


import argparse

import torch

from diffusers import UNetaDConditionModel


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__=None   ,UpperCAmelCase__=None   ,UpperCAmelCase__=None							):

				"""simple docstring"""






				assert isinstance(UpperCAmelCase__   ,UpperCAmelCase__							), "Paths should be a list of dicts containing 'old' and 'new' keys."

				# Splits the attention layers into three variables.
				if attention_paths_to_split is not None:
								for path, path_map in attention_paths_to_split.items():
												_SCREAMING_SNAKE_CASE      = old_checkpoint[path]
												_SCREAMING_SNAKE_CASE      = old_tensor.shape[0] // 3

												_SCREAMING_SNAKE_CASE      = (-1, channels) if len(old_tensor.shape							) == 3 else (-1)

												_SCREAMING_SNAKE_CASE      = old_tensor.shape[0] // config['num_head_channels'] // 3

												_SCREAMING_SNAKE_CASE      = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]							)
												_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = old_tensor.split(channels // num_heads   ,dim=1							)

												_SCREAMING_SNAKE_CASE      = query.reshape(UpperCAmelCase__							)
												_SCREAMING_SNAKE_CASE      = key.reshape(UpperCAmelCase__							)
												_SCREAMING_SNAKE_CASE      = value.reshape(UpperCAmelCase__							)

				for path in paths:
								_SCREAMING_SNAKE_CASE      = path['new']

								# These have already been assigned
								if attention_paths_to_split is not None and new_path in attention_paths_to_split:
												continue

								if additional_replacements is not None:
												for replacement in additional_replacements:
																_SCREAMING_SNAKE_CASE      = new_path.replace(replacement['old']   ,replacement['new']							)

        # proj_attn.weight has to be converted from conv 1D to linear
								_SCREAMING_SNAKE_CASE      = old_checkpoint[path['old']]
								_SCREAMING_SNAKE_CASE      = ['proj_attn.weight']
								_SCREAMING_SNAKE_CASE      = ['proj_out.weight', 'proj_in.weight']
								if any(k in new_path for k in names							):
												_SCREAMING_SNAKE_CASE      = weight[:, :, 0]
								elif any(k in new_path for k in names_a							) and len(weight.shape							) > 2 and ".attentions." not in new_path:
												_SCREAMING_SNAKE_CASE      = weight[:, :, 0]
								else:
												_SCREAMING_SNAKE_CASE      = weight


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__=0							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = []
				for old_item in old_list:
								_SCREAMING_SNAKE_CASE      = old_item

								#         new_item = new_item.replace('norm.weight', 'group_norm.weight')
								#         new_item = new_item.replace('norm.bias', 'group_norm.bias')

								#         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
								#         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

								#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

								mapping.append({'old': old_item, 'new': new_item}							)

				return mapping


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__=1							):

				"""simple docstring"""






				if n_shave_prefix_segments >= 0:
								return ".".join(path.split('.'							)[n_shave_prefix_segments:]							)
				else:
								return ".".join(path.split('.'							)[:n_shave_prefix_segments]							)


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__=0							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = []
				for old_item in old_list:
								mapping.append({'old': old_item, 'new': old_item}							)

				return mapping


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__=0							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = []
				for old_item in old_list:
								_SCREAMING_SNAKE_CASE      = old_item.replace('in_layers.0'   ,'norm1'							)
								_SCREAMING_SNAKE_CASE      = new_item.replace('in_layers.2'   ,'conv1'							)

								_SCREAMING_SNAKE_CASE      = new_item.replace('out_layers.0'   ,'norm2'							)
								_SCREAMING_SNAKE_CASE      = new_item.replace('out_layers.3'   ,'conv2'							)

								_SCREAMING_SNAKE_CASE      = new_item.replace('emb_layers.1'   ,'time_emb_proj'							)
								_SCREAMING_SNAKE_CASE      = new_item.replace('skip_connection'   ,'conv_shortcut'							)

								_SCREAMING_SNAKE_CASE      = shave_segments(UpperCAmelCase__   ,n_shave_prefix_segments=UpperCAmelCase__							)

								if "temopral_conv" not in old_item:
												mapping.append({'old': old_item, 'new': new_item}							)

				return mapping


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__=None   ,UpperCAmelCase__=False							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = {}
				_SCREAMING_SNAKE_CASE      = list(checkpoint.keys()							)

				_SCREAMING_SNAKE_CASE      = 'model.diffusion_model.'

				# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
				if sum(k.startswith('model_ema'							) for k in keys							) > 100 and extract_ema:
								print(f'''Checkpoint {path} has both EMA and non-EMA weights.'''							)
								print(
								    'In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'
								    ' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.'							)
								for key in keys:
												if key.startswith('model.diffusion_model'							):
																_SCREAMING_SNAKE_CASE      = 'model_ema.' + ''.join(key.split('.'							)[1:]							)
																_SCREAMING_SNAKE_CASE      = checkpoint.pop(UpperCAmelCase__							)
				else:
								if sum(k.startswith('model_ema'							) for k in keys							) > 100:
												print(
												    'In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'
												    ' weights (usually better for inference), please make sure to add the `--extract_ema` flag.'							)

								for key in keys:
												_SCREAMING_SNAKE_CASE      = checkpoint.pop(UpperCAmelCase__							)

				_SCREAMING_SNAKE_CASE      = {}

				_SCREAMING_SNAKE_CASE      = unet_state_dict['time_embed.0.weight']
				_SCREAMING_SNAKE_CASE      = unet_state_dict['time_embed.0.bias']
				_SCREAMING_SNAKE_CASE      = unet_state_dict['time_embed.2.weight']
				_SCREAMING_SNAKE_CASE      = unet_state_dict['time_embed.2.bias']

				if config["class_embed_type"] is None:
								# No parameters to port
								...
				elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
								_SCREAMING_SNAKE_CASE      = unet_state_dict['label_emb.0.0.weight']
								_SCREAMING_SNAKE_CASE      = unet_state_dict['label_emb.0.0.bias']
								_SCREAMING_SNAKE_CASE      = unet_state_dict['label_emb.0.2.weight']
								_SCREAMING_SNAKE_CASE      = unet_state_dict['label_emb.0.2.bias']
				else:
								raise NotImplementedError(f'''Not implemented `class_embed_type`: {config["class_embed_type"]}'''							)

				_SCREAMING_SNAKE_CASE      = unet_state_dict['input_blocks.0.0.weight']
				_SCREAMING_SNAKE_CASE      = unet_state_dict['input_blocks.0.0.bias']

				_SCREAMING_SNAKE_CASE      = [v for v in unet_state_dict if v.startswith('input_blocks.0.1'							)]
				_SCREAMING_SNAKE_CASE      = renew_attention_paths(UpperCAmelCase__							)
				_SCREAMING_SNAKE_CASE      = {'old': 'input_blocks.0.1', 'new': 'transformer_in'}
				assign_to_checkpoint(UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,additional_replacements=[meta_path]   ,config=UpperCAmelCase__							)

				_SCREAMING_SNAKE_CASE      = unet_state_dict['out.0.weight']
				_SCREAMING_SNAKE_CASE      = unet_state_dict['out.0.bias']
				_SCREAMING_SNAKE_CASE      = unet_state_dict['out.2.weight']
				_SCREAMING_SNAKE_CASE      = unet_state_dict['out.2.bias']

				# Retrieves the keys for the input blocks only
				_SCREAMING_SNAKE_CASE      = len({'.'.join(layer.split('.'							)[:2]							) for layer in unet_state_dict if 'input_blocks' in layer}							)
				_SCREAMING_SNAKE_CASE      = {
				    layer_id: [key for key in unet_state_dict if f'''input_blocks.{layer_id}''' in key]
				    for layer_id in range(UpperCAmelCase__							)
				}

