'''simple docstring'''

import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowercase_     		= '''src/diffusers'''
lowercase_     		= '''.'''


# This is to make sure the diffusers module imported is the one in the repo.
lowercase_     		= importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
    submodule_search_locations=[DIFFUSERS_PATH],
)
lowercase_     		= spec.loader.load_module()



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						return line.startswith(a__      ) or len(a__      ) <= 1 or re.search(r'^\s*\)(\s*->.*:|:)\s*$'			,      a__      ) is not None



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=object_name.split('.'      )
						_lowerCAmelCase										=0

						# First let's find the module where our object lives.
						_lowerCAmelCase										=parts[i]
						while i < len(a__      ) and not os.path.isfile(os.path.join(a__			,      F'''{module}.py'''      )      ):
												i += 1
												if i < len(a__      ):
																		_lowerCAmelCase										=os.path.join(a__			,      parts[i]      )
						if i >= len(a__      ):
												raise ValueError(F'''`object_name` should begin with the name of a module of diffusers but got {object_name}.'''      )

						with open(os.path.join(a__			,      F'''{module}.py'''      )			,      'r'			,      encoding='utf-8'			,      newline='\n'      ) as f:
												_lowerCAmelCase										=f.readlines()

						# Now let's find the class / func in the code!
						_lowerCAmelCase										=''
						_lowerCAmelCase										=0
						for name in parts[i + 1 :]:
												while (
												    line_index < len(a__      ) and re.search(rF'''^{indent}(class|def)\s+{name}(\(|\:)'''			,      lines[line_index]      ) is None
												):
																		line_index += 1
												indent += "    "
												line_index += 1

						if line_index >= len(a__      ):
												raise ValueError(F''' {object_name} does not match any function or class in {module}.'''      )

						# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
						_lowerCAmelCase										=line_index
						while line_index < len(a__      ) and _should_continue(lines[line_index]			,      a__      ):
												line_index += 1
						# Clean up empty lines at the end (if any).
						while len(lines[line_index - 1]      ) <= 1:
												line_index -= 1

						_lowerCAmelCase										=lines[start_index:line_index]
						return "".join(a__      )


lowercase_     		= re.compile(r'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
lowercase_     		= re.compile(r'''^\s*(\S+)->(\S+)(\s+.*|$)''')
lowercase_     		= re.compile(r'''<FILL\s+[^>]*>''')



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=code.split('\n'      )
						_lowerCAmelCase										=0
						while idx < len(a__      ) and len(lines[idx]      ) == 0:
												idx += 1
						if idx < len(a__      ):
												return re.search(r'^(\s*)\S'			,      lines[idx]      ).groups()[0]
						return ""



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=len(get_indent(a__      )      ) > 0
						if has_indent:
												_lowerCAmelCase										=F'''class Bla:\n{code}'''
						_lowerCAmelCase										=black.Mode(target_versions={black.TargetVersion.PYaa}			,      line_length=1_1_9			,      preview=a__      )
						_lowerCAmelCase										=black.format_str(a__			,      mode=a__      )
						_lowerCAmelCase						,				_lowerCAmelCase										=style_docstrings_in_code(a__      )
						return result[len('class Bla:\n'      ) :] if has_indent else result



def 							UpperCamelCase__			(			a__			,      a__=False      ):


						'''simple docstring'''






						with open(a__			,      'r'			,      encoding='utf-8'			,      newline='\n'      ) as f:
												_lowerCAmelCase										=f.readlines()
						_lowerCAmelCase										=[]
						_lowerCAmelCase										=0
						# Not a for loop cause `lines` is going to change (if `overwrite=True`).
						while line_index < len(a__      ):
												_lowerCAmelCase										=_re_copy_warning.search(lines[line_index]      )
												if search is None:
																		line_index += 1
																		continue

												# There is some copied code here, let's retrieve the original.
												_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=search.groups()
												_lowerCAmelCase										=find_code_in_diffusers(a__      )
												_lowerCAmelCase										=get_indent(a__      )

												_lowerCAmelCase										=line_index + 1 if indent == theoretical_indent else line_index + 2
												_lowerCAmelCase										=theoretical_indent
												_lowerCAmelCase										=start_index

												# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
												_lowerCAmelCase										=True
												while line_index < len(a__      ) and should_continue:
																		line_index += 1
																		if line_index >= len(a__      ):
																								break
																		_lowerCAmelCase										=lines[line_index]
																		_lowerCAmelCase										=_should_continue(a__			,      a__      ) and re.search(F'''^{indent}# End copy'''			,      a__      ) is None
												# Clean up empty lines at the end (if any).
												while len(lines[line_index - 1]      ) <= 1:
																		line_index -= 1

												_lowerCAmelCase										=lines[start_index:line_index]
												_lowerCAmelCase										=''.join(a__      )

												# Remove any nested `Copied from` comments to avoid circular copies
												_lowerCAmelCase										=[line for line in theoretical_code.split('\n'      ) if _re_copy_warning.search(a__      ) is None]
												_lowerCAmelCase										='\n'.join(a__      )

												# Before comparing, use the `replace_pattern` on the original code.
												if len(a__      ) > 0:
																		_lowerCAmelCase										=replace_pattern.replace('with'			,      ''      ).split(','      )
																		_lowerCAmelCase										=[_re_replace_pattern.search(a__      ) for p in patterns]
																		for pattern in patterns:
																								if pattern is None:
																														continue
																								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=pattern.groups()
																								_lowerCAmelCase										=re.sub(a__			,      a__			,      a__      )
																								if option.strip() == "all-casing":
																														_lowerCAmelCase										=re.sub(obja.lower()			,      obja.lower()			,      a__      )
																														_lowerCAmelCase										=re.sub(obja.upper()			,      obja.upper()			,      a__      )

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
																		_lowerCAmelCase										=blackify(lines[start_index - 1] + theoretical_code      )
																		_lowerCAmelCase										=theoretical_code[len(lines[start_index - 1]      ) :]

												# Test for a diff and act accordingly.
												if observed_code != theoretical_code:
																		diffs.append([object_name, start_index]      )
																		if overwrite:
																								_lowerCAmelCase										=lines[:start_index] + [theoretical_code] + lines[line_index:]
																								_lowerCAmelCase										=start_index + 1

						if overwrite and len(a__      ) > 0:
												# Warn the user a file has been modified.
												print(F'''Detected changes, rewriting {filename}.'''      )
												with open(a__			,      'w'			,      encoding='utf-8'			,      newline='\n'      ) as f:
																		f.writelines(a__      )
						return diffs



def 							UpperCamelCase__			(			a__ = False      ):


						'''simple docstring'''






						_lowerCAmelCase										=glob.glob(os.path.join(a__			,      '**/*.py'      )			,      recursive=a__      )
						_lowerCAmelCase										=[]
						for filename in all_files:
												_lowerCAmelCase										=is_copy_consistent(a__			,      a__      )
												diffs += [F'''- {filename}: copy does not match {d[0]} at line {d[1]}''' for d in new_diffs]
						if not overwrite and len(a__      ) > 0:
												_lowerCAmelCase										='\n'.join(a__      )
												raise Exception(
												    'Found the following copy inconsistencies:\n'
												    + diff
												    + '\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.'      )


if __name__ == "__main__":
							lowercase_     		= argparse.ArgumentParser()
							parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
							lowercase_     		= parser.parse_args()

							check_copies(args.fix_and_overwrite)






'''simple docstring'''

import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


lowercase_     		= logging.getLogger(__name__)
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      int       =							'sequence-classification'






		def __init__(   self		,						__A				) ->     List[Any]:
								if type(__A				) == dict:
														_lowerCAmelCase										=Namespace(**__A				)
								_lowerCAmelCase										=glue_output_modes[hparams.task]
								_lowerCAmelCase										=glue_tasks_num_labels[hparams.task]

								super().__init__(__A		,						__A		,						self.mode				)






		def 		UpperCamelCase__       (   self		,						**__A				) ->     Any:
								return self.model(**__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     Union[str, Any]:
								_lowerCAmelCase										={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

								if self.config.model_type not in ["distilbert", "bart"]:
														_lowerCAmelCase										=batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

								_lowerCAmelCase										=self(**__A				)
								_lowerCAmelCase										=outputs[0]

								_lowerCAmelCase										=self.trainer.lr_schedulers[0]['scheduler']
								_lowerCAmelCase										={'loss': loss, 'rate': lr_scheduler.get_last_lr()[-1]}
								return {"loss": loss, "log": tensorboard_logs}






		def 		UpperCamelCase__       (   self				) ->     Any:
								_lowerCAmelCase										=self.hparams
								_lowerCAmelCase										=processors[args.task]()
								_lowerCAmelCase										=processor.get_labels()

								for mode in ["train", "dev"]:
														_lowerCAmelCase										=self._feature_file(__A				)
														if os.path.exists(__A				) and not args.overwrite_cache:
																				logger.info('Loading features from cached file %s'		,						__A				)
														else:
																				logger.info('Creating features from dataset file at %s'		,						args.data_dir				)
																				_lowerCAmelCase										=(
																				    processor.get_dev_examples(args.data_dir				)
																				    if mode == 'dev'
																				    else processor.get_train_examples(args.data_dir				)
																				)
																				_lowerCAmelCase										=convert_examples_to_features(
																				    __A		,						self.tokenizer		,						max_length=args.max_seq_length		,						label_list=self.labels		,						output_mode=args.glue_output_mode		,						)
																				logger.info('Saving features into cached file %s'		,						__A				)
																				torch.save(__A		,						__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A = False				) ->     DataLoader:
								_lowerCAmelCase										='dev' if mode == 'test' else mode

