



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 	a						(				_UpperCAmelCase							: str			,       _UpperCAmelCase							: Optional[int]=False			,       _UpperCAmelCase							: Union[str, Any]=False						):







		'''simple docstring'''





		__UpperCAmelCase   :					str													=     '''backbone.''' if is_semantic else ''''''

		__UpperCAmelCase   :					str													=     []
		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 	a						(				_UpperCAmelCase							: Union[str, Any]			,       _UpperCAmelCase							: List[str]			,       _UpperCAmelCase							: Optional[int]=False			,       _UpperCAmelCase							: int=False						):







		'''simple docstring'''





		for i in range(config.num_hidden_layers						):
				__UpperCAmelCase   :					List[str]													=     '''backbone.''' if is_semantic else ''''''
				# queries, keys and values
				__UpperCAmelCase   :					int													=     state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'						)
				__UpperCAmelCase   :					int													=     state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'						)
				__UpperCAmelCase   :					Optional[Any]													=     state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'						)

				__UpperCAmelCase   :					str													=     in_proj_weight[
				    : config.hidden_size, :
				]
				__UpperCAmelCase   :					Union[str, Any]													=     q_bias
				__UpperCAmelCase   :					int													=     in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				__UpperCAmelCase   :					Union[str, Any]													=     in_proj_weight[
				    -config.hidden_size :, :
				]
				__UpperCAmelCase   :					Optional[int]													=     v_bias

				# gamma_1 and gamma_2
				# we call them lambda because otherwise they are renamed when using .from_pretrained
				__UpperCAmelCase   :					List[Any]													=     state_dict.pop(f'{prefix}blocks.{i}.gamma_1'						)
				__UpperCAmelCase   :					List[Any]													=     state_dict.pop(f'{prefix}blocks.{i}.gamma_2'						)

				__UpperCAmelCase   :					Optional[int]													=     gamma_a
				__UpperCAmelCase   :					Union[str, Any]													=     gamma_a

				# relative_position bias table + index
				if not has_lm_head:
						# each layer has its own relative position bias
						__UpperCAmelCase   :					Dict													=     state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_bias_table'						)
						__UpperCAmelCase   :					Optional[int]													=     state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_index'						)

						__UpperCAmelCase   :					Union[str, Any]													=     table
						__UpperCAmelCase   :					Optional[int]													=     index

def 	a						(				_UpperCAmelCase							: Any			,       _UpperCAmelCase							: List[str]			,       _UpperCAmelCase							: Optional[int]						):







		'''simple docstring'''





		__UpperCAmelCase   :					Optional[Any]													=     dct.pop(_UpperCAmelCase						)
		__UpperCAmelCase   :					Optional[int]													=     val

def 	a						(				):







		'''simple docstring'''





		__UpperCAmelCase   :					Dict													=     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		__UpperCAmelCase   :					Any													=     Image.open(requests.get(_UpperCAmelCase			,       stream=_UpperCAmelCase						).raw						)
		return im

@torch.no_grad()
def 	a						(				_UpperCAmelCase							: Optional[Any]			,       _UpperCAmelCase							: List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase   :					List[Any]													=     BeitConfig()
		__UpperCAmelCase   :					Any													=     False
		__UpperCAmelCase   :					int													=     False
		__UpperCAmelCase   :					int													=     '''huggingface/label-files'''
		# set config parameters based on URL
		if checkpoint_url[-9:-4] == "pt22k":
				# masked image modeling
				__UpperCAmelCase   :					Union[str, Any]													=     True
				__UpperCAmelCase   :					str													=     True
				__UpperCAmelCase   :					List[str]													=     True
		elif checkpoint_url[-9:-4] == "ft22k":
				# intermediate fine-tuning on ImageNet-22k
				__UpperCAmelCase   :					Tuple													=     True
				__UpperCAmelCase   :					Any													=     2_18_41
				__UpperCAmelCase   :					Any													=     '''imagenet-22k-id2label.json'''
				__UpperCAmelCase   :					Any													=     json.load(open(hf_hub_download(_UpperCAmelCase			,       _UpperCAmelCase			,       repo_type='''dataset'''						)			,       '''r'''						)						)
				__UpperCAmelCase   :					List[Any]													=     {int(_UpperCAmelCase						): 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[92_05]
				del idalabel[1_50_27]
				__UpperCAmelCase   :					int													=     idalabel
				__UpperCAmelCase   :					Union[str, Any]													=     {v: k for k, v in idalabel.items()}
		elif checkpoint_url[-8:-4] == "to1k":
				# fine-tuning on ImageNet-1k
				__UpperCAmelCase   :					int													=     True
				__UpperCAmelCase   :					int													=     10_00
				__UpperCAmelCase   :					Dict													=     '''imagenet-1k-id2label.json'''
				__UpperCAmelCase   :					str													=     json.load(open(hf_hub_download(_UpperCAmelCase			,       _UpperCAmelCase			,       repo_type='''dataset'''						)			,       '''r'''						)						)
				__UpperCAmelCase   :					List[str]													=     {int(_UpperCAmelCase						): v for k, v in idalabel.items()}
				__UpperCAmelCase   :					Any													=     idalabel
				__UpperCAmelCase   :					List[Any]													=     {v: k for k, v in idalabel.items()}
				if "384" in checkpoint_url:
						__UpperCAmelCase   :					int													=     3_84
				if "512" in checkpoint_url:
						__UpperCAmelCase   :					Optional[int]													=     5_12
		elif "ade20k" in checkpoint_url:
				# fine-tuning
				__UpperCAmelCase   :					int													=     True
				__UpperCAmelCase   :					Any													=     1_50
				__UpperCAmelCase   :					int													=     '''ade20k-id2label.json'''
				__UpperCAmelCase   :					Dict													=     json.load(open(hf_hub_download(_UpperCAmelCase			,       _UpperCAmelCase			,       repo_type='''dataset'''						)			,       '''r'''						)						)
				__UpperCAmelCase   :					Dict													=     {int(_UpperCAmelCase						): v for k, v in idalabel.items()}
				__UpperCAmelCase   :					Optional[Any]													=     idalabel
				__UpperCAmelCase   :					List[str]													=     {v: k for k, v in idalabel.items()}
				__UpperCAmelCase   :					Union[str, Any]													=     6_40
				__UpperCAmelCase   :					List[Any]													=     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:
				__UpperCAmelCase   :					List[Any]													=     10_24
				__UpperCAmelCase   :					int													=     40_96
				__UpperCAmelCase   :					List[str]													=     24
				__UpperCAmelCase   :					Optional[int]													=     16
				if "ade20k" in checkpoint_url:
						__UpperCAmelCase   :					Tuple													=     6_40
						__UpperCAmelCase   :					List[str]													=     [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
		__UpperCAmelCase   :					Union[str, Any]													=     torch.hub.load_state_dict_from_url(_UpperCAmelCase			,       map_location='''cpu'''			,       check_hash=_UpperCAmelCase						)
		__UpperCAmelCase   :					Tuple													=     state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

