





'''simple docstring'''

import argparse

import torch

from diffusers import UNetaDConditionModel


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase=None,       _UpperCAmelCase=None,       _UpperCAmelCase=None  ):
		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():
						__UpperCAmelCase	:							Any     =     old_checkpoint[path]
						__UpperCAmelCase	:							Union[str, Any]     =     old_tensor.shape[0] // 3

						__UpperCAmelCase	:							Optional[int]     =     (-1, channels) if len(old_tensor.shape  ) == 3 else (-1)

						__UpperCAmelCase	:							int     =     old_tensor.shape[0] // config["num_head_channels"] // 3

						__UpperCAmelCase	:							Optional[int]     =     old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]  )
						__UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase	:							List[Any]     =     old_tensor.split(channels // num_heads,       dim=1  )

						__UpperCAmelCase	:							Optional[int]     =     query.reshape(_UpperCAmelCase  )
						__UpperCAmelCase	:							List[str]     =     key.reshape(_UpperCAmelCase  )
						__UpperCAmelCase	:							Dict     =     value.reshape(_UpperCAmelCase  )

		for path in paths:
				__UpperCAmelCase	:							Any     =     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:
								__UpperCAmelCase	:							str     =     new_path.replace(replacement["old"],       replacement["new"]  )

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


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase=0  ):
		__UpperCAmelCase	:							Any     =     []
		for old_item in old_list:
				__UpperCAmelCase	:							Optional[int]     =     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 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase=1  ):
		if n_shave_prefix_segments >= 0:
				return ".".join(path.split("."  )[n_shave_prefix_segments:]  )
		else:
				return ".".join(path.split("."  )[:n_shave_prefix_segments]  )


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase=0  ):
		__UpperCAmelCase	:							List[Any]     =     []
		for old_item in old_list:
				mapping.append({"old": old_item, "new": old_item}  )

		return mapping


def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase=0  ):
		__UpperCAmelCase	:							int     =     []
		for old_item in old_list:
				__UpperCAmelCase	:							Optional[int]     =     old_item.replace("in_layers.0",       "norm1"  )
				__UpperCAmelCase	:							List[str]     =     new_item.replace("in_layers.2",       "conv1"  )

				__UpperCAmelCase	:							Union[str, Any]     =     new_item.replace("out_layers.0",       "norm2"  )
				__UpperCAmelCase	:							Any     =     new_item.replace("out_layers.3",       "conv2"  )

				__UpperCAmelCase	:							List[str]     =     new_item.replace("emb_layers.1",       "time_emb_proj"  )
				__UpperCAmelCase	:							int     =     new_item.replace("skip_connection",       "conv_shortcut"  )

				__UpperCAmelCase	:							Optional[int]     =     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 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase=None,       _UpperCAmelCase=False  ):
		__UpperCAmelCase	:							str     =     {}
		__UpperCAmelCase	:							List[Any]     =     list(checkpoint.keys()  )

		__UpperCAmelCase	:							str     =     "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"  ):
								__UpperCAmelCase	:							List[str]     =     "model_ema." + "".join(key.split("."  )[1:]  )
								__UpperCAmelCase	:							int     =     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:
						__UpperCAmelCase	:							Any     =     checkpoint.pop(_UpperCAmelCase  )

		__UpperCAmelCase	:							Optional[Any]     =     {}

		__UpperCAmelCase	:							List[Any]     =     unet_state_dict["time_embed.0.weight"]
		__UpperCAmelCase	:							Union[str, Any]     =     unet_state_dict["time_embed.0.bias"]
		__UpperCAmelCase	:							Tuple     =     unet_state_dict["time_embed.2.weight"]
		__UpperCAmelCase	:							Any     =     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":
				__UpperCAmelCase	:							Dict     =     unet_state_dict["label_emb.0.0.weight"]
				__UpperCAmelCase	:							Tuple     =     unet_state_dict["label_emb.0.0.bias"]
				__UpperCAmelCase	:							str     =     unet_state_dict["label_emb.0.2.weight"]
				__UpperCAmelCase	:							Dict     =     unet_state_dict["label_emb.0.2.bias"]
		else:
				raise NotImplementedError(F"Not implemented `class_embed_type`: {config['class_embed_type']}"  )

		__UpperCAmelCase	:							Optional[int]     =     unet_state_dict["input_blocks.0.0.weight"]
		__UpperCAmelCase	:							Union[str, Any]     =     unet_state_dict["input_blocks.0.0.bias"]

