




'''simple docstring'''

import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					List[Any]:

					'''simple docstring'''

					if isinstance(__UpperCAmelCase,      __UpperCAmelCase	):
										snake_case_									=				np.full((len(__UpperCAmelCase	), sequence_length, 2),      __UpperCAmelCase	)
					else:
										snake_case_									=				np.full((len(__UpperCAmelCase	), sequence_length),      __UpperCAmelCase	)

					for i, tensor in enumerate(__UpperCAmelCase	):
										if padding_side == "right":
															if isinstance(__UpperCAmelCase,      __UpperCAmelCase	):
																				snake_case_									=				tensor[:sequence_length]
															else:
																				snake_case_									=				tensor[:sequence_length]
										else:
															if isinstance(__UpperCAmelCase,      __UpperCAmelCase	):
																				snake_case_									=				tensor[:sequence_length]
															else:
																				snake_case_									=				tensor[:sequence_length]

					return out_tensor.tolist()


def 		__magic_name__    (	__UpperCAmelCase	)		->					Dict:

					'''simple docstring'''

					snake_case_									=				ord(__UpperCAmelCase	)
					if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
										return True
					snake_case_									=				unicodedata.category(__UpperCAmelCase	)
					if cat.startswith('''P'''	):
										return True
					return False






@dataclass
class 						a				(   _lowerCamelCase     ):
				snake_case_      =		42
				snake_case_      =		True
				snake_case_      =		None
				snake_case_      =		None
				snake_case_      =		-100
				snake_case_      =		"pt"


				def   A_ (		self    :					int       ,				lowercase_    :					List[str]					):
									import torch

									snake_case_									=				'''label''' if '''label''' in features[0].keys() else '''labels'''
									snake_case_									=				[feature[label_name] for feature in features] if label_name in features[0].keys() else None
									snake_case_									=				self.tokenizer.pad(
									    lowercase_       ,				padding=self.padding       ,				max_length=self.max_length       ,				pad_to_multiple_of=self.pad_to_multiple_of       ,				return_tensors='''pt''' if labels is None else None       ,				)

									if labels is None:
														return batch

									snake_case_									=				torch.tensor(batch['''entity_ids''']					).shape[1]
									snake_case_									=				self.tokenizer.padding_side
									if padding_side == "right":
														snake_case_									=				[
														    list(lowercase_					) + [self.label_pad_token_id] * (sequence_length - len(lowercase_					)) for label in labels
														]
									else:
														snake_case_									=				[
														    [self.label_pad_token_id] * (sequence_length - len(lowercase_					)) + list(lowercase_					) for label in labels
														]

									snake_case_									=				[feature['''ner_tags'''] for feature in features]
									snake_case_									=				padding_tensor(lowercase_       ,				-1       ,				lowercase_       ,				lowercase_					)
									snake_case_									=				[feature['''original_entity_spans'''] for feature in features]
									snake_case_									=				padding_tensor(lowercase_       ,				(-1, -1)       ,				lowercase_       ,				lowercase_					)
									snake_case_									=				{k: torch.tensor(lowercase_       ,				dtype=torch.intaa					) for k, v in batch.items()}

									return batch








"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__A											=		{
    """configuration_owlvit""": [
        """OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """OwlViTConfig""",
        """OwlViTOnnxConfig""",
        """OwlViTTextConfig""",
        """OwlViTVisionConfig""",
    ],
    """processing_owlvit""": ["""OwlViTProcessor"""],
}


try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		["""OwlViTFeatureExtractor"""]
  __A											=		["""OwlViTImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		[
      """OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """OwlViTModel""",
      """OwlViTPreTrainedModel""",
      """OwlViTTextModel""",
      """OwlViTVisionModel""",
      """OwlViTForObjectDetection""",
  ]

if TYPE_CHECKING:
  from .configuration_owlvit import (
      OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      OwlViTConfig,
      OwlViTOnnxConfig,
      OwlViTTextConfig,
      OwlViTVisionConfig,
  )
  from .processing_owlvit import OwlViTProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_owlvit import OwlViTFeatureExtractor
    from .image_processing_owlvit import OwlViTImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_owlvit import (
        OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OwlViTForObjectDetection,
        OwlViTModel,
        OwlViTPreTrainedModel,
        OwlViTTextModel,
        OwlViTVisionModel,
    )

else:
  import sys

  __A											=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