		__UpperCAmelCase   :					Dict													=     create_rename_keys(_UpperCAmelCase			,       has_lm_head=_UpperCAmelCase			,       is_semantic=_UpperCAmelCase						)
		for src, dest in rename_keys:
				rename_key(_UpperCAmelCase			,       _UpperCAmelCase			,       _UpperCAmelCase						)
		read_in_q_k_v(_UpperCAmelCase			,       _UpperCAmelCase			,       has_lm_head=_UpperCAmelCase			,       is_semantic=_UpperCAmelCase						)
		if is_semantic:
				# add prefix to decoder keys
				for key, val in state_dict.copy().items():
						__UpperCAmelCase   :					Union[str, Any]													=     state_dict.pop(_UpperCAmelCase						)
						if key.startswith('''backbone.fpn'''						):
								__UpperCAmelCase   :					Any													=     key.replace('''backbone.fpn'''			,       '''fpn'''						)
						__UpperCAmelCase   :					List[Any]													=     val

    # load HuggingFace model
		if checkpoint_url[-9:-4] == "pt22k":
				__UpperCAmelCase   :					List[Any]													=     BeitForMaskedImageModeling(_UpperCAmelCase						)
		elif "ade20k" in checkpoint_url:
				__UpperCAmelCase   :					Tuple													=     BeitForSemanticSegmentation(_UpperCAmelCase						)
		else:
				__UpperCAmelCase   :					Optional[Any]													=     BeitForImageClassification(_UpperCAmelCase						)
		model.eval()
		model.load_state_dict(_UpperCAmelCase						)

		# Check outputs on an image
		if is_semantic:
				__UpperCAmelCase   :					Union[str, Any]													=     BeitImageProcessor(size=config.image_size			,       do_center_crop=_UpperCAmelCase						)
				__UpperCAmelCase   :					Any													=     load_dataset('''hf-internal-testing/fixtures_ade20k'''			,       split='''test'''						)
				__UpperCAmelCase   :					Tuple													=     Image.open(ds[0]['''file''']						)
		else:
				__UpperCAmelCase   :					List[Any]													=     BeitImageProcessor(
				    size=config.image_size			,       resample=PILImageResampling.BILINEAR			,       do_center_crop=_UpperCAmelCase						)
				__UpperCAmelCase   :					Union[str, Any]													=     prepare_img()

		__UpperCAmelCase   :					Optional[int]													=     image_processor(images=_UpperCAmelCase			,       return_tensors='''pt'''						)
		__UpperCAmelCase   :					Optional[int]													=     encoding['''pixel_values''']

		__UpperCAmelCase   :					Dict													=     model(_UpperCAmelCase						)
		__UpperCAmelCase   :					List[str]													=     outputs.logits

