





'''simple docstring'''


import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowerCAmelCase_ : str	  =       logging.getLogger(__name__)







def  _lowerCamelCase	(__lowerCamelCase	:  Tuple       ,	__lowerCamelCase	:  Optional[int]  ) ->     Optional[int]:
			a__	 =			np.argmax(__snake_case       ,	axis=1  )
			return np.sum(outputs == labels  )







def  _lowerCamelCase	(__lowerCamelCase	:  Optional[int]  ) ->     int:
			with open(__snake_case       ,	encoding="utf_8"  ) as f:
						a__	 =			csv.reader(__snake_case  )
						a__	 =			[]
						next(__snake_case  )  # skip the first line
						for line in tqdm(__snake_case  ):
									output.append((" ".join(line[1:5]  ), line[5], line[6], int(line[-1]  ) - 1)  )
			return output







def  _lowerCamelCase	(__lowerCamelCase	:  Tuple       ,	__lowerCamelCase	:  List[Any]       ,	__lowerCamelCase	:  Dict       ,	__lowerCamelCase	:  Dict       ,	__lowerCamelCase	:  List[str]       ,	__lowerCamelCase	:  Union[str, Any]  ) ->     Any:
			a__	 =			[]
			for dataset in encoded_datasets:
						a__	 =			len(__snake_case  )
						a__	 =			np.zeros((n_batch, 2, input_len)       ,	dtype=np.intaa  )
						a__	 =			np.zeros((n_batch, 2)       ,	dtype=np.intaa  )
						a__	 =			np.full((n_batch, 2, input_len)       ,	fill_value=-100       ,	dtype=np.intaa  )
						a__	 =			np.zeros((n_batch,)       ,	dtype=np.intaa  )
						for (
						    i,
						    (story, conta, conta, mc_label),
						) in enumerate(__snake_case  ):
									a__	 =			[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									a__	 =			[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
									a__	 =			with_conta
									a__	 =			with_conta
									a__	 =			len(__snake_case  ) - 1
									a__	 =			len(__snake_case  ) - 1
									a__	 =			with_conta
									a__	 =			with_conta
									a__	 =			mc_label
						a__	 =			(input_ids, mc_token_ids, lm_labels, mc_labels)
						tensor_datasets.append(tuple(torch.tensor(__snake_case  ) for t in all_inputs  )  )
			return tensor_datasets







def  _lowerCamelCase	() ->     Union[str, Any]:
			a__	 =			argparse.ArgumentParser()
			parser.add_argument("--model_name"       ,	type=__snake_case       ,	default="openai-gpt"       ,	help="pretrained model name"  )
			parser.add_argument("--do_train"       ,	action="store_true"       ,	help="Whether to run training."  )
			parser.add_argument("--do_eval"       ,	action="store_true"       ,	help="Whether to run eval on the dev set."  )
			parser.add_argument(
			    "--output_dir"       ,	default=__snake_case       ,	type=__snake_case       ,	required=__snake_case       ,	help="The output directory where the model predictions and checkpoints will be written."       ,	)
			parser.add_argument("--train_dataset"       ,	type=__snake_case       ,	default=""  )
			parser.add_argument("--eval_dataset"       ,	type=__snake_case       ,	default=""  )
			parser.add_argument("--seed"       ,	type=__snake_case       ,	default=42  )
			parser.add_argument("--num_train_epochs"       ,	type=__snake_case       ,	default=3  )
			parser.add_argument("--train_batch_size"       ,	type=__snake_case       ,	default=8  )
			parser.add_argument("--eval_batch_size"       ,	type=__snake_case       ,	default=16  )
			parser.add_argument("--adam_epsilon"       ,	default=1e-8       ,	type=__snake_case       ,	help="Epsilon for Adam optimizer."  )
			parser.add_argument("--max_grad_norm"       ,	type=__snake_case       ,	default=1  )
			parser.add_argument(
			    "--max_steps"       ,	default=-1       ,	type=__snake_case       ,	help=(
			        "If > 0: set total number of training                         steps to perform. Override num_train_epochs."
			    )       ,	)
			parser.add_argument(
			    "--gradient_accumulation_steps"       ,	type=__snake_case       ,	default=1       ,	help="Number of updates steps to accumulate before                        performing a backward/update pass."       ,	)
			parser.add_argument("--learning_rate"       ,	type=__snake_case       ,	default=6.25e-5  )
			parser.add_argument("--warmup_steps"       ,	default=0       ,	type=__snake_case       ,	help="Linear warmup over warmup_steps."  )
			parser.add_argument("--lr_schedule"       ,	type=__snake_case       ,	default="warmup_linear"  )
			parser.add_argument("--weight_decay"       ,	type=__snake_case       ,	default=0.01  )
			parser.add_argument("--lm_coef"       ,	type=__snake_case       ,	default=0.9  )
			parser.add_argument("--n_valid"       ,	type=__snake_case       ,	default=374  )