				# Retrieves the keys for the middle blocks only
				_SCREAMING_SNAKE_CASE      = len({'.'.join(layer.split('.'							)[:2]							) for layer in unet_state_dict if 'middle_block' in layer}							)
				_SCREAMING_SNAKE_CASE      = {
				    layer_id: [key for key in unet_state_dict if f'''middle_block.{layer_id}''' in key]
				    for layer_id in range(UpperCAmelCase__							)
				}

				# Retrieves the keys for the output blocks only
				_SCREAMING_SNAKE_CASE      = len({'.'.join(layer.split('.'							)[:2]							) for layer in unet_state_dict if 'output_blocks' in layer}							)
				_SCREAMING_SNAKE_CASE      = {
				    layer_id: [key for key in unet_state_dict if f'''output_blocks.{layer_id}''' in key]
				    for layer_id in range(UpperCAmelCase__							)
				}

				for i in range(1   ,UpperCAmelCase__							):
								_SCREAMING_SNAKE_CASE      = (i - 1) // (config['layers_per_block'] + 1)
								_SCREAMING_SNAKE_CASE      = (i - 1) % (config['layers_per_block'] + 1)

								_SCREAMING_SNAKE_CASE      = [
								    key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key and f'''input_blocks.{i}.0.op''' not in key
								]
								_SCREAMING_SNAKE_CASE      = [key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]
								_SCREAMING_SNAKE_CASE      = [key for key in input_blocks[i] if f'''input_blocks.{i}.2''' in key]

								if f'''input_blocks.{i}.op.weight''' in unet_state_dict:
												_SCREAMING_SNAKE_CASE      = unet_state_dict.pop(
												    f'''input_blocks.{i}.op.weight'''							)
												_SCREAMING_SNAKE_CASE      = unet_state_dict.pop(
												    f'''input_blocks.{i}.op.bias'''							)

								_SCREAMING_SNAKE_CASE      = renew_resnet_paths(UpperCAmelCase__							)
								_SCREAMING_SNAKE_CASE      = {'old': f'''input_blocks.{i}.0''', 'new': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
								assign_to_checkpoint(
								    UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,additional_replacements=[meta_path]   ,config=UpperCAmelCase__							)

								_SCREAMING_SNAKE_CASE      = [key for key in resnets if 'temopral_conv' in key]
								_SCREAMING_SNAKE_CASE      = renew_temp_conv_paths(UpperCAmelCase__							)
								_SCREAMING_SNAKE_CASE      = {
								    'old': f'''input_blocks.{i}.0.temopral_conv''',
								    'new': f'''down_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
								}
								assign_to_checkpoint(
								    UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,additional_replacements=[meta_path]   ,config=UpperCAmelCase__							)

								if len(UpperCAmelCase__							):
												_SCREAMING_SNAKE_CASE      = renew_attention_paths(UpperCAmelCase__							)
												_SCREAMING_SNAKE_CASE      = {'old': f'''input_blocks.{i}.1''', 'new': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}'''}
												assign_to_checkpoint(
												    UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,additional_replacements=[meta_path]   ,config=UpperCAmelCase__							)

								if len(UpperCAmelCase__							):
												_SCREAMING_SNAKE_CASE      = renew_attention_paths(UpperCAmelCase__							)
												_SCREAMING_SNAKE_CASE      = {
												    'old': f'''input_blocks.{i}.2''',
												    'new': f'''down_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
												}
												assign_to_checkpoint(
												    UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,additional_replacements=[meta_path]   ,config=UpperCAmelCase__							)

				_SCREAMING_SNAKE_CASE      = middle_blocks[0]
				_SCREAMING_SNAKE_CASE      = [key for key in resnet_a if 'temopral_conv' in key]
				_SCREAMING_SNAKE_CASE      = middle_blocks[1]
				_SCREAMING_SNAKE_CASE      = middle_blocks[2]
				_SCREAMING_SNAKE_CASE      = middle_blocks[3]
				_SCREAMING_SNAKE_CASE      = [key for key in resnet_a if 'temopral_conv' in key]

