





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()
snake_case			:      str 							=   logging.get_logger(__name__)


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__=False   ,UpperCAmelCase__=False							):

				"""simple docstring"""






				_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'),
				    ]							)

				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 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__=False   ,UpperCAmelCase__=False							):

				"""simple docstring"""






				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

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

												_SCREAMING_SNAKE_CASE      = table
												_SCREAMING_SNAKE_CASE      = index


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = dct.pop(_SCREAMING_SNAKE_CASE							)
				_SCREAMING_SNAKE_CASE      = val


def 				SCREAMING_SNAKE_CASE (     ):

				"""simple docstring"""






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


@torch.no_grad()
def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = BeitConfig()
				_SCREAMING_SNAKE_CASE      = False
				_SCREAMING_SNAKE_CASE      = False
				_SCREAMING_SNAKE_CASE      = 'huggingface/label-files'
				# set config parameters based on URL
				if checkpoint_url[-9:-4] == "pt22k":
								# masked image modeling
								_SCREAMING_SNAKE_CASE      = True
								_SCREAMING_SNAKE_CASE      = True
								_SCREAMING_SNAKE_CASE      = True
				elif checkpoint_url[-9:-4] == "ft22k":
								# intermediate fine-tuning on ImageNet-22k
								_SCREAMING_SNAKE_CASE      = True
								_SCREAMING_SNAKE_CASE      = 2_1841
								_SCREAMING_SNAKE_CASE      = 'imagenet-22k-id2label.json'
								_SCREAMING_SNAKE_CASE      = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,repo_type='dataset'							)   ,'r'							)							)
								_SCREAMING_SNAKE_CASE      = {int(_SCREAMING_SNAKE_CASE							): 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]
								_SCREAMING_SNAKE_CASE      = idalabel
								_SCREAMING_SNAKE_CASE      = {v: k for k, v in idalabel.items()}
				elif checkpoint_url[-8:-4] == "to1k":
								# fine-tuning on ImageNet-1k
								_SCREAMING_SNAKE_CASE      = True
								_SCREAMING_SNAKE_CASE      = 1000
								_SCREAMING_SNAKE_CASE      = 'imagenet-1k-id2label.json'
								_SCREAMING_SNAKE_CASE      = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,repo_type='dataset'							)   ,'r'							)							)
								_SCREAMING_SNAKE_CASE      = {int(_SCREAMING_SNAKE_CASE							): v for k, v in idalabel.items()}
								_SCREAMING_SNAKE_CASE      = idalabel
								_SCREAMING_SNAKE_CASE      = {v: k for k, v in idalabel.items()}
								if "384" in checkpoint_url:
												_SCREAMING_SNAKE_CASE      = 384
								if "512" in checkpoint_url:
												_SCREAMING_SNAKE_CASE      = 512
				elif "ade20k" in checkpoint_url:
								# fine-tuning
								_SCREAMING_SNAKE_CASE      = True
								_SCREAMING_SNAKE_CASE      = 150
								_SCREAMING_SNAKE_CASE      = 'ade20k-id2label.json'
								_SCREAMING_SNAKE_CASE      = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,repo_type='dataset'							)   ,'r'							)							)
								_SCREAMING_SNAKE_CASE      = {int(_SCREAMING_SNAKE_CASE							): v for k, v in idalabel.items()}
								_SCREAMING_SNAKE_CASE      = idalabel
								_SCREAMING_SNAKE_CASE      = {v: k for k, v in idalabel.items()}
								_SCREAMING_SNAKE_CASE      = 640
								_SCREAMING_SNAKE_CASE      = 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:
								_SCREAMING_SNAKE_CASE      = 1024
								_SCREAMING_SNAKE_CASE      = 4096
								_SCREAMING_SNAKE_CASE      = 24
								_SCREAMING_SNAKE_CASE      = 16
								if "ade20k" in checkpoint_url:
												_SCREAMING_SNAKE_CASE      = 640
												_SCREAMING_SNAKE_CASE      = [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
				_SCREAMING_SNAKE_CASE      = torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE   ,map_location='cpu'   ,check_hash=_SCREAMING_SNAKE_CASE							)
				_SCREAMING_SNAKE_CASE      = state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