		__UpperCAmelCase	:							Optional[Any]     =     [v for v in unet_state_dict if v.startswith("input_blocks.0.1"  )]
		__UpperCAmelCase	:							Optional[int]     =     renew_attention_paths(_UpperCAmelCase  )
		__UpperCAmelCase	:							List[str]     =     {"old": "input_blocks.0.1", "new": "transformer_in"}
		assign_to_checkpoint(_UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       additional_replacements=[meta_path],       config=_UpperCAmelCase  )

		__UpperCAmelCase	:							List[str]     =     unet_state_dict["out.0.weight"]
		__UpperCAmelCase	:							int     =     unet_state_dict["out.0.bias"]
		__UpperCAmelCase	:							int     =     unet_state_dict["out.2.weight"]
		__UpperCAmelCase	:							str     =     unet_state_dict["out.2.bias"]

		# Retrieves the keys for the input blocks only
		__UpperCAmelCase	:							Tuple     =     len({".".join(layer.split("."  )[:2]  ) for layer in unet_state_dict if "input_blocks" in layer}  )
		__UpperCAmelCase	:							List[str]     =     {
		    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
		__UpperCAmelCase	:							Any     =     len({".".join(layer.split("."  )[:2]  ) for layer in unet_state_dict if "middle_block" in layer}  )
		__UpperCAmelCase	:							List[str]     =     {
		    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
		__UpperCAmelCase	:							Union[str, Any]     =     len({".".join(layer.split("."  )[:2]  ) for layer in unet_state_dict if "output_blocks" in layer}  )
		__UpperCAmelCase	:							Optional[int]     =     {
		    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  ):
				__UpperCAmelCase	:							List[Any]     =     (i - 1) // (config["layers_per_block"] + 1)
				__UpperCAmelCase	:							Optional[Any]     =     (i - 1) % (config["layers_per_block"] + 1)

				__UpperCAmelCase	:							Dict     =     [
				    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
				]
				__UpperCAmelCase	:							List[Any]     =     [key for key in input_blocks[i] if F"input_blocks.{i}.1" in key]
				__UpperCAmelCase	:							Optional[int]     =     [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:
						__UpperCAmelCase	:							Tuple     =     unet_state_dict.pop(
						    F"input_blocks.{i}.op.weight"  )
						__UpperCAmelCase	:							Tuple     =     unet_state_dict.pop(
						    F"input_blocks.{i}.op.bias"  )

				__UpperCAmelCase	:							Optional[int]     =     renew_resnet_paths(_UpperCAmelCase  )
				__UpperCAmelCase	:							Any     =     {"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  )

				__UpperCAmelCase	:							List[str]     =     [key for key in resnets if "temopral_conv" in key]
				__UpperCAmelCase	:							Optional[Any]     =     renew_temp_conv_paths(_UpperCAmelCase  )
				__UpperCAmelCase	:							Any     =     {
				    "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  ):
						__UpperCAmelCase	:							Any     =     renew_attention_paths(_UpperCAmelCase  )
						__UpperCAmelCase	:							List[Any]     =     {"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  ):
						__UpperCAmelCase	:							Dict     =     renew_attention_paths(_UpperCAmelCase  )
						__UpperCAmelCase	:							List[Any]     =     {
						    "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  )

		__UpperCAmelCase	:							Optional[Any]     =     middle_blocks[0]
		__UpperCAmelCase	:							List[Any]     =     [key for key in resnet_a if "temopral_conv" in key]
		__UpperCAmelCase	:							Dict     =     middle_blocks[1]
		__UpperCAmelCase	:							Union[str, Any]     =     middle_blocks[2]
		__UpperCAmelCase	:							List[Any]     =     middle_blocks[3]
		__UpperCAmelCase	:							str     =     [key for key in resnet_a if "temopral_conv" in key]

		__UpperCAmelCase	:							Union[str, Any]     =     renew_resnet_paths(_UpperCAmelCase  )
		__UpperCAmelCase	:							str     =     {"old": "middle_block.0", "new": "mid_block.resnets.0"}
		assign_to_checkpoint(
		    _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       config=_UpperCAmelCase,       additional_replacements=[meta_path]  )

		__UpperCAmelCase	:							Any     =     renew_temp_conv_paths(_UpperCAmelCase  )
		__UpperCAmelCase	:							List[str]     =     {"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
		assign_to_checkpoint(
		    _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       config=_UpperCAmelCase,       additional_replacements=[meta_path]  )