			parser.add_argument("--server_ip"       ,	type=__snake_case       ,	default=""       ,	help="Can be used for distant debugging."  )
			parser.add_argument("--server_port"       ,	type=__snake_case       ,	default=""       ,	help="Can be used for distant debugging."  )
			a__	 =			parser.parse_args()
			print(__snake_case  )

			if args.server_ip and args.server_port:
						# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
						import ptvsd

						print("Waiting for debugger attach"  )
						ptvsd.enable_attach(address=(args.server_ip, args.server_port)       ,	redirect_output=__snake_case  )
						ptvsd.wait_for_attach()

			random.seed(args.seed  )
			np.random.seed(args.seed  )
			torch.manual_seed(args.seed  )
			torch.cuda.manual_seed_all(args.seed  )

			a__	 =			torch.device("cuda" if torch.cuda.is_available() else "cpu"  )
			a__	 =			torch.cuda.device_count()
			logger.info("device: {}, n_gpu {}".format(__snake_case       ,	__snake_case  )  )

			if not args.do_train and not args.do_eval:
						raise ValueError("At least one of `do_train` or `do_eval` must be True."  )

			if not os.path.exists(args.output_dir  ):
						os.makedirs(args.output_dir  )

			# Load tokenizer and model
			# This loading functions also add new tokens and embeddings called `special tokens`
			# These new embeddings will be fine-tuned on the RocStories dataset
			a__	 =			['_start_', '_delimiter_', '_classify_']
			a__	 =			OpenAIGPTTokenizer.from_pretrained(args.model_name  )
			tokenizer.add_tokens(__snake_case  )
			a__	 =			tokenizer.convert_tokens_to_ids(__snake_case  )
			a__	 =			OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name  )
			model.resize_token_embeddings(len(__snake_case  )  )
			model.to(__snake_case  )

			# Load and encode the datasets
			def tokenize_and_encode(__lowerCamelCase	:  Optional[Any]  ):
						if isinstance(__snake_case       ,	__snake_case  ):
									return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(__snake_case  )  )
						elif isinstance(__snake_case       ,	__snake_case  ):
									return obj
						return [tokenize_and_encode(__snake_case  ) for o in obj]

			logger.info("Encoding dataset..."  )
			a__	 =			load_rocstories_dataset(args.train_dataset  )
			a__	 =			load_rocstories_dataset(args.eval_dataset  )
			a__	 =			(train_dataset, eval_dataset)
			a__	 =			tokenize_and_encode(__snake_case  )

			# Compute the max input length for the Transformer
			a__	 =			model.config.n_positions // 2 - 2
			a__	 =			max(
			    len(story[:max_length]  ) + max(len(conta[:max_length]  )       ,	len(conta[:max_length]  )  ) + 3
			    for dataset in encoded_datasets
			    for story, conta, conta, _ in dataset  )
			a__	 =			min(__snake_case       ,	model.config.n_positions  )  # Max size of input for the pre-trained model

			# Prepare inputs tensors and dataloaders
			a__	 =			pre_process_datasets(__snake_case       ,	__snake_case       ,	__snake_case       ,	*__snake_case  )
			a__	 =			tensor_datasets[0], tensor_datasets[1]

			a__	 =			TensorDataset(*__snake_case  )
			a__	 =			RandomSampler(__snake_case  )
			a__	 =			DataLoader(__snake_case       ,	sampler=__snake_case       ,	batch_size=args.train_batch_size  )

			a__	 =			TensorDataset(*__snake_case  )
			a__	 =			SequentialSampler(__snake_case  )
			a__	 =			DataLoader(__snake_case       ,	sampler=__snake_case       ,	batch_size=args.eval_batch_size  )

