



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     List[Any]=1					)       ->				int:







			'''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 							A			(  _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     Tuple=0					)       ->				List[str]:







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

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

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

						_UpperCAmelCase	 =     shave_segments(_UpperCAmelCase							,     n_shave_prefix_segments=_UpperCAmelCase					)

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

			return mapping





def 							A			(  _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     List[str]=0					)       ->				int:







			'''simple docstring'''
			_UpperCAmelCase	 =     []
			for old_item in old_list:
						_UpperCAmelCase	 =     old_item

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

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

						_UpperCAmelCase	 =     shave_segments(_UpperCAmelCase							,     n_shave_prefix_segments=_UpperCAmelCase					)

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

			return mapping





def 							A			(  _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     Optional[int]=None							,     _UpperCAmelCase       :     List[Any]=None							,     _UpperCAmelCase       :     Optional[int]=None					)       ->				Tuple:







			'''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():
									_UpperCAmelCase	 =     old_checkpoint[path]
									_UpperCAmelCase	 =     old_tensor.shape[0] // 3

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

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

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

									_UpperCAmelCase	 =     query.reshape(_UpperCAmelCase					)
									_UpperCAmelCase	 =     key.reshape(_UpperCAmelCase					)
									_UpperCAmelCase	 =     value.reshape(_UpperCAmelCase					)

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

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

						# Global renaming happens here
						_UpperCAmelCase	 =     new_path.replace('middle_block.0'							,     'mid_block.resnets.0'					)
						_UpperCAmelCase	 =     new_path.replace('middle_block.1'							,     'mid_block.attentions.0'					)
						_UpperCAmelCase	 =     new_path.replace('middle_block.2'							,     'mid_block.resnets.1'					)

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

        # proj_attn.weight has to be converted from conv 1D to linear
						if "proj_attn.weight" in new_path:
									_UpperCAmelCase	 =     old_checkpoint[path['old']][:, :, 0]
						else:
									_UpperCAmelCase	 =     old_checkpoint[path['old']]





def 							A			(  _UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     str					)       ->				int:







			'''simple docstring'''
			_UpperCAmelCase	 =     {}

			_UpperCAmelCase	 =     checkpoint['time_embed.0.weight']
			_UpperCAmelCase	 =     checkpoint['time_embed.0.bias']
			_UpperCAmelCase	 =     checkpoint['time_embed.2.weight']
			_UpperCAmelCase	 =     checkpoint['time_embed.2.bias']

			_UpperCAmelCase	 =     checkpoint['input_blocks.0.0.weight']
			_UpperCAmelCase	 =     checkpoint['input_blocks.0.0.bias']

			_UpperCAmelCase	 =     checkpoint['out.0.weight']
			_UpperCAmelCase	 =     checkpoint['out.0.bias']
			_UpperCAmelCase	 =     checkpoint['out.2.weight']
			_UpperCAmelCase	 =     checkpoint['out.2.bias']

			# Retrieves the keys for the input blocks only
			_UpperCAmelCase	 =     len({'.'.join(layer.split('.'					)[:2]					) for layer in checkpoint if 'input_blocks' in layer}					)
			_UpperCAmelCase	 =     {
			    layer_id: [key for key in checkpoint if F"input_blocks.{layer_id}" in key]
			    for layer_id in range(_UpperCAmelCase					)
			}

			# Retrieves the keys for the middle blocks only
			_UpperCAmelCase	 =     len({'.'.join(layer.split('.'					)[:2]					) for layer in checkpoint if 'middle_block' in layer}					)
			_UpperCAmelCase	 =     {
			    layer_id: [key for key in checkpoint if F"middle_block.{layer_id}" in key]
			    for layer_id in range(_UpperCAmelCase					)
			}

