





"""simple docstring"""



import collections
import importlib.util
import os
import re
from pathlib import Path


_snake_case          =						'''src/transformers'''


# Matches is_xxx_available()
_snake_case          =						re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
_snake_case          =						re.compile(R'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
_snake_case          =						re.compile(R'''\s+\"\S*\":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
_snake_case          =						re.compile(R'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
_snake_case          =						re.compile(R'''^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
_snake_case          =						re.compile(R'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma:     "MyModel",
_snake_case          =						re.compile('''^\s+\"([^\"]+)\",''')
# Catches a line with objects between brackets only:    ["foo", "bar"],
_snake_case          =						re.compile('''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
_snake_case          =						re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
_snake_case          =						re.compile(R'''^\s*try:''')
# Catches a line with else:
_snake_case          =						re.compile(R'''^\s*else:''')






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[str]						):






							"""simple docstring"""



							if _re_test_backend.search(__snake_case						) is None:
														return None
							_lowerCAmelCase  	=			[b[0] for b in _re_backend.findall(__snake_case						)]
							backends.sort()
							return "_and_".join(__snake_case						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Dict						):






							"""simple docstring"""



							with open(__snake_case     ,  'r'     ,  encoding='utf-8'     ,  newline='\n'						) as f:
														_lowerCAmelCase  	=			f.readlines()

							_lowerCAmelCase  	=			0
							while line_index < len(__snake_case						) and not lines[line_index].startswith('_import_structure = {'						):
														line_index += 1

							# If this is a traditional init, just return.
							if line_index >= len(__snake_case						):
														return None

							# First grab the objects without a specific backend in _import_structure
							_lowerCAmelCase  	=			[]
							while not lines[line_index].startswith('if TYPE_CHECKING'						) and find_backend(lines[line_index]						) is None:
														_lowerCAmelCase  	=			lines[line_index]
														# If we have everything on a single line, let's deal with it.
														if _re_one_line_import_struct.search(__snake_case						):
																					_lowerCAmelCase  	=			_re_one_line_import_struct.search(__snake_case						).groups()[0]
																					_lowerCAmelCase  	=			re.findall('\[([^\]]+)\]'     ,  __snake_case						)
																					for imp in imports:
																												objects.extend([obj[1:-1] for obj in imp.split(', '						)]						)
																					line_index += 1
																					continue
														_lowerCAmelCase  	=			_re_import_struct_key_value.search(__snake_case						)
														if single_line_import_search is not None:
																					_lowerCAmelCase  	=			[obj[1:-1] for obj in single_line_import_search.groups()[0].split(', '						) if len(__snake_case						) > 0]
																					objects.extend(__snake_case						)
														elif line.startswith(' ' * 8 + '"'						):
																					objects.append(line[9:-3]						)
														line_index += 1

							_lowerCAmelCase  	=			{'none': objects}
							# Let's continue with backend-specific objects in _import_structure
							while not lines[line_index].startswith('if TYPE_CHECKING'						):
														# If the line is an if not is_backend_available, we grab all objects associated.
														_lowerCAmelCase  	=			find_backend(lines[line_index]						)
														# Check if the backend declaration is inside a try block:
														if _re_try.search(lines[line_index - 1]						) is None:
																					_lowerCAmelCase  	=			None

														if backend is not None:
																					line_index += 1

																					# Scroll until we hit the else block of try-except-else
																					while _re_else.search(lines[line_index]						) is None:
																												line_index += 1

