





import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
a__: int							   =						logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a__: Optional[int]							   =						[]
for i in range(6):
       # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
       rename_keys.append(
           (F"transformer.encoder.layers.{i}.self_attn.out_proj.weight", F"encoder.layers.{i}.self_attn.out_proj.weight")
       )
       rename_keys.append(
           (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")
       )
       rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight"))
       rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias"))
       rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight"))
       rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias"))
       rename_keys.append(
           (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")
       )
       rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias"))
       rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight"))
       rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias"))
       # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
       rename_keys.append(
           (F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"decoder.layers.{i}.self_attn.out_proj.weight")
       )
       rename_keys.append(
           (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")
       )
       rename_keys.append(
           (
               F"transformer.decoder.layers.{i}.cross_attn.out_proj.weight",
               F"decoder.layers.{i}.encoder_attn.out_proj.weight",
           )
       )
       rename_keys.append(
           (
               F"transformer.decoder.layers.{i}.cross_attn.out_proj.bias",
               F"decoder.layers.{i}.encoder_attn.out_proj.bias",
           )
       )
       rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight"))
       rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias"))
       rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight"))
       rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias"))
       rename_keys.append(
           (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")
       )
       rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias"))
       rename_keys.append(
           (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")
       )
       rename_keys.append(
           (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")
       )
       rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight"))
       rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias"))

       # q, k, v projections in self/cross-attention in decoder for conditional DETR
       rename_keys.append(
           (F"transformer.decoder.layers.{i}.sa_qcontent_proj.weight", F"decoder.layers.{i}.sa_qcontent_proj.weight")
       )
       rename_keys.append(
           (F"transformer.decoder.layers.{i}.sa_kcontent_proj.weight", F"decoder.layers.{i}.sa_kcontent_proj.weight")
       )
       rename_keys.append(
           (F"transformer.decoder.layers.{i}.sa_qpos_proj.weight", F"decoder.layers.{i}.sa_qpos_proj.weight")
       )
       rename_keys.append(
           (F"transformer.decoder.layers.{i}.sa_kpos_proj.weight", F"decoder.layers.{i}.sa_kpos_proj.weight")
       )
       rename_keys.append((F"transformer.decoder.layers.{i}.sa_v_proj.weight", F"decoder.layers.{i}.sa_v_proj.weight"))
       rename_keys.append(
           (F"transformer.decoder.layers.{i}.ca_qcontent_proj.weight", F"decoder.layers.{i}.ca_qcontent_proj.weight")
       )
       # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
       rename_keys.append(
           (F"transformer.decoder.layers.{i}.ca_kcontent_proj.weight", F"decoder.layers.{i}.ca_kcontent_proj.weight")
       )
       rename_keys.append(
           (F"transformer.decoder.layers.{i}.ca_kpos_proj.weight", F"decoder.layers.{i}.ca_kpos_proj.weight")
       )
       rename_keys.append((F"transformer.decoder.layers.{i}.ca_v_proj.weight", F"decoder.layers.{i}.ca_v_proj.weight"))
       rename_keys.append(
           (F"transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight", F"decoder.layers.{i}.ca_qpos_sine_proj.weight")
       )

       rename_keys.append(
           (F"transformer.decoder.layers.{i}.sa_qcontent_proj.bias", F"decoder.layers.{i}.sa_qcontent_proj.bias")
       )
       rename_keys.append(
           (F"transformer.decoder.layers.{i}.sa_kcontent_proj.bias", F"decoder.layers.{i}.sa_kcontent_proj.bias")
       )
       rename_keys.append((F"transformer.decoder.layers.{i}.sa_qpos_proj.bias", F"decoder.layers.{i}.sa_qpos_proj.bias"))
       rename_keys.append((F"transformer.decoder.layers.{i}.sa_kpos_proj.bias", F"decoder.layers.{i}.sa_kpos_proj.bias"))
       rename_keys.append((F"transformer.decoder.layers.{i}.sa_v_proj.bias", F"decoder.layers.{i}.sa_v_proj.bias"))
       rename_keys.append(
           (F"transformer.decoder.layers.{i}.ca_qcontent_proj.bias", F"decoder.layers.{i}.ca_qcontent_proj.bias")
       )
       # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
       rename_keys.append(
           (F"transformer.decoder.layers.{i}.ca_kcontent_proj.bias", F"decoder.layers.{i}.ca_kcontent_proj.bias")
       )
       rename_keys.append((F"transformer.decoder.layers.{i}.ca_kpos_proj.bias", F"decoder.layers.{i}.ca_kpos_proj.bias"))
       rename_keys.append((F"transformer.decoder.layers.{i}.ca_v_proj.bias", F"decoder.layers.{i}.ca_v_proj.bias"))
       rename_keys.append(
           (F"transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias", F"decoder.layers.{i}.ca_qpos_sine_proj.bias")
       )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)


def       UpperCamelCase__(			UpperCamelCase__     :			Optional[Any]					,	UpperCamelCase__     :			Optional[int]					,	UpperCamelCase__     :			Optional[int]    )->List[Any]:
       A__		   =		state_dict.pop(UpperCamelCase__    )
       A__		   =		val


def       UpperCamelCase__(			UpperCamelCase__     :			Optional[Any]    )->Union[str, Any]:
       A__		   =		OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     A__		   =		key.replace('''backbone.0.body'''					,	'''backbone.conv_encoder.model'''    )
                     A__		   =		value
              else:
                     A__		   =		value

       return new_state_dict


def       UpperCamelCase__(			UpperCamelCase__     :			Optional[int]					,	UpperCamelCase__     :			Tuple=False    )->List[str]:
       A__		   =		''''''
       if is_panoptic:
              A__		   =		'''conditional_detr.'''

       # first: transformer encoder
       for i in range(6    ):
              # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
              A__		   =		state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"    )
              A__		   =		state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"    )
              # next, add query, keys and values (in that order) to the state dict
              A__		   =		in_proj_weight[:2_56, :]
              A__		   =		in_proj_bias[:2_56]
              A__		   =		in_proj_weight[2_56:5_12, :]
              A__		   =		in_proj_bias[2_56:5_12]
              A__		   =		in_proj_weight[-2_56:, :]
              A__		   =		in_proj_bias[-2_56:]


def       UpperCamelCase__(			)->Optional[Any]:
       A__		   =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       A__		   =		Image.open(requests.get(UpperCamelCase__					,	stream=UpperCamelCase__    ).raw    )

       return im


@torch.no_grad()
def       UpperCamelCase__(			UpperCamelCase__     :			str					,	UpperCamelCase__     :			List[Any]    )->Optional[Any]:
       A__		   =		ConditionalDetrConfig()
       # set backbone and dilation attributes
       if "resnet101" in model_name:
              A__		   =		'''resnet101'''
       if "dc5" in model_name:
              A__		   =		True
       A__		   =		'''panoptic''' in model_name
       if is_panoptic:
              A__		   =		2_50
       else:
              A__		   =		91
              A__		   =		'''huggingface/label-files'''
              A__		   =		'''coco-detection-id2label.json'''
              A__		   =		json.load(open(hf_hub_download(UpperCamelCase__					,	UpperCamelCase__					,	repo_type='''dataset'''    )					,	'''r'''    )    )
              A__		   =		{int(UpperCamelCase__    ): v for k, v in idalabel.items()}
              A__		   =		idalabel
              A__		   =		{v: k for k, v in idalabel.items()}

       # load image processor
       A__		   =		'''coco_panoptic''' if is_panoptic else '''coco_detection'''
       A__		   =		ConditionalDetrImageProcessor(format=UpperCamelCase__    )

       # prepare image
       A__		   =		prepare_img()
       A__		   =		image_processor(images=UpperCamelCase__					,	return_tensors='''pt'''    )
       A__		   =		encoding['''pixel_values''']

       logger.info(f"Converting model {model_name}..."    )