								_lowerCAmelCase										=self._feature_file(__A				)
								logger.info('Loading features from cached file %s'		,						__A				)
								_lowerCAmelCase										=torch.load(__A				)
								_lowerCAmelCase										=torch.tensor([f.input_ids for f in features]		,						dtype=torch.long				)
								_lowerCAmelCase										=torch.tensor([f.attention_mask for f in features]		,						dtype=torch.long				)
								_lowerCAmelCase										=torch.tensor([f.token_type_ids for f in features]		,						dtype=torch.long				)
								if self.hparams.glue_output_mode == "classification":
														_lowerCAmelCase										=torch.tensor([f.label for f in features]		,						dtype=torch.long				)
								elif self.hparams.glue_output_mode == "regression":
														_lowerCAmelCase										=torch.tensor([f.label for f in features]		,						dtype=torch.float				)

								return DataLoader(
								    TensorDataset(__A		,						__A		,						__A		,						__A				)		,						batch_size=__A		,						shuffle=__A		,						)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     List[str]:
								_lowerCAmelCase										={'input_ids': batch[0], 'attention_mask': batch[1], 'labels': batch[3]}

								if self.config.model_type not in ["distilbert", "bart"]:
														_lowerCAmelCase										=batch[2] if self.config.model_type in ['bert', 'xlnet', 'albert'] else None

								_lowerCAmelCase										=self(**__A				)
								_lowerCAmelCase						,				_lowerCAmelCase										=outputs[:2]
								_lowerCAmelCase										=logits.detach().cpu().numpy()
								_lowerCAmelCase										=inputs['labels'].detach().cpu().numpy()

								return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}






		def 		UpperCamelCase__       (   self		,						__A				) ->     tuple:
								_lowerCAmelCase										=torch.stack([x['val_loss'] for x in outputs]				).mean().detach().cpu().item()
								_lowerCAmelCase										=np.concatenate([x['pred'] for x in outputs]		,						axis=0				)

								if self.hparams.glue_output_mode == "classification":
														_lowerCAmelCase										=np.argmax(__A		,						axis=1				)
								elif self.hparams.glue_output_mode == "regression":
														_lowerCAmelCase										=np.squeeze(__A				)

								_lowerCAmelCase										=np.concatenate([x['target'] for x in outputs]		,						axis=0				)
								_lowerCAmelCase										=[[] for _ in range(out_label_ids.shape[0]				)]
								_lowerCAmelCase										=[[] for _ in range(out_label_ids.shape[0]				)]

								_lowerCAmelCase										={**{'val_loss': val_loss_mean}, **compute_metrics(self.hparams.task		,						__A		,						__A				)}

								_lowerCAmelCase										=dict(results.items()				)
								_lowerCAmelCase										=results
								return ret, preds_list, out_label_list






		def 		UpperCamelCase__       (   self		,						__A				) ->     dict:
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=self._eval_end(__A				)
								_lowerCAmelCase										=ret['log']
								return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






		def 		UpperCamelCase__       (   self		,						__A				) ->     dict:
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=self._eval_end(__A				)
								_lowerCAmelCase										=ret['log']
								# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
								return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






		@staticmethod
		def 		UpperCamelCase__       (   __A		,						__A				) ->     Any:
								BaseTransformer.add_model_specific_args(__A		,						__A				)
								parser.add_argument(
								    '--max_seq_length'		,						default=128		,						type=__A		,						help=(
								        'The maximum total input sequence length after tokenization. Sequences longer '
								        'than this will be truncated, sequences shorter will be padded.'
								    )		,						)

								parser.add_argument(
								    '--task'		,						default=''		,						type=__A		,						required=__A		,						help='The GLUE task to run'		,						)
								parser.add_argument(
								    '--gpus'		,						default=0		,						type=__A		,						help='The number of GPUs allocated for this, it is by default 0 meaning none'		,						)

								parser.add_argument(
								    '--overwrite_cache'		,						action='store_true'		,						help='Overwrite the cached training and evaluation sets'				)

								return parser



def 							UpperCamelCase__			(			):


						'''simple docstring'''






						_lowerCAmelCase										=argparse.ArgumentParser()
						add_generic_args(a__			,      os.getcwd()      )
						_lowerCAmelCase										=GLUETransformer.add_model_specific_args(a__			,      os.getcwd()      )
						_lowerCAmelCase										=parser.parse_args()

						# If output_dir not provided, a folder will be generated in pwd
						if args.output_dir is None:
												_lowerCAmelCase										=os.path.join(
												    './results'			,      F'''{args.task}_{time.strftime('%Y%m%d_%H%M%S'      )}'''			,      )
												os.makedirs(args.output_dir      )

						_lowerCAmelCase										=GLUETransformer(a__      )
						_lowerCAmelCase										=generic_train(a__			,      a__      )

						# Optionally, predict on dev set and write to output_dir
						if args.do_predict:
												_lowerCAmelCase										=sorted(glob.glob(os.path.join(args.output_dir			,      'checkpoint-epoch=*.ckpt'      )			,      recursive=a__      )      )
												_lowerCAmelCase										=model.load_from_checkpoint(checkpoints[-1]      )
												return trainer.test(a__      )


if __name__ == "__main__":
							main()