			# Prepare optimizer
			if args.do_train:
						if args.max_steps > 0:
									a__	 =			args.max_steps
									a__	 =			args.max_steps // (len(__snake_case  ) // args.gradient_accumulation_steps) + 1
						else:
									a__	 =			len(__snake_case  ) // args.gradient_accumulation_steps * args.num_train_epochs

						a__	 =			list(model.named_parameters()  )
						a__	 =			['bias', 'LayerNorm.bias', 'LayerNorm.weight']
						a__	 =			[
						    {
						        'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay  )],
						        'weight_decay': args.weight_decay,
						    },
						    {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay  )], 'weight_decay': 0.0},
						]
						a__	 =			AdamW(__snake_case       ,	lr=args.learning_rate       ,	eps=args.adam_epsilon  )
						a__	 =			get_linear_schedule_with_warmup(
						    __snake_case       ,	num_warmup_steps=args.warmup_steps       ,	num_training_steps=__snake_case  )

			if args.do_train:
						a__	 =			0, 0, None
						model.train()
						for _ in trange(int(args.num_train_epochs  )       ,	desc="Epoch"  ):
									a__	 =			0
									a__	 =			0
									a__	 =			tqdm(__snake_case       ,	desc="Training"  )
									for step, batch in enumerate(__snake_case  ):
												a__	 =			tuple(t.to(__snake_case  ) for t in batch  )
												a__	 =			batch
												a__	 =			model(__snake_case       ,	mc_token_ids=__snake_case       ,	lm_labels=__snake_case       ,	mc_labels=__snake_case  )
												a__	 =			args.lm_coef * losses[0] + losses[1]
												loss.backward()
												optimizer.step()
												scheduler.step()
												optimizer.zero_grad()
												tr_loss += loss.item()
												a__	 =			(
												    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
												)
												nb_tr_steps += 1
												a__	 =			'Training loss: {:.2e} lr: {:.2e}'.format(__snake_case       ,	scheduler.get_lr()[0]  )

    # Save a trained model
			if args.do_train:
						# Save a trained model, configuration and tokenizer
						a__	 =			model.module if hasattr(__snake_case       ,	"module"  ) else model  # Only save the model itself

						# If we save using the predefined names, we can load using `from_pretrained`
						a__	 =			os.path.join(args.output_dir       ,	__snake_case  )
						a__	 =			os.path.join(args.output_dir       ,	__snake_case  )

						torch.save(model_to_save.state_dict()       ,	__snake_case  )
						model_to_save.config.to_json_file(__snake_case  )
						tokenizer.save_vocabulary(args.output_dir  )

						# Load a trained model and vocabulary that you have fine-tuned
						a__	 =			OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir  )
						a__	 =			OpenAIGPTTokenizer.from_pretrained(args.output_dir  )
						model.to(__snake_case  )

			if args.do_eval:
						model.eval()
						a__	 =			0, 0
						a__	 =			0, 0
						for batch in tqdm(__snake_case       ,	desc="Evaluating"  ):
									a__	 =			tuple(t.to(__snake_case  ) for t in batch  )
									a__	 =			batch
									with torch.no_grad():
												a__	 =			model(
												    __snake_case       ,	mc_token_ids=__snake_case       ,	lm_labels=__snake_case       ,	mc_labels=__snake_case  )

									a__	 =			mc_logits.detach().cpu().numpy()
									a__	 =			mc_labels.to("cpu"  ).numpy()
									a__	 =			accuracy(__snake_case       ,	__snake_case  )

									eval_loss += mc_loss.mean().item()
									eval_accuracy += tmp_eval_accuracy

									nb_eval_examples += input_ids.size(0  )
									nb_eval_steps += 1

						a__	 =			eval_loss / nb_eval_steps
						a__	 =			eval_accuracy / nb_eval_examples
						a__	 =			tr_loss / nb_tr_steps if args.do_train else None
						a__	 =			{'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

						a__	 =			os.path.join(args.output_dir       ,	"eval_results.txt"  )
						with open(__snake_case       ,	"w"  ) as writer:
									logger.info("***** Eval results *****"  )
									for key in sorted(result.keys()  ):
												logger.info("  %s = %s"       ,	__snake_case       ,	str(result[key]  )  )
												writer.write("%s = %s\n" % (key, str(result[key]  ))  )


if __name__ == "__main__":
		main()










'''simple docstring'''




import argparse
import os
import re


lowercase__       : Optional[int]         =       '''src/diffusers'''

# Pattern that looks at the indentation in a line.
lowercase__       : Dict         =       re.compile(r'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
lowercase__       : List[str]         =       re.compile(r'''^\s*"([^"]+)":''')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowercase__       : Tuple         =       re.compile(r'''^\s*_import_structure\["([^"]+)"\]''')
# Pattern that matches `"key",` and puts `key` in group 0.
lowercase__       : str         =       re.compile(r'''^\s*"([^"]+)",\s*$''')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowercase__       : str         =       re.compile(r'''\[([^\]]+)\]''')






def 			_lowerCAmelCase     (    __snake_case    :					str     )	->	Tuple:
     __A		:					List[Any]						=			_re_indent.search(__snake_case     )
     return "" if search is None else search.groups()[0]






def 			_lowerCAmelCase     (    __snake_case    :					Optional[Any]					,				__snake_case    :					str=""					,				__snake_case    :					Any=None					,				__snake_case    :					List[Any]=None     )	->	Optional[int]:
     __A		:					Tuple						=			0
     __A		:					Optional[int]						=			code.split('\n'     )
     if start_prompt is not None:
          while not lines[index].startswith(__snake_case     ):
               index += 1
          __A		:					Optional[int]						=			['\n'.join(lines[:index]     )]
     else:
          __A		:					Any						=			[]