				_SCREAMING_SNAKE_CASE      = renew_resnet_paths(UpperCAmelCase__							)
				_SCREAMING_SNAKE_CASE      = {'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
				assign_to_checkpoint(
				    UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,config=UpperCAmelCase__   ,additional_replacements=[meta_path]							)

				_SCREAMING_SNAKE_CASE      = renew_temp_conv_paths(UpperCAmelCase__							)
				_SCREAMING_SNAKE_CASE      = {'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
				assign_to_checkpoint(
				    UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,config=UpperCAmelCase__   ,additional_replacements=[meta_path]							)

				_SCREAMING_SNAKE_CASE      = renew_resnet_paths(UpperCAmelCase__							)
				_SCREAMING_SNAKE_CASE      = {'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
				assign_to_checkpoint(
				    UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,config=UpperCAmelCase__   ,additional_replacements=[meta_path]							)

				_SCREAMING_SNAKE_CASE      = renew_temp_conv_paths(UpperCAmelCase__							)
				_SCREAMING_SNAKE_CASE      = {'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
				assign_to_checkpoint(
				    UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,config=UpperCAmelCase__   ,additional_replacements=[meta_path]							)

				_SCREAMING_SNAKE_CASE      = renew_attention_paths(UpperCAmelCase__							)
				_SCREAMING_SNAKE_CASE      = {'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
				assign_to_checkpoint(
				    UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,additional_replacements=[meta_path]   ,config=UpperCAmelCase__							)

				_SCREAMING_SNAKE_CASE      = renew_attention_paths(UpperCAmelCase__							)
				_SCREAMING_SNAKE_CASE      = {'old': 'middle_block.2', 'new': 'mid_block.temp_attentions.0'}
				assign_to_checkpoint(
				    UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,additional_replacements=[meta_path]   ,config=UpperCAmelCase__							)

				for i in range(UpperCAmelCase__							):
								_SCREAMING_SNAKE_CASE      = i // (config['layers_per_block'] + 1)
								_SCREAMING_SNAKE_CASE      = i % (config['layers_per_block'] + 1)
								_SCREAMING_SNAKE_CASE      = [shave_segments(UpperCAmelCase__   ,2							) for name in output_blocks[i]]
								_SCREAMING_SNAKE_CASE      = {}

								for layer in output_block_layers:
												_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = layer.split('.'							)[0], shave_segments(UpperCAmelCase__   ,1							)
												if layer_id in output_block_list:
																output_block_list[layer_id].append(UpperCAmelCase__							)
												else:
																_SCREAMING_SNAKE_CASE      = [layer_name]

								if len(UpperCAmelCase__							) > 1:
												_SCREAMING_SNAKE_CASE      = [key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
												_SCREAMING_SNAKE_CASE      = [key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]
												_SCREAMING_SNAKE_CASE      = [key for key in output_blocks[i] if f'''output_blocks.{i}.2''' in key]

												_SCREAMING_SNAKE_CASE      = renew_resnet_paths(UpperCAmelCase__							)
												_SCREAMING_SNAKE_CASE      = renew_resnet_paths(UpperCAmelCase__							)

												_SCREAMING_SNAKE_CASE      = {'old': f'''output_blocks.{i}.0''', 'new': f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
												assign_to_checkpoint(
												    UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,additional_replacements=[meta_path]   ,config=UpperCAmelCase__							)

												_SCREAMING_SNAKE_CASE      = [key for key in resnets if 'temopral_conv' in key]
												_SCREAMING_SNAKE_CASE      = renew_temp_conv_paths(UpperCAmelCase__							)
												_SCREAMING_SNAKE_CASE      = {
												    'old': f'''output_blocks.{i}.0.temopral_conv''',
												    'new': f'''up_blocks.{block_id}.temp_convs.{layer_in_block_id}''',
												}
												assign_to_checkpoint(
												    UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,additional_replacements=[meta_path]   ,config=UpperCAmelCase__							)

												_SCREAMING_SNAKE_CASE      = {k: sorted(UpperCAmelCase__							) for k, v in output_block_list.items()}
												if ["conv.bias", "conv.weight"] in output_block_list.values():
																_SCREAMING_SNAKE_CASE      = list(output_block_list.values()							).index(['conv.bias', 'conv.weight']							)
																_SCREAMING_SNAKE_CASE      = unet_state_dict[
																    f'''output_blocks.{i}.{index}.conv.weight'''
																]
																_SCREAMING_SNAKE_CASE      = unet_state_dict[
																    f'''output_blocks.{i}.{index}.conv.bias'''
																]

																# Clear attentions as they have been attributed above.
																if len(UpperCAmelCase__							) == 2:
																				_SCREAMING_SNAKE_CASE      = []

												if len(UpperCAmelCase__							):
																_SCREAMING_SNAKE_CASE      = renew_attention_paths(UpperCAmelCase__							)
																_SCREAMING_SNAKE_CASE      = {
																    'old': f'''output_blocks.{i}.1''',
																    'new': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
																}
																assign_to_checkpoint(
																    UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,additional_replacements=[meta_path]   ,config=UpperCAmelCase__							)

												if len(UpperCAmelCase__							):
																_SCREAMING_SNAKE_CASE      = renew_attention_paths(UpperCAmelCase__							)
																_SCREAMING_SNAKE_CASE      = {
																    'old': f'''output_blocks.{i}.2''',
																    'new': f'''up_blocks.{block_id}.temp_attentions.{layer_in_block_id}''',
																}
																assign_to_checkpoint(
																    UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__   ,additional_replacements=[meta_path]   ,config=UpperCAmelCase__							)
								else:
												_SCREAMING_SNAKE_CASE      = renew_resnet_paths(UpperCAmelCase__   ,n_shave_prefix_segments=1							)
												for path in resnet_0_paths:
																_SCREAMING_SNAKE_CASE      = '.'.join(['output_blocks', str(UpperCAmelCase__							), path['old']]							)
																_SCREAMING_SNAKE_CASE      = '.'.join(['up_blocks', str(UpperCAmelCase__							), 'resnets', str(UpperCAmelCase__							), path['new']]							)
																_SCREAMING_SNAKE_CASE      = unet_state_dict[old_path]

												_SCREAMING_SNAKE_CASE      = [l for l in output_block_layers if 'temopral_conv' in l]
												for path in temopral_conv_paths:
																_SCREAMING_SNAKE_CASE      = path.split('temopral_conv.'							)[-1]
																_SCREAMING_SNAKE_CASE      = '.'.join(['output_blocks', str(UpperCAmelCase__							), str(UpperCAmelCase__							), 'temopral_conv', pruned_path]							)
																_SCREAMING_SNAKE_CASE      = '.'.join(['up_blocks', str(UpperCAmelCase__							), 'temp_convs', str(UpperCAmelCase__							), pruned_path]							)
																_SCREAMING_SNAKE_CASE      = unet_state_dict[old_path]

				return new_checkpoint


if __name__ == "__main__":
							snake_case			:      str 							=   argparse.ArgumentParser()

							parser.add_argument(
							    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
							)
							parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
							snake_case			:      Optional[int] 							=   parser.parse_args()

							snake_case			:      Optional[int] 							=   torch.load(args.checkpoint_path, map_location='cpu')
							snake_case			:      List[str] 							=   UNetaDConditionModel()

							snake_case			:      int 							=   convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

							snake_case			:      Dict 							=   set(unet.state_dict().keys()) - set(converted_ckpt.keys())
							snake_case			:      Union[str, Any] 							=   set(converted_ckpt.keys()) - set(unet.state_dict().keys())

							assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

							# load state_dict
							unet.load_state_dict(converted_ckpt)

							unet.save_pretrained(args.dump_path)

							# -- finish converting the unet --




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
