'''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          =  logging.getLogger(__name__)






@dataclass
class 				a__:


			'''simple docstring'''






			UpperCAmelCase_			:     str
			UpperCAmelCase_			:     List[str]
			UpperCAmelCase_			:     Optional[List[str]]







@dataclass
class 				a__:


			'''simple docstring'''






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







class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     List[str]      =       '''train'''
			UpperCAmelCase_			:     Tuple      =       '''dev'''
			UpperCAmelCase_			:     Union[str, Any]      =       '''test'''







class 				a__:


			'''simple docstring'''






			@staticmethod
			def 							a_       (   __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					raise NotImplementedError



			@staticmethod
			def 							a_       (   __lowerCAmelCase):




					"""simple docstring"""


					raise NotImplementedError







			@staticmethod
			def 							a_       (   __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase=False       ,    __lowerCAmelCase="[CLS]"       ,    __lowerCAmelCase=1       ,    __lowerCAmelCase="[SEP]"       ,    __lowerCAmelCase=False       ,    __lowerCAmelCase=False       ,    __lowerCAmelCase=0       ,    __lowerCAmelCase=0       ,    __lowerCAmelCase=-100       ,    __lowerCAmelCase=0       ,    __lowerCAmelCase=True       ,    ):




					"""simple docstring"""


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

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

							lowerCAmelCase   					=  []
							lowerCAmelCase   					=  []
							for word, label in zip(example.words       ,    example.labels):
									lowerCAmelCase   					=  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.
							lowerCAmelCase   					=  tokenizer.num_special_tokens_to_add()
							if len(__lowerCAmelCase) > 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(__lowerCAmelCase)

							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(__lowerCAmelCase)

							# 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(__lowerCAmelCase)

							# Zero-pad up to the sequence length.
							lowerCAmelCase   					=  max_seq_length - len(__lowerCAmelCase)
							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(__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:
									lowerCAmelCase   					=  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 				a__(					lowerCAmelCase__	):


									'''simple docstring'''






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



									def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase=False       ,    __lowerCAmelCase = Split.train       ,    ):




											"""simple docstring"""


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




											"""simple docstring"""


											return len(self.features)







									def __getitem__(   self       ,    __lowerCAmelCase):




											"""simple docstring"""


											return self.features[i]


if is_tf_available():
						import tensorflow as tf






						class 				a__:


									'''simple docstring'''






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



									def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase=False       ,    __lowerCAmelCase = Split.train       ,    ):




											"""simple docstring"""


											lowerCAmelCase   					=  token_classification_task.read_examples_from_file(__lowerCAmelCase       ,    __lowerCAmelCase)
											# TODO clean up all this to leverage built-in features of tokenizers
											lowerCAmelCase   					=  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:
													lowerCAmelCase   					=  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:
													lowerCAmelCase   					=  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):




											"""simple docstring"""


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

											return self.dataset



									def __len__(   self):




											"""simple docstring"""


											return len(self.features)







									def __getitem__(   self       ,    __lowerCAmelCase):




											"""simple docstring"""


											return self.features[i]





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowercase          =  {
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''InstructBlipQFormerModel''',
						    '''InstructBlipPreTrainedModel''',
						    '''InstructBlipForConditionalGeneration''',
						    '''InstructBlipVisionModel''',
						]

if TYPE_CHECKING:
						from .configuration_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    InstructBlipConfig,
						    InstructBlipQFormerConfig,
						    InstructBlipVisionConfig,
						)
						from .processing_instructblip import InstructBlipProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_instructblip import (
												    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InstructBlipForConditionalGeneration,
												    InstructBlipPreTrainedModel,
												    InstructBlipQFormerModel,
												    InstructBlipVisionModel,
												)

else:
						import sys

						__lowercase          =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