			# Retrieves the keys for the output blocks only
			_UpperCAmelCase	 =     len({'.'.join(layer.split('.'					)[:2]					) for layer in checkpoint if 'output_blocks' in layer}					)
			_UpperCAmelCase	 =     {
			    layer_id: [key for key in checkpoint if F"output_blocks.{layer_id}" in key]
			    for layer_id in range(_UpperCAmelCase					)
			}

			for i in range(1							,     _UpperCAmelCase					):
						_UpperCAmelCase	 =     (i - 1) // (config['num_res_blocks'] + 1)
						_UpperCAmelCase	 =     (i - 1) % (config['num_res_blocks'] + 1)

						_UpperCAmelCase	 =     [key for key in input_blocks[i] if F"input_blocks.{i}.0" in key]
						_UpperCAmelCase	 =     [key for key in input_blocks[i] if F"input_blocks.{i}.1" in key]

						if F"input_blocks.{i}.0.op.weight" in checkpoint:
									_UpperCAmelCase	 =     checkpoint[
									    F"input_blocks.{i}.0.op.weight"
									]
									_UpperCAmelCase	 =     checkpoint[
									    F"input_blocks.{i}.0.op.bias"
									]
									continue

						_UpperCAmelCase	 =     renew_resnet_paths(_UpperCAmelCase					)
						_UpperCAmelCase	 =     {'old': F"input_blocks.{i}.0", 'new': F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
						_UpperCAmelCase	 =     {'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
						assign_to_checkpoint(
						    _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     additional_replacements=[meta_path, resnet_op]							,     config=_UpperCAmelCase					)

						if len(_UpperCAmelCase					):
									_UpperCAmelCase	 =     renew_attention_paths(_UpperCAmelCase					)
									_UpperCAmelCase	 =     {
									    'old': F"input_blocks.{i}.1",
									    'new': F"down_blocks.{block_id}.attentions.{layer_in_block_id}",
									}
									_UpperCAmelCase	 =     {
									    F"input_blocks.{i}.1.qkv.bias": {
									        'key': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
									        'query': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
									        'value': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
									    },
									    F"input_blocks.{i}.1.qkv.weight": {
									        'key': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
									        'query': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
									        'value': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
									    },
									}
									assign_to_checkpoint(
									    _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     additional_replacements=[meta_path]							,     attention_paths_to_split=_UpperCAmelCase							,     config=_UpperCAmelCase							,     )

			_UpperCAmelCase	 =     middle_blocks[0]
			_UpperCAmelCase	 =     middle_blocks[1]
			_UpperCAmelCase	 =     middle_blocks[2]

			_UpperCAmelCase	 =     renew_resnet_paths(_UpperCAmelCase					)
			assign_to_checkpoint(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     config=_UpperCAmelCase					)

			_UpperCAmelCase	 =     renew_resnet_paths(_UpperCAmelCase					)
			assign_to_checkpoint(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     config=_UpperCAmelCase					)

			_UpperCAmelCase	 =     renew_attention_paths(_UpperCAmelCase					)
			_UpperCAmelCase	 =     {
			    'middle_block.1.qkv.bias': {
			        'key': 'mid_block.attentions.0.key.bias',
			        'query': 'mid_block.attentions.0.query.bias',
			        'value': 'mid_block.attentions.0.value.bias',
			    },
			    'middle_block.1.qkv.weight': {
			        'key': 'mid_block.attentions.0.key.weight',
			        'query': 'mid_block.attentions.0.query.weight',
			        'value': 'mid_block.attentions.0.value.weight',
			    },
			}
			assign_to_checkpoint(
			    _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     attention_paths_to_split=_UpperCAmelCase							,     config=_UpperCAmelCase					)

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

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

						if len(_UpperCAmelCase					) > 1:
									_UpperCAmelCase	 =     [key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
									_UpperCAmelCase	 =     [key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]

									_UpperCAmelCase	 =     renew_resnet_paths(_UpperCAmelCase					)
									_UpperCAmelCase	 =     renew_resnet_paths(_UpperCAmelCase					)

