


import argparse
import os
import re


UpperCAmelCase_				:			Dict   =	'''src/diffusers'''

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







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Dict    )     ->		Any:

       """simple docstring"""

       a_ :		Union[str, Any]							=  _re_indent.search(snake_case_    )
       return "" if search is None else search.groups()[0]







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Union[str, Any]  ,   __A			:      Dict=""  ,   __A			:      Dict=None  ,   __A			:      Any=None    )     ->		Optional[Any]:

       """simple docstring"""

       a_ :		Optional[int]							=  0
       a_ :		List[Any]							=  code.split('\n'    )
       if start_prompt is not None:
              while not lines[index].startswith(snake_case_    ):
                     index += 1
              a_ :		Optional[Any]							=  ["""\n""".join(lines[:index]    )]
       else:
              a_ :		int							=  []

       # We split into blocks until we get to the `end_prompt` (or the end of the block).
       a_ :		Any							=  [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_ :		Any							=  [lines[index + 1]]
                                   index += 1
                            else:
                                   a_ :		List[str]							=  []
                     else:
                            blocks.append('\n'.join(snake_case_    )    )
                            a_ :		int							=  [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 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[Any]    )     ->		str:

       """simple docstring"""

       def _inner(__A			:      Tuple    ):
              return key(snake_case_    ).lower().replace('_'  ,   ''    )

       return _inner







def 	SCREAMING_SNAKE_CASE_						(      __A			:      List[Any]  ,   __A			:      Optional[int]=None    )     ->		int:

       """simple docstring"""

       def noop(__A			:      Dict    ):
              return x

       if key is None:
              a_ :		int							=  noop
       # Constants are all uppercase, they go first.
       a_ :		List[Any]							=  [obj for obj in objects if key(snake_case_    ).isupper()]
       # Classes are not all uppercase but start with a capital, they go second.
       a_ :		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_ :		List[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 	SCREAMING_SNAKE_CASE_						(      __A			:      int    )     ->		List[Any]:

       """simple docstring"""

       def _replace(__A			:      List[Any]    ):
              a_ :		Any							=  match.groups()[0]
              if "," not in imports:
                     return F"""[{imports}]"""
              a_ :		Union[str, Any]							=  [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_ :		List[str]							=  keys[:-1]
              return "[" + ", ".join([F"""\"{k}\"""" for k in sort_objects(snake_case_    )]    ) + "]"

       a_ :		str							=  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_ :		str							=  2 if lines[1].strip() == """[""" else 1
              a_ :		Dict							=  [(i, _re_strip_line.search(snake_case_    ).groups()[0]) for i, line in enumerate(lines[idx:-idx]    )]
              a_ :		int							=  sort_objects(snake_case_  ,   key=lambda __A    : 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_ :		List[Any]							=  _re_bracket_content.sub(_replace  ,   lines[1]    )
              else:
                     a_ :		Optional[Any]							=  [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_ :		List[Any]							=  keys[:-1]
                     a_ :		int							=  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_ :		List[str]							=  _re_bracket_content.sub(_replace  ,   snake_case_    )
              return import_statement







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Tuple  ,   __A			:      str=True    )     ->		Dict:

       """simple docstring"""

       with open(snake_case_  ,   'r'    ) as f:
              a_ :		int							=  f.read()

       if "_import_structure" not in code:
              return

       # Blocks of indent level 0
       a_ :		Dict							=  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_ :		Optional[Any]							=  main_blocks[block_idx]
              a_ :		Optional[int]							=  block.split('\n'    )

              # Get to the start of the imports.
              a_ :		Union[str, Any]							=  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_ :		List[str]							=  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_ :		Union[str, Any]							=  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_ :		Union[str, 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_ :		Union[str, Any]							=  [(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_ :		List[Any]							=  [x[0] for x in sorted(snake_case_  ,   key=lambda __A    : x[1]    )]

              # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
              a_ :		str							=  0
              a_ :		List[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_ :		Tuple							=  """\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 	SCREAMING_SNAKE_CASE_						(      __A			:      int=True    )     ->		str:

       """simple docstring"""

       a_ :		Any							=  []
       for root, _, files in os.walk(snake_case_    ):
              if "__init__.py" in files:
                     a_ :		Union[str, Any]							=  sort_imports(os.path.join(snake_case_  ,   '__init__.py'    )  ,   check_only=snake_case_    )
                     if result:
                            a_ :		Any							=  [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__":
    UpperCAmelCase_				:			Any   =	argparse.ArgumentParser()
    parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
    UpperCAmelCase_				:			str   =	parser.parse_args()

    sort_imports_in_all_inits(check_only=args.check_only)






import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Any    )     ->		Optional[int]:

       """simple docstring"""

       a_ :		str							=  filter(lambda __A    : p.requires_grad  ,   model.parameters()    )
       a_ :		List[Any]							=  sum([np.prod(p.size()    ) for p in model_parameters]    )
       return params


UpperCAmelCase_				:			Dict   =	logging.getLogger(__name__)







def 	SCREAMING_SNAKE_CASE_						(      __A			:      int  ,   __A			:      int    )     ->		List[str]:

       """simple docstring"""

       if metric == "rouge2":
              a_ :		Dict							=  '{val_avg_rouge2:.4f}-{step_count}'
       elif metric == "bleu":
              a_ :		Dict							=  '{val_avg_bleu:.4f}-{step_count}'
       elif metric == "em":
              a_ :		Union[str, Any]							=  '{val_avg_em:.4f}-{step_count}'
       elif metric == "loss":
              a_ :		str							=  '{val_avg_loss:.4f}-{step_count}'
       else:
              raise NotImplementedError(
                  F"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
                  ' function.'    )

       a_ :		Dict							=  ModelCheckpoint(
           dirpath=__A  ,   filename=__A  ,   monitor=F"""val_{metric}"""  ,   mode='max'  ,   save_top_k=1  ,   every_n_epochs=1  ,   )

       return checkpoint_callback







def 	SCREAMING_SNAKE_CASE_						(      __A			:      int  ,   __A			:      List[Any]    )     ->		int:

       """simple docstring"""

       return EarlyStopping(
           monitor=F"""val_{metric}"""  ,   mode='min' if 'loss' in metric else 'max'  ,   patience=__A  ,   verbose=__A  ,   )
class 			SCREAMING_SNAKE_CASE__  (    pl.Callback      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	List[str]					)		->      Any:
             a_ :		int							=  {F"""lr_group_{i}""": param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups					)}
             pl_module.logger.log_metrics(SCREAMING_SNAKE_CASE__					)
      @rank_zero_only
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	pl.Trainer					,							SCREAMING_SNAKE_CASE__    :	pl.LightningModule					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	List[str]=True					)		->      None:
             logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""					)
             a_ :		List[str]							=  trainer.callback_metrics
             trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}					)
             # Log results
             a_ :		Optional[int]							=  Path(pl_module.hparams.output_dir					)
             if type_path == "test":
                    a_ :		Tuple							=  od / 'test_results.txt'
                    a_ :		Optional[int]							=  od / 'test_generations.txt'
             else:
                    # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
                    # If people want this it will be easy enough to add back.
                    a_ :		str							=  od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
                    a_ :		Optional[Any]							=  od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
                    results_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__					)
                    generations_file.parent.mkdir(exist_ok=SCREAMING_SNAKE_CASE__					)
             with open(SCREAMING_SNAKE_CASE__					,							'a+'					) as writer:
                    for key in sorted(SCREAMING_SNAKE_CASE__					):
                           if key in ["log", "progress_bar", "preds"]:
                                  continue
                           a_ :		int							=  metrics[key]
                           if isinstance(SCREAMING_SNAKE_CASE__					,							torch.Tensor					):
                                  a_ :		List[str]							=  val.item()
                           a_ :		int							=  F"""{key}: {val:.6f}\n"""
                           writer.write(SCREAMING_SNAKE_CASE__					)

             if not save_generations:
                    return

             if "preds" in metrics:
                    a_ :		Optional[Any]							=  '\n'.join(metrics['preds']					)
                    generations_file.open('w+'					).write(SCREAMING_SNAKE_CASE__					)
      @rank_zero_only
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					)		->      Dict:
             try:
                    a_ :		Any							=  pl_module.model.model.num_parameters()
             except AttributeError:
                    a_ :		List[str]							=  pl_module.model.num_parameters()

             a_ :		Any							=  count_trainable_parameters(SCREAMING_SNAKE_CASE__					)
             # mp stands for million parameters
             trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6}					)
      @rank_zero_only
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	pl.Trainer					,							SCREAMING_SNAKE_CASE__    :	pl.LightningModule					)		->      Optional[int]:
             save_json(pl_module.metrics					,							pl_module.metrics_save_path					)
             return self._write_logs(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							'test'					)

      @rank_zero_only
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	pl.Trainer					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					)		->      Optional[Any]:
             save_json(pl_module.metrics					,							pl_module.metrics_save_path					)
             # Uncommenting this will save val generations
             # return self._write_logs(trainer, pl_module, "valid")


