
     return ".".join(snake_case							)


if __name__ == "__main__":
 import pprint
 import time

 _lowerCAmelCase     :     Optional[Any]            =	time.time()
 with open("""dictionary.txt""") as in_file:
  _lowerCAmelCase     :     Optional[int]            =	in_file.read().splitlines()

 _lowerCAmelCase     :     dict            =	{}
 for word in word_list:
  _lowerCAmelCase     :     int            =	get_word_pattern(word)
  if pattern in all_patterns:
   all_patterns[pattern].append(word)
  else:
   _lowerCAmelCase     :     Any            =	[word]

 with open("""word_patterns.txt""", """w""") as out_file:
  out_file.write(pprint.pformat(all_patterns))

 _lowerCAmelCase     :     Union[str, Any]            =	round(time.time() - start_time, 2)
 print(F"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
 # Done!  9,581 word patterns found in 0.58 seconds.