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

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

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

				_SCREAMING_SNAKE_CASE      = image_processor(images=_SCREAMING_SNAKE_CASE   ,return_tensors='pt'							)
				_SCREAMING_SNAKE_CASE      = encoding['pixel_values']

				_SCREAMING_SNAKE_CASE      = model(_SCREAMING_SNAKE_CASE							)
				_SCREAMING_SNAKE_CASE      = outputs.logits

				# verify logits
				_SCREAMING_SNAKE_CASE      = torch.Size([1, 1000]							)
				if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'							):
								_SCREAMING_SNAKE_CASE      = torch.Size([1, 196, 8192]							)
				elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'							):
								_SCREAMING_SNAKE_CASE      = torch.Size([1, 196, 8192]							)
				elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'							):
								_SCREAMING_SNAKE_CASE      = torch.Size([1, 2_1841]							)
								_SCREAMING_SNAKE_CASE      = torch.tensor([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5]							)
								_SCREAMING_SNAKE_CASE      = 2397
				elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'							):
								_SCREAMING_SNAKE_CASE      = torch.Size([1, 2_1841]							)
								_SCREAMING_SNAKE_CASE      = torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]							)
								_SCREAMING_SNAKE_CASE      = 2396
				elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'							):
								_SCREAMING_SNAKE_CASE      = torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9]							)
								_SCREAMING_SNAKE_CASE      = 285
				elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'							):
								_SCREAMING_SNAKE_CASE      = torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]							)
								_SCREAMING_SNAKE_CASE      = 281
				elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'							):
								_SCREAMING_SNAKE_CASE      = torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7]							)
								_SCREAMING_SNAKE_CASE      = 761
				elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'							):
								_SCREAMING_SNAKE_CASE      = torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6]							)
								_SCREAMING_SNAKE_CASE      = 761
				elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'							):
								_SCREAMING_SNAKE_CASE      = torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7]							)
								_SCREAMING_SNAKE_CASE      = 761
				elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'							):
								_SCREAMING_SNAKE_CASE      = torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]]							)
								_SCREAMING_SNAKE_CASE      = 761
				elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'							):
								_SCREAMING_SNAKE_CASE      = torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2]							)
								_SCREAMING_SNAKE_CASE      = 761
				elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'							):
								_SCREAMING_SNAKE_CASE      = (1, 150, 160, 160)
								_SCREAMING_SNAKE_CASE      = torch.tensor(
								    [
								        [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
								        [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
								        [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
								    ]							)
				elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'							):
								_SCREAMING_SNAKE_CASE      = (1, 150, 160, 160)
								_SCREAMING_SNAKE_CASE      = torch.tensor(
								    [
								        [[-4.3_3_0_5, -2.3_0_4_9, -3.0_1_6_1], [-2.9_5_9_1, -1.5_3_0_5, -2.2_2_5_1], [-3.4_1_9_8, -1.8_0_0_4, -2.9_0_6_2]],
								        [[-5.8_9_2_2, -3.7_4_3_5, -4.3_9_7_8], [-4.2_0_6_3, -2.7_8_7_2, -3.4_7_5_5], [-4.2_7_9_1, -3.1_8_7_4, -4.1_6_8_1]],
								        [[0.9_8_9_5, 4.3_4_6_7, 4.7_6_6_3], [4.2_4_7_6, 5.6_8_3_0, 6.1_5_1_8], [4.5_5_5_0, 6.2_4_9_5, 6.5_1_5_4]],
								    ]							)
				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]   ,_SCREAMING_SNAKE_CASE   ,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]   ,_SCREAMING_SNAKE_CASE   ,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(_SCREAMING_SNAKE_CASE							).mkdir(exist_ok=_SCREAMING_SNAKE_CASE							)
				print(f'''Saving model to {pytorch_dump_folder_path}'''							)
				model.save_pretrained(_SCREAMING_SNAKE_CASE							)
				print(f'''Saving image processor to {pytorch_dump_folder_path}'''							)
				image_processor.save_pretrained(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							snake_case			:      Optional[Any] 							=   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.'
							)
							snake_case			:      Dict 							=   parser.parse_args()
							convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation

snake_case			:      Any 							=   logging.get_logger(__name__)

snake_case			:      str 							=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

snake_case			:      Optional[int] 							=   {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

snake_case			:      Tuple 							=   {
    'microsoft/deberta-base': 5_12,
    'microsoft/deberta-large': 5_12,
    'microsoft/deberta-xlarge': 5_12,
    'microsoft/deberta-base-mnli': 5_12,
    'microsoft/deberta-large-mnli': 5_12,
    'microsoft/deberta-xlarge-mnli': 5_12,
}

snake_case			:      Any 							=   {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}


def 				SCREAMING_SNAKE_CASE (     ):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = (
				    list(range(ord('!'							)   ,ord('~'							) + 1							)							) + list(range(ord('¡'							)   ,ord('¬'							) + 1							)							) + list(range(ord('®'							)   ,ord('ÿ'							) + 1							)							)
				)
				_SCREAMING_SNAKE_CASE      = bs[:]
				_SCREAMING_SNAKE_CASE      = 0
				for b in range(2**8							):
								if b not in bs:
												bs.append(UpperCAmelCase__							)
												cs.append(2**8 + n							)
												n += 1
				_SCREAMING_SNAKE_CASE      = [chr(UpperCAmelCase__							) for n in cs]
				return dict(zip(UpperCAmelCase__   ,UpperCAmelCase__							)							)


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = set()
				_SCREAMING_SNAKE_CASE      = word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)							)
								_SCREAMING_SNAKE_CASE      = char
				return pairs



class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					int        =						VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE   :					List[Any]        =						PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE   :					Any        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						["input_ids", "attention_mask", "token_type_ids"]





							def __init__(    self				, A_				, A_				, A_="replace"				, A_="[CLS]"				, A_="[SEP]"				, A_="[SEP]"				, A_="[CLS]"				, A_="[UNK]"				, A_="[PAD]"				, A_="[MASK]"				, A_=False				, A_=False				, **A_				, )->	List[str]:
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else bos_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else eos_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else sep_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else cls_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else unk_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else pad_token

											# Mask token behave like a normal word, i.e. include the space before it
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else mask_token

											super().__init__(
											    errors=A_				, bos_token=A_				, eos_token=A_				, unk_token=A_				, sep_token=A_				, cls_token=A_				, pad_token=A_				, mask_token=A_				, add_prefix_space=A_				, add_bos_token=A_				, **A_				, )
											_SCREAMING_SNAKE_CASE      = add_bos_token

											with open(A_				, encoding='utf-8'	) as vocab_handle:
															_SCREAMING_SNAKE_CASE      = json.load(A_	)
											_SCREAMING_SNAKE_CASE      = {v: k for k, v in self.encoder.items()}
											_SCREAMING_SNAKE_CASE      = errors  # how to handle errors in decoding
											_SCREAMING_SNAKE_CASE      = bytes_to_unicode()
											_SCREAMING_SNAKE_CASE      = {v: k for k, v in self.byte_encoder.items()}
											with open(A_				, encoding='utf-8'	) as merges_handle:
															_SCREAMING_SNAKE_CASE      = merges_handle.read().split('\n'	)[1:-1]
											_SCREAMING_SNAKE_CASE      = [tuple(merge.split()	) for merge in bpe_merges]
											_SCREAMING_SNAKE_CASE      = dict(zip(A_				, range(len(A_	)	)	)	)
											_SCREAMING_SNAKE_CASE      = {}
											_SCREAMING_SNAKE_CASE      = add_prefix_space

											# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
											_SCREAMING_SNAKE_CASE      = re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'	)





							@property
							# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
							def 		__magic_name__			(    self	)->	int:
											return len(self.encoder	)





							def 		__magic_name__			(    self	)->	Tuple:
											return dict(self.encoder				, **self.added_tokens_encoder	)





							def 		__magic_name__			(    self				, A_	)->	Optional[int]:
											if token in self.cache:
															return self.cache[token]
											_SCREAMING_SNAKE_CASE      = tuple(A_	)
											_SCREAMING_SNAKE_CASE      = get_pairs(A_	)

