


"""simple docstring"""






# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint


class 	A_       :





  """simple docstring"""

  def __init__(						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					List[str]:
     a   :	int						=    key
     a   :	List[str]						=    freq






  def __str__(						self						)				->					Optional[int]:
     return f'Node(key={self.key}, freq={self.freq})'




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     List[Any]:
   if i > j or i < 0 or j > len(UpperCAmelCase__				) - 1:
      return

   a   :	Tuple						=    root[i][j]
   if parent == -1:  # root does not have a parent
      print(F'{key[node]} is the root of the binary search tree.'				)
   elif is_left:
      print(F'{key[node]} is the left child of key {parent}.'				)
   else:
      print(F'{key[node]} is the right child of key {parent}.'				)

   print_binary_search_tree(UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   node - 1       ,   key[node]       ,   UpperCAmelCase__				)
   print_binary_search_tree(UpperCAmelCase__       ,   UpperCAmelCase__       ,   node + 1       ,   UpperCAmelCase__       ,   key[node]       ,   UpperCAmelCase__				)




def        A_					(					UpperCAmelCase__				)     ->     Tuple:
   nodes.sort(key=lambda UpperCAmelCase__				: node.key				)

   a   :	Union[str, Any]						=    len(UpperCAmelCase__				)

   a   :	Optional[int]						=    [nodes[i].key for i in range(UpperCAmelCase__				)]
   a   :	Dict						=    [nodes[i].freq for i in range(UpperCAmelCase__				)]

   # This 2D array stores the overall tree cost (which's as minimized as possible);
   # for a single key, cost is equal to frequency of the key.
   a   :	str						=    [[freqs[i] if i == j else 0 for j in range(UpperCAmelCase__				)] for i in range(UpperCAmelCase__				)]
   # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
   # array
   a   :	List[str]						=    [[freqs[i] if i == j else 0 for j in range(UpperCAmelCase__				)] for i in range(UpperCAmelCase__				)]
   # stores tree roots that will be used later for constructing binary search tree
   a   :	Dict						=    [[i if i == j else 0 for j in range(UpperCAmelCase__				)] for i in range(UpperCAmelCase__				)]

   for interval_length in range(2       ,   n + 1				):
      for i in range(n - interval_length + 1				):
         a   :	List[str]						=    i + interval_length - 1

         a   :	Tuple						=    sys.maxsize  # set the value to "infinity"
         a   :	Dict						=    total[i][j - 1] + freqs[j]

         # Apply Knuth's optimization
         # Loop without optimization: for r in range(i, j + 1):
         for r in range(root[i][j - 1]       ,   root[i + 1][j] + 1				):  # r is a temporal root
            a   :	Union[str, Any]						=    dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
            a   :	List[Any]						=    dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
            a   :	int						=    left + total[i][j] + right

            if dp[i][j] > cost:
               a   :	Union[str, Any]						=    cost
               a   :	Optional[Any]						=    r

   print('Binary search tree nodes:'				)
   for node in nodes:
      print(UpperCAmelCase__				)

   print(F'\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.'				)
   print_binary_search_tree(UpperCAmelCase__       ,   UpperCAmelCase__       ,   0       ,   n - 1       ,   -1       ,   UpperCAmelCase__				)




def        A_					(					)     ->     Tuple:
   # A sample binary search tree
   a   :	Optional[Any]						=    [Node(UpperCAmelCase__       ,   randint(1       ,   50				)				) for i in range(10       ,   0       ,   -1				)]
   find_optimal_binary_search_tree(UpperCAmelCase__				)


if __name__ == "__main__":
 main()









"""simple docstring"""






