
"""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


__magic_name__       =			logging.getLogger(__name__)
@dataclass
class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		__lowercase							:  str
		__lowercase							:  List[str]
		__lowercase							:  Optional[List[str]]



@dataclass
class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





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



class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  str						   =		'''train'''
		__lowercase							:  Optional[int]						   =		'''dev'''
		__lowercase							:  Tuple						   =		'''test'''



class 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		@staticmethod
		def 	snake_case_     (     lowerCAmelCase__							,  lowerCAmelCase__):
				raise NotImplementedError




		@staticmethod
		def 	snake_case_     (     lowerCAmelCase__):
				raise NotImplementedError




		@staticmethod
		def 	snake_case_     (     lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__=False							,  lowerCAmelCase__="[CLS]"							,  lowerCAmelCase__=1							,  lowerCAmelCase__="[SEP]"							,  lowerCAmelCase__=False							,  lowerCAmelCase__=False							,  lowerCAmelCase__=0							,  lowerCAmelCase__=0							,  lowerCAmelCase__=-1_0_0							,  lowerCAmelCase__=0							,  lowerCAmelCase__=True							,  ):
				__SCREAMING_SNAKE_CASE            = {label: i for i, label in enumerate(lowerCAmelCase__)}

				__SCREAMING_SNAKE_CASE            = []
				for ex_index, example in enumerate(lowerCAmelCase__):
						if ex_index % 1_0_0_0_0 == 0:
								logger.info("""Writing example %d of %d"""							,  lowerCAmelCase__							,  len(lowerCAmelCase__))

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

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

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

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

						__SCREAMING_SNAKE_CASE            = tokenizer.convert_tokens_to_ids(lowerCAmelCase__)

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

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

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

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

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


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




									"""simple docstring"""





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




									def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__ = None							,  lowerCAmelCase__=False							,  lowerCAmelCase__ = Split.train							,  ):
											# Load data features from cache or dataset file
											__SCREAMING_SNAKE_CASE            = os.path.join(
											    lowerCAmelCase__							,  """cached_{}_{}_{}""".format(mode.value							,  tokenizer.__class__.__name__							,  str(lowerCAmelCase__))							,  )

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




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




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


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




									"""simple docstring"""





									__lowercase							:  List[InputFeatures]
									__lowercase							:  int						   =		-100




									def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__ = None							,  lowerCAmelCase__=False							,  lowerCAmelCase__ = Split.train							,  ):
											__SCREAMING_SNAKE_CASE            = token_classification_task.read_examples_from_file(lowerCAmelCase__							,  lowerCAmelCase__)
											# TODO clean up all this to leverage built-in features of tokenizers
											__SCREAMING_SNAKE_CASE            = token_classification_task.convert_examples_to_features(
											    lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  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=lowerCAmelCase__							,  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:
													__SCREAMING_SNAKE_CASE            = tf.data.Dataset.from_generator(
													    lowerCAmelCase__							,  ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)							,  (
													        {"""input_ids""": tf.TensorShape([None]), """attention_mask""": tf.TensorShape([None])},
													        tf.TensorShape([None]),
													    )							,  )
											else:
													__SCREAMING_SNAKE_CASE            = tf.data.Dataset.from_generator(
													    lowerCAmelCase__							,  ({"""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 	snake_case_     (     self):
											__SCREAMING_SNAKE_CASE            = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))

											return self.dataset




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




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






"""simple docstring"""





import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs



@pytest.mark.parametrize(
    """kwargs, expected"""  ,				[
        ({"""num_shards""": 0, """max_num_jobs""": 1}, []),
        ({"""num_shards""": 10, """max_num_jobs""": 1}, [range(10				)]),
        ({"""num_shards""": 10, """max_num_jobs""": 10}, [range(UpperCamelCase_  ,				i + 1				) for i in range(10				)]),
        ({"""num_shards""": 1, """max_num_jobs""": 10}, [range(1				)]),
        ({"""num_shards""": 10, """max_num_jobs""": 3}, [range(0  ,				4				), range(4  ,				7				), range(7  ,				10				)]),
        ({"""num_shards""": 3, """max_num_jobs""": 10}, [range(0  ,				1				), range(1  ,				2				), range(2  ,				3				)]),
    ]  ,				)
def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = _distribute_shards(**UpperCamelCase_				)
		assert out == expected



@pytest.mark.parametrize(
    """gen_kwargs, max_num_jobs, expected"""  ,				[
        ({"""foo""": 0}, 10, [{"""foo""": 0}]),
        ({"""shards""": [0, 1, 2, 3]}, 1, [{"""shards""": [0, 1, 2, 3]}]),
        ({"""shards""": [0, 1, 2, 3]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}, {"""shards""": [2]}, {"""shards""": [3]}]),
        ({"""shards""": [0, 1]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}]),
        ({"""shards""": [0, 1, 2, 3]}, 2, [{"""shards""": [0, 1]}, {"""shards""": [2, 3]}]),
    ]  ,				)
def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = _split_gen_kwargs(UpperCamelCase_  ,				UpperCamelCase_				)
		assert out == expected



@pytest.mark.parametrize(
    """gen_kwargs, expected"""  ,				[
        ({"""foo""": 0}, 1),
        ({"""shards""": [0]}, 1),
        ({"""shards""": [0, 1, 2, 3]}, 4),
        ({"""shards""": [0, 1, 2, 3], """foo""": 0}, 4),
        ({"""shards""": [0, 1, 2, 3], """other""": (0, 1)}, 4),
        ({"""shards""": [0, 1, 2, 3], """shards2""": [0, 1]}, RuntimeError),
    ]  ,				)
def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		if expected is RuntimeError:
				with pytest.raises(UpperCamelCase_				):
						_number_of_shards_in_gen_kwargs(UpperCamelCase_				)
		else:
				__SCREAMING_SNAKE_CASE            = _number_of_shards_in_gen_kwargs(UpperCamelCase_				)
				assert out == expected