																					line_index += 1

																					_lowerCAmelCase  	=			[]
																					# Until we unindent, add backend objects to the list
																					while len(lines[line_index]						) <= 1 or lines[line_index].startswith(' ' * 4						):
																												_lowerCAmelCase  	=			lines[line_index]
																												if _re_import_struct_add_one.search(__snake_case						) is not None:
																																			objects.append(_re_import_struct_add_one.search(__snake_case						).groups()[0]						)
																												elif _re_import_struct_add_many.search(__snake_case						) is not None:
																																			_lowerCAmelCase  	=			_re_import_struct_add_many.search(__snake_case						).groups()[0].split(', '						)
																																			_lowerCAmelCase  	=			[obj[1:-1] for obj in imports if len(__snake_case						) > 0]
																																			objects.extend(__snake_case						)
																												elif _re_between_brackets.search(__snake_case						) is not None:
																																			_lowerCAmelCase  	=			_re_between_brackets.search(__snake_case						).groups()[0].split(', '						)
																																			_lowerCAmelCase  	=			[obj[1:-1] for obj in imports if len(__snake_case						) > 0]
																																			objects.extend(__snake_case						)
																												elif _re_quote_object.search(__snake_case						) is not None:
																																			objects.append(_re_quote_object.search(__snake_case						).groups()[0]						)
																												elif line.startswith(' ' * 8 + '"'						):
																																			objects.append(line[9:-3]						)
																												elif line.startswith(' ' * 12 + '"'						):
																																			objects.append(line[13:-3]						)
																												line_index += 1

																					_lowerCAmelCase  	=			objects
														else:
																					line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
							_lowerCAmelCase  	=			[]
							while (
							    line_index < len(__snake_case						)
							    and find_backend(lines[line_index]						) is None
							    and not lines[line_index].startswith('else'						)
							):
														_lowerCAmelCase  	=			lines[line_index]
														_lowerCAmelCase  	=			_re_import.search(__snake_case						)
														if single_line_import_search is not None:
																					objects.extend(single_line_import_search.groups()[0].split(', '						)						)
														elif line.startswith(' ' * 8						):
																					objects.append(line[8:-2]						)
														line_index += 1

							_lowerCAmelCase  	=			{'none': objects}
							# Let's continue with backend-specific objects
							while line_index < len(__snake_case						):
														# If the line is an if is_backend_available, we grab all objects associated.
														_lowerCAmelCase  	=			find_backend(lines[line_index]						)
														# Check if the backend declaration is inside a try block:
														if _re_try.search(lines[line_index - 1]						) is None:
																					_lowerCAmelCase  	=			None

														if backend is not None:
																					line_index += 1

																					# Scroll until we hit the else block of try-except-else
																					while _re_else.search(lines[line_index]						) is None:
																												line_index += 1

																					line_index += 1

																					_lowerCAmelCase  	=			[]
																					# Until we unindent, add backend objects to the list
																					while len(lines[line_index]						) <= 1 or lines[line_index].startswith(' ' * 8						):
																												_lowerCAmelCase  	=			lines[line_index]
																												_lowerCAmelCase  	=			_re_import.search(__snake_case						)
																												if single_line_import_search is not None:
																																			objects.extend(single_line_import_search.groups()[0].split(', '						)						)
																												elif line.startswith(' ' * 12						):
																																			objects.append(line[12:-2]						)
																												line_index += 1

																					_lowerCAmelCase  	=			objects
														else:
																					line_index += 1

							return import_dict_objects, type_hint_objects






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					List[Any]						):






							"""simple docstring"""



							def find_duplicates(SCREAMING_SNAKE_CASE:					Optional[int]						):
														return [k for k, v in collections.Counter(__snake_case						).items() if v > 1]

							if list(import_dict_objects.keys()						) != list(type_hint_objects.keys()						):
														return ["Both sides of the init do not have the same backends!"]

