


'''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


_lowercase      :      List[Any]            =					logging.getLogger(__name__)






@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				str
				__magic_name__    :				List[str]
				__magic_name__    :				Optional[List[str]]







@dataclass
class 	UpperCamelCase__:
				__magic_name__    :				List[int]
				__magic_name__    :				List[int]
				__magic_name__    :				Optional[List[int]]  		=	None
				__magic_name__    :				Optional[List[int]]  		=	None







class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				Union[str, Any]  		=	"train"
				__magic_name__    :				Any  		=	"dev"
				__magic_name__    :				Any  		=	"test"







class 	UpperCamelCase__:




				@staticmethod
				def 					a__(		lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			Union[Split, str]				)->					List[InputExample]:

										"""simple docstring"""




										raise NotImplementedError




				@staticmethod
				def 					a__(		lowerCAmelCase      :			str				)->					List[str]:

										"""simple docstring"""




										raise NotImplementedError





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

										"""simple docstring"""




										UpperCAmelCase					    =     {label: i for i, label in enumerate(lowerCAmelCase				)}

										UpperCAmelCase					    =     []
										for ex_index, example in enumerate(lowerCAmelCase				):
																if ex_index % 10000 == 0:
																						logger.info('''Writing example %d of %d'''				,   lowerCAmelCase				,   len(lowerCAmelCase				)				)

																UpperCAmelCase					    =     []
																UpperCAmelCase					    =     []
																for word, label in zip(example.words				,   example.labels				):
																						UpperCAmelCase					    =     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.
																UpperCAmelCase					    =     tokenizer.num_special_tokens_to_add()
																if len(lowerCAmelCase				) > max_seq_length - special_tokens_count:
																						UpperCAmelCase					    =     tokens[: (max_seq_length - special_tokens_count)]
																						UpperCAmelCase					    =     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]
																UpperCAmelCase					    =     [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:
																						UpperCAmelCase					    =     [cls_token] + tokens
																						UpperCAmelCase					    =     [pad_token_label_id] + label_ids
																						UpperCAmelCase					    =     [cls_token_segment_id] + segment_ids

																UpperCAmelCase					    =     tokenizer.convert_tokens_to_ids(lowerCAmelCase				)

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

																# Zero-pad up to the sequence length.
																UpperCAmelCase					    =     max_seq_length - len(lowerCAmelCase				)
																if pad_on_left:
																						UpperCAmelCase					    =     ([pad_token] * padding_length) + input_ids
																						UpperCAmelCase					    =     ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																						UpperCAmelCase					    =     ([pad_token_segment_id] * padding_length) + segment_ids
																						UpperCAmelCase					    =     ([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:
																						UpperCAmelCase					    =     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 	UpperCamelCase__(       lowerCAmelCase	):
					__magic_name__    :				List[InputFeatures]
					__magic_name__    :				int  		=	nn.CrossEntropyLoss().ignore_index




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

											"""simple docstring"""




											UpperCAmelCase					    =     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.
											UpperCAmelCase					    =     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}"""				)
																							UpperCAmelCase					    =     torch.load(lowerCAmelCase				)
																	else:
																							logger.info(F"""Creating features from dataset file at {data_dir}"""				)
																							UpperCAmelCase					    =     token_classification_task.read_examples_from_file(lowerCAmelCase				,   lowerCAmelCase				)
																							# TODO clean up all this to leverage built-in features of tokenizers
																							UpperCAmelCase					    =     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      :			int				)->					Any:

											"""simple docstring"""




											return len(self.features				)





					def __getitem__(		self      :			Optional[Any]				,   lowerCAmelCase      :			List[Any]				)->					InputFeatures:

											"""simple docstring"""




											return self.features[i]


if is_tf_available():
	import tensorflow as tf






	class 	UpperCamelCase__:
					__magic_name__    :				List[InputFeatures]
					__magic_name__    :				int  		=	-100




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

											"""simple docstring"""




											UpperCAmelCase					    =     token_classification_task.read_examples_from_file(lowerCAmelCase				,   lowerCAmelCase				)
											# TODO clean up all this to leverage built-in features of tokenizers
											UpperCAmelCase					    =     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:
																	UpperCAmelCase					    =     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:
																	UpperCAmelCase					    =     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 					a__(		self      :			List[Any]				)->					Dict:

											"""simple docstring"""




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

											return self.dataset




					def __len__(		self      :			Optional[Any]				)->					Union[str, Any]:

											"""simple docstring"""




											return len(self.features				)





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

											"""simple docstring"""




											return self.features[i]









'''simple docstring'''



# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    """pipelines_utils""",
    """0.22.0""",
    """Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
    standard_warn=False,
    stacklevel=3,
)





