

'''simple docstring'''




import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_lowerCAmelCase  :		Tuple		     =	logging.getLogger(__name__)



@dataclass
class  lowerCAmelCase				:
	_lowerCamelCase   :					str
	_lowerCamelCase   :					List[str]
	_lowerCamelCase   :					Optional[List[str]]



@dataclass
class  lowerCAmelCase				:
	_lowerCamelCase   :					List[int]
	_lowerCamelCase   :					List[int]
	_lowerCamelCase   :					Optional[List[int]]			      = None
	_lowerCamelCase   :					Optional[List[int]]			      = None



class  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					List[str]			      = """train"""
	_lowerCamelCase   :					Optional[int]			      = """dev"""
	_lowerCamelCase   :					Tuple			      = """test"""



class  lowerCAmelCase				:



	@staticmethod
	def 				lowercase     (					snake_case__ ,					snake_case__    ):
		raise NotImplementedError



	@staticmethod
	def 				lowercase     (					snake_case__    ):
		raise NotImplementedError



	@staticmethod
	def 				lowercase     (					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__=False ,					snake_case__="[CLS]" ,					snake_case__=1 ,					snake_case__="[SEP]" ,					snake_case__=False ,					snake_case__=False ,					snake_case__=0 ,					snake_case__=0 ,					snake_case__=-100 ,					snake_case__=0 ,					snake_case__=True ,					):
		lowerCAmelCase					:    Optional[int]      							= {label: i for i, label in enumerate(snake_case__    )}

		lowerCAmelCase					:    Tuple      							= []
		for ex_index, example in enumerate(snake_case__    ):
			if ex_index % 1_0000 == 0:
				logger.info('Writing example %d of %d' ,					snake_case__ ,					len(snake_case__    )    )

			lowerCAmelCase					:    Dict      							= []
			lowerCAmelCase					:    Union[str, Any]      							= []
			for word, label in zip(example.words ,					example.labels    ):
				lowerCAmelCase					:    Any      							= tokenizer.tokenize(snake_case__    )

				# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
				if len(snake_case__    ) > 0:
					tokens.extend(snake_case__    )
					# Use the real label id for the first token of the word, and padding ids for the remaining tokens
					label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(snake_case__    ) - 1)    )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
			lowerCAmelCase					:    Optional[int]      							= tokenizer.num_special_tokens_to_add()
			if len(snake_case__    ) > max_seq_length - special_tokens_count:
				lowerCAmelCase					:    Optional[Any]      							= tokens[: (max_seq_length - special_tokens_count)]
				lowerCAmelCase					:    List[Any]      							= label_ids[: (max_seq_length - special_tokens_count)]

			# The convention in BERT is:
			# (a) For sequence pairs:
			#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
			#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
			# (b) For single sequences:
			#  tokens:   [CLS] the dog is hairy . [SEP]
			#  type_ids:   0   0   0   0  0     0   0
			#
			# Where "type_ids" are used to indicate whether this is the first
			# sequence or the second sequence. The embedding vectors for `type=0` and
			# `type=1` were learned during pre-training and are added to the wordpiece
			# embedding vector (and position vector). This is not *strictly* necessary
			# since the [SEP] token unambiguously separates the sequences, but it makes
			# it easier for the model to learn the concept of sequences.
			#
			# For classification tasks, the first vector (corresponding to [CLS]) is
			# used as the "sentence vector". Note that this only makes sense because
			# the entire model is fine-tuned.
			tokens += [sep_token]
			label_ids += [pad_token_label_id]
			if sep_token_extra:
				# roberta uses an extra separator b/w pairs of sentences
				tokens += [sep_token]
				label_ids += [pad_token_label_id]
			lowerCAmelCase					:    Optional[Any]      							= [sequence_a_segment_id] * len(snake_case__    )

			if cls_token_at_end:
				tokens += [cls_token]
				label_ids += [pad_token_label_id]
				segment_ids += [cls_token_segment_id]
			else:
				lowerCAmelCase					:    List[str]      							= [cls_token] + tokens
				lowerCAmelCase					:    List[str]      							= [pad_token_label_id] + label_ids
				lowerCAmelCase					:    List[str]      							= [cls_token_segment_id] + segment_ids

			lowerCAmelCase					:    Optional[Any]      							= tokenizer.convert_tokens_to_ids(snake_case__    )

			# The mask has 1 for real tokens and 0 for padding tokens. Only real
			# tokens are attended to.
			lowerCAmelCase					:    Dict      							= [1 if mask_padding_with_zero else 0] * len(snake_case__    )

			# Zero-pad up to the sequence length.
			lowerCAmelCase					:    Any      							= max_seq_length - len(snake_case__    )
			if pad_on_left:
				lowerCAmelCase					:    Union[str, Any]      							= ([pad_token] * padding_length) + input_ids
				lowerCAmelCase					:    Tuple      							= ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
				lowerCAmelCase					:    Union[str, Any]      							= ([pad_token_segment_id] * padding_length) + segment_ids
				lowerCAmelCase					:    Any      							= ([pad_token_label_id] * padding_length) + label_ids
			else:
				input_ids += [pad_token] * padding_length
				input_mask += [0 if mask_padding_with_zero else 1] * padding_length
				segment_ids += [pad_token_segment_id] * padding_length
				label_ids += [pad_token_label_id] * padding_length

			assert len(snake_case__    ) == max_seq_length
			assert len(snake_case__    ) == max_seq_length
			assert len(snake_case__    ) == max_seq_length
			assert len(snake_case__    ) == max_seq_length

			if ex_index < 5:
				logger.info('*** Example ***'    )
				logger.info('guid: %s' ,					example.guid    )
				logger.info('tokens: %s' ,					' '.join([str(snake_case__    ) for x in tokens]    )    )
				logger.info('input_ids: %s' ,					' '.join([str(snake_case__    ) for x in input_ids]    )    )
				logger.info('input_mask: %s' ,					' '.join([str(snake_case__    ) for x in input_mask]    )    )
				logger.info('segment_ids: %s' ,					' '.join([str(snake_case__    ) for x in segment_ids]    )    )
				logger.info('label_ids: %s' ,					' '.join([str(snake_case__    ) for x in label_ids]    )    )

			if "token_type_ids" not in tokenizer.model_input_names:
				lowerCAmelCase					:    Optional[Any]      							= None

			features.append(
			    InputFeatures(
			        input_ids=snake_case__ ,					attention_mask=snake_case__ ,					token_type_ids=snake_case__ ,					label_ids=snake_case__    )    )
		return features


if is_torch_available():
				import torch
				from torch import nn
				from torch.utils.data import Dataset



				class  lowerCAmelCase				(				a							):
					_lowerCamelCase   :					List[InputFeatures]
					_lowerCamelCase   :					int			      = nn.CrossEntropyLoss().ignore_index



					def __init__(					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ = None ,					snake_case__=False ,					snake_case__ = Split.train ,					):
						# Load data features from cache or dataset file
						lowerCAmelCase					:    List[Any]      							= os.path.join(
						    snake_case__ ,					'cached_{}_{}_{}'.format(mode.value ,					tokenizer.__class__.__name__ ,					str(snake_case__    )    ) ,					)