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__				:  int						= {
    "auto_factory": ["get_values"],
    "configuration_auto": ["ALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CONFIG_MAPPING", "MODEL_NAMES_MAPPING", "AutoConfig"],
    "feature_extraction_auto": ["FEATURE_EXTRACTOR_MAPPING", "AutoFeatureExtractor"],
    "image_processing_auto": ["IMAGE_PROCESSOR_MAPPING", "AutoImageProcessor"],
    "processing_auto": ["PROCESSOR_MAPPING", "AutoProcessor"],
    "tokenization_auto": ["TOKENIZER_MAPPING", "AutoTokenizer"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__				:  Optional[int]						= [
     "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
     "MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING",
     "MODEL_FOR_AUDIO_XVECTOR_MAPPING",
     "MODEL_FOR_BACKBONE_MAPPING",
     "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING",
     "MODEL_FOR_CAUSAL_LM_MAPPING",
     "MODEL_FOR_CTC_MAPPING",
     "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING",
     "MODEL_FOR_DEPTH_ESTIMATION_MAPPING",
     "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
     "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING",
     "MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING",
     "MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING",
     "MODEL_FOR_MASKED_LM_MAPPING",
     "MODEL_FOR_MASK_GENERATION_MAPPING",
     "MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
     "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
     "MODEL_FOR_OBJECT_DETECTION_MAPPING",
     "MODEL_FOR_PRETRAINING_MAPPING",
     "MODEL_FOR_QUESTION_ANSWERING_MAPPING",
     "MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING",
     "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
     "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
     "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
     "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING",
     "MODEL_FOR_TEXT_ENCODING_MAPPING",
     "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
     "MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING",
     "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING",
     "MODEL_FOR_VISION_2_SEQ_MAPPING",
     "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING",
     "MODEL_MAPPING",
     "MODEL_WITH_LM_HEAD_MAPPING",
     "MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING",
     "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING",
     "AutoModel",
     "AutoBackbone",
     "AutoModelForAudioClassification",
     "AutoModelForAudioFrameClassification",
     "AutoModelForAudioXVector",
     "AutoModelForCausalLM",
     "AutoModelForCTC",
     "AutoModelForDepthEstimation",
     "AutoModelForImageClassification",
     "AutoModelForImageSegmentation",
     "AutoModelForInstanceSegmentation",
     "AutoModelForMaskGeneration",
     "AutoModelForTextEncoding",
     "AutoModelForMaskedImageModeling",
     "AutoModelForMaskedLM",
     "AutoModelForMultipleChoice",
     "AutoModelForNextSentencePrediction",
     "AutoModelForObjectDetection",
     "AutoModelForPreTraining",
     "AutoModelForQuestionAnswering",
     "AutoModelForSemanticSegmentation",
     "AutoModelForSeq2SeqLM",
     "AutoModelForSequenceClassification",
     "AutoModelForSpeechSeq2Seq",
     "AutoModelForTableQuestionAnswering",
     "AutoModelForTokenClassification",
     "AutoModelForUniversalSegmentation",
     "AutoModelForVideoClassification",
     "AutoModelForVision2Seq",
     "AutoModelForVisualQuestionAnswering",
     "AutoModelForDocumentQuestionAnswering",
     "AutoModelWithLMHead",
     "AutoModelForZeroShotImageClassification",
     "AutoModelForZeroShotObjectDetection",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__				:  int						= [
     "TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
     "TF_MODEL_FOR_CAUSAL_LM_MAPPING",
     "TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
     "TF_MODEL_FOR_MASK_GENERATION_MAPPING",
     "TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING",
     "TF_MODEL_FOR_MASKED_LM_MAPPING",
     "TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
     "TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
     "TF_MODEL_FOR_PRETRAINING_MAPPING",
     "TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING",
     "TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING",
     "TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING",
     "TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
     "TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
     "TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
     "TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING",
     "TF_MODEL_FOR_TEXT_ENCODING_MAPPING",
     "TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
     "TF_MODEL_FOR_VISION_2_SEQ_MAPPING",
     "TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING",
     "TF_MODEL_MAPPING",
     "TF_MODEL_WITH_LM_HEAD_MAPPING",
     "TFAutoModel",
     "TFAutoModelForAudioClassification",
     "TFAutoModelForCausalLM",
     "TFAutoModelForImageClassification",
     "TFAutoModelForMaskedImageModeling",
     "TFAutoModelForMaskedLM",
     "TFAutoModelForMaskGeneration",
     "TFAutoModelForMultipleChoice",
     "TFAutoModelForNextSentencePrediction",
     "TFAutoModelForPreTraining",
     "TFAutoModelForDocumentQuestionAnswering",
     "TFAutoModelForQuestionAnswering",
     "TFAutoModelForSemanticSegmentation",
     "TFAutoModelForSeq2SeqLM",
     "TFAutoModelForSequenceClassification",
     "TFAutoModelForSpeechSeq2Seq",
     "TFAutoModelForTableQuestionAnswering",
     "TFAutoModelForTextEncoding",
     "TFAutoModelForTokenClassification",
     "TFAutoModelForVision2Seq",
     "TFAutoModelForZeroShotImageClassification",
     "TFAutoModelWithLMHead",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__				:  Optional[int]						= [
     "FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
     "FLAX_MODEL_FOR_CAUSAL_LM_MAPPING",
     "FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
     "FLAX_MODEL_FOR_MASKED_LM_MAPPING",
     "FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
     "FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
     "FLAX_MODEL_FOR_PRETRAINING_MAPPING",
     "FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING",
     "FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
     "FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
     "FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
     "FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
     "FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING",
     "FLAX_MODEL_MAPPING",
     "FlaxAutoModel",
     "FlaxAutoModelForCausalLM",
     "FlaxAutoModelForImageClassification",
     "FlaxAutoModelForMaskedLM",
     "FlaxAutoModelForMultipleChoice",
     "FlaxAutoModelForNextSentencePrediction",
     "FlaxAutoModelForPreTraining",
     "FlaxAutoModelForQuestionAnswering",
     "FlaxAutoModelForSeq2SeqLM",
     "FlaxAutoModelForSequenceClassification",
     "FlaxAutoModelForSpeechSeq2Seq",
     "FlaxAutoModelForTokenClassification",
     "FlaxAutoModelForVision2Seq",
 ]