       # load original model from torch hub
       A__		   =		torch.hub.load('''DeppMeng/ConditionalDETR'''					,	UpperCamelCase__					,	pretrained=UpperCamelCase__    ).eval()
       A__		   =		conditional_detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     A__		   =		'''conditional_detr.''' + src
              rename_key(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__    )
       A__		   =		rename_backbone_keys(UpperCamelCase__    )
       # query, key and value matrices need special treatment
       read_in_q_k_v(UpperCamelCase__					,	is_panoptic=UpperCamelCase__    )
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       A__		   =		'''conditional_detr.model.''' if is_panoptic else '''model.'''
       for key in state_dict.copy().keys():
              if is_panoptic:
                     if (
                         key.startswith('''conditional_detr'''    )
                         and not key.startswith('''class_labels_classifier'''    )
                         and not key.startswith('''bbox_predictor'''    )
                     ):
                            A__		   =		state_dict.pop(UpperCamelCase__    )
                            A__		   =		val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            A__		   =		state_dict.pop(UpperCamelCase__    )
                            A__		   =		val
                     elif key.startswith('''bbox_attention'''    ) or key.startswith('''mask_head'''    ):
                            continue
                     else:
                            A__		   =		state_dict.pop(UpperCamelCase__    )
                            A__		   =		val
              else:
                     if not key.startswith('''class_labels_classifier'''    ) and not key.startswith('''bbox_predictor'''    ):
                            A__		   =		state_dict.pop(UpperCamelCase__    )
                            A__		   =		val
    # finally, create HuggingFace model and load state dict
       A__		   =		ConditionalDetrForSegmentation(UpperCamelCase__    ) if is_panoptic else ConditionalDetrForObjectDetection(UpperCamelCase__    )
       model.load_state_dict(UpperCamelCase__    )
       model.eval()
       model.push_to_hub(repo_id=UpperCamelCase__					,	organization='''DepuMeng'''					,	commit_message='''Add model'''    )
       # verify our conversion
       A__		   =		conditional_detr(UpperCamelCase__    )
       A__		   =		model(UpperCamelCase__    )
       assert torch.allclose(outputs.logits					,	original_outputs['''pred_logits''']					,	atol=1e-4    )
       assert torch.allclose(outputs.pred_boxes					,	original_outputs['''pred_boxes''']					,	atol=1e-4    )
       if is_panoptic:
              assert torch.allclose(outputs.pred_masks					,	original_outputs['''pred_masks''']					,	atol=1e-4    )

       # Save model and image processor
       logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."    )
       Path(UpperCamelCase__    ).mkdir(exist_ok=UpperCamelCase__    )
       model.save_pretrained(UpperCamelCase__    )
       image_processor.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
       a__: Optional[int]							   =						argparse.ArgumentParser()

       parser.add_argument(
           '--model_name',
           default='conditional_detr_resnet50',
           type=str,
           help='Name of the CONDITIONAL_DETR model you\'d like to convert.',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
       )
       a__: Tuple							   =						parser.parse_args()
       convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)












import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


a__: List[Any]							   =						logging.getLogger(__name__)


a__: str							   =						list(MODEL_WITH_LM_HEAD_MAPPING.keys())
a__: Tuple							   =						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class        SCREAMING_SNAKE_CASE__					:
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={
         '''help''': (
             '''The model checkpoint for weights initialization. Leave None if you want to train a model from'''
             ''' scratch.'''
         )
     }							,   )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(UpperCamelCase__ )}							,   )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}							,   )




@dataclass
class        SCREAMING_SNAKE_CASE__					:
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''The input training data file (a text file).'''} )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={
         '''help''': (
             '''The input training data files (multiple files in glob format). '''
             '''Very often splitting large files to smaller files can prevent tokenizer going out of memory'''
         )
     }							,   )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}							,   )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''An optional input train ref data file for whole word mask in Chinese.'''}							,   )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''An optional input eval ref data file for whole word mask in Chinese.'''}							,   )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''Whether distinct lines of text in the dataset are to be handled as distinct sequences.'''}							,   )