									_UpperCAmelCase	 =     {'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					)

									if ["conv.weight", "conv.bias"] in output_block_list.values():
												_UpperCAmelCase	 =     list(output_block_list.values()					).index(['conv.weight', 'conv.bias']					)
												_UpperCAmelCase	 =     checkpoint[
												    F"output_blocks.{i}.{index}.conv.weight"
												]
												_UpperCAmelCase	 =     checkpoint[
												    F"output_blocks.{i}.{index}.conv.bias"
												]

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

									if len(_UpperCAmelCase					):
												_UpperCAmelCase	 =     renew_attention_paths(_UpperCAmelCase					)
												_UpperCAmelCase	 =     {
												    'old': F"output_blocks.{i}.1",
												    'new': F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
												}
												_UpperCAmelCase	 =     {
												    F"output_blocks.{i}.1.qkv.bias": {
												        'key': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
												        'query': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
												        'value': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
												    },
												    F"output_blocks.{i}.1.qkv.weight": {
												        'key': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
												        'query': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
												        'value': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
												    },
												}
												assign_to_checkpoint(
												    _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     additional_replacements=[meta_path]							,     attention_paths_to_split=to_split if any('qkv' in key for key in attentions					) else None							,     config=_UpperCAmelCase							,     )
						else:
									_UpperCAmelCase	 =     renew_resnet_paths(_UpperCAmelCase							,     n_shave_prefix_segments=1					)
									for path in resnet_0_paths:
												_UpperCAmelCase	 =     '.'.join(['output_blocks', str(_UpperCAmelCase					), path['old']]					)
												_UpperCAmelCase	 =     '.'.join(['up_blocks', str(_UpperCAmelCase					), 'resnets', str(_UpperCAmelCase					), path['new']]					)

												_UpperCAmelCase	 =     checkpoint[old_path]

			return new_checkpoint


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

							parser.add_argument(
							    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
							)

							parser.add_argument(
							    "--config_file",
							    default=None,
							    type=str,
							    required=True,
							    help="The config json file corresponding to the architecture.",
							)

							parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

							UpperCAmelCase__     = parser.parse_args()

							UpperCAmelCase__     = torch.load(args.checkpoint_path)

							with open(args.config_file) as f:
														UpperCAmelCase__     = json.loads(f.read())

							UpperCAmelCase__     = convert_ldm_checkpoint(checkpoint, config)

							if "ldm" in config:
														del config["ldm"]

							UpperCAmelCase__     = UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

							try:
														UpperCAmelCase__     = DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
														UpperCAmelCase__     = VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

														UpperCAmelCase__     = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
														pipe.save_pretrained(args.dump_path)
							except:  # noqa: E722
														model.save_pretrained(args.dump_path)





import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


UpperCAmelCase__     = logging.getLogger(__name__)
UpperCAmelCase__     = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
UpperCAmelCase__     = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class     __lowerCAmelCase					:
			UpperCamelCase           =  field(
			    default=A       ,		metadata={
			        '''help''': (
			            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
			        )
			    }       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(A		)}       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={
			        '''help''': (
			            '''Override some existing default config settings when a model is trained from scratch. Example: '''
			            '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
			        )
			    }       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}       ,		)
			UpperCamelCase           =  field(
			    default='''main'''       ,		metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={
			        '''help''': (
			            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
			            '''with private models).'''
			        )
			    }       ,		)



			def 					_lowerCamelCase (		self     :						Any)     ->					Dict:



						"""simple docstring"""






						if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
									raise ValueError(
									    '--config_overrides can\'t be used in combination with --config_name or --model_name_or_path')







@dataclass
class     __lowerCAmelCase					:
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}		)
			UpperCamelCase           =  field(default=A       ,		metadata={'''help''': '''The input training data file (a text file).'''}		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''An optional input train ref data file for whole word masking in Chinese.'''}       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''An optional input validation ref data file for whole word masking in Chinese.'''}       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}		)
			UpperCamelCase           =  field(
			    default=5       ,		metadata={
			        '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
			    }       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={
			        '''help''': (
			            '''The maximum total input sequence length after tokenization. Sequences longer '''
			            '''than this will be truncated. Default to the max input length of the model.'''
			        )
			    }       ,		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={'''help''': '''The number of processes to use for the preprocessing.'''}       ,		)
			UpperCamelCase           =  field(
			    default=0.15       ,		metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''}		)
			UpperCamelCase           =  field(
			    default=A       ,		metadata={
			        '''help''': (
			            '''Whether to pad all samples to `max_seq_length`. '''
			            '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
			        )
			    }       ,		)



			def 					_lowerCamelCase (		self     :						Dict)     ->					Union[str, Any]:



						"""simple docstring"""






						if self.train_file is not None:
									_UpperCAmelCase	 =     self.train_file.split('.')[-1]
									assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
						if self.validation_file is not None:
									_UpperCAmelCase	 =     self.validation_file.split('.')[-1]
									assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Union[str, Any]					)       ->				Optional[Any]:







			'''simple docstring'''
			with open(_UpperCAmelCase							,     'r'							,     encoding='utf-8'					) as f:
						_UpperCAmelCase	 =     [json.loads(_UpperCAmelCase					) for line in f.read().splitlines() if (len(_UpperCAmelCase					) > 0 and not line.isspace())]
			assert len(_UpperCAmelCase					) == len(_UpperCAmelCase					)

			_UpperCAmelCase	 =     {c: dataset[c] for c in dataset.column_names}
			_UpperCAmelCase	 =     refs
			return Dataset.from_dict(_UpperCAmelCase					)





def 							A			(  )       ->				Optional[Any]:







			'''simple docstring'''
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			_UpperCAmelCase	 =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
			if len(sys.argv					) == 2 and sys.argv[1].endswith('.json'					):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						_UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase	 =     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
			else:
						_UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase	 =     parser.parse_args_into_dataclasses()

			# Detecting last checkpoint.
			_UpperCAmelCase	 =     None
			if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
						_UpperCAmelCase	 =     get_last_checkpoint(training_args.output_dir					)
						if last_checkpoint is None and len(os.listdir(training_args.output_dir					)					) > 0:
									raise ValueError(
									    F"Output directory ({training_args.output_dir}) already exists and is not empty. "
									    'Use --overwrite_output_dir to overcome.'					)
						elif last_checkpoint is not None:
									logger.info(
									    F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
									    'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'					)

    # Setup logging
			logging.basicConfig(
			    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,     datefmt='%m/%d/%Y %H:%M:%S'							,     handlers=[logging.StreamHandler(sys.stdout					)]							,     )
			logger.setLevel(logging.INFO if is_main_process(training_args.local_rank					) else logging.WARN					)

			# Log on each process the small summary:
			logger.warning(
			    F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
			    + F"distributed training: {bool(training_args.local_rank != -1					)}, 16-bits training: {training_args.fpaa}"					)
			# Set the verbosity to info of the Transformers logger (on main process only):
			if is_main_process(training_args.local_rank					):
						transformers.utils.logging.set_verbosity_info()
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()
			logger.info('Training/evaluation parameters %s'							,     _UpperCAmelCase					)