							_lowerCAmelCase  	=			[]
							for key in import_dict_objects.keys():
														_lowerCAmelCase  	=			find_duplicates(import_dict_objects[key]						)
														if duplicate_imports:
																					errors.append(f"""Duplicate _import_structure definitions for: {duplicate_imports}"""						)
														_lowerCAmelCase  	=			find_duplicates(type_hint_objects[key]						)
														if duplicate_type_hints:
																					errors.append(f"""Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}"""						)

														if sorted(set(import_dict_objects[key]						)						) != sorted(set(type_hint_objects[key]						)						):
																					_lowerCAmelCase  	=			'base imports' if key == 'none' else f"""{key} backend"""
																					errors.append(f"""Differences for {name}:"""						)
																					for a in type_hint_objects[key]:
																												if a not in import_dict_objects[key]:
																																			errors.append(f"""  {a} in TYPE_HINT but not in _import_structure."""						)
																					for a in import_dict_objects[key]:
																												if a not in type_hint_objects[key]:
																																			errors.append(f"""  {a} in _import_structure but not in TYPE_HINT."""						)
							return errors






def 	__snake_case  (    ):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							for root, _, files in os.walk(__snake_case						):
														if "__init__.py" in files:
																					_lowerCAmelCase  	=			os.path.join(__snake_case     ,  '__init__.py'						)
																					_lowerCAmelCase  	=			parse_init(__snake_case						)
																					if objects is not None:
																												_lowerCAmelCase  	=			analyze_results(*__snake_case						)
																												if len(__snake_case						) > 0:
																																			_lowerCAmelCase  	=			f"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
																																			failures.append('\n'.join(__snake_case						)						)
							if len(__snake_case						) > 0:
														raise ValueError('\n\n'.join(__snake_case						)						)






def 	__snake_case  (    ):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							for path, directories, files in os.walk(__snake_case						):
														for folder in directories:
																					# Ignore private modules
																					if folder.startswith('_'						):
																												directories.remove(__snake_case						)
																												continue
																					# Ignore leftovers from branches (empty folders apart from pycache)
																					if len(list((Path(__snake_case						) / folder).glob('*.py'						)						)						) == 0:
																												continue
																					_lowerCAmelCase  	=			str((Path(__snake_case						) / folder).relative_to(__snake_case						)						)
																					_lowerCAmelCase  	=			short_path.replace(os.path.sep     ,  '.'						)
																					submodules.append(__snake_case						)
														for fname in files:
																					if fname == "__init__.py":
																												continue
																					_lowerCAmelCase  	=			str((Path(__snake_case						) / fname).relative_to(__snake_case						)						)
																					_lowerCAmelCase  	=			short_path.replace('.py'     ,  ''						).replace(os.path.sep     ,  '.'						)
																					if len(submodule.split('.'						)						) == 1:
																												submodules.append(__snake_case						)
							return submodules


_snake_case          =						[
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
]






def 	__snake_case  (    ):






							"""simple docstring"""



							_lowerCAmelCase  	=			importlib.util.spec_from_file_location(
							    'transformers'     ,  os.path.join(__snake_case     ,  '__init__.py'						)     ,  submodule_search_locations=[PATH_TO_TRANSFORMERS]     ,  )
							_lowerCAmelCase  	=			spec.loader.load_module()

							_lowerCAmelCase  	=			[
							    module
							    for module in get_transformers_submodules()
							    if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
							]
							if len(__snake_case						) > 0:
														_lowerCAmelCase  	=			'\n'.join(f"""- {module}""" for module in module_not_registered						)
														raise ValueError(
														    'The following submodules are not properly registered in the main init of Transformers:\n'
														    f"""{list_of_modules}\n"""
														    'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.'						)


if __name__ == "__main__":
	check_all_inits()
	check_submodules()








"""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


_snake_case          =						logging.getLogger(__name__)
@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							str
				SCREAMING_SNAKE_CASE_:							List[str]
				SCREAMING_SNAKE_CASE_:							Optional[List[str]]






@dataclass
class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							List[int]
				SCREAMING_SNAKE_CASE_:							List[int]
				SCREAMING_SNAKE_CASE_:							Optional[List[int]]							=					None
				SCREAMING_SNAKE_CASE_:							Optional[List[int]]							=					None






class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Union[str, Any]							=					"train"
				SCREAMING_SNAKE_CASE_:							Tuple							=					"dev"
				SCREAMING_SNAKE_CASE_:							List[str]							=					"test"






class        _SCREAMING_SNAKE_CASE  :






				'''simple docstring'''



				@staticmethod
				def 				__lowerCamelCase	(					UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Union[Split, str]  )		->  List[InputExample]:







											"""simple docstring"""




											raise NotImplementedError


				@staticmethod
				def 				__lowerCamelCase	(					UpperCAmelCase_	:			str  )		->  List[str]:







											"""simple docstring"""




											raise NotImplementedError







				@staticmethod
				def 				__lowerCamelCase	(					UpperCAmelCase_	:			List[InputExample]				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			int				,     UpperCAmelCase_	:			PreTrainedTokenizer				,     UpperCAmelCase_	:			Union[str, Any]=False				,     UpperCAmelCase_	:			Optional[int]="[CLS]"				,     UpperCAmelCase_	:			Tuple=1				,     UpperCAmelCase_	:			str="[SEP]"				,     UpperCAmelCase_	:			Optional[Any]=False				,     UpperCAmelCase_	:			Union[str, Any]=False				,     UpperCAmelCase_	:			Union[str, Any]=0				,     UpperCAmelCase_	:			Dict=0				,     UpperCAmelCase_	:			List[Any]=-100				,     UpperCAmelCase_	:			Dict=0				,     UpperCAmelCase_	:			List[Any]=True				,     )		->  List[InputFeatures]:







											"""simple docstring"""




											_lowerCAmelCase  	=			{label: i for i, label in enumerate(UpperCAmelCase_  )}

											_lowerCAmelCase  	=			[]
											for ex_index, example in enumerate(UpperCAmelCase_  ):
																		if ex_index % 10_000 == 0:
																									logger.info('Writing example %d of %d'				,     UpperCAmelCase_				,     len(UpperCAmelCase_  )  )

																		_lowerCAmelCase  	=			[]
																		_lowerCAmelCase  	=			[]
																		for word, label in zip(example.words				,     example.labels  ):
																									_lowerCAmelCase  	=			tokenizer.tokenize(UpperCAmelCase_  )

																									# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																									if len(UpperCAmelCase_  ) > 0:
																																tokens.extend(UpperCAmelCase_  )
																																# 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(UpperCAmelCase_  ) - 1)  )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
																		_lowerCAmelCase  	=			tokenizer.num_special_tokens_to_add()
																		if len(UpperCAmelCase_  ) > max_seq_length - special_tokens_count:
																									_lowerCAmelCase  	=			tokens[: (max_seq_length - special_tokens_count)]
																									_lowerCAmelCase  	=			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  	=			[sequence_a_segment_id] * len(UpperCAmelCase_  )

																		if cls_token_at_end:
																									tokens += [cls_token]
																									label_ids += [pad_token_label_id]
																									segment_ids += [cls_token_segment_id]
																		else:
																									_lowerCAmelCase  	=			[cls_token] + tokens
																									_lowerCAmelCase  	=			[pad_token_label_id] + label_ids
																									_lowerCAmelCase  	=			[cls_token_segment_id] + segment_ids

																		_lowerCAmelCase  	=			tokenizer.convert_tokens_to_ids(UpperCAmelCase_  )

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

																		# Zero-pad up to the sequence length.
																		_lowerCAmelCase  	=			max_seq_length - len(UpperCAmelCase_  )
																		if pad_on_left:
																									_lowerCAmelCase  	=			([pad_token] * padding_length) + input_ids
																									_lowerCAmelCase  	=			([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																									_lowerCAmelCase  	=			([pad_token_segment_id] * padding_length) + segment_ids
																									_lowerCAmelCase  	=			([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(UpperCAmelCase_  ) == max_seq_length
																		assert len(UpperCAmelCase_  ) == max_seq_length
																		assert len(UpperCAmelCase_  ) == max_seq_length
																		assert len(UpperCAmelCase_  ) == max_seq_length

