from typing import TYPE_CHECKING

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


A   :     List[str]       =				{
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A   :     List[Any]       =				["LongformerTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A   :     Union[str, Any]       =				[
          "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "LongformerForMaskedLM",
          "LongformerForMultipleChoice",
          "LongformerForQuestionAnswering",
          "LongformerForSequenceClassification",
          "LongformerForTokenClassification",
          "LongformerModel",
          "LongformerPreTrainedModel",
          "LongformerSelfAttention",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A   :     Optional[int]       =				[
          "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFLongformerForMaskedLM",
          "TFLongformerForMultipleChoice",
          "TFLongformerForQuestionAnswering",
          "TFLongformerForSequenceClassification",
          "TFLongformerForTokenClassification",
          "TFLongformerModel",
          "TFLongformerPreTrainedModel",
          "TFLongformerSelfAttention",
      ]


if TYPE_CHECKING:
      from .configuration_longformer import (
          LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          LongformerConfig,
          LongformerOnnxConfig,
      )
      from .tokenization_longformer import LongformerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_longformer_fast import LongformerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_longformer import (
                LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                LongformerForMaskedLM,
                LongformerForMultipleChoice,
                LongformerForQuestionAnswering,
                LongformerForSequenceClassification,
                LongformerForTokenClassification,
                LongformerModel,
                LongformerPreTrainedModel,
                LongformerSelfAttention,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_longformer import (
                TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFLongformerForMaskedLM,
                TFLongformerForMultipleChoice,
                TFLongformerForQuestionAnswering,
                TFLongformerForSequenceClassification,
                TFLongformerForTokenClassification,
                TFLongformerModel,
                TFLongformerPreTrainedModel,
                TFLongformerSelfAttention,
            )

else:
      import sys

      A   :     Dict       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  42
  lowerCamelCase__   =  42


if is_transformers_available() and is_torch_available():
      from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