     # We split into blocks until we get to the `end_prompt` (or the end of the block).
     __A		:					Tuple						=			[lines[index]]
     index += 1
     while index < len(__snake_case     ) and (end_prompt is None or not lines[index].startswith(__snake_case     )):
          if len(lines[index]     ) > 0 and get_indent(lines[index]     ) == indent_level:
               if len(__snake_case     ) > 0 and get_indent(current_block[-1]     ).startswith(indent_level + ' '     ):
                    current_block.append(lines[index]     )
                    blocks.append('\n'.join(__snake_case     )     )
                    if index < len(__snake_case     ) - 1:
                         __A		:					Union[str, Any]						=			[lines[index + 1]]
                         index += 1
                    else:
                         __A		:					Union[str, Any]						=			[]
               else:
                    blocks.append('\n'.join(__snake_case     )     )
                    __A		:					Optional[Any]						=			[lines[index]]
          else:
               current_block.append(lines[index]     )
          index += 1

     # Adds current block if it's nonempty.
     if len(__snake_case     ) > 0:
          blocks.append('\n'.join(__snake_case     )     )

     # Add final block after end_prompt if provided.
     if end_prompt is not None and index < len(__snake_case     ):
          blocks.append('\n'.join(lines[index:]     )     )

     return blocks






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	int:

     def _inner(__snake_case    :					List[Any]     ):
          return key(__snake_case     ).lower().replace('_'					,				''     )

     return _inner






def 			_lowerCAmelCase     (    __snake_case    :					Dict					,				__snake_case    :					Any=None     )	->	List[Any]:

     # If no key is provided, we use a noop.
     def noop(__snake_case    :					List[Any]     ):
          return x

     if key is None:
          __A		:					Optional[Any]						=			noop
     # Constants are all uppercase, they go first.
     __A		:					str						=			[obj for obj in objects if key(__snake_case     ).isupper()]
     # Classes are not all uppercase but start with a capital, they go second.
     __A		:					List[str]						=			[obj for obj in objects if key(__snake_case     )[0].isupper() and not key(__snake_case     ).isupper()]
     # Functions begin with a lowercase, they go last.
     __A		:					str						=			[obj for obj in objects if not key(__snake_case     )[0].isupper()]

     __A		:					Tuple						=			ignore_underscore(__snake_case     )
     return sorted(__snake_case					,				key=__snake_case     ) + sorted(__snake_case					,				key=__snake_case     ) + sorted(__snake_case					,				key=__snake_case     )






def 			_lowerCAmelCase     (    __snake_case    :					Optional[int]     )	->	Tuple:

     # This inner function sort imports between [ ].
     def _replace(__snake_case    :					Tuple     ):
          __A		:					List[str]						=			match.groups()[0]
          if "," not in imports:
               return f'[{imports}]'
          __A		:					int						=			[part.strip().replace('"'					,				''     ) for part in imports.split(','     )]
          # We will have a final empty element if the line finished with a comma.
          if len(keys[-1]     ) == 0:
               __A		:					Dict						=			keys[:-1]
          return "[" + ", ".join([f'"{k}"' for k in sort_objects(__snake_case     )]     ) + "]"

     __A		:					List[Any]						=			import_statement.split('\n'     )
     if len(__snake_case     ) > 3:
          # Here we have to sort internal imports that are on several lines (one per name):
          # key: [
          #     "object1",
          #     "object2",
          #     ...
          # ]