						# Make sure only the first process in distributed training processes the dataset,
						# and the others will use the cache.
						lowerCAmelCase					:    str      							= cached_features_file + '.lock'
						with FileLock(snake_case__    ):
							if os.path.exists(snake_case__    ) and not overwrite_cache:
								logger.info(f"Loading features from cached file {cached_features_file}"    )
								lowerCAmelCase					:    Any      							= torch.load(snake_case__    )
							else:
								logger.info(f"Creating features from dataset file at {data_dir}"    )
								lowerCAmelCase					:    Union[str, Any]      							= token_classification_task.read_examples_from_file(snake_case__ ,					snake_case__    )
								# TODO clean up all this to leverage built-in features of tokenizers
								lowerCAmelCase					:    str      							= token_classification_task.convert_examples_to_features(
								    snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					cls_token_at_end=bool(model_type in ['xlnet']    ) ,					cls_token=tokenizer.cls_token ,					cls_token_segment_id=2 if model_type in ['xlnet'] else 0 ,					sep_token=tokenizer.sep_token ,					sep_token_extra=snake_case__ ,					pad_on_left=bool(tokenizer.padding_side == 'left'    ) ,					pad_token=tokenizer.pad_token_id ,					pad_token_segment_id=tokenizer.pad_token_type_id ,					pad_token_label_id=self.pad_token_label_id ,					)
								logger.info(f"Saving features into cached file {cached_features_file}"    )
								torch.save(self.features ,					snake_case__    )



					def __len__(					self    ):
						return len(self.features    )



					def __getitem__(					self ,					snake_case__    ):
						return self.features[i]


if is_tf_available():
				import tensorflow as tf



				class  lowerCAmelCase				:
					_lowerCamelCase   :					List[InputFeatures]
					_lowerCamelCase   :					int			      = -100



					def __init__(					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ = None ,					snake_case__=False ,					snake_case__ = Split.train ,					):
						lowerCAmelCase					:    Any      							= token_classification_task.read_examples_from_file(snake_case__ ,					snake_case__    )
						# TODO clean up all this to leverage built-in features of tokenizers
						lowerCAmelCase					:    Dict      							= token_classification_task.convert_examples_to_features(
						    snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					cls_token_at_end=bool(model_type in ['xlnet']    ) ,					cls_token=tokenizer.cls_token ,					cls_token_segment_id=2 if model_type in ['xlnet'] else 0 ,					sep_token=tokenizer.sep_token ,					sep_token_extra=snake_case__ ,					pad_on_left=bool(tokenizer.padding_side == 'left'    ) ,					pad_token=tokenizer.pad_token_id ,					pad_token_segment_id=tokenizer.pad_token_type_id ,					pad_token_label_id=self.pad_token_label_id ,					)

						def gen():
							for ex in self.features:
								if ex.token_type_ids is None:
									yield (
									    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
									    ex.label_ids,
									)
								else:
									yield (
									    {
									        "input_ids": ex.input_ids,
									        "attention_mask": ex.attention_mask,
									        "token_type_ids": ex.token_type_ids,
									    },
									    ex.label_ids,
									)

						if "token_type_ids" not in tokenizer.model_input_names:
							lowerCAmelCase					:    List[Any]      							= tf.data.Dataset.from_generator(
							    snake_case__ ,					({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa) ,					(
							        {'input_ids': tf.TensorShape([None]    ), 'attention_mask': tf.TensorShape([None]    )},
							        tf.TensorShape([None]    ),
							    ) ,					)
						else:
							lowerCAmelCase					:    Tuple      							= tf.data.Dataset.from_generator(
							    snake_case__ ,					({'input_ids': tf.intaa, 'attention_mask': tf.intaa, 'token_type_ids': tf.intaa}, tf.intaa) ,					(
							        {
							            'input_ids': tf.TensorShape([None]    ),
							            'attention_mask': tf.TensorShape([None]    ),
							            'token_type_ids': tf.TensorShape([None]    ),
							        },
							        tf.TensorShape([None]    ),
							    ) ,					)



					def 				lowercase     (					self    ):
						lowerCAmelCase					:    List[str]      							= self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features    )    )    )

						return self.dataset



					def __len__(					self    ):
						return len(self.features    )



					def __getitem__(					self ,					snake_case__    ):
						return self.features[i]




'''simple docstring'''




import argparse
import os
import re


_lowerCAmelCase  :		Dict		     =	'src/diffusers'

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

def 			__UpperCamelCase    (    _A    :				Union[str, Any]				)    ->      Dict:




	"""simple docstring"""





	lowerCAmelCase					:    Any      							= _re_indent.search(_A				)
	return "" if search is None else search.groups()[0]

def 			__UpperCamelCase    (    _A    :				Dict					,   _A    :				Any=""					,   _A    :				List[str]=None					,   _A    :				Any=None				)    ->      Tuple:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[int]      							= 0
	lowerCAmelCase					:    Tuple      							= code.split('\n'				)
	if start_prompt is not None:
		while not lines[index].startswith(_A				):
			index += 1
		lowerCAmelCase					:    Optional[int]      							= ['\n'.join(lines[:index]				)]
	else:
		lowerCAmelCase					:    int      							= []

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

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

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

	return blocks

def 			__UpperCamelCase    (    _A    :				Dict				)    ->      List[Any]:




	"""simple docstring"""





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

	return _inner

def 			__UpperCamelCase    (    _A    :				Union[str, Any]					,   _A    :				Any=None				)    ->      Optional[Any]:




	"""simple docstring"""





	def noop(_A    :				Any				):
		return x

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

	lowerCAmelCase					:    Tuple      							= ignore_underscore(_A				)
	return sorted(_A					,   key=_A				) + sorted(_A					,   key=_A				) + sorted(_A					,   key=_A				)

def 			__UpperCamelCase    (    _A    :				Union[str, Any]				)    ->      int:




	"""simple docstring"""





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

	lowerCAmelCase					:    Optional[int]      							= import_statement.split('\n'				)
	if len(_A				) > 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.
		lowerCAmelCase					:    Optional[Any]      							= 2 if lines[1].strip() == '[' else 1
		lowerCAmelCase					:    List[str]      							= [(i, _re_strip_line.search(_A				).groups()[0]) for i, line in enumerate(lines[idx:-idx]				)]
		lowerCAmelCase					:    Optional[Any]      							= sort_objects(_A					,   key=lambda _A				: x[1]				)
		lowerCAmelCase					:    Dict      							= [lines[x[0] + idx] for x in sorted_indices]
		return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]				)
	elif len(_A				) == 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:
			lowerCAmelCase					:    Optional[int]      							= _re_bracket_content.sub(_replace					,   lines[1]				)
		else:
			lowerCAmelCase					:    List[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:
				lowerCAmelCase					:    int      							= keys[:-1]
			lowerCAmelCase					:    Tuple      							= get_indent(lines[1]				) + ', '.join([F"\"{k}\"" for k in sort_objects(_A				)]				)
		return "\n".join(_A				)
	else:
		# Finally we have to deal with imports fitting on one line
		lowerCAmelCase					:    Union[str, Any]      							= _re_bracket_content.sub(_replace					,   _A				)
		return import_statement

def 			__UpperCamelCase    (    _A    :				str					,   _A    :				Tuple=True				)    ->      Optional[Any]:




	"""simple docstring"""





	with open(_A					,   'r'				) as f:
		lowerCAmelCase					:    Optional[int]      							= f.read()

	if "_import_structure" not in code:
		return

	# Blocks of indent level 0
	lowerCAmelCase					:    List[Any]      							= split_code_in_indented_blocks(
	    _A					,   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(_A				) - 1				):
		# Check if the block contains some `_import_structure`s thingy to sort.
		lowerCAmelCase					:    List[str]      							= main_blocks[block_idx]
		lowerCAmelCase					:    Union[str, Any]      							= block.split('\n'				)

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

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

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

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

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

def 			__UpperCamelCase    (    _A    :				Tuple=True				)    ->      Any:




	"""simple docstring"""





	lowerCAmelCase					:    Tuple      							= []
	for root, _, files in os.walk(_A				):
		if "__init__.py" in files:
			lowerCAmelCase					:    Any      							= sort_imports(os.path.join(_A					,   '__init__.py'				)					,   check_only=_A				)
			if result:
				lowerCAmelCase					:    Optional[Any]      							= [os.path.join(_A					,   '__init__.py'				)]
	if len(_A				) > 0:
		raise ValueError(F"Would overwrite {len(_A				)} files, run `make style`."				)


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

				sort_imports_in_all_inits(check_only=args.check_only)