if TYPE_CHECKING:
 from .auto_factory import get_values
 from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
 from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
 from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
 from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
 from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_auto import (
      MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
      MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
      MODEL_FOR_AUDIO_XVECTOR_MAPPING,
      MODEL_FOR_BACKBONE_MAPPING,
      MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
      MODEL_FOR_CAUSAL_LM_MAPPING,
      MODEL_FOR_CTC_MAPPING,
      MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
      MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
      MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
      MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
      MODEL_FOR_MASK_GENERATION_MAPPING,
      MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
      MODEL_FOR_MASKED_LM_MAPPING,
      MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
      MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
      MODEL_FOR_OBJECT_DETECTION_MAPPING,
      MODEL_FOR_PRETRAINING_MAPPING,
      MODEL_FOR_QUESTION_ANSWERING_MAPPING,
      MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
      MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
      MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
      MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
      MODEL_FOR_TEXT_ENCODING_MAPPING,
      MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
      MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
      MODEL_FOR_VISION_2_SEQ_MAPPING,
      MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
      MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
      MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
      MODEL_MAPPING,
      MODEL_WITH_LM_HEAD_MAPPING,
      AutoBackbone,
      AutoModel,
      AutoModelForAudioClassification,
      AutoModelForAudioFrameClassification,
      AutoModelForAudioXVector,
      AutoModelForCausalLM,
      AutoModelForCTC,
      AutoModelForDepthEstimation,
      AutoModelForDocumentQuestionAnswering,
      AutoModelForImageClassification,
      AutoModelForImageSegmentation,
      AutoModelForInstanceSegmentation,
      AutoModelForMaskedImageModeling,
      AutoModelForMaskedLM,
      AutoModelForMaskGeneration,
      AutoModelForMultipleChoice,
      AutoModelForNextSentencePrediction,
      AutoModelForObjectDetection,
      AutoModelForPreTraining,
      AutoModelForQuestionAnswering,
      AutoModelForSemanticSegmentation,
      AutoModelForSeqaSeqLM,
      AutoModelForSequenceClassification,
      AutoModelForSpeechSeqaSeq,
      AutoModelForTableQuestionAnswering,
      AutoModelForTextEncoding,
      AutoModelForTokenClassification,
      AutoModelForUniversalSegmentation,
      AutoModelForVideoClassification,
      AutoModelForVisionaSeq,
      AutoModelForVisualQuestionAnswering,
      AutoModelForZeroShotImageClassification,
      AutoModelForZeroShotObjectDetection,
      AutoModelWithLMHead,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_auto import (
      TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
      TF_MODEL_FOR_CAUSAL_LM_MAPPING,
      TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
      TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      TF_MODEL_FOR_MASK_GENERATION_MAPPING,
      TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
      TF_MODEL_FOR_MASKED_LM_MAPPING,
      TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
      TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
      TF_MODEL_FOR_PRETRAINING_MAPPING,
      TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
      TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
      TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
      TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
      TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
      TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
      TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
      TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
      TF_MODEL_MAPPING,
      TF_MODEL_WITH_LM_HEAD_MAPPING,
      TFAutoModel,
      TFAutoModelForAudioClassification,
      TFAutoModelForCausalLM,
      TFAutoModelForDocumentQuestionAnswering,
      TFAutoModelForImageClassification,
      TFAutoModelForMaskedImageModeling,
      TFAutoModelForMaskedLM,
      TFAutoModelForMaskGeneration,
      TFAutoModelForMultipleChoice,
      TFAutoModelForNextSentencePrediction,
      TFAutoModelForPreTraining,
      TFAutoModelForQuestionAnswering,
      TFAutoModelForSemanticSegmentation,
      TFAutoModelForSeqaSeqLM,
      TFAutoModelForSequenceClassification,
      TFAutoModelForSpeechSeqaSeq,
      TFAutoModelForTableQuestionAnswering,
      TFAutoModelForTextEncoding,
      TFAutoModelForTokenClassification,
      TFAutoModelForVisionaSeq,
      TFAutoModelForZeroShotImageClassification,
      TFAutoModelWithLMHead,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_auto import (
      FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
      FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
      FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      FLAX_MODEL_FOR_MASKED_LM_MAPPING,
      FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
      FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
      FLAX_MODEL_FOR_PRETRAINING_MAPPING,
      FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
      FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
      FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
      FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
      FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
      FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
      FLAX_MODEL_MAPPING,
      FlaxAutoModel,
      FlaxAutoModelForCausalLM,
      FlaxAutoModelForImageClassification,
      FlaxAutoModelForMaskedLM,
      FlaxAutoModelForMultipleChoice,
      FlaxAutoModelForNextSentencePrediction,
      FlaxAutoModelForPreTraining,
      FlaxAutoModelForQuestionAnswering,
      FlaxAutoModelForSeqaSeqLM,
      FlaxAutoModelForSequenceClassification,
      FlaxAutoModelForSpeechSeqaSeq,
      FlaxAutoModelForTokenClassification,
      FlaxAutoModelForVisionaSeq,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE__				:  Optional[int]						= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