          # We may have to ignore one or two lines on each side.
          __A		:					Optional[int]						=			2 if lines[1].strip() == '[' else 1
          __A		:					Any						=			[(i, _re_strip_line.search(__snake_case     ).groups()[0]) for i, line in enumerate(lines[idx:-idx]     )]
          __A		:					Optional[int]						=			sort_objects(__snake_case					,				key=lambda __snake_case     : x[1]     )
          __A		:					Any						=			[lines[x[0] + idx] for x in sorted_indices]
          return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]     )
     elif len(__snake_case     ) == 3:
          # Here we have to sort internal imports that are on one separate line:
          # key: [
          #     "object1", "object2", ...
          # ]
          if _re_bracket_content.search(lines[1]     ) is not None:
               __A		:					Union[str, Any]						=			_re_bracket_content.sub(_replace					,				lines[1]     )
          else:
               __A		:					Dict						=			[part.strip().replace('"'					,				''     ) for part in lines[1].split(','     )]
               # We will have a final empty element if the line finished with a comma.
               if len(keys[-1]     ) == 0:
                    __A		:					Tuple						=			keys[:-1]
               __A		:					List[Any]						=			get_indent(lines[1]     ) + ', '.join([f'"{k}"' for k in sort_objects(__snake_case     )]     )
          return "\n".join(__snake_case     )
     else:
          # Finally we have to deal with imports fitting on one line
          __A		:					Optional[Any]						=			_re_bracket_content.sub(_replace					,				__snake_case     )
          return import_statement






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]					,				__snake_case    :					List[Any]=True     )	->	Optional[Any]:
     with open(__snake_case					,				'r'     ) as f:
          __A		:					Dict						=			f.read()

     if "_import_structure" not in code:
          return

     # Blocks of indent level 0
     __A		:					str						=			split_code_in_indented_blocks(
         __snake_case					,				start_prompt='_import_structure = {'					,				end_prompt='if TYPE_CHECKING:'     )

     # We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
     for block_idx in range(1					,				len(__snake_case     ) - 1     ):
          # Check if the block contains some `_import_structure`s thingy to sort.
          __A		:					Tuple						=			main_blocks[block_idx]
          __A		:					int						=			block.split('\n'     )

          # Get to the start of the imports.
          __A		:					Tuple						=			0
          while line_idx < len(__snake_case     ) and "_import_structure" not in block_lines[line_idx]:
               # Skip dummy import blocks
               if "import dummy" in block_lines[line_idx]:
                    __A		:					Optional[int]						=			len(__snake_case     )
               else:
                    line_idx += 1
          if line_idx >= len(__snake_case     ):
               continue

          # Ignore beginning and last line: they don't contain anything.
          __A		:					Dict						=			'\n'.join(block_lines[line_idx:-1]     )
          __A		:					int						=			get_indent(block_lines[1]     )
          # Slit the internal block into blocks of indent level 1.
          __A		:					Optional[int]						=			split_code_in_indented_blocks(__snake_case					,				indent_level=__snake_case     )
          # We have two categories of import key: list or _import_structure[key].append/extend
          __A		:					Any						=			_re_direct_key if '_import_structure' in block_lines[0] else _re_indirect_key
          # Grab the keys, but there is a trap: some lines are empty or just comments.
          __A		:					Dict						=			[(pattern.search(__snake_case     ).groups()[0] if pattern.search(__snake_case     ) is not None else None) for b in internal_blocks]
          # We only sort the lines with a key.
          __A		:					Optional[Any]						=			[(i, key) for i, key in enumerate(__snake_case     ) if key is not None]
          __A		:					Tuple						=			[x[0] for x in sorted(__snake_case					,				key=lambda __snake_case     : x[1]     )]

          # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
          __A		:					str						=			0
          __A		:					Any						=			[]
          for i in range(len(__snake_case     )     ):
               if keys[i] is None:
                    reordered_blocks.append(internal_blocks[i]     )
               else:
                    __A		:					str						=			sort_objects_in_import(internal_blocks[sorted_indices[count]]     )
                    reordered_blocks.append(__snake_case     )
                    count += 1

        # And we put our main block back together with its first and last line.
          __A		:					int						=			'\n'.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]     )

     if code != "\n".join(__snake_case     ):
          if check_only:
               return True
          else:
               print(f'Overwriting {file}.'     )
               with open(__snake_case					,				'w'     ) as f:
                    f.write('\n'.join(__snake_case     )     )






def 			_lowerCAmelCase     (    __snake_case    :					int=True     )	->	Optional[Any]:
     __A		:					Tuple						=			[]
     for root, _, files in os.walk(__snake_case     ):
          if "__init__.py" in files:
               __A		:					List[Any]						=			sort_imports(os.path.join(__snake_case					,				'__init__.py'     )					,				check_only=__snake_case     )
               if result:
                    __A		:					Dict						=			[os.path.join(__snake_case					,				'__init__.py'     )]
     if len(__snake_case     ) > 0:
          raise ValueError(f'Would overwrite {len(__snake_case     )} files, run `make style`.'     )


if __name__ == "__main__":
  lowercase__       : Optional[int]         =       argparse.ArgumentParser()
  parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
  lowercase__       : Union[str, Any]         =       parser.parse_args()

  sort_imports_in_all_inits(check_only=args.check_only)