 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''Train with masked-language modeling loss instead of language modeling.'''} )
 __SCREAMING_SNAKE_CASE							=      field(default=UpperCamelCase__							,   metadata={'''help''': '''Whether ot not to use whole word mask.'''} )
 __SCREAMING_SNAKE_CASE							=      field(
     default=0.1_5							,   metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''} )
 __SCREAMING_SNAKE_CASE							=      field(
     default=1 / 6							,   metadata={
         '''help''': (
             '''Ratio of length of a span of masked tokens to surrounding context length for permutation language'''
             ''' modeling.'''
         )
     }							,   )
 __SCREAMING_SNAKE_CASE							=      field(
     default=5							,   metadata={'''help''': '''Maximum length of a span of masked tokens for permutation language modeling.'''} )

 __SCREAMING_SNAKE_CASE							=      field(
     default=-1							,   metadata={
         '''help''': (
             '''Optional input sequence length after tokenization.'''
             '''The training dataset will be truncated in block of this size for training.'''
             '''Default to the model max input length for single sentence inputs (take into account special tokens).'''
         )
     }							,   )
 __SCREAMING_SNAKE_CASE							=      field(
     default=UpperCamelCase__							,   metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )


def       UpperCamelCase__(			UpperCamelCase__     :			DataTrainingArguments					,	UpperCamelCase__     :			PreTrainedTokenizer					,	UpperCamelCase__     :			bool = False					,	UpperCamelCase__     :			Optional[str] = None					,	)->str:
       def _dataset(UpperCamelCase__     :			List[str]					,	UpperCamelCase__     :			Optional[int]=None    ):
              if args.line_by_line:
                     if ref_path is not None:
                            if not args.whole_word_mask or not args.mlm:
                                   raise ValueError('''You need to set world whole masking and mlm to True for Chinese Whole Word Mask'''    )
                            return LineByLineWithRefDataset(
                                tokenizer=UpperCamelCase__					,	file_path=UpperCamelCase__					,	block_size=args.block_size					,	ref_path=UpperCamelCase__					,	)

                     return LineByLineTextDataset(tokenizer=UpperCamelCase__					,	file_path=UpperCamelCase__					,	block_size=args.block_size    )
              else:
                     return TextDataset(
                         tokenizer=UpperCamelCase__					,	file_path=UpperCamelCase__					,	block_size=args.block_size					,	overwrite_cache=args.overwrite_cache					,	cache_dir=UpperCamelCase__					,	)

       if evaluate:
              return _dataset(args.eval_data_file					,	args.eval_ref_file    )
       elif args.train_data_files:
              return ConcatDataset([_dataset(UpperCamelCase__    ) for f in glob(args.train_data_files    )]    )
       else:
              return _dataset(args.train_data_file					,	args.train_ref_file    )


def       UpperCamelCase__(			)->List[Any]:
       # 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.

       A__		   =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
       A__   ,      A__   ,      A__		   =		parser.parse_args_into_dataclasses()

       if data_args.eval_data_file is None and training_args.do_eval:
              raise ValueError(
                  '''Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '''
                  '''or remove the --do_eval argument.'''    )
       if (
           os.path.exists(training_args.output_dir    )
           and os.listdir(training_args.output_dir    )
           and training_args.do_train
           and not training_args.overwrite_output_dir
       ):
              raise ValueError(
                  f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
                  ''' --overwrite_output_dir to overcome.'''    )

       # Setup logging
       logging.basicConfig(
           format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''					,	datefmt='''%m/%d/%Y %H:%M:%S'''					,	level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN					,	)
       logger.warning(
           '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''					,	training_args.local_rank					,	training_args.device					,	training_args.n_gpu					,	bool(training_args.local_rank != -1    )					,	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
       set_seed(training_args.seed    )