			# Set seed before initializing model.
			set_seed(training_args.seed					)

			# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
			# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
			# (the dataset will be downloaded automatically from the datasets Hub).
			#
			# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
			# 'text' is found. You can easily tweak this behavior (see below).
			#
			# In distributed training, the load_dataset function guarantee that only one local process can concurrently
			# download the dataset.
			if data_args.dataset_name is not None:
						# Downloading and loading a dataset from the hub.
						_UpperCAmelCase	 =     load_dataset(data_args.dataset_name							,     data_args.dataset_config_name					)
						if "validation" not in datasets.keys():
									_UpperCAmelCase	 =     load_dataset(
									    data_args.dataset_name							,     data_args.dataset_config_name							,     split=F"train[:{data_args.validation_split_percentage}%]"							,     )
									_UpperCAmelCase	 =     load_dataset(
									    data_args.dataset_name							,     data_args.dataset_config_name							,     split=F"train[{data_args.validation_split_percentage}%:]"							,     )
			else:
						_UpperCAmelCase	 =     {}
						if data_args.train_file is not None:
									_UpperCAmelCase	 =     data_args.train_file
						if data_args.validation_file is not None:
									_UpperCAmelCase	 =     data_args.validation_file
						_UpperCAmelCase	 =     data_args.train_file.split('.'					)[-1]
						if extension == "txt":
									_UpperCAmelCase	 =     'text'
						_UpperCAmelCase	 =     load_dataset(_UpperCAmelCase							,     data_files=_UpperCAmelCase					)
			# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
			# https://huggingface.co/docs/datasets/loading_datasets.html.

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			_UpperCAmelCase	 =     {
			    'cache_dir': model_args.cache_dir,
			    'revision': model_args.model_revision,
			    'use_auth_token': True if model_args.use_auth_token else None,
			}
			if model_args.config_name:
						_UpperCAmelCase	 =     AutoConfig.from_pretrained(model_args.config_name							,     **_UpperCAmelCase					)
			elif model_args.model_name_or_path:
						_UpperCAmelCase	 =     AutoConfig.from_pretrained(model_args.model_name_or_path							,     **_UpperCAmelCase					)
			else:
						_UpperCAmelCase	 =     CONFIG_MAPPING[model_args.model_type]()
						logger.warning('You are instantiating a new config instance from scratch.'					)
						if model_args.config_overrides is not None:
									logger.info(F"Overriding config: {model_args.config_overrides}"					)
									config.update_from_string(model_args.config_overrides					)
									logger.info(F"New config: {config}"					)

			_UpperCAmelCase	 =     {
			    'cache_dir': model_args.cache_dir,
			    'use_fast': model_args.use_fast_tokenizer,
			    'revision': model_args.model_revision,
			    'use_auth_token': True if model_args.use_auth_token else None,
			}
			if model_args.tokenizer_name:
						_UpperCAmelCase	 =     AutoTokenizer.from_pretrained(model_args.tokenizer_name							,     **_UpperCAmelCase					)
			elif model_args.model_name_or_path:
						_UpperCAmelCase	 =     AutoTokenizer.from_pretrained(model_args.model_name_or_path							,     **_UpperCAmelCase					)
			else:
						raise ValueError(
						    'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
						    'You can do it from another script, save it, and load it from here, using --tokenizer_name.'					)

			if model_args.model_name_or_path:
						_UpperCAmelCase	 =     AutoModelForMaskedLM.from_pretrained(
						    model_args.model_name_or_path							,     from_tf=bool('.ckpt' in model_args.model_name_or_path					)							,     config=_UpperCAmelCase							,     cache_dir=model_args.cache_dir							,     revision=model_args.model_revision							,     use_auth_token=True if model_args.use_auth_token else None							,     )
			else:
						logger.info('Training new model from scratch'					)
						_UpperCAmelCase	 =     AutoModelForMaskedLM.from_config(_UpperCAmelCase					)

			model.resize_token_embeddings(len(_UpperCAmelCase					)					)

			# Preprocessing the datasets.
			# First we tokenize all the texts.
			if training_args.do_train:
						_UpperCAmelCase	 =     datasets['train'].column_names
			else:
						_UpperCAmelCase	 =     datasets['validation'].column_names
			_UpperCAmelCase	 =     'text' if 'text' in column_names else column_names[0]