		# verify logits
		__UpperCAmelCase   :					Union[str, Any]													=     torch.Size([1, 10_00]						)
		if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''						):
				__UpperCAmelCase   :					Dict													=     torch.Size([1, 1_96, 81_92]						)
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''						):
				__UpperCAmelCase   :					Union[str, Any]													=     torch.Size([1, 1_96, 81_92]						)
		elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''						):
				__UpperCAmelCase   :					Union[str, Any]													=     torch.Size([1, 2_18_41]						)
				__UpperCAmelCase   :					Tuple													=     torch.tensor([2.22_88, 2.46_71, 0.73_95]						)
				__UpperCAmelCase   :					Tuple													=     23_97
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''						):
				__UpperCAmelCase   :					str													=     torch.Size([1, 2_18_41]						)
				__UpperCAmelCase   :					Union[str, Any]													=     torch.tensor([1.68_81, -0.27_87, 0.59_01]						)
				__UpperCAmelCase   :					Optional[int]													=     23_96
		elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''						):
				__UpperCAmelCase   :					Any													=     torch.tensor([0.12_41, 0.07_98, -0.65_69]						)
				__UpperCAmelCase   :					str													=     2_85
		elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''						):
				__UpperCAmelCase   :					List[str]													=     torch.tensor([-1.23_85, -1.09_87, -1.01_08]						)
				__UpperCAmelCase   :					Optional[int]													=     2_81
		elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''						):
				__UpperCAmelCase   :					Union[str, Any]													=     torch.tensor([-1.53_03, -0.94_84, -0.31_47]						)
				__UpperCAmelCase   :					List[Any]													=     7_61
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''						):
				__UpperCAmelCase   :					Tuple													=     torch.tensor([0.46_10, -0.09_28, 0.20_86]						)
				__UpperCAmelCase   :					Any													=     7_61
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''						):
				__UpperCAmelCase   :					Optional[Any]													=     torch.tensor([-0.48_04, 0.62_57, -0.18_37]						)
				__UpperCAmelCase   :					Dict													=     7_61
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''						):
				__UpperCAmelCase   :					Optional[Any]													=     torch.tensor([[-0.51_22, 0.51_17, -0.21_13]]						)
				__UpperCAmelCase   :					Tuple													=     7_61
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''						):
				__UpperCAmelCase   :					Any													=     torch.tensor([-0.30_62, 0.72_61, 0.48_52]						)
				__UpperCAmelCase   :					List[str]													=     7_61
		elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''						):
				__UpperCAmelCase   :					int													=     (1, 1_50, 1_60, 1_60)
				__UpperCAmelCase   :					Dict													=     torch.tensor(
				    [
				        [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
				        [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
				        [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
				    ]						)
		elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k'''						):
				__UpperCAmelCase   :					Optional[int]													=     (1, 1_50, 1_60, 1_60)
				__UpperCAmelCase   :					Union[str, Any]													=     torch.tensor(
				    [
				        [[-4.33_05, -2.30_49, -3.01_61], [-2.95_91, -1.53_05, -2.22_51], [-3.41_98, -1.80_04, -2.90_62]],
				        [[-5.89_22, -3.74_35, -4.39_78], [-4.20_63, -2.78_72, -3.47_55], [-4.27_91, -3.18_74, -4.16_81]],
				        [[0.98_95, 4.34_67, 4.76_63], [4.24_76, 5.68_30, 6.15_18], [4.55_50, 6.24_95, 6.51_54]],
				    ]						)
		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]			,       _UpperCAmelCase			,       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]			,       _UpperCAmelCase			,       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(_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 __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)







import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_reformer import ReformerTokenizer
else:
				__A             =None


__A             =logging.get_logger(__name__)


__A             ="▁"

__A             ={"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__A             ={
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

__A             ={
    "google/reformer-crime-and-punishment": 5_2_4_2_8_8,
}

class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	VOCAB_FILES_NAMES
			UpperCamelCase      						=	PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase      						=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase      						=	["""input_ids""", """attention_mask"""]
			UpperCamelCase      						=	ReformerTokenizer




			def __init__(       self							:			Tuple	,	a_							:			Optional[int]=None	,	a_							:			int=None	,	a_							:			Dict="</s>"	,	a_							:			Dict="<unk>"	,	a_							:			List[Any]=[]	,	**a_							:			int	,	):
					'''simple docstring'''





					super().__init__(
					    a_	,	tokenizer_file=a_	,	eos_token=a_	,	unk_token=a_	,	additional_special_tokens=a_	,	**a_	,	)

					__UpperCAmelCase   :					Optional[Any]													=     vocab_file
					__UpperCAmelCase   :					Optional[Any]													=     False if not self.vocab_file else True




			def 			snake_case__					(       self							:			Dict	,	a_							:			str	,	a_							:			Optional[str] = None			):
					'''simple docstring'''





					if not self.can_save_slow_tokenizer:
							raise ValueError(
							    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
							    '''tokenizer.'''			)

					if not os.path.isdir(a_			):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
							return
					__UpperCAmelCase   :					Tuple													=     os.path.join(
					    a_	,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

					if os.path.abspath(self.vocab_file			) != os.path.abspath(a_			):
							copyfile(self.vocab_file	,	a_			)

					return (out_vocab_file,)