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       if model_args.config_name:
              A__		   =		AutoConfig.from_pretrained(model_args.config_name					,	cache_dir=model_args.cache_dir    )
       elif model_args.model_name_or_path:
              A__		   =		AutoConfig.from_pretrained(model_args.model_name_or_path					,	cache_dir=model_args.cache_dir    )
       else:
              A__		   =		CONFIG_MAPPING[model_args.model_type]()
              logger.warning('''You are instantiating a new config instance from scratch.'''    )

       if model_args.tokenizer_name:
              A__		   =		AutoTokenizer.from_pretrained(model_args.tokenizer_name					,	cache_dir=model_args.cache_dir    )
       elif model_args.model_name_or_path:
              A__		   =		AutoTokenizer.from_pretrained(model_args.model_name_or_path					,	cache_dir=model_args.cache_dir    )
       else:
              raise ValueError(
                  '''You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another'''
                  ''' script, save it,and load it from here, using --tokenizer_name'''    )

       if model_args.model_name_or_path:
              A__		   =		AutoModelWithLMHead.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					,	)
       else:
              logger.info('''Training new model from scratch'''    )
              A__		   =		AutoModelWithLMHead.from_config(UpperCamelCase__    )

       model.resize_token_embeddings(len(UpperCamelCase__    )    )

       if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
              raise ValueError(
                  '''BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'''
                  '''--mlm flag (masked language modeling).'''    )

       if data_args.block_size <= 0:
              A__		   =		tokenizer.max_len
              # Our input block size will be the max possible for the model
       else:
              A__		   =		min(data_args.block_size					,	tokenizer.max_len    )

       # Get datasets

       A__		   =		(
           get_dataset(UpperCamelCase__					,	tokenizer=UpperCamelCase__					,	cache_dir=model_args.cache_dir    ) if training_args.do_train else None
       )
       A__		   =		(
           get_dataset(UpperCamelCase__					,	tokenizer=UpperCamelCase__					,	evaluate=UpperCamelCase__					,	cache_dir=model_args.cache_dir    )
           if training_args.do_eval
           else None
       )
       if config.model_type == "xlnet":
              A__		   =		DataCollatorForPermutationLanguageModeling(
                  tokenizer=UpperCamelCase__					,	plm_probability=data_args.plm_probability					,	max_span_length=data_args.max_span_length					,	)
       else:
              if data_args.mlm and data_args.whole_word_mask:
                     A__		   =		DataCollatorForWholeWordMask(
                         tokenizer=UpperCamelCase__					,	mlm_probability=data_args.mlm_probability    )
              else:
                     A__		   =		DataCollatorForLanguageModeling(
                         tokenizer=UpperCamelCase__					,	mlm=data_args.mlm					,	mlm_probability=data_args.mlm_probability    )

    # Initialize our Trainer
       A__		   =		Trainer(
           model=UpperCamelCase__					,	args=UpperCamelCase__					,	data_collator=UpperCamelCase__					,	train_dataset=UpperCamelCase__					,	eval_dataset=UpperCamelCase__					,	prediction_loss_only=UpperCamelCase__					,	)

       # Training
       if training_args.do_train:
              A__		   =		(
                  model_args.model_name_or_path
                  if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path    )
                  else None
              )
              trainer.train(model_path=UpperCamelCase__    )
              trainer.save_model()
              # For convenience, we also re-save the tokenizer to the same directory,
              # so that you can share your model easily on huggingface.co/models =)
              if trainer.is_world_master():
                     tokenizer.save_pretrained(training_args.output_dir    )

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

              A__		   =		trainer.evaluate()

              A__		   =		math.exp(eval_output['''eval_loss''']    )
              A__		   =		{'''perplexity''': perplexity}

              A__		   =		os.path.join(training_args.output_dir					,	'''eval_results_lm.txt'''    )
              if trainer.is_world_master():
                     with open(UpperCamelCase__					,	'''w'''    ) as writer:
                            logger.info('''***** Eval results *****'''    )
                            for key in sorted(result.keys()    ):
                                   logger.info('''  %s = %s'''					,	UpperCamelCase__					,	str(result[key]    )    )
                                   writer.write('''%s = %s\n''' % (key, str(result[key]    ))    )

              results.update(UpperCamelCase__    )

       return results


def       UpperCamelCase__(			UpperCamelCase__     :			List[str]    )->List[str]:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
       main()





