

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

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

						return out_tensor.tolist()


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      ord(SCREAMING_SNAKE_CASE__			)
						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(SCREAMING_SNAKE_CASE__			)
						if cat.startswith('''P'''			):
												return True
						return False





@dataclass
class       snake_case_  (						__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  PreTrainedTokenizerBase
			SCREAMING_SNAKE_CASE	:  Union[bool, str, PaddingStrategy]		     =							True
			SCREAMING_SNAKE_CASE	:  Optional[int]		     =							None
			SCREAMING_SNAKE_CASE	:  Optional[int]		     =							None
			SCREAMING_SNAKE_CASE	:  int		     =							-100
			SCREAMING_SNAKE_CASE	:  str		     =							"pt"







			def 							snake_case__(	self					:							Optional[int]				,  _UpperCamelCase					:							str				) ->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(
									    _UpperCamelCase				,  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(_UpperCamelCase				) + [self.label_pad_token_id] * (sequence_length - len(_UpperCamelCase				)) for label in labels
															]
									else:
															snake_case_  =      [
															    [self.label_pad_token_id] * (sequence_length - len(_UpperCamelCase				)) + list(_UpperCamelCase				) for label in labels
															]

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

									return batch






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase_							=				{
    """configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
    """configuration_maskformer_swin""": ["""MaskFormerSwinConfig"""],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				["""MaskFormerFeatureExtractor"""]
							UpperCAmelCase_							=				["""MaskFormerImageProcessor"""]


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				[
							    """MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MaskFormerForInstanceSegmentation""",
							    """MaskFormerModel""",
							    """MaskFormerPreTrainedModel""",
							]
							UpperCAmelCase_							=				[
							    """MaskFormerSwinBackbone""",
							    """MaskFormerSwinModel""",
							    """MaskFormerSwinPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
							from .configuration_maskformer_swin import MaskFormerSwinConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_maskformer import MaskFormerFeatureExtractor
														from .image_processing_maskformer import MaskFormerImageProcessor
							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_maskformer import (
														    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MaskFormerForInstanceSegmentation,
														    MaskFormerModel,
														    MaskFormerPreTrainedModel,
														)
														from .modeling_maskformer_swin import (
														    MaskFormerSwinBackbone,
														    MaskFormerSwinModel,
														    MaskFormerSwinPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase_							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure)