

from typing import TYPE_CHECKING

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


A_						:     Union[str, Any]			  =				{
    '''configuration_segformer''': ['''SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SegformerConfig''', '''SegformerOnnxConfig''']
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_						:     List[Any]			  =				['''SegformerFeatureExtractor''']
   A_						:     Tuple			  =				['''SegformerImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_						:     Optional[int]			  =				[
       '''SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SegformerDecodeHead''',
       '''SegformerForImageClassification''',
       '''SegformerForSemanticSegmentation''',
       '''SegformerLayer''',
       '''SegformerModel''',
       '''SegformerPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_						:     List[str]			  =				[
       '''TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFSegformerDecodeHead''',
       '''TFSegformerForImageClassification''',
       '''TFSegformerForSemanticSegmentation''',
       '''TFSegformerModel''',
       '''TFSegformerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_segformer import SegformerFeatureExtractor
      from .image_processing_segformer import SegformerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_segformer import (
          SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          SegformerDecodeHead,
          SegformerForImageClassification,
          SegformerForSemanticSegmentation,
          SegformerLayer,
          SegformerModel,
          SegformerPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_segformer import (
          TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFSegformerDecodeHead,
          TFSegformerForImageClassification,
          TFSegformerForSemanticSegmentation,
          TFSegformerModel,
          TFSegformerPreTrainedModel,
      )

else:
   import sys

   A_						:     Dict			  =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/