		__UpperCAmelCase	:							Tuple     =     renew_resnet_paths(_UpperCAmelCase  )
		__UpperCAmelCase	:							Optional[Any]     =     {"old": "middle_block.3", "new": "mid_block.resnets.1"}
		assign_to_checkpoint(
		    _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       config=_UpperCAmelCase,       additional_replacements=[meta_path]  )

		__UpperCAmelCase	:							Optional[Any]     =     renew_temp_conv_paths(_UpperCAmelCase  )
		__UpperCAmelCase	:							List[Any]     =     {"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
		assign_to_checkpoint(
		    _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       config=_UpperCAmelCase,       additional_replacements=[meta_path]  )

		__UpperCAmelCase	:							str     =     renew_attention_paths(_UpperCAmelCase  )
		__UpperCAmelCase	:							Dict     =     {"old": "middle_block.1", "new": "mid_block.attentions.0"}
		assign_to_checkpoint(
		    _UpperCAmelCase,       _UpperCAmelCase,       _UpperCAmelCase,       additional_replacements=[meta_path],       config=_UpperCAmelCase  )

		__UpperCAmelCase	:							str     =     renew_attention_paths(_UpperCAmelCase  )
		__UpperCAmelCase	:							List[str]     =     {"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  ):
				__UpperCAmelCase	:							List[Any]     =     i // (config["layers_per_block"] + 1)
				__UpperCAmelCase	:							str     =     i % (config["layers_per_block"] + 1)
				__UpperCAmelCase	:							Any     =     [shave_segments(_UpperCAmelCase,       2  ) for name in output_blocks[i]]
				__UpperCAmelCase	:							Tuple     =     {}

				for layer in output_block_layers:
						__UpperCAmelCase  ,			__UpperCAmelCase	:							int     =     layer.split("."  )[0], shave_segments(_UpperCAmelCase,       1  )
						if layer_id in output_block_list:
								output_block_list[layer_id].append(_UpperCAmelCase  )
						else:
								__UpperCAmelCase	:							int     =     [layer_name]

				if len(_UpperCAmelCase  ) > 1:
						__UpperCAmelCase	:							List[str]     =     [key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
						__UpperCAmelCase	:							Optional[int]     =     [key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]
						__UpperCAmelCase	:							Union[str, Any]     =     [key for key in output_blocks[i] if F"output_blocks.{i}.2" in key]

						__UpperCAmelCase	:							Dict     =     renew_resnet_paths(_UpperCAmelCase  )
						__UpperCAmelCase	:							Optional[int]     =     renew_resnet_paths(_UpperCAmelCase  )

						__UpperCAmelCase	:							Optional[int]     =     {"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  )

						__UpperCAmelCase	:							List[str]     =     [key for key in resnets if "temopral_conv" in key]
						__UpperCAmelCase	:							List[str]     =     renew_temp_conv_paths(_UpperCAmelCase  )
						__UpperCAmelCase	:							Optional[int]     =     {
						    "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  )

						__UpperCAmelCase	:							Optional[Any]     =     {k: sorted(_UpperCAmelCase  ) for k, v in output_block_list.items()}
						if ["conv.bias", "conv.weight"] in output_block_list.values():
								__UpperCAmelCase	:							List[str]     =     list(output_block_list.values()  ).index(["conv.bias", "conv.weight"]  )
								__UpperCAmelCase	:							Optional[int]     =     unet_state_dict[
								    F"output_blocks.{i}.{index}.conv.weight"
								]
								__UpperCAmelCase	:							Any     =     unet_state_dict[
								    F"output_blocks.{i}.{index}.conv.bias"
								]

								# Clear attentions as they have been attributed above.
								if len(_UpperCAmelCase  ) == 2:
										__UpperCAmelCase	:							List[str]     =     []

						if len(_UpperCAmelCase  ):
								__UpperCAmelCase	:							List[Any]     =     renew_attention_paths(_UpperCAmelCase  )
								__UpperCAmelCase	:							Any     =     {
								    "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  ):
								__UpperCAmelCase	:							int     =     renew_attention_paths(_UpperCAmelCase  )
								__UpperCAmelCase	:							str     =     {
								    "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:
						__UpperCAmelCase	:							Optional[Any]     =     renew_resnet_paths(_UpperCAmelCase,       n_shave_prefix_segments=1  )
						for path in resnet_0_paths:
								__UpperCAmelCase	:							int     =     ".".join(["output_blocks", str(_UpperCAmelCase  ), path["old"]]  )
								__UpperCAmelCase	:							str     =     ".".join(["up_blocks", str(_UpperCAmelCase  ), "resnets", str(_UpperCAmelCase  ), path["new"]]  )
								__UpperCAmelCase	:							List[Any]     =     unet_state_dict[old_path]

						__UpperCAmelCase	:							Optional[Any]     =     [l for l in output_block_layers if "temopral_conv" in l]
						for path in temopral_conv_paths:
								__UpperCAmelCase	:							List[str]     =     path.split("temopral_conv."  )[-1]
								__UpperCAmelCase	:							List[Any]     =     ".".join(["output_blocks", str(_UpperCAmelCase  ), str(_UpperCAmelCase  ), "temopral_conv", pruned_path]  )
								__UpperCAmelCase	:							str     =     ".".join(["up_blocks", str(_UpperCAmelCase  ), "temp_convs", str(_UpperCAmelCase  ), pruned_path]  )
								__UpperCAmelCase	:							int     =     unet_state_dict[old_path]

		return new_checkpoint


if __name__ == "__main__":
			lowerCAmelCase__      :   List[Any]      	=			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.")
			lowerCAmelCase__      :   Optional[Any]      	=			parser.parse_args()

			lowerCAmelCase__      :   Optional[Any]      	=			torch.load(args.checkpoint_path, map_location="cpu")
			lowerCAmelCase__      :   Dict      	=			UNetaDConditionModel()

			lowerCAmelCase__      :   str      	=			convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

			lowerCAmelCase__      :   int      	=			set(unet.state_dict().keys()) - set(converted_ckpt.keys())
			lowerCAmelCase__      :   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 --











'''simple docstring'''

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

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


if is_sentencepiece_available():
			from .tokenization_xglm import XGLMTokenizer
else:
			lowerCAmelCase__      :   List[str]      	=			None


lowerCAmelCase__      :   Union[str, Any]      	=			logging.get_logger(__name__)

lowerCAmelCase__      :   Tuple      	=			{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase__      :   List[str]      	=			{
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json",
    },
}

lowerCAmelCase__      :   int      	=			{
    "facebook/xglm-564M": 20_48,
}


class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE							=	PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE							=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE							=	['''input_ids''', '''attention_mask''']
					SCREAMING_SNAKE_CASE							=	XGLMTokenizer
					def __init__(   self       :      Optional[int]		,	UpperCAmelCase_       :      List[Any]=None		,	UpperCAmelCase_       :      str=None		,	UpperCAmelCase_       :      str="<s>"		,	UpperCAmelCase_       :      List[Any]="</s>"		,	UpperCAmelCase_       :      Dict="</s>"		,	UpperCAmelCase_       :      Optional[Any]="<s>"		,	UpperCAmelCase_       :      List[str]="<unk>"		,	UpperCAmelCase_       :      Union[str, Any]="<pad>"		,	**UpperCAmelCase_       :      List[Any]		,	):







							"""simple docstring"""
							# Compatibility with the original tokenizer
							__UpperCAmelCase	:							Union[str, Any]     =     7
							__UpperCAmelCase	:							Tuple     =     [f"<madeupword{i}>" for i in range(self.num_madeup_words		)]

							__UpperCAmelCase	:							List[Any]     =     kwargs.get("additional_special_tokens"		,	[]		)
							kwargs["additional_special_tokens"] += [
							    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    UpperCAmelCase_		,	tokenizer_file=UpperCAmelCase_		,	bos_token=UpperCAmelCase_		,	eos_token=UpperCAmelCase_		,	sep_token=UpperCAmelCase_		,	cls_token=UpperCAmelCase_		,	unk_token=UpperCAmelCase_		,	pad_token=UpperCAmelCase_		,	**UpperCAmelCase_		,	)

							__UpperCAmelCase	:							Dict     =     vocab_file
							__UpperCAmelCase	:							Optional[int]     =     False if not self.vocab_file else True
					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      List[int]		,	UpperCAmelCase_       :      Optional[List[int]] = None		):







							"""simple docstring"""

							if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
							__UpperCAmelCase	:							Any     =     [self.sep_token_id]
							return sep + token_ids_a + sep + sep + token_ids_a
					def 		lowerCamelCase_   (   self       :      Optional[Any]		,	UpperCAmelCase_       :      List[int]		,	UpperCAmelCase_       :      Optional[List[int]] = None		):







							"""simple docstring"""
							__UpperCAmelCase	:							List[Any]     =     [self.sep_token_id]

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






					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      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(UpperCAmelCase_		):
									logger.error(f"Vocabulary path ({save_directory}) should be a directory."		)
									return
							__UpperCAmelCase	:							str     =     os.path.join(
							    UpperCAmelCase_		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

							return (out_vocab_file,)