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

																		if "token_type_ids" not in tokenizer.model_input_names:
																									_lowerCAmelCase  	=			None

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


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






					'''simple docstring'''



					SCREAMING_SNAKE_CASE_:							List[InputFeatures]
					SCREAMING_SNAKE_CASE_:							int							=					nn.CrossEntropyLoss().ignore_index


					def __init__(					self	:			List[Any]				,     UpperCAmelCase_	:			TokenClassificationTask				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			PreTrainedTokenizer				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Optional[int] = None				,     UpperCAmelCase_	:			List[str]=False				,     UpperCAmelCase_	:			Split = Split.train				,     )		->  List[str]:







												"""simple docstring"""




												_lowerCAmelCase  	=			os.path.join(
												    UpperCAmelCase_				,     'cached_{}_{}_{}'.format(mode.value				,     tokenizer.__class__.__name__				,     str(UpperCAmelCase_  )  )				,     )

												# Make sure only the first process in distributed training processes the dataset,
												# and the others will use the cache.
												_lowerCAmelCase  	=			cached_features_file + '.lock'
												with FileLock(UpperCAmelCase_  ):
																			if os.path.exists(UpperCAmelCase_  ) and not overwrite_cache:
																										logger.info(F"""Loading features from cached file {cached_features_file}"""  )
																										_lowerCAmelCase  	=			torch.load(UpperCAmelCase_  )
																			else:
																										logger.info(F"""Creating features from dataset file at {data_dir}"""  )
																										_lowerCAmelCase  	=			token_classification_task.read_examples_from_file(UpperCAmelCase_				,     UpperCAmelCase_  )
																										# TODO clean up all this to leverage built-in features of tokenizers
																										_lowerCAmelCase  	=			token_classification_task.convert_examples_to_features(
																										    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     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=UpperCAmelCase_				,     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				,     UpperCAmelCase_  )


					def __len__(					self	:			Dict  )		->  int:







												"""simple docstring"""




												return len(self.features  )







					def __getitem__(					self	:			List[str]				,     UpperCAmelCase_	:			List[Any]  )		->  InputFeatures:







												"""simple docstring"""




												return self.features[i]


if is_tf_available():
	import tensorflow as tf
	class        _SCREAMING_SNAKE_CASE  :






					'''simple docstring'''



					SCREAMING_SNAKE_CASE_:							List[InputFeatures]
					SCREAMING_SNAKE_CASE_:							int							=					-1_0_0


					def __init__(					self	:			Tuple				,     UpperCAmelCase_	:			TokenClassificationTask				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			PreTrainedTokenizer				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			str				,     UpperCAmelCase_	:			Optional[int] = None				,     UpperCAmelCase_	:			Optional[Any]=False				,     UpperCAmelCase_	:			Split = Split.train				,     )		->  Dict:







												"""simple docstring"""




												_lowerCAmelCase  	=			token_classification_task.read_examples_from_file(UpperCAmelCase_				,     UpperCAmelCase_  )
												# TODO clean up all this to leverage built-in features of tokenizers
												_lowerCAmelCase  	=			token_classification_task.convert_examples_to_features(
												    UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     UpperCAmelCase_				,     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=UpperCAmelCase_				,     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  	=			tf.data.Dataset.from_generator(
																			    UpperCAmelCase_				,     ({'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  	=			tf.data.Dataset.from_generator(
																			    UpperCAmelCase_				,     ({'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 				__lowerCamelCase	(					self	:			str  )		->  Tuple:







												"""simple docstring"""




												_lowerCAmelCase  	=			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features  )  )  )

												return self.dataset


					def __len__(					self	:			List[Any]  )		->  Optional[Any]:







												"""simple docstring"""




												return len(self.features  )







					def __getitem__(					self	:			int				,     UpperCAmelCase_	:			List[Any]  )		->  InputFeatures:







												"""simple docstring"""




												return self.features[i]