											if not pairs:
															return token

											while True:
															_SCREAMING_SNAKE_CASE      = min(A_				, key=lambda A_	: self.bpe_ranks.get(A_				, float('inf'	)	)	)
															if bigram not in self.bpe_ranks:
																			break
															_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = bigram
															_SCREAMING_SNAKE_CASE      = []
															_SCREAMING_SNAKE_CASE      = 0
															while i < len(A_	):
																			try:
																							_SCREAMING_SNAKE_CASE      = word.index(A_				, A_	)
																			except ValueError:
																							new_word.extend(word[i:]	)
																							break
																			else:
																							new_word.extend(word[i:j]	)
																							_SCREAMING_SNAKE_CASE      = j

																			if word[i] == first and i < len(A_	) - 1 and word[i + 1] == second:
																							new_word.append(first + second	)
																							i += 2
																			else:
																							new_word.append(word[i]	)
																							i += 1
															_SCREAMING_SNAKE_CASE      = tuple(A_	)
															_SCREAMING_SNAKE_CASE      = new_word
															if len(A_	) == 1:
																			break
															else:
																			_SCREAMING_SNAKE_CASE      = get_pairs(A_	)
											_SCREAMING_SNAKE_CASE      = ' '.join(A_	)
											_SCREAMING_SNAKE_CASE      = word
											return word





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [self.cls_token_id]
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											return cls + token_ids_a + sep + token_ids_a + sep





							def 		__magic_name__			(    self				, A_				, A_ = None				, A_ = False	)->	List[int]:
											if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=A_				, token_ids_a=A_				, already_has_special_tokens=A_	)

											if token_ids_a is None:
															return [1] + ([0] * len(A_	)) + [1]
											return [1] + ([0] * len(A_	)) + [1] + ([0] * len(A_	)) + [1]





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [self.cls_token_id]

											if token_ids_a is None:
															return len(cls + token_ids_a + sep	) * [0]
											return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]





							def 		__magic_name__			(    self				, A_	)->	Dict:
											_SCREAMING_SNAKE_CASE      = []
											for token in re.findall(self.pat				, A_	):
															_SCREAMING_SNAKE_CASE      = ''.join(
															    self.byte_encoder[b] for b in token.encode('utf-8'	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
															bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A_	).split(' '	)	)
											return bpe_tokens





							def 		__magic_name__			(    self				, A_	)->	Optional[Any]:
											return self.encoder.get(A_				, self.encoder.get(self.unk_token	)	)





							def 		__magic_name__			(    self				, A_	)->	Dict:
											return self.decoder.get(A_	)





							def 		__magic_name__			(    self				, A_	)->	Dict:
											_SCREAMING_SNAKE_CASE      = ''.join(A_	)
											_SCREAMING_SNAKE_CASE      = bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'				, errors=self.errors	)
											return text





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	Tuple[str]:
											if not os.path.isdir(A_	):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
															return
											_SCREAMING_SNAKE_CASE      = os.path.join(
											    A_				, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
											_SCREAMING_SNAKE_CASE      = os.path.join(
											    A_				, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']	)

											with open(A_				, 'w'				, encoding='utf-8'	) as f:
															f.write(json.dumps(self.encoder				, indent=2				, sort_keys=A_				, ensure_ascii=A_	) + '\n'	)

											_SCREAMING_SNAKE_CASE      = 0
											with open(A_				, 'w'				, encoding='utf-8'	) as writer:
															writer.write('#version: 0.2\n'	)
															for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				, key=lambda A_	: kv[1]	):
																			if index != token_index:
																							logger.warning(
																							    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																							    ' Please check that the tokenizer is not corrupted!'	)
																							_SCREAMING_SNAKE_CASE      = token_index
																			writer.write(' '.join(A_	) + '\n'	)
																			index += 1

											return vocab_file, merge_file





							def 		__magic_name__			(    self				, A_				, A_=False				, **A_	)->	str:
											_SCREAMING_SNAKE_CASE      = kwargs.pop('add_prefix_space'				, self.add_prefix_space	)
											if (is_split_into_words or add_prefix_space) and (len(A_	) > 0 and not text[0].isspace()):
															_SCREAMING_SNAKE_CASE      = ' ' + text
											return (text, kwargs)





							def 		__magic_name__			(    self				, A_	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = []
											for is_user, text in conversation.iter_texts():
															input_ids.extend(self.encode(A_				, add_special_tokens=A_	) + [self.eos_token_id]	)
											if len(A_	) > self.model_max_length:
															_SCREAMING_SNAKE_CASE      = input_ids[-self.model_max_length :]
											return input_ids