			_UpperCAmelCase	 =     'max_length' if data_args.pad_to_max_length else False

			def tokenize_function(_UpperCAmelCase       :     str					):
						# Remove empty lines
						_UpperCAmelCase	 =     [line for line in examples['text'] if len(_UpperCAmelCase					) > 0 and not line.isspace()]
						return tokenizer(examples['text']							,     padding=_UpperCAmelCase							,     truncation=_UpperCAmelCase							,     max_length=data_args.max_seq_length					)

			_UpperCAmelCase	 =     datasets.map(
			    _UpperCAmelCase							,     batched=_UpperCAmelCase							,     num_proc=data_args.preprocessing_num_workers							,     remove_columns=[text_column_name]							,     load_from_cache_file=not data_args.overwrite_cache							,     )

			# Add the chinese references if provided
			if data_args.train_ref_file is not None:
						_UpperCAmelCase	 =     add_chinese_references(tokenized_datasets['train']							,     data_args.train_ref_file					)
			if data_args.validation_ref_file is not None:
						_UpperCAmelCase	 =     add_chinese_references(
						    tokenized_datasets['validation']							,     data_args.validation_ref_file					)
			# If we have ref files, need to avoid it removed by trainer
			_UpperCAmelCase	 =     data_args.train_ref_file or data_args.validation_ref_file
			if has_ref:
						_UpperCAmelCase	 =     False

			# Data collator
			# This one will take care of randomly masking the tokens.
			_UpperCAmelCase	 =     DataCollatorForWholeWordMask(tokenizer=_UpperCAmelCase							,     mlm_probability=data_args.mlm_probability					)

			# Initialize our Trainer
			_UpperCAmelCase	 =     Trainer(
			    model=_UpperCAmelCase							,     args=_UpperCAmelCase							,     train_dataset=tokenized_datasets['train'] if training_args.do_train else None							,     eval_dataset=tokenized_datasets['validation'] if training_args.do_eval else None							,     tokenizer=_UpperCAmelCase							,     data_collator=_UpperCAmelCase							,     )

			# Training
			if training_args.do_train:
						if last_checkpoint is not None:
									_UpperCAmelCase	 =     last_checkpoint
						elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path					):
									_UpperCAmelCase	 =     model_args.model_name_or_path
						else:
									_UpperCAmelCase	 =     None
						_UpperCAmelCase	 =     trainer.train(resume_from_checkpoint=_UpperCAmelCase					)
						trainer.save_model()  # Saves the tokenizer too for easy upload

						_UpperCAmelCase	 =     os.path.join(training_args.output_dir							,     'train_results.txt'					)
						if trainer.is_world_process_zero():
									with open(_UpperCAmelCase							,     'w'					) as writer:
												logger.info('***** Train results *****'					)
												for key, value in sorted(train_result.metrics.items()					):
															logger.info(F"  {key} = {value}"					)
															writer.write(F"{key} = {value}\n"					)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
									trainer.state.save_to_json(os.path.join(training_args.output_dir							,     'trainer_state.json'					)					)

    # Evaluation
			_UpperCAmelCase	 =     {}
			if training_args.do_eval:
						logger.info('*** Evaluate ***'					)

						_UpperCAmelCase	 =     trainer.evaluate()

						_UpperCAmelCase	 =     math.exp(eval_output['eval_loss']					)
						_UpperCAmelCase	 =     perplexity

						_UpperCAmelCase	 =     os.path.join(training_args.output_dir							,     'eval_results_mlm_wwm.txt'					)
						if trainer.is_world_process_zero():
									with open(_UpperCAmelCase							,     'w'					) as writer:
												logger.info('***** Eval results *****'					)
												for key, value in sorted(results.items()					):
															logger.info(F"  {key} = {value}"					)
															writer.write(F"{key} = {value}\n"					)

			return results





def 							A			(  _UpperCAmelCase       :     str					)       ->				Optional[Any]:







			'''simple docstring'''
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
							main()
